Recycling of Used
INTERNATIONAL DE VELOPMENT IN FOCUS

Lead-Acid Batteries
Guidelines for Appraisal of
Environmental Health Impacts
Katherine von Stackelberg, Pamela R. D. Williams,
Ernesto Sánchez-Triana, Santiago Enriquez, and Claudia Serrano
INTERNATIONAL DEVELOPMENT IN FOCUS

Recycling of Used
Lead-Acid Batteries
Guidelines for Appraisal of
Environmental Health Impacts

KATHERINE VON STACKELBERG, PAMELA R. D. WILLIAMS,
ERNESTO SÁNCHEZ-TRIANA, SANTIAGO ENRIQUEZ, AND CLAUDIA SERRANO
© 2022 International Bank for Reconstruction and Development / The World Bank
1818 H Street NW, Washington, DC 20433
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Contents

Acknowledgments vii
Executive Summary ix
Abbreviations xv

CHAPTER 1 Introduction 1
Structure of the report 5
References 6

CHAPTER 2 Overview: Used Lead-Acid Battery Recycling 7
Description of the process 7
Conceptual site model (CSM) of exposure 9
Linking environmental contamination to human exposures and health
outcomes 11
References 17

CHAPTER 3 Study Sampling Design 19
Introduction 20
Identifying households and sampling locations 20
Note 23
References 23

CHAPTER 4 General Guidelines for Environmental Sampling 25
Introduction 25
Soil sampling 26
Dust sampling 28
Water sampling 30
Agricultural product sampling 32
Resources 35
Reference 35

CHAPTER 5 General Guidelines for Biological Sampling 37
Introduction 37
Biological sampling matrices 38
Biological sampling protocol 41
Resources 42

iii
iv | Recycling of Used Lead-Acid Batteries

CHAPTER 6 General Guidelines for Assessing Health Outcomes 43
Introduction 43
Self-reported health status and medical history 44
Medical exams and biological testing 45
Resources 53
References 53

APPENDIX A Overview of Contaminants 55

APPENDIX B Guidelines for Designing and Conducting Home
Surveys 59

APPENDIX C Key References and Resource Guides for Environmental
Sampling 67

APPENDIX D Biomonitoring Resources 73

APPENDIX E Resources for Health-Outcomes Assessment 83

APPENDIX F Bibliography 87

Boxes
2.1 Hypothetical ULAB-recycling facility 14
4.1 Soil sampling at a hypothetical ULAB-recycling site 27
4.2 Dust sampling at a hypothetical ULAB-recycling site 29
4.3 Water sampling at a hypothetical ULAB-recycling site 31
4.4 Agricultural sampling at a hypothetical ULAB-recycling site 33
4.5 Total environmental samples 34

Figures
ES.1 General conceptual site model of sources for health outcomes
at ULAB sites xii
1.1 Disability-adjusted life years caused by lead exposure by income level,
1990–2019 2
1.2 Disability-adjusted life years caused by lead exposure by region,
1990–2019 3
1.3 Children’s average blood lead levels by country (µg/dl) 5
2.1 Schematic of ULAB activities 8
2.2 General conceptual site model for ULAB recycling 10
2.3 Overview of how site data will be used to link environmental contamination
to human exposures and health outcomes 13
6.1 Dermatological outcomes associated with As exposures 49

Tables
ES.1 Summary of biomonitoring and health-outcome measurement
recommendations xiii
1.1 Summary of national-level estimates of the cost of lead exposure 3
1.2 Summary of subnational-level estimates of the cost of lead exposure 3
2.1 Primary inputs to ULAB-recycling process 9
2.2 Primary outputs from the ULAB-recycling process 9
2.3 Potential exposure pathways by exposure route and environmental
media at ULAB-recycling sites 10
2.4 Health outcomes associated with CoCs at ULAB-recycling sites 12
3.1 Categories of questions in the home survey (appendix B) 22
B4.1.1 Soil sampling scenarios 27
B4.2.1 Dust sampling scenarios 29
B4.4.1 Agricultural product sampling scenarios 33
5.1 Hierarchy of preferred biomarkers of exposure 39
5.2 Overview of biomarkers of exposure for Pb 39
Contents | v

5.3 Overview of biomarkers of exposure for As 40
5.4 Overview of biomarkers of exposure for Cd 41
6.1 Overview of recommended biomarkers of effect 46
6.2 Health outcomes associated with As exposures 50
A.1 Additional information and detailed profiles on arsenic 56
A.2 Additional information and detailed profiles on cadmium 57
A.3 Additional information and detailed profiles on lead 58
C.1 Site-characterization resources 67
C.2 Criteria for analytical-method selection 68
C.3 Sources of analytical guidelines for contaminated-site assessments 69
C.4 US EPA laboratory methods 69
D.1 Selected biomonitoring studies for lead and metals with
application to LMICs 73
E.1 Links to resources for health-outcomes assessment 83
Acknowledgments

The background research and drafts for this report were prepared by Katherine
von Stackelberg and Pamela Williams. The task team included Montserrat
Meiro-Lorenzo, Gabriela Elizondo, Maria Rosa Puech, Lek Gjon Kadeli, Mayra
Guerra Lopez, Claudia Serrano, Santiago Enriquez, and Ernesto Sánchez-Triana
(Task Team Leader). Frank Van Woerden, A. S. Harinath, and Shafick Hoossein
were peer reviewers for this analytical work. Editorial support was provided by
Stan Wanat (Stanford University).
This analytical work was supervised by Karin Kemper (Global Director,
Environment, Natural Resources and Blue Economy Global Practice—ENB), and
Iain Shuker and Christian Peter (Practice Managers, Global Platform Unit,
ENB). This analytical work was funded by the Pollution Management and
Environmental Health Multi-Donor Trust Fund (PMEH).

vii
Executive Summary

Despite robust evidence documenting the tragic and widespread consequences
of lead exposure, some environmental and health authorities around the world,
particularly in low- and middle-income countries (LMICs), have yet to develop
regulatory responses. The 2019 Global Burden of Disease (GBD 2019 Diseases
and Injuries Collaborators 2020) report estimated that lead exposure resulted in
more than 900,000 deaths and 21.7 million years of healthy life lost (measured in
disability-adjusted life years, or DALYs) worldwide due to long-term effects of
lead exposure on health. Since 1990, between 84 percent and 88 percent of the
health impacts of lead exposure have occurred in lower-middle-income and
upper-middle-income countries. These estimates likely represent an underesti-
mation since they underestimate the full costs of lead exposure in that they do
not include all endpoints. Notable among all such endpoints is the loss of IQ
points in children.
The available evidence suggests that lead exposure represents a significant
risk in most countries around the world. The US Centers for Disease Control and
Prevention has determined that blood lead levels at or above 5 micrograms per
deciliter are a threshold for regulatory action. Based on this criterion, it is esti-
mated that 1 in 3 children, or approximately 800 million children, have high
blood lead levels.
In low- and middle-income countries, small-scale informal industries are
known to operate with little regulatory oversight. These small, informal opera-
tions routinely dispose of chemicals directly onto and into the land, water, and
air, generating the potential for significant exposures and resulting health risks
for workers and surrounding communities. The small scale of these operations
may create a false perception that they are a minor concern; however, they

significantly outnumber large operations across a number of market segments.
A primary obstacle in addressing chemical pollution, including lead expo-
sure, is the uncertainty about the sources of chemical exposures and their rela-
tionships to health outcomes. Information on individual exposure factors and
behaviors contributing to exposure is typically lacking, having traditionally been
collected for high-income countries (HICs), which may not be reliable or accu-
rate for assessing exposures in LMICs.

ix
x | Recycling of Used Lead-Acid Batteries

The general guidelines presented in this report provide a pragmatic
framework for designing representative studies and developing uniform sam-

pling guidelines to support estimates of morbidity that are explicitly linked to
exposure to land-based contaminants from used lead-acid battery (ULAB) recy-
cling activities. A primary goal is to support environmental burden of disease
evaluations, which attempt to attribute health outcomes to specific sources of
pollution. The guidelines provide recommendations on the most appropriate
and cost-effective sampling and analysis methods to ensure the collection of rep-
resentative population-level data. Additionally, the guidelines will provide sam-
ple-size recommendations for each contaminant, as well as environmental
media, biological sampling data, household-survey data, and health-outcome
data.
These guidelines focus on small-scale ULABs that are known to generate sig-
nificant amounts of lead waste through the smelting process, as well as other
metal waste, including arsenic and cadmium. A primary concern with lead expo-
sures is the documented association with neurodevelopmental outcomes in
children, as demonstrated by statistically significantly reduced performance on

a battery of cognitive tests. These associations are evident even in the youngest
children, and toxicological and epidemiologic data indicate these effects have no
threshold (that is, there is no safe level of exposure). Other potential exposures
include arsenic and cadmium, and exposure to these contaminants is also asso-
ciated with neurodevelopmental outcomes in children, as well as arsenicosis;
bladder, lung, and skin cancers; and renal outcomes.
The primary objective of these guidelines is to guide research to assess the
relationship between environmental contamination, exposures, and health out-
comes related to a subset of contaminants originating from ULAB activities for
particularly vulnerable populations, such as children, and the general popula-
tion within a single household in the vicinity of ULAB sites in LMICs. To achieve
this objective, biomonitoring and health-outcome data are linked to house-
hold-survey data and environmental data (for example, soil, dust, water, agricul-
tural products, fish) at the individual level from an exposed population compared
to individuals from an unexposed (reference) population. Data on exposures and
health outcomes in the same individual, across a representative set of individu-
als, is required to support an understanding of the potential impact of ULAB
activities on local populations. The guidelines can also assist in building local
capacity to conduct environmental assessments following a consistent method-
ology to facilitate comparability across ULAB sites in different geographic areas.
Sampling strategies and methods are prioritized given information needs,
resource availability, and other constraints or considerations. The guidelines
include a number of supporting appendixes, where additional resources and ref-
erences on relevant topics can be found.
The Conceptual Site Model (CSM) provides a qualitative, graphical overview
of the relationship between sources of contaminants, migration of contaminants
through the environment, exposure pathways, and health outcomes. Figure ES.1
provides a general conceptual site model for ULAB activities. The CSM demon-
strates how contaminants that are released, emitted, or discharged from ULAB
sources can migrate through the environment, depending on local conditions.
The CSM also demonstrates the pathways and routes by which individuals in the
population can be exposed. This general CSM provides the starting point for
developing a site-specific CSM.
Executive Summary | xi

Problem formulation is the process of (a) establishing study objectives,
(b) supporting the identification of data-quality objectives associated with sta-
tistical analyses, and (c) developing a strategy. The objective of the strategy is to
characterize the zone of influence or community footprint associated with
ULAB activities in a specific geographic area. A detailed checklist is provided to
(a) assist in developing a land-use map of the area and to (b) refine the general
CSM for the site of interest based on existing site-specific information and
knowledge.
To meet the primary research objective, the sampling design is structured to
link environmental contamination and individual exposures to multiple contam-
inants of concern (CoCs) with different health outcomes associated with expo-
sure to these CoCs at the household level. Selected households and sampling
locations should therefore provide data on how environmental contamination
contributes to household exposures. The process of identifying local hotspots or
fully characterizing environmental concentrations across the entire site will
likely require a different sampling strategy. These guidelines recommend a pri-
mary grid-based sampling-design strategy augmented by targeted sampling
where individuals spend significant amounts of time (for example, schools, play-
grounds, agricultural locations). Typical grid densities range from 20 x 20 m to
100 x 100 m, with most falling generally in the 40 x 40 m to 60 x 60 m range.
A household is selected from each grid node with an example provided.
Recommendations for a Home Survey Questionnaire provide the detailed
information on exposure factors. These factors include time-activity patterns,
food-frequency questionnaires, and other demographic information. This infor-
mation is used to link environmental sampling data with biomonitoring data and
health-outcome data in a specific community. As these data are collected, they
should be compiled into country- or region-specific databases. These databases
can support development of risk assessments and other analyses that require
quantifying exposure factors (for example, consumption rates, body weight, and
so forth). Such quantification can be used to predict, for example, contami-
nant-specific intake rates applicable to analyses for pollution sources beyond
ULAB facilities.
Media-specific environmental sampling recommendations are provided as
follows:

Soil

• Collect four individual soil samples and analyze for metals using in-field
X-ray fluorescence (XRF).
• Composite the samples and send them to a laboratory for a multi-metal
screen.
• If resources allow, 50 percent of household samples—randomly selected—
and 100 percent of targeted samples undergo bioavailability testing for Pb.

Dust

• Collect two individual dust samples from interior surfaces using an
appropriate wipe (for example, GhostWipe™) and analyze using in-field XRF.
• Composite the samples and send to an accredited laboratory for a multi-metal
screen.
xii | Recycling of Used Lead-Acid Batteries

Water

• Water samples should be collected at the point of release at the individual
household or dwelling where the water is available for consumption or use,
unless there is a communal water source (for example, a community well or
common surface water).
• Samples can also be collected directly from an off-site surface-water body if it
is commonly used for recreational or other purposes (or if the in-field research
team requires additional data on potential site contamination).
• Collect 1-liter samples and send to an accredited laboratory for a multi-metal
screen.

Agricultural products

• The household survey and food-frequency questionnaire should inform the
selection of specific items being collected, with an emphasis on composite
samples that reflect dietary items for the largest number of participating
households.
• Agricultural samples can include meat, dairy (including processed items such
as cheese), eggs, fruits, and vegetables, and should be collected from the point
of consumption (for example, kitchen), garden, or market, as determined by
the in-field team.
• Collect somewhere between 40 and 100 grams of biomass, depending on spe-
cific laboratory guidance.

Individuals from participating households provide biomonitoring and
health-outcome data. These are related as shown in table ES.1.

FIGURE ES.1
General conceptual site model of sources for health outcomes at ULAB sites
Source/Process Release/Discharge Transport mechansim Exposure pathway Exposure route Contaminant Health outcomes

Particulate
Hyperkeratosis; Skin,
Extraction bladder, lung cancers;
Surface soil Fugitive dust (deposition) Neurodevelopmental
Surface water (runoff) and cognitive outcomes
Particulate Groundwater (leaching) in children; chronic
Outdoor/indoor dust obstructive pulmonary
Processing and Surface soil
Soils (yards, etc.) Inhalation disease (COPD)
concentrating As
Drinking water Dermal
Surface water Neurodevelopmental
Irrigated crops Ingestion
and cognitive outcomes
Aquatic food chain
Vapor and Ambient air (dispersion) in children; COPD;
Pb
particulate Fugitive dust (deposition) cardiovascular disease
Surface water (runoff) (CVD); Chronic kidney
Amalgamation
disease
Surface soil Groundwater (leaching)

Surface water MeHg
Neurodevelopmental
Smelting and cognitive outcomes
Vapor and Drinking water
Bioaccumulation in children; CVD
particulate Ingestion Hg
Groundwater (leaching) Irrigated crops
Aquatic food chain
Surface soil
Wastewater
Surface water
Executive Summary | xiii

TABLE ES.1 Summary of biomonitoring and health-outcome measurement recommendations
CoC BIOMARKER OF EXPOSURE HEALTH OUTCOMES AND BIOMARKERS OF EFFECT
arsenic Gold standard is metabolite • Conduct age-specific, culturally relevant cognitive testing for each child.
monomethylarsonic acid
(As) • Conduct in-field screening for keratosis on the soles of the feet as part of the
(%MMA) obtained from a household survey or as part of a more formal medical examination.
speciated creatinine-adjusted
urine sample • If keratosis is observed, consider a carcinogenic biomarker, such as DNA adduct
assay or micronucleus formation assay.
• Measure C-reactive protein as a nonspecific biomarker of intermediate effects on
the renal and cardiovascular systems.
cadmium International consensus on • Measure sensitive urinary biomarkers, including β2-m (urinary β2-microglobulin)
use of creatine-adjusted and glomerular filtration rate (GfR).
(Cd)
urine • If elevated, consider measuring additional carcinogenic biomarkers, such as DNA
adduct formation or micronucleus formation.
lead Venous blood is the gold • Measure blood pressure in adults in the field or as part of a medical examination.
standard; dried capillary
(Pb) • Measure specific biomarkers including proteinuria (for example, albumin); anemia
blood spot using in-field status (for example, hematocrit); cardiovascular risk (for example, C-reactive
LeadCare Analyzer protein); ALA for Pb exposures.
• Conduct age-specific, culturally relevant cognitive testing for each child.
Source: von Stackelberg, Williams, and Sánchez-Triana 2021.
Note: CoC = contaminant of concern.

CONCLUSIONS

Data obtained following these recommendations can be used to support

consistent, comparable, and standardized community-risk and health-impact
assessments at contaminated sites in LMICs. These data can also be used to
s upport subsequent economic-burden analyses and risk-management

decision-making with respect to site cleanup and risk-mitigation options in the

most cost- effective and efficient manner. Adherence to this framework will
facilitate comparisons and meta-analyses across studies by standardizing

data-collection efforts at the community level.

REFERENCES

As arsenic
ATSDR Agency for Toxic Substances and Disease Registry
BLL blood lead levels
Cd cadmium
CDC Centers for Disease Control and Prevention (US)
CoC contaminant of concern
CSM conceptual site model
DALY disability-adjusted life year
GBD Global Burden of Disease
HICs high-income countries
ICP-MS inductively coupled plasma mass spectrometry
LMICs low- and middle-income countries
Pb lead
PMEH Pollution Management and Environmental Health
ULAB used lead-acid battery
US EPA United States Environmental Protection Agency
WHO World Health Organization
XRF X-ray fluorescence
1 Introduction

Despite robust evidence documenting the tragic and widespread consequences
of lead exposure, many environmental and health authorities around the world,
particularly in low- and middle-income countries (LMICs), have yet to develop
regulatory responses. The issue has received little attention, perhaps because it
has low visibility compared to other environmental issues. When water is con-
taminated, people become acutely ill with diarrhea and other symptoms that can
lead to death. Similarly, poor air quality leads to overt symptoms and premature
mortality. Exposure to environmental contaminants such as lead is associated
with more insidious outcomes, such as reduced performance on cognitive tests
and other neurological disorders.
Although exposure to lead can cause death and acute illnesses in individuals,
the problem is more subtle for most-affected populations. Affected children do
not perform as well in school. They are late to read. They are slow to learn how
to perform common tasks. Perhaps a few more children are born with cognitive
deficits. Perhaps these children have less impulse control. Perhaps they exhibit
more violence. These symptoms are not always understood as an environmental
or a public health issue—or indeed a development problem. Instead, people will
say it is an issue of morals or of education. They will discipline the children, and
then they will take themselves to task and ask how and why they are failing to
raise these children correctly. It is not always clear that the issue may be an envi-
ronmental exposure.
Historically, high lead levels were caused by lead in gasoline. The phaseout of
leaded gasoline is in many ways a public health and environmental success story,
but it may also have contributed to a false perception that lead is no longer a
major environmental and health challenge. Outbreaks of childhood lead poison-
ing in low-, middle-, and high-income countries have occasionally surfaced in
the news, generally documenting contamination from a nearby mine or smelter,
creating the impression that lead exposure is a problem that affects only a few
intermittent communities.
The 2019 Global Burden of Disease report (GBD 2019 Diseases and Injuries
Collaborators 2020) estimated that lead exposure resulted in more than 900,000
deaths and 21.7 million years of healthy life lost (measured in disability-adjusted

1
2 | Recycling of Used Lead-Acid Batteries

life years or DALYs) worldwide due to long-term effects of lead exposure
on health. Lead exposure accounted for 62.5 percent of the global burden of

idiopathic developmental intellectual disability, 8.2 percent of the global burden
of hypertensive heart disease, 7.2 percent of the global burden of ischemic heart
disease, and 5.6 percent of global burden of strokes (Murray et al. 2020).
Since 1990, between 84 percent and 88 percent of the health impacts of lead
exposure have occurred in lower-middle- and upper-middle-income countries.
Between 1990 and 2019, the health impacts of lead exposure grew by more than
35 percent globally, with LMICs experiencing rapid growth and high-income
countries achieving a decline of more than 30 percent (figure 1.1). South Asia and
East Asia and the Pacific are the regions where lead exposure has led to the larg-
est number of predicted DALYs (see figure 1.2).
These figures demonstrate the significant health effects caused by lead expo-
sure predicted by DALYs, which may underestimate the full costs of lead expo-
sure because they do not explicitly include the loss of IQ points in children. The
World Bank has conducted several studies to estimate the health effects and
costs of environmental degradation, including lead exposure. Table 1.1 summa-
rizes the results of the studies conducted in Argentina, Bolivia, Lao People’s
Democratic Republic (PDR), and Mexico. In all countries, lead exposure results
in increased mortality and morbidity among adults, and in significant neuropsy-
chological effects in children. In addition to the pain and suffering associated
with these premature deaths and illnesses, lead exposure results in costs that
represent a significant share of each country’s GDP. The biggest share of this cost
stems from the loss of IQ points.
Similar studies have also been conducted to estimate the health effects and
costs of lead exposure at the subnational level, as summarized in table 1.2. These
studies have addressed geographic areas with very different characteristics.
What is consistent across all of them is that lead represents a significant health
risk and causes significant economic costs.

FIGURE 1.1
Disability-adjusted life years caused by lead exposure by income level, 1990–2019
25,000,000

20,000,000

15,000,000

10,000,000

5,000,000

0
90
91
92
93
94
95
96
97
98
99
00
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
19
19
19
19
19
19
19
19
19
19

20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20

High income Low income Lower-middle income Upper-middle income

Source: Based on data from the GBD 2019.
Introduction | 3

FIGURE 1.2
Disability-adjusted life years caused by lead exposure by region, 1990–2019
25,000,000

20,000,000

15,000,000

10,000,000

5,000,000

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
98
99
00
90
91
92
93
94
95
96
97

20
20
20

20
20
20
20
20
20
20
20
20

20
20
19
19
19
19
19
19
19
19
19
20
19

EAP ECA LAC MENA North America SAR SSA

Source: Based on data from the GBD 2019.
Note: EAP = East Asia and Pacific region; ECA = Europe and Central Asia region; LAC = Latin America and the Caribbean region;
MENA = Middle East and Northern Africa region; SAR = South Asia region; SSA = Sub-Saharan Africa region.

TABLE 1.1 Summary of national-level estimates of the cost of lead exposure
ARGENTINA BOLIVIA LAO PDR MEXICO
COST MEASURE (2012) (2014) (2017) (2018)
Total population (million) 41.1 11 6.86 126
GDP per capita (US$) 11,573 3,150 2,500 9,763
Labor force participation rate (15–64 years) 68% 74% 81% 65%
IQ points lost per year 619,581 345,576 341,615 3,838,340
Cost of IQ loss (% of GDP) 0.60% 1.35% 1.9% 0.97%
Annual deaths from adult lead exposure 2,082 371 562 5,105
Days of illness from adult lead exposure 9.7 2.2 2.2 116
(million)
Cost of increased mortality and morbidity of 0.31% 0.21% 0.65% 0.39%
adult lead exposure (% of GDP)
Total cost (% of GDP) 0.91% 1.56% 2.55% 1.36%
Source: World Bank compilation.

TABLE 1.2 Summary of subnational-level estimates of the cost of lead exposure
SINDH, YUCATÁN
APURIMAC, PAKISTAN HIDALGO, PENINSULA,
COST MEASURE PERU (2012) (2009) MEXICO (2012) MEXICO (2013)
Total population 452,000 36,000,000 2,800,000 4,300,000
GDP per capita (US$) $1,931 1,279 $6,980 8,967
IQ points lost per year 11,200 1,984,840 55,200 142,000
Cost of IQ % of GDP, 2018 1.34% 2.54% 0.63% 1.14%
Annual deaths from adult lead exposure 11 — 63 138
continued
4 | Recycling of Used Lead-Acid Batteries

TABLE 1.2, Continued

SINDH, YUCATÁN
APURIMAC, PAKISTAN HIDALGO, PENINSULA,
COST MEASURE PERU (2012) (2009) MEXICO (2012) MEXICO (2013)
Days of illness from adult lead exposure 58,000 — 232,000 505,000
Cost of increased mortality and morbidity of 0.15% — 0.13% 0.18%
adult lead exposure
Total cost (% of GDP) 1.49% 2.54% 0.76% 1.33%
Source: World Bank compilation.
Note: — = not available.

Among the subnational areas included in these studies, lead had the highest
costs in the Pakistani province of Sindh. There were multiple sources of lead
exposure in Sindh at the time of the study, including drinking water. In a study
of 18 districts of Karachi in 2007 and 2008, lead concentrations exceeded the
World Health Organization (WHO) guideline limit of 10 micrograms per liter of
water (µg/L) in 89 percent of the sampled sources. The average lead concentra-
tion was 77 µg/L in drinking water originating from surface sources and 146 µg/L
in groundwater sources. Other potentially important sources of lead exposure
included the traditional cosmetic surma, which often had a very high lead
concentration of > 65 percent. Children’s ornaments and jewelry often also con-
tained lead (Sánchez-Triana et al. 2015).
Available evidence indicates that lead exposure represents a significant expo-
sure and potential risk in most countries around the world. The US Centers for
Disease Control and Prevention has determined that blood lead levels at or above
5 micrograms per deciliter is a cause for action. Based on this criterion, it is esti-
mated that 1 in 3 children, or approximately 800 million children, have high lead
blood levels (figure 1.3). What is even more worrisome is that scientific evidence
is increasingly showing that there is no threshold blood lead level below which
there are no impacts on children’s health. That means that an even larger number
of children could be at risk of being affected by lead exposure and the resulting
health effects, which might include increased aggressive behavior, impulsivity,
attention-deficit hyperactivity disorder (ADHD), and mild retardation, all of
which are characteristics known to be associated with violent and criminal
behavior.
The reduction in lead exposure observed in high-income countries has been
associated with improved regulations and strict enforcement. However, in
LMICs, environmental regulations are lacking. Moreover, while environmental
enforcement agencies in LMICs may dedicate their limited resources to regulat-
ing the operations of larger polluting manufacturing operations, small-scale
informal industries tend to operate without any form of regulatory supervision.
These small, informal operations routinely dispose of chemicals directly onto
and into land, water, and air, generating the potential for significant health risks
for workers and surrounding communities. The small scale of these operations
may create a false perception that they are a minor concern; however, they sig-
nificantly outnumber large operations in a number of market segments.
A major obstacle in addressing chemical pollution, including lead exposure,
is the uncertainty about sources of chemical-pollution exposures and their rela-
tionship to health outcomes. Exposure factors and behaviors contributing to
exposures have traditionally been collected for high-income countries and may
not be reliable or accurate for assessing exposures in low- and middle-income
countries.
Introduction | 5

FIGURE 1.3
Children’s average blood lead levels by country (µg/dl)

Lead (pollution)
Lead pollution
Average BLL (µg/dL)

15 5 3 2 1 0

Source: UNICEF/Pure Earth (2020), based on data from IHME (2019).
Note: BLL = blood lead level.

These general guidelines aim to overcome this obstacle by assisting field
researchers in applying a consistent and uniform, yet flexible and practical,
approach for information gathering and data collection across different used
lead-acid battery (ULAB) recycling and reference sites in LMICs. The informa-
tion and data obtained can be used to assess the relationship between environ-
mental contamination generated by ULAB-recycling activities and
individual-based biomonitoring and health-outcome data among the general
population living near these sites, along with associated reference sites. The
guidelines can also be used to assist in building local capacity to conduct envi-
ronmental assessments following a consistent methodology to facilitate compa-
rability across ULAB sites in different geographic areas. Sampling strategies are
prioritized given information needs, resource availability, and other constraints
or considerations. The guidelines include a number of supporting appendixes
where additional resources and references on various topics can be found.

STRUCTURE OF THE REPORT

Chapter 2 of this report provides an overview of the ULAB-recycling process,
including a description of the primary contaminants released or discharged
during each step of the process. This chapter also presents a general conceptual
site model (CSM) for ULAB-recycling sites that identifies the transport mecha-
nisms, exposure pathways, and routes of exposure for local populations that may
6 | Recycling of Used Lead-Acid Batteries

be exposed to these contaminants. Lastly, this chapter highlights key site-specific
questions or issues that should be considered to inform the selection of partici-
pating households and sampling locations at ULAB-recycling sites.
Subsequent chapters of the guidelines provide guidance for information
gathering and data collection during field implementation at the identified
ULAB-recycling sites. Chapter 3 describes the process for identifying participat-
ing households and individuals within those households that will provide
household-survey data (appendix B), environmental sampling data (chapter 4),

biomonitoring data (chapter 5), and health-outcome data (chapter 6). This is a
critical step that will determine where to conduct subsequent environmental
sampling of soil, sediment, dust, water, fish, or agricultural and food products,
and for whom to collect biological and health outcome data, in order to assess
the potential contribution of ULAB-related contamination to population-level
exposures and health outcomes in exposed individuals. Chapter 4 provides gen-
eral guidelines for conducting environmental sampling of soil, dust, sediment,
water, fish, and/or agricultural and food products. Chapter 5 provides general
guidelines for collecting biological samples in blood, urine, hair, or other matri-
ces. Chapter 6 provides general guidelines for evaluating health outcomes using
medical exams, health surveys, and diagnostic tests.
The guidelines focus on data collection as opposed to data analysis. The
choice of statistical models and analyses will depend on site-specific study
objectives, the site-specific CSM, and the ultimate number of households and
samples available.

REFERENCES

GBD (Global Burden of Disease) 2019 Diseases and Injuries Collaborators. 2020. “Global
Burden of 369 Diseases and Injuries in 204 Countries and Territories, 1990–2019:
A Systematic Analysis for the Global Burden of Disease Study 2019.” Lancet 396 (10258):
1204–22. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30925-9
/fulltext.
IHME (Institute for Health Metrics and Evaluation). 2019. “Global Burden of Disease Study
Resources.” http://www.healthdata.org/.
Murray, C. J., A. Y. Aravkin, P. Zheng, C. Abbafati, K. M. Abbas, M. Abbasi-Kangevari,
F. Abd-Allah, A. Abdelalim, M. Abdollahi, and I. Abdollahpour. 2020. “Global Burden of 87
Risk Factors in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the
Global Burden of Disease Study 2019.” Lancet 396 (10258): 1223–49.
Sánchez-Triana, Ernesto, Santiago Enriquez, Bjorn Larsen, Peter Webster, and Javaid Afzal.
2015. Sustainability and Poverty Alleviation: Confronting Environmental Threats in Sindh,
Pakistan. Directions in Development—Environment and Sustainable Development;.
Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/22149.
UNICEF/Pure Earth. 2020. The Toxic Truth: Children’s Exposure to Lead Pollution Undermines
a Generation of Future Potential. https://www.unicef.org/media/109361/file/The%20toxic
%20truth.pdf.
2 Overview: Used Lead-Acid
Battery Recycling

This chapter provides an overview of the used lead-acid battery (ULAB) recy-
cling process, including a description of the primary contaminants released or
discharged during each step of the process. This chapter also presents a general
conceptual site model (CSM) for ULAB sites that identifies the transport mech-
anisms, exposure pathways, and routes of exposure for local populations that
may be exposed to these contaminants. Problem formulation is the process of
establishing study objectives, supporting the identification of data-quality objec-
tives associated with statistical analyses, and developing a strategy for character-
izing the zone of influence or community footprint associated with ULAB
activities in a specific geographic area. A detailed checklist is provided to assist
in developing a land-use map of the area and to refine the general CSM for the
site of interest based on site-specific existing information and knowledge. The
provided checklists highlight key site-specific questions or issues required to
inform the selection of participating households and sampling locations at ULAB
and associated reference sites.

DESCRIPTION OF THE PROCESS

Used lead-acid battery (ULAB) recycling consists of dismantling and recycling
used batteries, usually acquired from motor vehicles. Figure 2.1 provides an
overview of the steps involved in the process:

• Collection and transportation of used batteries to a recycling facility
• Dismantling of used batteries
• Separation of component battery parts in a water bath (lead sinks to the bot-
tom and plastics rise to the top)
• Air drying of lead and lead oxide-containing materials; mixing material with
coal, soda ash, and scrap metal
• transferring material to uncovered vessels for heating
• Smelting and refining of lead components
• Washing and shredding or melting of plastic components

7
8 | Recycling of Used Lead-Acid Batteries

FIGURE 2.1
Schematic of ULAB activities

Draining Lead contamination of
electrolyte soil and water

Collection and
transport of Plastic /
Toxic smoke including
batteries ebonite
sulfur dioxide, dioxins,
components
dibenzofurans.
burnt or
Lead–contaminated waste
bumped
Breaking up
batteries into
component
parts

Lead-containing
components Lead fragments and lead
broken up oxide dust disperse into air
and settle on soil, other
surfaces, and workers’ hair
and clothes. Surrounding
Conveying environment contaminated
broken battery with lead
parts to the
smelter

Lead fumes dispersed in air
and inhaled by workers.
Smelting and Fumes condense as
refining particles that settle on soil,
other surfaces, and workers’
hair and clothes

Workers go
home without Lead dust is carried home
washing and and contaminates domestic
changing environment
clothes

Source: WHO 2017.

• Purification and treatment of sulfuric acid electrolyte (fluid from the
batteries)
• Treatment and disposal of waste products (for example, plastics, residual
lead, water).

Contaminants and other materials encountered during the ULAB-recycling pro-
cess arise primarily from the battery components themselves, although some
additional materials are added during the smelting and refining process. The
primary inputs to the process are summarized in table 2.1.
Contaminants, primarily metals, may enter the environment during various
phases of the ULAB-recycling process. Appendix 1 provides a brief overview of
the contaminants (metals) typically found at ULAB sites. The primary outputs
Overview: Used Lead-Acid Battery Recycling | 9

TABLE 2.1 Primary inputs to ULAB-recycling process
INPUTS SOURCE
Lead (Pb), arsenic (As), and cadmium (Cd) Components of battery
Water Added to separate battery components
Lead oxide, battery acid, and plastics Components of battery
Coal, soda ash, and scrap metal Added during smelting and refining
Source: World Bank compilation.

TABLE 2.2 Primary outputs from the ULAB-recycling process
OUTPUTS MECHANISM
Lead (Pb), arsenic (As), and cadmium (Cd) Released to soil after dismantling batteries
Pb vapor and particulate (including fly ash) Released to air and soil after smelting
Solid waste containing Pb, As, and Cd Discharged to unlined lagoons or pits
Wastewater containing Pb, As, and Cd Discharged to soil or surface water
Source: World Bank compilation.

and contaminants of concern (CoCs) generated as part of the ULAB-recycling
process that will be the focus of these guidelines given the stated research objec-
tives are shown in table 2.2.
Once released or discharged into the environment, the contaminants from
ULAB-recycling sites can migrate through different environmental media based
on their chemical and physical properties and local conditions. The primary
transport mechanisms at ULAB-recycling sites include the following:

• Airborne transport of fugitive dust from contaminated surface soil
• Airborne transport of vapors and particulate downwind from the source
• Leaching or runoff of contaminated soil to surface water or groundwater
(particularly following rain or flooding events)
• Leaching of waste products from lagoons or pits to surface water or ground-
water sources
• Migration of contaminated surface water from wastewater discharges to
other surface water sources or groundwater.

CONCEPTUAL SITE MODEL (CSM) OF EXPOSURE

Once contaminants have migrated offsite at ULAB-recycling sites, additional
processes or activities can lead to population exposures to these contaminants
through direct or indirect contact with contaminated environmental media. For
example, populations may be indirectly exposed to contaminants that are pres-
ent in contaminated water used to irrigate crops or that are present in contami-
nated soils where livestock are grazed. An exposure pathway refers to the physical
movement of an agent from a source or point of release through the environment
to a receptor (for example, air, groundwater, surface water, soil, sediment, dust,
food chain). Exposure routes describe the different ways by which agents may
enter the body following external contact (for example, inhalation, ingestion,
dermal). Potential exposure pathways and routes of exposure related to ULAB-
recycling sites are presented in table 2.3.
Figure 2.2 provides a general conceptual site model (CSM) for ULAB-
recycling sites that shows how contaminants that are released, emitted, or
10 | Recycling of Used Lead-Acid Batteries

TABLE 2.3 Potential exposure pathways by exposure route and environmental media at ULAB-recycling sites
ENVIRONMENTAL MEDIA
EXPOSURE
ROUTE AIR SOIL/DUST WATER
Inhalation Inhalation of Pb vapors and Inhalation of Pb soil vapors and Pb, As, Inhalation of Pb vapors released from
particles in outdoor air due to or Cd particles or dust in outdoor air tap, surface, or groundwater (for
releases to air from smelting due to dismantling batteries, releases example, bathing, showering, washing,
to soil from smelting, and solid waste swimming) due to solid waste or
or wastewater discharges to soil wastewater discharges to water
Inhalation of Pb vapors and Inhalation of Pb soil vapors and Pb, As,
particles in indoor air due to or Cd particles or dust in indoor air
releases to air from smelting due to dismantling batteries, releases
to soil from smelting, and solid waste
or wastewater discharges to soil
Ingestion Ingestion of agricultural Incidental ingestion of Pb, As, or Cd in Ingestion of Pb, As, or Cd in tap, surface,
products contaminated with Pb, soil or dust (indoors or outdoors) due or groundwater due to solid waste or
As, or Cd due to deposition of to dismantling batteries, releases to wastewater discharges to water
vapors or particles (for example, soil from smelting, and solid waste or
fruits, vegetables, grains) wastewater discharges to soil
Ingestion of agricultural Ingestion of agricultural products Ingestion of agricultural products
products contaminated with Pb, contaminated with Pb, As, or Cd due contaminated with Pb, As, or Cd due to
As, or Cd due to transfer of to transfer of contaminants from soil to being irrigated with contaminated water
contaminants from air to plants, animals, or plants to animals
animals or plants to animals (for
example, meat, milk, eggs)
Ingestion of agricultural products
contaminated with Pb, As, or Cd due to
transfer of contaminants from water to
animals
Dermal Dermal contact with Pb vapors Dermal contact with Pb, As, or Cd in Dermal contact with Pb, As, or Cd in tap,
contact and particles due to releases to soil or dust (indoors or outdoors) due surface, or groundwater due to solid
air from smelting to dismantling batteries, releases to waste or wastewater discharges to water
soil from smelting, and solid waste or
wastewater discharges to soil
Source: World Bank compilation.
Note: As = arsenic; Cd = cadmium; Pb = lead.

FIGURE 2.2
General conceptual site model for ULAB recycling

Source/process Release/discharge Transport mechansim Exposure pathway Exposure route Contaminant Health outcome

Fugitive dust (deposition) Output/indoor dust Lung, skin, bladder
Dismantling Surface soil Surface water (runoff) Drinking water cancer;
used batteries Hyperkeratosis;
Groundwater (leaching) Irrigate crops
Neurodevelopmental
and cognitive outcomes
Ambient air (dispersion) Ambient air Inhalation As
in children; chronic
Vapor and Fugitive dust (deposition) Outdoor/indoor dust Dermal obstructive pulmonary
particulate Surface soil (deposition) Soil (yard, school) disease (COPD)
Ingestion
Smelting
Fugitive dust (deposition) Outdoor/indoor dust Neurodevelopmental
Surface soil Surface water (runoff) Drinking water and cognitive outcomes
Groundwater (leaching) Irrigate crops Pb in children; COPD;
cardiovascular disease
Surface and Surface water (runoff) (CVD); Renal outcomes
Solid waste Drinking water
Subsurface soil Ingestion
Groundwater (leaching) Irrigate crops
(lagoon/pit)

Surface water (runoff) Drinking water Cd
Surface soil Renal cancer / chronic
Groundwater (leaching) Irrigate crops kidney disease (CKD)
Dermal
Wastewater
Ingestion
Drinking water
Surface water Groundwater (leaching)
Irrigate crops

Source: von Stackelberg, Williams, and Sánchez-Triana 2021.
Overview: Used Lead-Acid Battery Recycling | 11

discharged from ULAB sources can migrate through the environment, and the
pathways and routes by which individuals in the population can be exposed to
those contaminants. Per the General Guidelines for Environmental Sampling
(chapter 4), samples should be collected from different environmental media at
each ULAB-recycling site to provide data on exposure point concentrations for
a subset of the most relevant contaminants, exposure pathways, and populations
of interest. Appendix C provides links to key references and resource guides to
facilitate environmental field sampling and sample laboratory analysis.

LINKING ENVIRONMENTAL CONTAMINATION TO HUMAN
EXPOSURES AND HEALTH OUTCOMES

Exposure is the amount of an agent in the environmental media with which a
person comes into contact and is a function of the exposure point concentration
and the amount of time the individual is in contact with the contaminated media.
Intake is the amount of an agent that enters the human body via an exposure
route. Characterizing exposure and intake therefore requires information about
various exposure factors—such as human behavior, time, and activity patterns—
and contact rates. Common exposure factors relevant for ULAB-recycling sites
include the following:

• Soil and dust ingestion rates
• Water-ingestion rates and liquid-ingestion rates
• Food-ingestion rates and fish-ingestion rates
• Inhalation rates
• Mouthing frequency in children (hand-to-mouth and object-to-mouth)
• Dermal exposure factors (for example, skin-surface area, skin adherence,
residue transfer)
• Time spent indoors vs. outdoors
• Time spent in various activities (for example, sleeping, at school, at work)
• Time spent bathing, showering, or swimming
• Time spent playing on various surfaces (for example, dirt, grass, sand, gravel)
• Body weight.

Although information on typical or recommended exposure factors is often
available for high-income countries (HICs), these data may not be reliable or accu-
rate for assessing exposures in LMICs. Per the General Guidelines for Conducting
Household Surveys (appendix B), site- and population-specific information should
be collected to provide relevant exposure-factors data for use at ULAB-recycling
sites and associated reference sites. This information will be linked to the environ-
mental sampling data (obtained from chapter 4) as one way to estimate popula-
tion-level exposures at ULAB-recycling sites as compared to reference sites.
Dose is the amount of an agent that crosses the outer boundary of an organism
and is absorbed into the body and thus is available for interaction with metabolic
processes. The internal dose of a chemical (or its metabolite) can be measured
directly from biological sampling (often called biomonitoring). Depending on
the contaminant, common biological matrices that may be relevant for ULAB-
recycling sites include the following:

• Blood
• Urine
12 | Recycling of Used Lead-Acid Batteries

TABLE 2.4 Health outcomes associated with CoCs at ULAB-recycling sites
METALS MEASURABLE HEALTH OUTCOMES
Pb Developmental health outcomes in children (for example, reduction in IQ,
cognitive deficits)
Cardiovascular-health outcomes in adults
Renal-health outcomes in children and adults
As Skin rashes and lesions and hyperkeratosis, possible precursors to skin cancer
Developmental and cognitive deficits in children
Lung cancer in adults
Bladder cancer in adults
Cd Nephrotoxicity and renal effects, possible precursors to kidney cancer
Source: World Bank compilation.

• Hair
• Nails (that is, toenails, fingernails)
• Breast milk / cord blood.

Per the General Guidelines for Biological Sampling (chapter 5), samples should
be collected from relevant biological matrices, where feasible, at each ULAB site
to provide data on total exposures from all sources and pathways (as reflected by
the measured internal dose). This information will be linked to the house-
hold-survey data (chapter 3) and exposure concentration (chapter 4) to assess
the relationship between estimates of exposure and biomarkers of exposure.
Appendix D provides links to key resources and methods for collecting biologi-
cal samples. Where possible, these data can be used to validate or update existing
modeling tools (appendix D) for estimating population exposures and doses.
Note that prior to collecting any biological samples, the in-field team will need
to ensure that all Institutional Review Board (IRB), human subjects, and ethical
clearances are completed as required. Population exposures to the predominant
contaminants at ULAB-recycling sites may be associated with different types of
health outcomes, as shown in table 2.4.
Per the General Guidelines for Assessing Medical and Health Outcomes
(
chapter 6), medical exams, surveys, and diagnostic testing should be conducted,
where feasible and appropriate, at each small-scale ULAB site and associated ref-
erence site to provide data on reported, observed, or measured symptoms
and health effects. This information will be linked to the household survey
chapter 3), environmental concentrations (chapter 4), and biological dose mea-
(
surements (chapter 5) to assess the potential relationship between exposures and
health outcomes at these ULAB sites as compared to reference sites. Appendix E
provides links to key tools and resources for assessing health outcomes.
Figure 2.3 provides an overview of how the data collected at each ULAB-
recycling site will be used to link environmental contamination to human expo-
sures and health outcomes.

Problem formulation and site-specific characterization
Problem formulation is the process of establishing study objectives, supporting
the identification of data-quality objectives associated with statistical analyses,
and developing a strategy for characterizing the zone of influence or community
footprint associated with ULAB activities in a specific geographic area. A key
Overview: Used Lead-Acid Battery Recycling | 13

FIGURE 2.3
Overview of how site data will be used to link environmental contamination to
human exposures and health outcomes

• Population behaviors, activity patterns, and contact rates

• Chapter 2 Overview: Used Lead-Acid Battery Recycling, and appendix B,
Exposure Guidelines for Designing and Conducting Home Surveys
factors

• Chemical concentrations in environmental media

Exposure • Chapter 3, Study Sampling Design
point
concentration

• Chemical concentrations in biological matrices—biomarkers
of exposure and effect

Internal • Chapter 4, General Guidelines for Environmental Sampling
dose

• Symptoms, intermediate health outcomes, and health effects

• Chapter 5, General Guidelines for Biological Sampling
Health
outcomes

Source: World Bank compilation.

first step is to develop a land-use map of the area and refine the general CSM for
the site of interest based on site-specific existing information and knowledge.
This will set the stage for subsequent collection of environmental, household
survey, biomonitoring, and health-outcomes data given the goal of relating mea-
sured environmental exposures to biomonitoring and health-outcome data at
the individual level.
The primary objective of these guidelines is to guide research to assess the
relationship between environmental contamination, exposures, and health out-
comes related to a subset of contaminants originating from ULAB activities (for
example, lead [Pb], arsenic [As], and cadmium [Cd]) for particularly vulnerable
populations (for example, children and women of child-bearing age) within a
single household at ULAB-recycling sites in LMICs. To evaluate this objective,
biomonitoring, health-outcome, and household-survey data are linked to envi-
ronmental data (for example, soil, dust, water, agricultural products) for individ-
uals comprising an “exposed” population compared to individuals comprising
an “unexposed” population. Statistical analyses of the data obtained during this
research can help answer questions such as the following:

• What are the environmental concentrations of Pb, As, and Cd in the vicinity
of ULAB facilities? Are these higher than in comparable areas without such
facilities?
• What are the biological concentrations of Pb, As, and Cd in blood, hair, or
urine? Do these levels correlate with environmental concentrations? Do they
14 | Recycling of Used Lead-Acid Batteries

differ between ULAB-exposed populations and non–ULAB-exposed
populations?
• What is the incidence of specific health outcomes in ULAB-exposed popula-
tions? Do these correlate with environmental or biological concentrations?
Do they differ from non–ULAB-exposed populations?
• How do time-activity patterns and exposure factors differ across popula-
tions? Do these differences help explain the biological or health-outcome
findings?
• Which data sets are most predictive of exposures or health outcomes? Is
there a reduced set of data that can be collected in the future to streamline the
evaluation of potential impacts from ULAB activities?
• Can an assessment framework be developed to evaluate the benefits and costs
of potential interventions to reduce exposures or improve population health?

The problem-formulation stage defines the questions that the analysis
will address. A goal of problem formulation is to assemble existing site informa-
tion and data to inform an understanding of how ULAB-recycling activities
might affect the local population. Such data regarding sites and effects are used
to develop a land-use map of the area. The map may be developed using local
topographical maps, Google Maps, Google Earth, GIS programs, or similar soft-
ware. The map should include a defined geographic area (for example, village,
town, city) that locates the physical small-scale ULAB facility or processing area
(that is, “source area”) relative to other infrastructure or areas where popula-
tions, particularly children, spend the most time (for example, housing units,
schools, town center, and so forth) since these define the potential zone of influ-
ence or footprint associated with ULAB activities. For the purposes of these
guidelines, these areas are collectively referred to as the “ULAB study site”
(see box 2.1) and include both the source area and the broader zone of influence.
Additional maps or insets provide the spatial context for activities that may lead
to contaminant exposures. For example, within the source area, the map should
identify processing activities and physical waste stored onsite (for example, slag,
matte, fly-ash lagoons, waste pits). The map should also identify the prevailing
wind direction and the location of local wells or water bodies (particularly those
used as drinking-water sources or for recreational purposes) as well as direct
and indirect wastewater discharges (including proximity to freshwater and
sources of drinking water). The site map will also serve as the basis for

BOX 2.1

Hypothetical ULAB-recycling facility
Consider a hypothetical ULAB-recycling facility most households obtain drinking water from private
located in a town setting (population 12,000) with or shared wells, while the remaining households
some residences located within 100 m of the source. obtain their water from a shared surface-water source.
Land use at the site is primarily residential, agricul- Nearly half the residents have backyard gardens, and
tural, and light industrial. There are three schools at the rest obtain much of their produce and meat from a
the site. The source area experiences frequent rain local market. There is a large-scale agricultural field
and runoff into the nearby residential area, as well as near the ULAB-recycling facility. There are also unin-
truck traffic along the road leading into the facility. A habited areas that have a low potential for exposures
number of creeks and ditches are found at the site, and to occur.
Overview: Used Lead-Acid Battery Recycling | 15

identifying relevant environmental sampling locations (chapter 4) as well as
households from which to collect home survey data (chapter 3 and appendix B),
biomonitoring data (chapter 5), and health-outcome data (chapter 6).
Another goal of problem formulation is to refine the general CSM for ULAB
sites to reflect any unique characteristics of the study area, identify the site-spe-
cific relevant exposure pathways and exposed populations of interest. Thus,
problem formulation is used to characterize all aspects of the environmental set-
ting and determine where and under what conditions general population expo-
sures are likeliest to occur. The following checklist is designed as a guide to assist
in characterizing and mapping the environmental setting and establishing the
zone of influence to develop the site-specific CSM.

Characterize the general environmental setting on a map:

• Locate ULAB activities in the context of local populations, noting where dif-
ferent aspects of the process may occur. In some areas, battery breaking may
occur in areas separate from the primary smelting and refining.
• Identify locations of all surface waters, including ditches, creeks, streams,
rivers, and lakes.

• Identify what is known about groundwater, depth to the water table, and
aquifers in the study area.

• Identify the prevailing wind direction, particularly relative to residential
areas, local bodies of water, and small- or large-scale agricultural activities.
Dispersion and deposition of lead dust and other metals are likely to be signif-
icant and can occur over large areas.
• Identify water bodies within a depositional area of ULAB activities, or
affected by wastewaters or soil runoff, both of which are likely to contain lead
and other metals.
• Identify agricultural areas, community gardens, and the potential for back-
yard gardening.
• Locate sources of irrigation water that might be affected by ULAB wastewa-
ter discharges, including direct or indirect surface-water discharges or
releases to soils that can run off or erode. Establish whether groundwater is
used for irrigation and whether there is a leaching pathway.
• Identify locations where animals or animal products (for example, milk, eggs)
are raised for consumption.

Describe the ULAB process:

• Identify how many batteries are being processed, and whether other lead-
containing items are also recycled (for example, metal parts).
• Identify where batteries are stored and what happens to the plastic casings
once battery breaking is completed. In some communities, these plastic cas-
ings are repurposed in homes in different ways and may become a source of
exposures.
• Battery dismantling typically involves a water bath to separate the plastic
from the lead—where does this water come from and how does disposal
occur?
• Describe how smelting occurs (for example, blast furnace, open barrels, other
vessels). Identify the number and capacity of smelting vessels.
• Describe the refining process, including identifying the number and capacity
of refining vessels.
• Describe any measures for air-pollution control utilized during smelting and
refining.
16 | Recycling of Used Lead-Acid Batteries

• Identify what is done with any waste products from the stack (for example,
dust from filters on any equipment for air-pollution control).
• Describe the specific ULAB process utilized and identify all inputs and out-
puts (for example, the processes identified in figure 2.1).
• Identify how much slag is produced for each ton of metallic lead. Typically,
300–350 kg of slag is produced per ton of metallic lead, and approximately
5 percent of this slag is composed of lead compounds. Generally, slag is recy-
cled until there is no viable metal left to be extracted; at that point, the slag
is typically disposed of in specific designated areas. Locate these areas and
describe the conditions of storage and opportunities for material to enter
the environment (for example, runoff from routine precipitation events,
flooding, and so on).
• Identify the disposition of leachate that may be produced; unstable, water-
soluble slag that comes into contact with water or moist air is likely to lead to
downstream contamination.
• Describe the process for handling the electrolyte solution. This solution will
contain high levels of sulfuric acid that require neutralization.
• Cooling water is often used and, although it typically does not come into con-
tact with contaminants, there may be aspects to the site-specific process in
which this is not true and must be identified.

Waste releases and potential fate and transport:

• Develop a qualitative (or quantitative, if possible) mass balance for ULAB
activities by identifying all materials used in the process, where they come
from, and what products, including waste, are generated.
• Locate wastewater discharges on the site map and identify the specific hydro-
logic connections between wastewater discharges and surface waters (for
example, ditches, lagoons, receiving waters).
• Establish whether typical precipitation events lead to routine ponding and
discharges to nearby surface waters.
• Locate communal surface or groundwater sources of drinking water relative
to potentially affected surface waters on the site map to identify potential
sampling areas.
• Establish the potential for wastewater discharges (directly or indirectly
through surface water) to be used as irrigation water for local agricultural
products or animals.
• In some areas, the plastic from battery breaking.

Population demographics and exposure pathways:

• Establish the local population and population size (for example, village,
urban, peri-urban).
• Quantify or estimate population size and age/sex distribution.
• Identify the fraction of the local population that participates in ULAB
activities.
• Identify residential areas relative to ULAB activities on the site map.
• Identify and map community spaces within the study area, including schools,
hospitals and health centers, community centers, places of worship, play-
grounds, and places where individuals, particularly children, are likely to
spend significant amounts of time.
• If processing activities occur in homes (for example, grinding and milling),
these specific locations should be explicitly identified.
Overview: Used Lead-Acid Battery Recycling | 17

• Establish site-specific exposure pathways (as shown in figure 2.2).
• Identify whether unique or additional exposure pathways should be consid-
ered. Particular emphasis should be given to identifying the sources of drink-
ing water, since Pb in drinking water can represent a significant exposure
pathway at lead-contaminated sites.

The general guidelines provided in the following chapters should be used to
support data-collection efforts at each ULAB-recycling site. These guidelines
provide a general (uniform) approach for sampling and analysis, but detailed
field protocols (for example, physical process of collecting samples, storing and
shipping samples, laboratory analysis of samples) and sampling data sheets will
need to be provided by the in-field research team, recognizing that local analyt-
ical capacity to implement these guidelines will differ across countries. Local
implementation may involve an iterative process, in which initially split sam-
ples are collected and sent for analysis locally as well as to an accredited inter-
national laboratory for a standard interlaboratory comparison. Enhancing and
leveraging local capacity to conduct sampling, analyze samples, and interpret
results is expected to require flexibility and collaboration.

REFERENCES

von Stackelberg, Katherine, Pamela R. D. Williams, and Ernesto Sánchez-Triana. 2021.
“A Systematic Framework for Collecting Site-Specific Sampling and Survey Data to Support
Analyses of Health Impacts from Land-Based Pollution in Low- and Middle-Income
Countries.” Int. J. Environ. Res. Public Health 18: 4676. https://doi.org/10.3390
/ijerph18094676.
WHO (World Health Organization). 2017. Recycling Used Lead-Acid Batteries: Health
Considerations. Geneva: WHO.
3 Study Sampling Design

This chapter describes the process for identifying participating households and
individuals within those households that will provide household-survey data
(appendix B), environmental sampling data (chapter 4), biomonitoring data
(chapter 5), and health-outcomes data (chapter 6). Identifying participating
households is a critical step that will determine where to conduct subsequent
environmental sampling of soil, sediment, dust, water, fish, or agricultural and
food products, and for whom to collect biological and health-outcome data for
assessing the potential contribution of used lead-acid battery (ULAB)–related
contamination to population-level exposures and health outcomes in exposed
individuals.
To meet the primary research objective, the sampling design is structured to
link environmental contamination and individual exposures to multiple contam-
inants of concern (CoCs), with different health outcomes associated with expo-
sure to these CoCs at the household level. Selected households and sampling
locations should therefore provide data on how environmental contamination
contributes to household exposures, rather than identifying local hot spots or
fully characterizing environmental concentrations across the entire site, which
will likely require a different sampling strategy. These guidelines recommend a
primary grid-based sampling-design strategy augmented by targeted sampling
on where individuals spend significant amounts of time (for example, schools,
playgrounds, agricultural locations, recreational or commercial fishing areas).
Targeted sampling will be required for households associated with fish con-
sumption from potentially affected aquatic areas. Typical grid densities range
from 20 x 20 m to 100 x 100 m, with most falling generally in the 40 x 40 m to
60 x 60 m range. A household is selected from each grid node with an example
provided.
Recommendations for the home-survey questionnaire are designed to pro-
vide the detailed information on exposure factors such as time-activity patterns,
food-frequency questionnaires, and other demographic information used to link
environmental sampling data with biomonitoring data and health-outcome data
in a specific community. As these data are collected, they should be compiled
into country- or region-specific databases to support development of risk

19
20 | Recycling of Used Lead-Acid Batteries

assessments and other analyses that require quantifying exposure factors (for
example, consumption rates, body weight, and so forth) to predict, for example,
contaminant-specific intake rates applicable to analyses beyond ULAB sites.

INTRODUCTION

To meet the primary objective outlined above, the sampling design is structured to
link environmental contamination and individual exposures to multiple CoCs
with different health outcomes associated with these CoCs at the household level.
Selected households and sampling locations should therefore provide data on how
environmental contamination contributes to household exposures, rather than
identifying local hot spots or fully characterizing environmental concentrations
across the entire site. For purposes of these guidelines, individuals selected within
each household should also reflect those populations of greatest vulnerability,
such as children and women of childbearing age. As noted in chapter 2, the pri-
mary CoCs at ULAB-recycling sites are lead (Pb), arsenic (As), and cadmium (Cd).
These metals will persist in the environment in their original form and are likely to
be found in soil, dust, water, and some agricultural products. Determining where
to collect environmental samples (that is, households and other targeted locations)
at each ULAB-recycling site should be informed by knowledge of the source loca-
tion, contaminant release and transport mechanisms, likely exposure pathways,
and location and activities of populations exposed, per the refined conceptual site
model (CSM) and household survey discussed in chapter 2.

IDENTIFYING HOUSEHOLDS AND SAMPLING LOCATIONS

Identifying the households and other targeted sampling locations linked to
where individuals spend time at each ULAB-recycling site is a critical first step
prior to any in-field data-collection efforts.
In these guidelines, a predominantly grid-based sampling design is recom-
mended for identifying participating households, which helps ensure random-
ization in the selection process, followed by targeted sampling as appropriate. In
grid-based sampling, households are identified using regularly spaced intervals
defined by a grid placed over the study area. Within each household, children
10 years of age and younger represent the primary population of interest for
these guidelines, given that (a) Pb is the primary CoC at ULAB-recycling sites,
(b) children have a greater opportunity for Pb exposures due to age-specific
activity patterns and on a per body weight basis, and (c) tools are available for
evaluating specific health outcomes (for example, cognitive deficits) associated
with elevated childhood Pb exposures. However, to maximize the participation
rate (in the event that sampling children only is a deterrent for some house-
holds), both the youngest child (3 years or older) and his or her mother should
be targeted for data collection once specific households have been identified.
Although a more refined approach for selecting households at each site can be
tailored by the in-field research team once the specific ULAB-recycling sites have
been identified, the following steps will aid in identifying households and environ-
mental sampling locations at these sites using a consistent and uniform approach:

• Step 1. Based on the site map developed in chapter 2, overlay an equally
spaced grid (typically a square or rectangle, but this can be a circle or other
Study Sampling Design | 21

shape) on the site map with the source area at the center, assuming the source
area is surrounded by residential areas. Depending on the site, the source
area may not be centered within a residential area and the grid will need to
be adjusted accordingly to capture locations designed to maximize potential
exposures. The goal is to identify households within the zone of influence of
the source area, as informed by the site-specific CSM (chapter 2). Determining
the exact grid size (which affects sample size) will require some flexibility,
depending on the site-specific CSM, population density, and resource con-
straints. For example, at a site with known wastewater discharges to a stream
that flows several kilometers downstream from the source area, ultimately
discharging to a pond, it may be beneficial to select both households within
1 km of the source area as well as other households from downstream loca-
tions. In general, the literature suggests that the majority of the influence of
ULAB facilities on soil and dust contamination occurs within several km of
the source and drops off after 1 km (see the bibliography in appendix F) but
will vary depending on site-specific attributes of the exposed population.
Typical grid densities range from 20 x 20 m to 100 x 100 m, with most falling
generally in the 40 x 40 m to 60 x 60 m range. A household is selected from
each grid node. If a selected household is not willing to participate in the
study, a neighboring household in the same grid space should be chosen. The
sample size can be altered by choice of grid size—that is, reducing the grid
size will increase the number of sample nodes and households sampled,
while increasing the grid size will reduce the number of sample nodes
and households sampled. Although it is not possible to identify a predeter-
mined sample size for each site using power calculations, in order to balance
research objectives and feasibility constraints, it is recommended that more
than 100 households, but fewer than 400 households, be selected per ULAB-
recycling site (average of 200 to 300 households).1 See appendix B for addi-
tional references.
• Step 2. For all households identified in Step 1, select two household members
to participate in the study. All subsequent environmental, biological, and
health-outcome sampling will link back to the specific characteristics and
activity patterns of these individuals, which will be informed by the home-
survey responses. As noted above, the primary population of interest for this
study is children ages 10 or younger, and one child and the child’s mother
should be selected. However, some households may not contain children
within this age grouping or provide permission for a young child to partici-
pate in the study. In these situations, an older child under the age of 18 and the
child’s mother should be selected, if possible; otherwise, seek permission for
any adult in the household.
As an example of how individual households might be selected, consider
the hypothetical ULAB-recycling facility mentioned earlier, which is (hypo-
thetically) centrally located within a residential area of approximately 800 x
1,100 m = 880,000 m2. A grid size of 50 x 50 m = 2,500 m2 placed over this area
yields 352 grid nodes (880,000 ÷ 2,500), but a number of these grid nodes do
not contain any nearby households. Subtracting these uninhabited areas from
the total results in 170 grid nodes, from which 170 households are selected for
inclusion in the study.
• Step 3. Individuals who agree to provide survey, biomonitoring, and
health-outcome data may not spend all of their time at home, particularly
children, who are likely to attend school. Given the objective to link the home
22 | Recycling of Used Lead-Acid Batteries

survey, biomonitoring, and health-outcome data to environmental exposures,
the home survey should be conducted as soon as the households and partici-
pating individuals have been identified. Information gathered from the sur-
vey will provide important data on additional targeted sampling locations (for
example, schools, playgrounds, community centers, and other areas where
participating individuals spend time), as well as communal drinking-water
sources and specific agricultural products being consumed. Appendix B pro-
vides sample questions and additional information on designing household
questionnaires.
The home-survey questionnaire (summarized in table 2.1 and described
more fully in appendix B) provides the detailed information on such expo-
sure factors as time-activity patterns, food-frequency questionnaires, and
other demographic information used to link environmental sampling data
chapter 4) with biomonitoring data (chapter 5) and health-outcome data
(
(chapter 6) in a specific community. However, as these kinds of data are
collected, this information should be compiled into country- or region-spe-
cific databases to support development of risk assessments and other
analyses that require quantifying exposure factors (for example, con-
sumption rates, body weight, and so forth) to predict, for example, con-
taminant-specific intake rates. Standardized tables of exposure factors
(for example, the US EPA Exposure Factors Handbook [US EPA 2011])
have been derived for specific countries, but it is not clear how these data
represent communities from areas with different cultural and lifestyle
attributes.
In general, each participating individual (and/or a parent on behalf of a
child) will answer the types of questions shown in table 3.1.
• Step 4. For each ULAB-recycling site, identify a matched reference site that
has similar features and population characteristics (but which does not par-
ticipate in ULAB recycling and is expected to experience similar

TABLE 3.1 Categories of questions in the home survey (appendix B)
CATEGORY TYPE OF QUESTIONS
General Age, sex, length of residence, education, income, household size
demographics and composition
Occupation and Work and school activities, possibility for take-home / exposures
school outside of the home
Time-activity Exposure factors and lifestyle and housing details, including other
patterns and possible sources of exposure
lifestyle
Dietary Calculate intake rates based on a food-frequency questionnaire
information (FFQ), with an emphasis on information about consumption of
locally produced agricultural products (either home garden or
purchased). FFQs can be done by keeping a diary over some time
period or recall over some time period (for example, 24-hour recall).
Can be combined with a duplicate diet analysis. Also note drink-
ing-water sources (communal untreated, municipal treated) and
amount of water and water-based beverage consumption
Economic data Cost-of-illness
Health status Self-reported symptoms: may be superseded by an on-site or
off-site medical examination in conjunction with biomonitoring
(chapter 5) and health-outcome evaluation and testing (chapter 6)
Source: World Bank compilation.
Study Sampling Design | 23

environmental exposures in the absence of ULAB activities) and conduct
sampling at this site in the same manner as at the ULAB-recycling site. This
step is recommended, given the objective to determine the association
between site-related contamination and health outcomes in the general pop-
ulation. The statistical comparison between two populations similar in every
way except for the exposure of interest (for example, no ULAB activities of
any kind in the reference population) will provide important insights into
site-related contamination. It is possible to evaluate associations without a
reference population, but the results may not be as definitive.

Note that achieving an objective other than the primary objective identified
here might require a different sampling approach or different sample-size
requirements. For example, a fuller characterization of environmental CoC con-
centrations throughout a study area without also collecting biomonitoring and
health-outcome data might require additional environmental sampling (for
example, soil, dust, or water) than is recommended in chapter 4, which targets
individual households and other locations where individuals spend the most
time in order to link environmental exposures most efficiently and effectively
with biomonitoring and health-outcome data. Similarly, a study focused on char-
acterizing population exposures to CoCs based on biomonitoring data (chapter 5)
in the absence of environmental data may require a larger grid over a larger area
to ensure a representative sample of the general population.

NOTE

1. Note that because the current study design is focused on multiple CoCs and exploratory
associations between multiple endpoints (for example, environmental contamination,
individual exposures, several possible health outcomes per CoC), it is not possible to con-
duct a single statistical power calculation to determine optimal sample size at ULAB-
recycling sites. A useful reference is the WHO publication by Lwanga and Lemeshow
(1991), titled Sample Size Determination in Health Studies: A Practical Manual, which pro-
vides tables of minimum numbers of samples given specific hypotheses and inputs. In gen-
eral, power calculations involving contaminant exposures and health outcomes will
depend on the statistical approach(es) to be used in analyzing the data, anticipated effect
sizes, or the difference between two populations. Additionally, these calculations will be
based on (a) anticipated probability of a health outcome given no exposure (general prev-
alence in the population), (b) anticipated relative risk, (c) confidence level, (d) significance
level, and (e) relative precision.

REFERENCES

Lwanga, Stephen Kaggwa, Stanley Lemeshow, and World Health Organization. 1991. Sample
Size Determination in Health Studies: A Practical Manual. Geneva: World Health
Organization. https://apps.who.int/iris/handle/10665/40062.
US EPA (US Environmental Protection Agency). 2011. Exposure Factors Handbook 2011 Edition
(Final Report); EPA/600/R-09/052F. US EPA Office for Research and Development.
Washington, DC: US EPA.
4 General Guidelines for
Environmental Sampling

This chapter provides an overview of environmental media that may be affected
by used lead-acid battery (ULAB) activities, as well as specific recommendations
for sampling strategies at each household and sampling area. It also makes
recommendations for appropriate analytical methodologies, and it provides a
running example for identifying sampling locations and collecting and analyzing
samples from each participating household and targeted sampling area.

INTRODUCTION

Environmental samples will be used to determine the overall magnitude of con-
tamination at each ULAB-recycling site, with an emphasis on those areas where
populations of interest (for example, children) spend the most time. This chap-
ter provides general guidelines for identifying where to collect environmental
samples at each site (sampling design) and what types of samples should be
collected from different environmental media. Important factors that will need
to be considered on a site-by-site basis are also noted. Detailed protocols and
procedures for collecting a physical sample, handling and preparing a physical
sample, and laboratory analysis of a physical samples are not addressed here and
will be developed by the in-field research team based on existing guidance, as
summarized in appendix C. It is essential that the environmental sampling be
conducted for the same homes and individuals for whom the home survey
(appendix C), biomonitoring (chapter 5), and health-outcome (chapter 6) data
are collected. The same type of environmental samples should also be collected
from both the identified ULAB-recycling sites and matched reference sites. Note
that any necessary ethical clearances will need to be obtained prior to sample
collection by the in-field research team.
The in-field research team should provide detailed protocols and procedures
for collecting a physical sample, handling and preparing physical samples, and
laboratory analysis of physical samples. Field observation and data sheets should
also be provided by the in-field research team. It is important that all sampling
tools and containers be clean and free of contaminants prior to sampling.

25
26 | Recycling of Used Lead-Acid Batteries

SOIL SAMPLING

Exposure pathways and routes
The primary contaminants of concern (CoCs) released or discharged at ULAB-
recycling sites into the environment are lead (Pb), arsenic (As), and cadmium (Cd).
Because these metals do not degrade easily in the environment, they are likely to
be found in both surface and subsurface soils at ULAB-recycling sites. Populations
in contact with soil, particularly surface soil, at ULAB-recycling sites include both
adults and children, although the latter are more likely to have direct and more
frequent contact with surface soil because of their behaviors and activity patterns.
Dermal contact and incidental, direct, and indirect ingestion of contaminated sur-
face soils are the primary routes and pathways of exposure at ULAB-recycling
sites. Dermal exposures can occur when adults or children walk barefoot on sur-
face soil or their body touches this soil (for example, during play outdoors).
Incidental ingestion can occur when individuals get soil on their skin (for example,
fingers) or an object (for example, a toy), which then comes into contact with their
mouth or food. Direct ingestion can occur when individuals eat dirt or soil (this is
a common practice among some children and generally still involves the top layer
of soil), whereas indirect ingestion can occur when crops (for example, below-
ground root vegetables) are grown in contaminated soil or are affected by fugitive
dust or airborne soils (for example, above-ground leafy vegetables). The agricul-
tural sampling exposure pathway is discussed below.

Sampling protocol and analysis
Soil samples should be collected at each ULAB-recycling site (and reference
site) using the grid-sampling approach described above. Specifically, soil sam-
ples should be collected near the ULAB source area and in areas corresponding
to selected households (for example, yard, garden). Additional targeted soil sam-
ples should be collected at schools or daycare facilities (for example, playground)
or from any other outdoor area where participating household members spend
significant amounts of time (for example, outdoor recreational areas). It is
important that specific sampling locations within each grid or targeted location
be optimized relative to where individuals spend the most time (for example, a
child playing in a yard versus near the front door, a child playing in a playground
at school versus in a parking lot). Determining where to collect these samples
should be informed by the refined conceptual site model (CSM) and household
survey (chapter 2). Note that sample locations should preferably consist of bare
soil that is not covered with grass, vegetation, or other material. Because individ-
uals are more likely to come into contact with surface soils than subsurface soils,
only surface-soil sampling is recommended at ULAB-recycling sites.
When collecting soil samples, the following general guidelines should be
followed (see box 4.1):

• Identify four individual undisturbed (or minimally disturbed) soil-sampling
locations per grid node or targeted location. The four locations should be
representative of the entire area(s) where the population of interest spends
the most time and over which activities occur. This could involve collecting
the four samples from different spots at the same location (for example, front
yard or garden) or from different spots at multiple locations (for example,
front yard and backyard).
General Guidelines for Environmental Sampling | 27

BOX 4.1

Soil sampling at a hypothetical ULAB-recycling site
A total of 170 households and three schools were Soil sampling at homes or schools includes those
selected under the grid-based and targeted sampling areas where individuals spend the most time (for
design. Since this site is located in a more rural setting, example, front yard or back yard, garden, play-
children spend the bulk of their time at home or at ground). The emphasis is on those individuals for
school, rather than central playgrounds or other out- whom the home survey, biological sampling, and
door settings. Consequently, soil sampling is only con- health-outcome data will be collected, and these
ducted at the identified households and schools, individuals also represent the focal point for all envi-
resulting in a total of 173 composite soil samples col- ronmental sampling.
lected at this site. Each composite sample comprises For example, consider the four sampling scenarios
four individual samples per location, which are in table B4.1.1. Important note: soil sampling should
analyzed for Pb, As, and Cd in the field using an XRF occur in the same households or locations where other
analyzer. The composite samples are packaged and sent environmental samples are being collected and which
to an accredited laboratory for analysis of Pb, As, and relate to individuals providing biomonitoring and
Cd at a minimum and possibly additional bioavailability health-outcome data.
analysis of Pb and As.
TABLE B4.1.1 Soil sampling scenarios
SCENARIO INDIVIDUAL TIME ACTIVITY SAMPLING LOCATIONS
1 5-year-old child (female) and her Front yard 4 random samples collected from front
28-year-old mother yard (1 composite)
2 6-year-old child (male) and his Front yard and backyard 2 random samples collected from both
30-year-old mother front yard and backyard (1 composite)
3 35-year-old woman (no child) Backyard garden 4 random samples collected from garden
(1 composite)
4 10-year-old child (male) (mother School playground 4 random samples collected from play
is not participating) area (1 composite)
Source: World Bank compilation.

• Record all four sampling locations using GPS and document coordinates on
the site map.
• Collect the surface-soil samples at a depth of 0 to 10 cm (note: the zero level
starts from the surface after removal of any vegetation, fresh litter, and sur-
face stones).
• First, use an in-field X-ray fluorescence (XRF) analyzer to measure the soil Pb,
As, and Cd concentration for each of the four individual soil samples per sam-
pling location. Note that the XRF instrument can be simultaneously calibrated
for additional metals.
• After XRF analysis, combine the four individual soil samples per sampling
location into a single composite soil sample. Package and send this composite
sample to an accredited laboratory for analysis using guidelines provided by
the laboratory for sample preservation, packaging, and shipping. It is recom-
mended that the composite samples be analyzed using ICP-MS methods,
which are considered state-of-the art for metals analysis (appendix C).
• Follow the directions and use the sampling equipment provided or recom-
mended by the in-field research team and analytical laboratory with respect
to sample collection, preparation, and shipping (including use of personal
protective equipment, such as gloves).
28 | Recycling of Used Lead-Acid Batteries

• Optional: Due to the specific environmental properties of both Pb and As, the
ideal analytical method in soil measures the bioavailable fraction rather than
the total fraction of these metals. Bioavailability is important from a risk-as-
sessment perspective since it measures the fraction of an ingested dose that
crosses the gastrointestinal epithelium and becomes available for distribution
to internal target tissues and organs. Therefore, in addition to the traditional
laboratory analysis for all soil samples, it is recommended that half of all
household samples (50 percent) and all (100 percent) of targeted samples (for
example, schools, playgrounds) be analyzed for bioavailable Pb using EPA
Method 1340 (US EPA, n.d.) or similar, if feasible. Note that, at this time, this
method has only been validated by the US EPA for Pb, although a method has
been proposed for As. The in-field research team will need to determine
whether it is feasible to conduct this extra analysis given possible resource
constraints. Appendix C provides more information on sampling methods
and links to standards and guidelines.

DUST SAMPLING

Exposure pathways and routes
ULAB-recycling sites result in direct releases of vapors and particulate as well
indirect releases of fugitive dust from contaminated surface soil. The particu-
lates and fugitive dust may contain Pb, As, and Cd, with which both adults and
children may come into contact at ULAB-recycling sites. Dermal contact, inci-
dental ingestion, and inhalation of contaminated dust are the primary expo-
sure routes and pathways at ULAB-recycling sites. Indoor exposures to
contaminated dust (the source of which may have been tracked in from out-
doors) is of particular concern due to the duration, frequency, and proximity of
individuals’ contact with indoor surfaces. Dermal exposures can occur when
adults or children walk barefoot on dust or when an individual’s skin touches
the dust (for example, during sleep or play). Incidental ingestion can occur
when individuals get dust on their skin (for example, fingers) or an object (for
example, a toy), which then comes into contact with their mouth or food. This
is a particularly significant exposure pathway for children at ULAB-recycling
sites. Inhalation can occur from direct releases of fugitive dust or when settled
dust becomes resuspended (for example, sweeping a floor, wiping surfaces).
Although it is possible for inhalation exposures to occur, this pathway is likely
to be small relative to the other exposure pathways, so air sampling is not rec-
ommended here.

Sampling protocol and analysis
Indoor dust samples should be collected at each ULAB-recycling site and refer-
ence site for each household where soil samples were collected as well as the
targeted locations (for example, schools) where soil samples were collected, if
applicable. As was the case for soil sampling, it is important that specific sam-
pling areas within each location be optimized relative to where individuals
spend the most time (for example, children’s bedroom and living room in homes,
classroom or lunchroom at school). Note that outdoor-dust samples are of lim-
ited utility given the collection of soil samples and are not recommended here.
General Guidelines for Environmental Sampling | 29

Typical dust samples are taken on indoor surfaces such as floors, tables, and
windowsills. Specific methods for sampling dust at ULAB-recycling sites will dif-
fer depending on the surface substrate. For dwellings with dirt floors, methods
analogous to soil sampling should be used. For dwellings with impervious and
smooth surfaces (for example, wood floors, wood tables, windowsills), wipe sam-
ples are preferred. In some instances, vacuum sampling may be required, such as
for carpeted surfaces or rough surfaces—for example, brick, stone, and so forth.
Similar to soil sampling, dust samples should undergo two levels of analysis if
possible. First, individual dust samples should be analyzed in the field using an
XRF analyzer calibrated for Pb, As, and Cd. The XRF can also be simultaneously
calibrated for additional metals. Second, composite dust samples should be
packaged and sent to an independent (accredited) laboratory for analysis of Pb,
As, and Cd. As noted above, other metals may also be evaluated by the analytical
laboratory if a multi-screen metals analysis is requested. The bioavailability
method described above for soil sampling is not suitable for dust samples, so it is
not recommended here.
When collecting dust samples using the wipe method, the following general
guidelines should be followed (see box 4.2):

• Collect two or three individual dust samples within each household or other
targeted indoor location. These locations should be representative of the

BOX 4.2

Dust sampling at a hypothetical ULAB-recycling site
Indoor dust sampling should be collected from the Dust sampling in homes and schools includes those
same households and targeted locations (for example, areas where individuals spend the most time (for
schools) identified for soil sampling. In this example, example. bedroom, kitchen, living room, classroom).
this yields sampling at 170 households and 3 schools, The focus is on those individuals for whom the home
for a total of 173 composite dust samples. Each com- survey, biological sampling, and health-outcome data
posite sample consists of two individual samples per will be collected.
indoor location, which are analyzed for Pb, As, and Cd For example, consider the four sampling scenarios
in the field using an XRF analyzer. The composite in table B4.2.1. Important note: dust sampling should
samples are packaged and sent to an accredited labo- occur in the same households or locations where other
ratory for analysis of Pb, As, and Cd at a minimum. No environmental samples are being collected and which
additional indoor locations were identified where the relate to individuals providing biomonitoring and
study population spent a significant amount of time. health-outcome data.

TABLE B4.2.1 Dust sampling scenarios
HOUSEHOLD INDIVIDUAL TIME ACTIVITY SAMPLING LOCATIONS
1 5-year-old child (female) Bedroom, kitchen 3 wipes collected from bedroom floor and 2 wipes
collected from kitchen table (1 composite)
2 6-year-old child (male) Bedroom, kitchen, 3 wipes collected from the bedroom floor, 2 wipes
living room collected from the kitchen floor, and 2 wipes collected
from windowsill in living room (1 composite)
3 35-year-old mother Bedroom, kitchen 3 wipes collected from bedroom floor and 2 wipes
collected from windowsill in kitchen (1 composite)
4 10-year-old child (male) School (classroom 2 wipes collected from classroom and 2 wipes collected
and lunchroom) from lunchroom (1 composite)
Source: World Bank compilation.
30 | Recycling of Used Lead-Acid Batteries

different indoor areas where the population of interest spends the most time
and over which activities occur (for example, bedroom, kitchen, living room).
Different surfaces can be sampled for any given room (for example, floor,
table, windowsill).
• Record all sampling locations on the in-field data sheets.
• The area to be sampled (that is, the area to be wiped) must be a rectangle or
square (preferred) with measurable dimensions so the total surface area can
be easily calculated, and either marked off with tape or using a cardboard
template. It is recommended that the wipe area be at least 900 cm2 (approxi-
mately 1 square foot) to obtain enough dust for analysis of Pb.
• Follow specific guidelines regarding how much pressure to apply on the wipe,
how to properly fold the wipe, and what type of wipe to use. The goal is to
pick up all dust from the sample area, including any debris (for example, paint
chips, chunks of dust or dirt). Disposable, moistened towelettes or baby wipes
(for example, GhostWipe™) are generally recommended. The wipe material
should meet appropriate performance criteria.
• Use an XRF analyzer in the field to measure the dust Pb, As, and Cd concen-
tration for each of the individual dust samples. Note that the XRF can be
simultaneously calibrated for additional metals, and it may be possible to use
the same instrument for both the soil and dust samples, but this will depend
on manufacturer specifications.
• Combine the individual dust samples into a single composite dust sample and
package and send this composite sample to an accredited laboratory for
analysis.
• Follow the directions and use the sampling equipment provided or recom-
mended by the in-field research team and analytical laboratory with respect
to sample collection, preparation, and shipping, including use of such per-
sonal protective equipment as gloves.

WATER SAMPLING

Exposure pathways and routes
ULAB-recycling sites have the potential to affect local water supplies due to
leaching or runoff of contaminated soil to surface water or groundwater,
leaching of waste products from lagoons or pits to surface water or ground-
water, or migration of contaminated surface water from wastewater dis-
charges to other surface water sources or groundwater. Various types of
surface-water sources at ULAB-recycling sites (for example, lakes, rivers,
streams) and groundwater sources of various depths may therefore contain
Pb, As, and Cd. Dermal contact and ingestion of contaminated water are the
primary exposure routes and pathways at ULAB-recycling sites. Dermal
exposures can occur when adults or children bathe, wash clothes or dishes,
swim, or wade in surface-water sources or if groundwater is used for bathing
or washing. Direct ingestion can occur when individuals drink water obtained
from surface or groundwater sources. This latter exposure pathway is likely
to be the most relevant for contributing to population exposures. Note that
either surface water or groundwater (or both) can be used as sources for
drinking water at ULAB-recycling sites.
General Guidelines for Environmental Sampling | 31

Sampling protocol and analysis
Water samples should be collected at each ULAB-recycling site and reference
site for each household where soil and dust samples were collected as well as
other targeted locations (for example, schools) where soil or dust samples were
collected, if applicable. Specifically, water samples should be collected at resi-
dences, schools, or daycare facilities where study participants spend significant
amounts of time and where water is used for drinking, bathing, washing, or rec-
reational purposes. It is important to recognize that the source of drinking water
may vary for individuals within a site as well as across ULAB-recycling sites, and
the collection of drinking-water samples will therefore depend on the water
source and population-activity patterns. Typical sources include tap water from
a municipal source (groundwater or surface water); an individual or shared
household well; a communal surface-water source (for example, a lake, river, or
stream); or a communal groundwater well. In some cases, multiple sources will
need to be considered. Determining where to collect these samples should be
informed by the refined CSM and household survey (chapters 2 and 3).
When collecting water samples, the following general guidelines should be
followed (see box 4.3):

• If possible, all water samples should be collected at the individual household
or targeted location (for example, school) where the water is available for
consumption or use. If the source is tap water, water samples should be col-
lected at the point of release (that is, the tap). If there is a communal water
source (for example, a community well) applicable to multiple participating
households, samples should be collected from the water source. Samples can
also be collected directly from an off-site surface-water body if it is commonly
used for recreational or other purposes (or if the in-field research team would
like additional data on potential site contamination).
• Water samples should reflect the actual water being used for drinking or
other purposes; therefore, if the water has undergone any type of treatment
(for example, chlorination), then the treated water should be sampled.

BOX 4.3

Water sampling at a hypothetical ULAB-recycling site
Water samples are collected at those households and 170 households that are sampled for soil and dust, a
targeted locations (for example, schools) with inde- duplicate water sample is collected from 85 residences
pendent sources of water used for drinking water, (170 ÷ 2) that have a private well. For the remaining
bathing, or washing or surface-water sources used for households, a duplicate water sample is collected from
swimming, wading, or other recreational purposes. In the central local surface-water source. A duplicate
this example, approximately half (50 percent) of water sample is also collected from each of the three
households obtain water from a private well for drink- targeted schools. Important note: water sampling
ing water and other activities, whereas the other half should occur in the same households or locations where
(50 percent) obtain their water directly from a central other environmental samples are being collected and
surface-water body. The targeted schools have private which relate to individuals providing biomonitoring and
wells used for drinking water only. Therefore, of the health-outcome data.
32 | Recycling of Used Lead-Acid Batteries

• For each water sample, duplicate samples of approximately 1 liter each should
be taken. The exact sample quantity should be determined with the labora-
tory conducting the analyses.
• Follow the directions and use the sampling equipment provided or recom-
mended by the in-field research team and analytical laboratory with respect
to sample collection, preparation, and shipping (including use of personal
protective equipment, such as gloves).

AGRICULTURAL PRODUCT SAMPLING

Exposure pathways and routes
Because ULAB-recycling sites have the potential to affect local soil and water
supplies, as discussed above, activities at ULAB-recycling sites may also
affect locally produced agricultural products that come into contact with
contaminated soil or water. In particular, contaminated irrigation water from
affected surface water or groundwater sources may be used to irrigate crops.
Crops may also be grown in contaminated soil or subject to aerial deposition,
with resulting uptake in root systems or deposition on foliage. Additionally,
animals may be grazed on contaminated soil or given contaminated water to
drink. Various types of agricultural products at ULAB-recycling sites may
therefore contain Pb, As, and Cd. Examples of locally produced agricultural
products include fruits, vegetables, and grains; animals consumed for meat
(for example, chickens, cattle); and various animal products (for example,
milk, cheese, eggs). These products may be produced at multiple scales, rang-
ing from small family gardens at the household level to large commercial
operations that sell products at local or off-site markets. Direct ingestion of
contaminated foodstuffs may represent an important exposure route and
pathway at ULAB-recycling sites.
The data on contaminant concentrations in agricultural products (which
can include fruits, vegetables, grains, dairy, meat, and eggs) are combined with
information on food consumption collected as part of the household survey
(chapter 3). Statistical models predicting intake or exploring correlations are
used to inform how exposure factors combine with environmental concentra-
tions and how those relate to body burdens from the biomonitoring
data. Another approach for this is to analyze food consumption based on a
composite sample using a duplicate diet approach. Participants consume food
as they normally would over some time period—for example, 24 or 48 hours—
making sure to both record what and how much of each food item they are
consuming as well as putting aside a small amount of food from each snack and
meal. These individual samples are combined into a composite sample and
analyzed for the CoCs. This method provides detailed information on intake,
and when combined with biomonitoring data, can be used to parameterize
models that predict CoC concentrations in humans (for example, intake or
physiologically based pharmacokinetic [PBPK] models).
Duplicate diet studies are not recommended here since the focus is on

exposures occurring over longer time periods. In addition, data on CoC
concentrations in agricultural products can be used to estimate population

intake rates, given assumptions on consumption frequencies, and therefore
represent more useful data.

General Guidelines for Environmental Sampling | 33

Sampling protocol and analysis
Selected agricultural samples should be collected at each ULAB-recycling site
and reference site for each household where soil, dust, and water samples have
been collected and perhaps other targeted locations. Specifically, the only agri-
cultural products that should be sampled are ones that are locally grown (either
at the household or community level), are frequently consumed by the popula-
tion of interest, and have the potential for contamination. Determining which
specific agricultural products to sample at each site will require further refine-
ment of the CSM, information obtained from the household survey, and a
detailed understanding by the in-field research team of the ways in which the
ULAB facility might affect local resources.
Examples of different types of foodstuffs that might be sampled at individual
ULAB-recycling sites include the following:

• Rice grown in surface water affected by wastewater from ULAB activities
• Chickens foraging directly at ULAB sites (focus on sampling what is most
commonly consumed—for example, chicken liver, eggs)

BOX 4.4

Agricultural sampling at a hypothetical ULAB-recycling site
Agricultural sampling should reflect products fre- households or locations where other environmental
quently consumed by individuals in the study that samples are being collected and which relate directly to
are affected by ULAB activities (for example, via the individuals providing biomonitoring and health-out-
use of contaminated irrigation water). In this exam- come data. In the context of linking environmental
ple, about half (50 percent) of the households have exposures to health outcomes in an exposed popula-
their own gardens, while those households located tion, there is limited utility to sampling products that
closest to the facility purchase products from a local are (a) not being consumed by individuals providing
market that are grown near the ULAB-recycling biomonitoring and health-outcome data, and (b) not
source area. Thus, agricultural samples are collected affected by ULAB activities (for example, beyond the
from 85 households that have home gardens, whereas zone of inf luence of ULAB-waste releases and dis-
additional targeted samples (2 samples each) are charges). Other studies with additional objectives—for
taken from the local market and agricultural field example, broader site characterization or evaluating
closest to the ULAB-recycling facility. See the sam- the possibility of contaminated products being sold or
pling scenarios in table B4.4.1. Important note: used outside the study area—may benefit from broad-
a gricultural sampling should occur from the same
ening the scope of sampling.

TABLE B4.4.1 Agricultural product sampling scenarios
HOUSEHOLD OR
SAMPLING LOCATION INDIVIDUAL PRODUCT SAMPLES
1 5-year-old child (female) Local chicken liver from the refrigerator
2 6-year-old child (male) Leafy greens from the home garden
Composite sample of leafy greens from the garden where soil
3 35-year-old mother
samples were obtained
Market Multiple households 1 leafy-green composite, 40 gr chicken liver
Agricultural field Multiple households 1 leafy-green composite, 1 cassava composite
Source: World Bank compilation.
34 | Recycling of Used Lead-Acid Batteries

• Root vegetables grown in soils from backyard gardens irrigated with surface
water affected by wastewater from ULAB activities
• Leafy greens grown downwind within a depositional area of ULAB activities
• Beans or other legumes grown in soils irrigated with contaminated surface
water and within the depositional area of the ULAB facility.

When collecting agricultural samples, the following general guidelines
should be followed (see box 4.4):

• If possible, all agricultural samples should be collected at the individual
household where the foodstuff is ready or available for consumption (for
example, kitchen), or taken directly from the garden for fruits and vegeta-
bles. Samples may be collected from the original source (for example,
community field crop) or marketplace for commonly consumed foodstuffs
that are not available at the household level (or if the in-field research
team requires additional data on potential site contamination). An empha-
sis should be placed on samples that may be applicable to the largest num-
ber of participating households (for example, foods obtained from a
market or similar source).
• For samples taken from household gardens, collect multiple samples of the
same item if possible and combine as a composite sample (for example, col-
lect a few lettuce leaves from several different heads of lettuce).
• Agricultural samples should reflect only the edible portion of foodstuffs and
target those portions of the food of greatest concern (for example, green
leaves, chicken liver).
• Sufficient amounts need to be collected per food sample; recommended
levels typically range from 40 to 100 grams for fruits, vegetables, and meat
products. The exact amount will depend on the specific laboratory
requirements.
• Follow the directions and use the sampling equipment provided or recom-
mended by the in-field research team and analytical laboratory with respect
to sample collection, preparation, and shipping (including use of personal
protective equipment, such as gloves).

BOX 4.5

Total environmental samples

MEDIUM HOUSEHOLDS OTHER LOCATIONS
Soil 170 composites (4 individual XRF) analyzed 3 composite samples per school (4 individual XRF)
for all metals; 85 (subset of 170) analyzed for analyzed for all metals and Pb and As bioavailability
Pb and As bioavailability
Dust 170 composites (4 individual XRF) analyzed 3 composite samples per school (4 individual XRF)
for all metals
Water 85 from individual homes 3 from schools; 1 from the central shared well
Agricultural products 85 from home gardens / individual homes 4 samples from market, agricultural field
Total 510 samples 17 samples
General Guidelines for Environmental Sampling | 35

Fish
In general, Pb, As, and Cd do not bioaccumulate in fish tissue. Therefore,
sampling for these CoCs in fish tissue at ULAB sites is not recommended.

RESOURCES

See appendix C for a listing of country-specific guidelines and guidance for sam-
pling and laboratory methods, including URLs.

REFERENCE

US EPA (US Environmental Protection Agency). N.d. EPA Method 1340 SW-846 Test Method
1340: In Vitro Bioaccessibility Assay for Lead in Soil. Washington, DC: US EPA. https://www
.epa.gov/hw-sw846/sw-846-test-method-1340-vitro-bioaccessibility-assay-lead-soil.
5 General Guidelines for
Biological Sampling

This chapter provides an overview of biological sampling conducted for each
participating individual in order to provide data on both internal exposure con-
centrations (for example, biomonitoring) and potential health outcomes associ-
ated with exposures to the contaminants of concern (CoCs). These data are
linked to household- and participant-specific environmental samples collected
per the recommendations in chapter 4 and clinical health-outcome data from
chapter 6.

INTRODUCTION

Biological samples provide the best evidence of combined exposure to CoCs
from all sources and exposure pathways or routes at each used lead-acid battery
(ULAB) site. The biological sampling data will also be used to confirm or validate
estimates of CoC exposure based on the exposure-factor data (chapter 3) and
environmental-sampling data (chapter 4) collected at each site. That is, the goal
is to avoid collecting biological samples at future sites if less intrusive expo-
sure-factor data and environmental data can be collected that are sufficiently
predictive of total exposures. Additionally, as discussed in chapter 6, biological
samples will be analyzed for possible indicators of health outcomes or nutri-
tional and health status. This chapter provides general guidelines for what types
of biological samples should be collected for each contaminant and recom-
mended approaches for sample collection. Important factors that will need to be
considered on a site-by-site basis are also noted. Detailed protocols and proce-
dures for collecting a biological sample, handling and preparing biological sam-
ples, and laboratory analysis of biological samples are not addressed here and
will be the final responsibility of the in-field research team and associated
trained professionals (although some useful resources are presented in
appendix D). It is essential that the biological sampling be conducted for the

same individuals for whom the household survey (chapter 3), environmental
(chapter 4), and health-outcome (chapter 6) data are collected. The same type of
biological samples should also be collected from both the identified ULAB sites

37
38 | Recycling of Used Lead-Acid Batteries

and matched reference sites. Note that any necessary ethical clearances will
need to be obtained prior to sample collection by the in-field research team.
The key factors to consider when evaluating potential biomarkers include (a)
how well the biomarker correlates with the dose (or external exposure) to
appropriate forms of the contaminant, for example, arsenic (As) versus organic
As); (b) how well the biomarker correlates with the contaminant concentration
in tissue relative to the health outcome; (c) how well the biomarker measure-
ment correlates with changes in the effective dose at the target tissue over time;
(d) an understanding of the cultural characteristics of the population; (e) tech-
nology availability; and (f ) invasiveness of the sample collection.
Because the goal of the biological sampling design is to both (a) quantify the
magnitude of exposure among individual population members to each CoC at
ULAB sites, and (b) to identify potential subclinical evidence of disease that
could be associated with exposures to these contaminants, the biological sam-
pling should attempt to provide the most accurate and relevant data on biomark-
ers of exposure or biomarkers of effect for each CoC (and also provide standard
information on nutritional and health status). Biological sampling matrices can
include urine, blood, toenails, and hair. Breast milk and cord blood are additional
matrices but are not recommended in this study due to the limited utility in relat-
ing exposure to health outcomes for an infant population. Each of these matrices
offers advantages and limitations depending on the contaminant, health out-
come or intermediate health outcome, and biomarker to be measured. An
emphasis is placed on point-of-care methods (for example, comparable to
in-field for environmental sampling) that provide rapid immunoassay-based
results.

BIOLOGICAL SAMPLING MATRICES

Biomarkers of exposure are measurements in biological matrices that reflect the
total absorbed or internal dose of a contaminant from all sources and exposure
routes and pathways. In some cases, metabolites as opposed to parent com-
pounds may provide the most reliable measures of exposure. Biological sampling
matrices can include urine, blood, toenails, and hair. Breast milk and cord blood
are additional matrices but are not recommended here due to the invasiveness of
data collection and limited utility in relating exposure to health outcomes for an
infant population. Each of these matrices offers advantages and limitations
depending on the contaminant and biomarker to be measured.
The following sections summarize potential biomarkers of exposure specific
to each CoC. Because several biological media are possible for each CoC, each of
which has advantages and limitations, a hierarchy of preferred options is pre-
sented in color-coded tables, as defined in table 5.1.

Lead
Lead (Pb) is the primary CoC at ULAB sites. Whole blood (not serum blood) is the
most reliable biological matrix for evaluating Pb exposures. Venous blood sam-
ples collected by a trained medical professional and submitted to an accredited
laboratory are considered the gold standard for Pb analysis. Somewhat less reli-
able, but also less invasive, is a dried blood spot collected as a capillary blood sam-
ple in-field and sent to a laboratory. Finally, an in-field testing instrument (that is,
General Guidelines for Biological Sampling | 39

TABLE 5.1 Hierarchy of preferred biomarkers of exposure
Gold standard. This biomarker has been well vetted in the literature with one or more
validated, cost-effective laboratory methods with high levels of precision. This is the
preferred biomarker given the primary research objectives in this document.
Screening level. This biomarker is an appropriate default for low-resource applications.
It is the least invasive, lowest cost, typically with in-field analysis. However, only the
total metal can be measured and will have high detection levels and may not have the
precision to evaluate statistical associations with outcomes.
Low preference. This biomarker (white color) can be used as a last resort but is
generally not preferred due to limitations with respect to associations (that is, they are
not the best measure of exposure or predictive of outcomes based on literature
studies).
To be avoided. This biomarker is not appropriate since it does not measure the
exposure of interest, is expensive, or does not have a validated method.
Source: World Bank compilation.

TABLE 5.2 Overview of biomarkers of exposure for Pb
BIOLOGICAL
MATRIX AND CAN MATRIX BE USED TO
CONTAMINANT ADVANTAGES LIMITATIONS EVALUATE HEALTH OUTCOME?
Lead
Venous blood Well-vetted, standardized routine Requires medical professional; Can do complete blood count
analysis with highest precision and requirements for processing, storage, and metabolic panel; additional
reliability handling markers related to anemia
Capillary blood LeadCare in-field analyzer; immediate Pb only; shows higher variability HemoCue in-field for hemoglo-
(including dried results. Alternatively, can use dried compared to venous blood sample bin (anemia) as marker of
blood spot) blood spot and send to laboratory. sent to laboratory. Field conditions potential intermediate effect.
Dried blood spot shows high variability may compromise ability to measure Can measure calcium
compared to venous blood accurately
Urine No advantages other than being less Can be used but not preferred Standard renal panel (for
invasive example, albumin, proteinuria)
as marker for renal damage—can
use spot sample
Hair No advantages other than being less Can be used but not preferred; No relevant outcome measure-
invasive reflects direct contact of hair with ment
dust rather than absorbed exposure
Toenail/ Least invasive; lowest cost and High detection levels. Needs to be No relevant outcome
fingernail immediate results in-field using XRF. No correlated with blood levels. measurement
specific storage or transport require- LeadCare in-field always preferred
ments. Provides information on
short-term and long-term exposures
Source: World Bank compilation.

LeadCare analyzer) calibrated for Pb can be used to collect a capillary blood sam-
ple, with immediate documentation of the results in a computer or on field-data
sheets. The least-cost and lowest-resource-intensive choice is an in-field XRF
analyzer to analyze toenail samples, but detection levels will be higher, with
greater variability in results. Urine and hair samples are the least reliable biologi-
cal matrices for evaluating Pb exposures and should generally be avoided for this
purpose. A test for chronic Pb exposures in adults is the zinc protoporphyrin
(ZPP) blood test, which is recommended by both the Secretariat of the Basel
Convention (2003) and the Occupational Health and Safety Administration
(OSHA) in the United States to evaluate long-term Pb exposures in adults. It is not
typically suitable for children, however, since it reflects exposures over longer
time periods. Table 5.2 provides an overview of the advantages and limitations of
40 | Recycling of Used Lead-Acid Batteries

different biological matrices for sampling Pb. The final column is used to identify
whether the matrix is useful for additionally capturing a biomarker of effect (for
example, obviating the need for additional sample collection).

Arsenic
The best measure of arsenic (As) exposure is the metabolite monomethylarsonic
acid (%MMA) obtained from a speciated creatinine-adjusted urine sample.
Although this method requires a separate laboratory analysis (for example,
high-performance liquid chromatography [HPLC] with hydride atomic absorp-
tion spectrometry [HG-AAS] or inductively coupled plasma mass spectrometry
[ICP-MS] or similar), it is a widely used measure of exposure in epidemiological
studies. It is also more often associated with health outcomes than measures of
total As. An in-field XRF analyzer can be used to analyze toenail samples for As,
although this is a much less robust approach. Blood and hair samples are the
least reliable biological matrices for evaluating As exposures and should gener-
ally be avoided for this purpose. Table 5.3 provides an overview of the advantages
and limitations of different biological matrices for sampling As.

TABLE 5.3 Overview of biomarkers of exposure for As
BIOLOGICAL
MATRIX AND CAN MATRIX BE USED TO
CONTAMINANT ADVANTAGES LIMITATIONSS EVALUATE HEALTH OUTCOME?
Arsenic
Venous blood Can use the same sample to evaluate Not considered reliable; clearance of As Can do complete blood count,
intermediate outcomes is rapid. Time between exposure and assays for pre-cancerous marker
sampling critical. Seafood sources (for example, DNA adduct
greatly influence blood levels formation, micronucleus
formation)
Capillary blood Not as invasive as venous blood; Still has to be sent to a laboratory; Can measure hemoglobin,
(for example, samples can be collected by sample volume can be an issue. Not calcium in-field but these have
dried blood nonmedical personnel. Storage and considered reliable for As given rapid not been associated with As
spot) transport significantly simplified clearance. Time between exposure and effects
sampling critical. Seafood sources
greatly influence levels. Does not show
good correlation with split-sample
venous blood
Urine Less expensive to measure total As. Total As does not always predict Standard renal panel (for
Can measure multiple metals using outcomes; associations not statistically example, albumin, proteinuria)
the same method significant. May do better collecting as marker for renal damage. If
toenail collecting for speciated As, then
only requires small additional
volume. Can also use spot
sample or dipstick
Speciated urine %MMA shows consistent relationship More expensive than total As; analysis is Standard renal panel
with lung, skin, bladder cancer from unique to As
oral exposures. Can use same sample
for standard renal panel; creatinine
Hair No advantages other than less Does not reflect internal / absorbed No relevant outcome measure-
invasive dose; reflects external exposures ment
Toenail/ Least invasive, lowest cost and High detection levels. Measures total No relevant outcome measure-
fingernail immediate results in-field if using As. Random within-person exposure ment
XRF. No specific storage or transport variability leads to attenuation of
requirements. Provides information measures of association between
on short- and long-term exposures exposure and outcome
Source: World Bank compilation.
General Guidelines for Biological Sampling | 41

TABLE 5.4 Overview of biomarkers of exposure for Cd
CAN MATRIX BE USED TO
BIOLOGICAL MATRIX EVALUATE HEALTH
AND CONTAMINANT ADVANTAGES LIMITATIONS OUTCOME?
Cadmium
Venous blood No advantages Reflects only recent exposures. Focus is on renal effects;
Meta-analyses show no association urine more useful
with outcomes. Invasive sample
Capillary blood (for No advantages other than somewhat Reflects only recent exposures. Focus is on renal effects;
example, dried blood less invasive than a venous sample Meta-analyses show no association urine more useful
spot) with outcomes
Urine Most widely used and well-vetted. Moderately invasive; requires urine Standard renal panel (for
Recommended by WHO, US EPA. sample example, albumin,
Not invasive. Measures long-term proteinuria) as marker for
low-level exposure. Can use spot renal damage; can use spot
sample sample for albumin alone

Hair No advantages other than less Not recommended No relevant outcome
invasive measurement
Toenail/fingernail Least invasive, lowest cost; can use High detection levels for XRF. Does not No relevant outcome
XRF in-field. No specific storage or correlate well with urine levels. Not measurement
transport requirements. Provides associated with renal outcomes. Can
information on short-term and be difficult to obtain required sample
long-term exposures mass
Source: World Bank compilation.

Cadmium
Given that cadmium (Cd) affects the renal system, the best exposure metric is
creatine-adjusted urine, which provides the most appropriate measure of Cd
exposures and has been widely used in many epidemiological studies. Table 5.4
provides an overview of the advantages and limitations of different biological
matrices for sampling Cd.

BIOLOGICAL SAMPLING PROTOCOL

The collection of biological samples should be done in conjunction with the
household survey (chapter 3) and evaluation of health outcomes (chapter 6). As
noted earlier, the selection of biological-sampling methods emphasizes rapid,
less invasive, in-field approaches where possible, recognizing that samples sent
to a laboratory represent the “gold standard.” Standardized guidelines exist for
collecting biological samples (see appendix D for examples of specific guidelines
from the United States Centers for Disease Control, World Health Organization,
and others) and the in-field team should be trained in those or rely on local med-
ical professionals.
When collecting and analyzing biological samples, the following general
guidelines should be followed:

• When collecting blood samples, particularly capillary blood samples, the skin
must first be thoroughly washed and dried to avoid contamination. This is a
common problem encountered during field studies of this kind.
• If collecting a dried blood spot, discard the first drop. Contamination is very
likely when collecting these samples. Follow all laboratory guidelines and
protocols.
42 | Recycling of Used Lead-Acid Batteries

• When collecting toenail samples, it may be possible to use the same XRF ana-
lyzer as is used for the soil and dust sampling, but this instrument will require
a separate calibration and samples cannot be collected simultaneously. Thus,
it may be more appropriate to have multiple XRF devices to address multiple
purposes.
• When collecting urine samples, the first morning void is preferred because it
is generally more concentrated. This will require some planning to obtain a
sample during this time period. Note that 24-hour urine samples may be too
cumbersome to collect and spot urine samples may be less robust.
• If the same venous blood or urine samples will be used to assess indicators of
nutrient status or health outcomes, greater sample volumes of whole blood or
urine may be needed.
• For both blood and urine samples, stringent guidelines related to the preser-
vation and transportation of biological samples must be followed.
• Urine samples should be adjusted for creatinine levels to account for dilu-
tion-dependent sample variation in urine concentrations (that is, individuals
who are well hydrated will have more diluted urinary concentrations of envi-
ronmental contaminants).

RESOURCES

See appendix D for a listing of relevant resources related to biomonitoring,
including recommended methods and guidance from health agencies, and
specific testing protocols.

6 General Guidelines for
Assessing Health Outcomes

This chapter provides general guidelines for collecting health-outcome data
based on a combination of possible approaches, including self-reported health
status and medical histories that can be administered by nonmedical personnel,
medical examinations and biological sampling conducted by health profession-
als, and diagnostic screening tools related to specific health outcomes that might
be administered by a physician or psychologist. The exact approach to be fol-
lowed at each site will depend on participant access to health care facilities
(where examinations and screenings are likely to occur); availability of in-field
methods (for surveys, exams, sample collection, or other tools that can be imple-
mented onsite); and researcher access to validated, culturally sensitive diagnos-
tic tools. Health-outcome data will be collected from individuals within the
households identified for sampling in chapter 3. The same health-outcome data
should be collected from individuals at both the identified used lead-acid battery
(ULAB) sites and matched reference sites to evaluate differences that may be
attributable to differences in contaminant of concern (CoC) exposures. Note
that any necessary ethical and institutional review board (IRB) clearances will
need to be obtained prior to data collection.

INTRODUCTION

There are three categories of possible health outcomes that can be measured.
The first is a medical diagnosis related to direct or measurable clinical outcomes
known to be associated with exposure to the CoC of interest—for example, blad-
der cancer or hyperkeratosis associated with arsenic (As) exposures, and

cognitive deficits as measured by age-specific standardized testing instruments
associated with exposures to lead (Pb) and As. The second is an intermediate,
nonspecific observation or measurement associated with the health outcome of
interest—for example, increased blood pressure associated with cardiovascular
outcomes that may be related to exposure to Pb and cadmium (Cd). The third is
an intermediate biochemical measurement (that is, biomarker of effect) that
requires laboratory or in-field analysis of a biological matrix—for example,

43
44 | Recycling of Used Lead-Acid Batteries

diagnosis of anemia based on hematocrit level in blood that may be related to Pb
exposures, and micronucleus formation in blood that may be associated with
genotoxic effects of As.
It should be noted that biomonitoring is a rapidly expanding field with
improvements in molecular techniques leading to the identification of novel bio-
markers, including oncogenes, tumor-suppressor genes, microRNAs and long
non-coding RNAs, DNA methylation, and others. The evolving discipline of
“omics,” including proteomics and genomics, has led to the identification of
genetic and epigenetic alterations, typically based on blood samples and utilizing
various laboratory-based assays. These methods are not yet mature enough to be
recommended for routine use in low- and middle-income countries (LMICs) but
could be considered in the future. A key drawback at the current time is the
requirement for specialized laboratory equipment, invasiveness of biological
sampling (typically a venous blood sample is required), and the increased
expense of such analyses.
Although it would be desirable to measure unique health outcomes associ-
ated with exposures to each CoC from one or more of these categories, a key
challenge of this type of investigation is that the primary CoCs from ULAB
sites (for example, Pb, As, and Cd) as well as other factors share common bio-
logical targets, so it is difficult to discern the relative contribution, if any, of
each CoC exposure to the identified health outcome. For example, exposure to
multiple CoCs has been associated with cognitive and neurodevelopmental
outcomes in children using age-specific standardized instruments (for exam-
ple, Bayley Scale of Infant Development or IQ tests). Additionally, intermedi-
ate measures of health outcomes in the absence of overt toxicity (biomarkers
of effect) may show associations with exposure concentrations as measured
through biomonitoring (chapter 5) or environmental concentrations of CoCs
(chapter 4). Therefore, it is important to note that while biomarkers of expo-
sure are CoC-specific, biomarkers of effect may not be CoC-specific. Moreover,
there are many other factors that could influence the same health outcomes
associated with these CoCs, ranging from such lifestyle factors as diet, exer-
cise, and smoking status to such common environmental exposures as air pol-
lution. It is anticipated that these latter factors will be captured during the
household survey and subsequently controlled for through statistical analyses
of the data.
Given these constraints, the primary goal of these guidelines is to try to collect
enough information about direct and indirect health outcomes from each partic-
ipant to evaluate (a) differences in these outcomes between the ULAB-exposed
population and a nonexposed reference population; and (b) possible relation-
ships between measured environmental concentrations (chapter 4), biomarkers
of exposure (chapter 5), and health outcomes at the individual level.

SELF-REPORTED HEALTH STATUS AND MEDICAL HISTORY

Each participant should provide basic health-related information, including the
following:

• Age, weight, and body mass index
• Smoking status
• Alcohol consumption
General Guidelines for Assessing Health Outcomes | 45

• Physical activity
• Medication use (for example, antihypertensive)
• Medical diagnoses and dates of diagnosis, including high blood pressure,
chronic illnesses such as cancer or kidney disease, or any other medical or
mental health conditions. If the medical history is for a child under the age of
18, a parent or caregiver will likely need to provide this information
• Health status relative to any known deficiencies (for example, rickets,
pyorrhea), particularly in children, including symptoms such as bleeding
gums
• Past and present illnesses, whether formally diagnosed or not, and dates:
information on any serious or chronic illnesses the person has experienced—
for instance, if the individual has ever had tuberculosis or if the individual has
asthma or diabetes
• Family medical history: information on any diagnosed health conditions of
immediate family members (for example, parents, siblings), including condi-
tions such as cancer, heart disease, and mental illnesses.

This information can be obtained in-field as part of the household survey
(see appendix B and the questions starting with number 4 in the Example of a
Household Questionnaire). Alternatively, if a medical examination will be
conducted, a more complete medical history (for example, see Bickley 2012)
can be obtained under the supervision of a medical professional, which is
likely to provide more detailed and refined data based on clinical
observations.

MEDICAL EXAMS AND BIOLOGICAL TESTING

Selected health outcomes can be measured using direct observation or testing,
some of which (for example, blood pressure) may be accomplished by
nonmedical personnel in the field, while others may require specific training

for nonmedical professionals (for example, arsenicosis versus a generalized
skin rash) or even a more formal medical diagnosis.
Biological sampling may also provide useful information about potential
health impacts or precursor effects. Biomarkers of effect are measurements in
biological matrices that serve as an indicator of a specific health outcome or
preclinical (upstream) change or effect at the molecular or cellular level or have

been shown to reliably predict health outcomes. These measurements are typi-
cally obtained from standard blood or urine tests, such as complete blood count
(CBC), or standard renal panel. Although most biomarkers of effect are nonspe-
cific with respect to exposure (that is, it is not possible to discern the source of
the observed biomarker or whether it is attributed to the contaminants of inter-
est), they can serve as potential indicators for the health outcome of interest.
However, it is important to recognize that upstream or precursor effects may not
lead to downstream outcomes or permanent effects. Biomarkers of effect
can therefore provide useful (although not definitive) information and indica-
tors on the continuum from exposure to overt health outcome. Table 6.1 provides
an overview of the recommended biomarkers of effect (including nutritional and
health status), with an emphasis on lower-cost, point-of-care, and in-field
approaches, followed by a discussion of each proposed biomarker by category of
health outcome.
46 | Recycling of Used Lead-Acid Batteries

TABLE 6.1 Overview of recommended biomarkers of effect
BIOMARKER SAMPLING APPROACH PURPOSE NOTES
ALA (aminolevulinic Impacts associated with acute Pb Not as useful for chronic Pb
24-hour urine
acid) poisoning exposures
Reagent strip point-of-
Proteinuria care testing device; Renal effects (Cd, Pb) Total protein in urine
dipstick
Renal effects; microalbuminuria
Reagent strip point-of-
noted as sensitive biomarker with Predominant protein found in
Albumin care testing device;
respect to renal outcomes (Cd, urine
dipstick
Pb)
Used with serum Selected by European Food
β2-m (urinary β2-micro- creatinine to evaluate Nonspecific urinary biomarker of Safety Authority (EFSA) as
globulin) glomerular filtration rate early proximal tubule effects (Cd) preferred biomarker of effect for
(GFR) Cd
Urine; can be calculated
Standard equations not
Glomerular filtration rate by laboratory or from Predictive measure of progressive
appropriate for children; see
(GFR) albumin, creatinine, renal dysfunction. (Cd, Pb)
text. Use Gao et al. 2013.
height, weight
Serum; can use dried Necessary for GFR estimating
Creatinine
blood spot equations (Cd, Pb)
Requires urine sample Necessary to adjust for urine Needed for biomarkers of
Creatinine
(no dipstick) volume exposure in urine
25hydroxyvitamin D3 Vitamin D deficiency, micronutri-
May be possible infield Vemulapati et al. 2017.
(25(OH)D3) ent status
Standard renal panel;
Calcium Health status, micronutrient status
may be in-field method
Complete blood count from a
Complete blood count Measures anemic status (Pb), also venous sample provides
Hemoglobin in laboratory; in-field affects absorption of metals additional markers such as
HemoCue generally hematocrit, platelet count,
corpuscular volume, and so forth
Dried blood spot; may Inflammatory biomarker; See appendix D for references
C-reactive protein be a rapid point-of-care associated with lung health (As) and links to point-of-care
method and cardiovascular outcomes (Pb) C-reactive protein (CRP) methods
Should be possible to
Associated with carcinogenic
DNA adduct formation use dried blood spot Recommended by the IPCS
outcomes (As, Cd)
sent to lab
Should be possible to
Associated with carcinogenic
Micronucleus formation use dried blood spot Recommended by the IPCS
outcomes (As, Cd)
sent to lab
Source: World Bank compilation.

Renal effects
Urinary and serum enzymes and low molecular weight (LMW) proteins have
been used as early markers of kidney dysfunction and are useful for the detection
of small changes in the function of tubular epithelial cells predictive of many
pathological conditions. Enzyme and protein excretion increases before eleva-
tion of other markers of renal function, such as creatinine, and well before overt
disease. Enzyme excretion rates in urine or blood are elevated following release
from cells damaged by exposure to exogenous substances such as CoCs, or from
regenerating cells that lead to increased enzyme induction. LMW proteins are
freely filtered across the glomerular capillary wall and almost completely reab-
sorbed by the proximal tubular cells. Functional or structural damage from
General Guidelines for Assessing Health Outcomes | 47

exposure to CoCs can lead to reduced reabsorption in the proximal tubule, lead-
ing to increases in proteins in both blood and urine. These biomarkers are asso-
ciated with exposures to Cd and Pb.

Proteinuria
Proteinuria is a classic early sign of kidney dysfunction and, although reversible,
its presence carries important prognostic information. Proteinuria is typically
measured as total protein and can be evaluated rapidly in-field using a reagent
strip in a spot urine sample. Early morning void is preferred, and urinary creati-
nine should be measured concurrently. Validated in-field or point-of-care assays
exist, but sending urine samples to a laboratory for a standard renal panel will
always yield more information—for example, albumin, BUN (blood urea nitro-
gen) /creatinine ratio (calculated), calcium, carbon dioxide, chloride, creatinine,
estimated glomerular filtration rate (calculated), glucose, phosphate, potassium,
sodium, and BUN. Proteinuria can be measured as total protein or albumin
(microalbuminuria), which has been an excellent predictor of kidney function
and is the preferred biomarker.
β2-m (urinary β2-microglobulin) is a urinary biomarker recommended by the
European Food Safety Authority (EFSA) biomonitoring program for evaluating
intermediate effects associated with Cd exposures, but may also reflect effects
from renal-acting agents other than the CoCs of interest at ULAB sites.

Glomerular filtration rate (GFR)
The best measure of kidney function is the glomerular filtration rate (GFR),
which reflects potential dysfunction and exposures in different areas of the
kidney as compared to proteinuria. However, measuring GFR can be chal-
lenging and is therefore typically estimated from a blood sample by using
equations (eGFR) based on the plasma concentration of creatinine or cysta-
tin C, another common protein. Well-vetted standardized equations exist for
adults based on height, weight, and serum creatinine, but these are not
appropriate for use in children. To estimate GFR in children, the recommen-
dation is to use a set of equations, as developed by Gao et al. (2013) and avail-
able as a stand-alone online calculator as endorsed by the International
Society of Nephrology.

Cardiovascular effects
Potential cardiovascular effects associated with exposure to CoCs found at
ULAB sites can vary from atherosclerosis to myocardial infarction to ischemic
events broadly referred to as cardiovascular disease (CVD). C-reactive protein
(CRP) is a blood biomarker of inflammation shown to be predictive of a range of
cardiovascular outcomes. While not as sensitive or as specific as homocysteine,
CRP can be measured using in-field assays, and has also been shown to be pre-
dictive of chronic kidney disease and chronic obstructive pulmonary disease
(COPD). Individuals with COPD face a two to five times greater risk of develop-
ing lung cancer, a key outcome associated with As exposures. Thus, CRP, while
nonspecific, may be indicative of intermediate health outcomes associated with
exposure to CoCs at ULAB sites.
Recently, bioactive molecules such as asymmetric dimethylarginine
(ADMA) and adipocyte fatty acid-binding protein (FABP4, also known as aP2
and AFABP) have emerged as new predictive biomarkers of CVD and have also
been associated with blood Pb levels. In the future, these may be considered in
lieu of CRP.
48 | Recycling of Used Lead-Acid Batteries

Carcinogenic effects
Exposures to As and Cd are associated with carcinogenic outcomes, including
lung, skin, and kidney cancers. Although the household survey (chapter 3)
includes a set of questions on health status, it is unlikely enough cases will be
observed to draw statistically meaningful conclusions, particularly given the
emphasis on selecting children from each participating household. Therefore, a
potential biomarker in the absence of disease may be indicative of changes at the
cellular level that are predictive of carcinogenic outcomes.
The International Programme on Chemical Safety has published guidelines
online to provide concise guidance on the planning, performance, and interpre-
tation of studies to monitor groups or individuals exposed to genotoxic agents.
Based on those guidelines, DNA adduct formation or micronucleus formation
are two standardized assays that provide important prognostic information on
exposure to potential carcinogens, including As and Cd.

CoC-specific health outcomes
The following sub sections describe the primary health outcomes associated
with each CoC and recommended methods for evaluating them using medical
exams and biological sampling. Appendix A provides a brief toxicity profile for
each CoC, as well as links to detailed toxicological profiles developed by the US
EPA, WHO, and others.

Lead
The key health outcomes associated with exposure to Pb include cognitive and
neurodevelopmental effects in children, as demonstrated through performance
on age-specific, culturally relevant standardized testing instruments (see the
section on neurodevelopmental and cognitive testing).
A secondary, nonspecific health outcome for Pb includes effects on the renal
system, which can be evaluated using nonspecific biomarkers of effect measured
using in-field approaches, at a minimum, and sent to a laboratory for the most
reliable, precise results. These effects on the renal system may ultimately lead to
cardiovascular outcomes. A key nonspecific risk factor for cardiovascular out-
comes that may also reflect increasing renal damage is blood pressure, which is
easy to measure in-field or at a health care facility. In addition, there are related
biomarkers that are predictive of clinical health outcomes and associated with
intermediate outcomes, including C-reactive protein and protein in the urine
(proteinuria; typically measured using albumin levels). Anemic status is also sig-
nificant and can be measured using in-field methods such as HemoCue. Anemia
may be related to health outcomes and may also influence Pb absorption.

Recommendations:

• Measure blood pressure in adults in the field or as part of a medical
examination
• Measure specific biomarkers, including proteinuria (for example, albumin,
ALA); anemia status (for example, hematocrit); and cardiovascular risk (for
example, C-reactive protein)
• Conduct age-specific, culturally relevant cognitive testing for each child (see
the sub section “Neurodevelopmental and Cognitive Testing” in this
chapter).
General Guidelines for Assessing Health Outcomes | 49

Arsenic
Exposure to As has been associated with a number of health outcomes,
including skin cancer, bladder cancer, lung cancer, neurodevelopmental
health outcomes in children, and arsenicosis. Because skin is a primary target
for As, hyperpigmentation and hyperkeratosis can be early symptoms of As
exposures and are often first seen on the feet, hands, and palms. Figure 6.1
provides an overview of the primary dermatological signs and symptoms
induced by As.
Table 6.2 provides an overview of the health outcomes that have been associ-
ated with exposure to As and recommended assessment methods.

Recommendations:

• Conduct age-specific, culturally relevant cognitive testing for each child.
• Conduct in-field screening for keratosis on the soles of the feet as part of
the household survey (appendix B) or as part of a more formal medical
examination.
• If keratosis is observed, consider a carcinogenic biomarker such as DNA
adduct assay or micronucleus formation assay.
• Measure C-reactive protein as a nonspecific biomarker of intermediate
effects on the renal and cardiovascular systems.

FIGURE 6.1
Dermatological outcomes associated with As exposures
Arsenic-induced primary
dermatological signs and symptoms

Signs: Melano-keratosis Melanosis Spotted melanosis Dorsal keratosis

Dry, rough, spotted Darkening of skin Occurring on Palpable nodules
Symptoms: nodules occur on (i.e., palms and chest, back, and develop on hands,
palms and soles whole body) limbs feet, and legs

Melanosis and
Spotted and
Signs: Leucomelanosis spotted palmoplantar
diffuse keratosis
keratsosis

Rough, dry, and Pigmented and Isolated pigmentation
Symptoms: spotted nodules seen depigmented spots and nodular rough
on palms and feet appear on legs or trunk skin on limbs

Source: Adapted from Abdul et al. 2015, figure 3.
50 | Recycling of Used Lead-Acid Batteries

TABLE 6.2 Health outcomes associated with As exposures

INTERMEDIATE HEALTH OUTCOME NONSPECIFIC BIOMARKER
HEALTH OUTCOME OR DIAGNOSTIC TEST OF EFFECT
Arsenicosis – (may be present in Keratosis on the soles of the feet Not applicable
children) precursor to squamous
cell carcinoma in adults
Lung cancer Lung-function tests DNA adduct formation;
micronucleus formation
Chronic pulmonary obstructive Lung-function tests C-reactive protein (CRP);
disease predictor of forced expirato-
ry volume
Heart disease (in adults) Blood pressure CRP
Cognitive and neurodevelopmental Age-specific, culturally relevant Not applicable
effects on children standardized test
Source: World Bank compilation.

Cadmium
The most significant health outcome associated with exposures to Cd is a variety
of renal effects, starting with proteinuria, leading to kidney disease, and ulti-
mately an increased risk of kidney cancer. In the absence of outright disease or
decreased kidney function, a number of well-vetted biomarkers exist for evalu-
ating intermediate outcomes associated with Cd exposures. These include β2-m
(urinary β2-microglobulin), a urinary biomarker recommended by the European
Food Safety Administration, WHO, and others as the most sensitive and appro-
priate biomarker of Cd effects, as well as measures of kidney function, including
glomerular filtration rate (GFR), which can either be measured from a standard
urinary panel or calculated from protein markers measured with rapid in-field
assays. In addition, there are carcinogenic biomarkers that should be considered
in participants with elevated β2-m levels, including assays for DNA adduct for-
mation and micronucleus formation.

Recommendations:

• Measure sensitive urinary biomarkers, including β2-m (urinary β2-micro-
globulin), and glomerular filtration rate (GFR).
• If elevated, consider measuring additional carcinogenic biomarkers, such as
DNA adduct formation or micronucleus formation.

Diagnostic screening tools
As with biomarkers of effect, there are non unique intermediate health outcomes
that may be both indicative of exposure to CoCs and apply to more than one CoC.
For example, neurodevelopmental and cognitive outcomes in children are asso-
ciated with exposures to Pb, As, and to a lesser extent Cd.

Pulmonary-function testing (PFT)
Lung cancer is a known health outcome associated with As exposures. While it
may not be possible to observe cancer cases, particularly in young participants,
simple lung-function tests that can be administered in-field may suggest effects
General Guidelines for Assessing Health Outcomes | 51

on the lung that indicate increased risk of other outcomes. Decreased lung func-
tion may be an intermediate indicator of exposures to As and may provide an
indication of increased risk of other adverse health outcomes. Standard
lung-function tests are noninvasive tests that show how well the lungs are work-
ing. The following standardized tests measure lung volume, capacity, rates of
flow, and gas exchange:

• Tidal volume (VT). This is the amount of air inhaled or exhaled during nor-
mal breathing.
• Minute volume (MV). This is the total amount of air exhaled per minute.
• Vital capacity (VC). This is the total volume of air that can be exhaled after
inhaling as much as possible.
• Functional residual capacity (FRC). This is the amount of air left in the lungs
after exhaling normally.
• Residual volume. This is the amount of air left in the lungs after exhaling as
much as possible.
• Total lung capacity. This is the total volume of the lungs when filled with as
much air as possible.
• Forced vital capacity (FVC). This is the amount of air exhaled forcefully and
quickly after inhaling as much as possible.
• Forced expiratory volume (FEV). This is the amount of air expired during the
first, second, and third seconds of the FVC test.
• Forced expiratory flow (FEF). This is the average rate of flow during the
middle half of the FVC test.

• Peak expiratory flow rate (PEFR). This is the fastest rate at which air can be
forced out of the lungs.

A variety of inexpensive peak-flow meters (one size for younger children and
a larger size for older children and adults) exist to measure FEV, FVC, and FEF.
Flow meters can be utilized in the field and do not require a medical professional
but do require training prior to use. The following guidelines are based on best
practices:

• Before each use of the meter, make sure the sliding marker or arrow is at the
bottom of the numbered scale (for example, zero or the lowest number on the
scale).
• Make sure the participant is standing up straight and have the participant
remove gum or any food from her or his mouth. Have the participant take in
as deep a breath as possible and put the peak-flow meter in the participant’s
mouth. Make sure her or his tongue is not on the mouthpiece. In one breath,
have the participant blow out as hard and as quickly as possible. This should
not be a slow exhalation but rather a fast, hard blast until nearly all of the air
has been removed from the lungs.
• The force of the air exhaled from the lungs causes the marker to move along
the numbered scale. Record the number.
• Repeat the entire routine three times. In general, when all three exhalations
are relatively close, this is an indication that the test is being performed
correctly.
• Record the highest of the three results. Do not calculate an average. It is never
possible to breathe out too much when using the peak-flow meter, but it is
possible to exhale too little.
52 | Recycling of Used Lead-Acid Batteries

Recommendations:

• Measure FEV, FVC, and FEF for each participant using a peak-flow meter.
This can be done in-field.
• Measure C-reactive protein, an inflammation biomarker predictive of FEV
and respiratory, cardiovascular, and renal outcomes.

Neurodevelopmental and cognitive testing
A key outcome of exposure to Pb, As, and to some extent Cd, includes neurode-
velopmental and cognitive health impacts, particularly in young children.
Methods to assess child development include the following:

• Direct assessment using standardized approaches by a trained medical pro-
fessional in a clinical environment
• Verbal reporting or completion of a questionnaire by parents or teachers
• Unstructured observation by a trained professional in a familiar environment
(for example, at home or school).

Direct assessment using standardized approaches is the preferred approach,
since parental reporting may be subject to recall bias. Unstructured observation,
although carried out by a professional, is difficult to reproduce, interpret, and
compare to other results.
Direct assessment using standardized tests is used to evaluate a range of out-
comes, including cognitive development; expressive and receptive language;
fine-motor and gross-motor development; academic performance (for example,
math, reading comprehension); and intelligence quotient (IQ). The specific
approach chosen depends on the availability of an appropriate age-specific
instrument. Results are scaled to a standardized, normative metric. Results can
also be expressed as percentile ranks relative to the standardization sample. In
general, normative populations for neurodevelopmental tests have been based
on Western or developed countries, which is of limited utility in an LMIC con-
text. Developmental assessments of children in LMICs face challenges due to
socioeconomic, cultural, and language differences in the populations being
tested. This leads to the necessity for adapting tests designed for one context and
makes it difficult to compare results across countries.
The testing protocol that is selected must demonstrate internal consistency,
interobserver agreement, test-retest reliability, sensitivity to maturational
changes, and the ability to identify relevant outcomes. These tests must be
administered by trained medical professionals (for example, child psycholo-
gists). Therefore, it is important to select a testing protocol that has been vali-
dated for the context in which it is being applied. The World Bank recently
published a toolkit that provides a practical “how-to” guide for selecting and
adapting of child development measurements for use in LMICs (Fernald et al.
2017). The toolkit proposes a step-by-step process to select, adapt, implement,
and analyze early childhood development data. The ECD Measurement
Inventory accompanies the toolkit and contains 147 measurement tools for
children under 8 years. For each test, it reports the domains assessed, age range
for which the tool is appropriate, method of administration, purpose of the
assessment, origin and locations of use, logistics, and cost. This guide, and
other, similar guides (discussed in appendix E), should be consulted (for exam-
ple, http://documents.worldbank.org/curated/en/384681513101293811/pdf
/WB-SIEF-ECD-MEASUREMENT -TOOLKIT.pdf ) in conjunction with a
trained professional on the in-field research team.
General Guidelines for Assessing Health Outcomes | 53

The following guidelines should be considered in selecting which testing pro-
tocol to use:

• Consultation with a medical professional with demonstrated experience in
LMIC settings. It is important to include a researcher with experience in
administering neurodevelopmental tests, particularly in an LMIC context.
Familiarity with specific testing protocols should be emphasized.
• Time availability and conditions under which testing is to be conducted.
A key consideration is how much time will be made available to administer
each test. In general, a comprehensive battery of neurodevelopmental testing
takes several hours and requires a clinical setting (for example, quiet, no dis-
tractions, stress-free, and so forth).
• Cultural relevance. The test must be appropriate for the cultural context and
age of the child, and the test must be acceptable to the local population.
• Availability of normative data. The testing protocol should have a standard-
ized, normative reference to evaluate the results in a consistent and compara-
ble way.
• Consideration of comorbidities. Stress, malnutrition and nutritional status
generally, low socioeconomic status, maternal education, and general house-
hold culture (for example, excessive alcohol use, smoking, drugs, and so forth)
have all been associated with performance on developmental tests, and these
should be noted as part of the background information on the individual
being tested.

RESOURCES

Appendix E provides additional resources and weblinks for assessing health
outcomes.

REFERENCES

Abdul, Khaja Shameem Mohammed, Sudheera Sammanthi Jayasinghe, Ediriweera P. S.
Chandana, Channa Jayasumana, and P. Mangala C. S. De Silva. 2015. “Arsenic and Human
Health Effects: A Review.” Environ. Toxicol. Pharmacol. 40 (3): 828–46. doi:10.1016/j
.etap.2015.09.016.
Bickley, L. S. 2012. Bates’ Guide to Physical Examination and History Taking, 11th ed. Philadelphia:
Lippincott Williams & Wilkins, an imprint of Wolters Kluwer.
Fernald, Lia C. H., Elizabeth Prado, Patricia Kariger, Abbie Raikes. 2017. A Toolkit for Measuring
Early Childhood Development in Low- and Middle-Income Countries. Strategic Impact
Evaluation Fund. Washington, DC: World Bank.
Gao, Anja, F. Cachat, M. Faouzi, B. J. Meyrat, E. Girardin, and Hassib Chehade. 2013. “Comparison
of the Glomerular Filtration Rate in Children by the New Revised Schwartz Formula and
New Generalized Formula.” Kidney International 83 (3): 524–30.
Vemulapati, S., E. Rey, D. O’Dell, S. Mehta, and D. Erickson. 2017. “A Quantitative Point-of-Need
Assay for the Assessment of Vitamin D3 Deficiency.” Scientific Reports 7: 14142.
APPENDIX A

Overview of Contaminants

ARSENIC (As); INORGANIC As (iAs)

Sources
Arsenic in its inorganic form occurs naturally in soil and water worldwide and is
found in many different types of ores. As is mobilized and released through a
variety of human activities, including smelting, use of arsenic-based pesticides,
and many other industrial processes. As also occurs naturally in groundwater in
many parts of the world, and people can be exposed from naturally occurring As
through household use of this water, including drinking, bathing, cooking, and
other activities. Naturally occurring As is also used as irrigation water in many
parts of the world, particularly for rice, and is therefore measurable in most rice
products, especially those from Southeast Asia.

Health outcomes
Exposures to arsenic have been associated with a variety of health outcomes
affecting virtually every organ system in the human body. Among the most
obvious clinical symptoms are skin rashes and lesions, such as hyperkeratosis
and hyperpigmentation, which may lead to skin cancer. Often these rashes
begin on the hands and extremities, and a noted delayed effect of acute or
chronic exposure may be seen as Mee’s lines in nails (for example, horizontal
lines). As is a known human carcinogen, with the strongest association seen
with skin cancer, followed by bladder and lung cancer. Arsenic is also

associated with neurotoxic and neurodevelopmental outcomes, including
cognitive deficits in children and peripheral neuropathy. A variety of other
health outcomes has been reported, ranging from gastrointestinal to
cardiovascular effects. Reported cardiac effects include altered myocardial

depolarization (prolonged QT interval and nonspecific ST-segment changes),
cardiac arrhythmias, and ischemic heart disease.

55
56 | Recycling of Used Lead-Acid Batteries

TABLE A.1 Additional information and detailed profiles on arsenic
SOURCE DESCRIPTION
ATSDR (US Agency for Toxic Substances and Disease Registry). 2007. Toxicological profile, community information,
“Toxicological Profile for Arsenic.” Atlanta: ATSDR. environmental health and medical education; many
resources for health professionals
WHO (World Health Organization). 1981. Arsenic (Environmental Health Detailed toxicological profile
Criteria 18). Geneva: WHO. file:///C:/Users/16507/Desktop/
Downloads/9241540788-eng.pdf.
EA (Environment Agency). 2009. “Soil Guideline Values for Inorganic Contains toxicological information; describes
Arsenic in Soil.” Science Report SC050021/arsenic SGV. Bristol, UK: EA. environmental fate and exposure pathways
A. Gomez-Caminero, P. Howe, M. Hughes, E. Kenyon, D. R. Lewis, M. Data and review to establish the scientific basis for
Moore, J. Ng, A. Aitio, and G. Becking. 2001. Environmental Health risk assessment of As
Criteria 224: Arsenic and Arsenic Compounds. Geneva: World Health
Organization.
NTP (National Toxicology Program). 2016. “Arsenic and Inorganic Arsenic US-based assessment of carcinogenicity of As
Compounds.” In Report on Carcinogens, 14th ed. Washington, DC: US
Department of Health and Human Services.
Source: World Bank compilation.

CADMIUM (Cd)

Sources
Cadmium occurs naturally at low concentrations in zinc, lead, and copper ores.
The primary sources of Cd in the environment include nonferrous metal mining
and refining, manufacture and application of phosphate fertilizers, fossil fuel
combustion, and waste incineration and disposal. The general population can be
exposed primarily through food ingestion, and smokers are exposed to high lev-
els of Cd.

Health outcomes
Sensitive targets of Cd toxicity include the kidneys and bones following oral
exposures. The earliest indication of kidney damage in humans is an increased
urinary excretion of low-molecular-weight proteins, particularly
β2-microglobulin, α1-microglobulin, and retinol binding protein. Increased
urinary levels of intracellular enzymes such as N-acetyl-β-glucosaminidase

(NAG) and increased excretion of calcium and metallothionein are also early
indicators of Cd toxicity. At higher exposure levels, decreases in glomerular
filtration rate associated with renal disease have been observed. Prolonged
inhalation or ingestion exposure of humans to cadmium at levels leading to
renal dysfunction have also been associated with bone disease in individuals
with risk factors such as poor nutrition.
Overview of Contaminants | 57

TABLE A.2 Additional information and detailed profiles on cadmium
SOURCE DESCRIPTION
ATSDR (US Agency for Toxic Substances and Disease Registry). 2015. Detailed toxicological profile, including sampling
“Toxicological Profile for Cadmium.” Atlanta: ATSDR. methods, exposure
ATSDR (US Agency for Toxic Substances and Disease Registry). 2013. Continuing medical education course on cadmium
“Environmental Health and Medicine Education, Cadmium Toxicity.” Atlanta: toxicity
ATSDR.
Health Canada. “Cadmium.” Health Canada supporting document for drinking
water guideline development
WHO (World Health Organization). 2010. “Exposure to Cadmium: A Major World Health Organization detailed supporting
Public Health Concern.” Geneva: WHO. toxicological information
IPCS (International Programme on Chemical Safety). 1992. “Environmental Health criteria document containing detailed
Health Criteria 134: Cadmium.” Geneva: WHO. toxicological review
Martin, Ian, Hannah Morgan, and Elizabeth Waterfall. 2009. “Soil Guideline Soil Guideline Values for cadmium in soil
Values for Cadmium in Soil.” Bristol: UK Environment Agency.
World Bank Group, United Nations Environment Programme, and United Pollution Prevention and Abatement Handbook,
Nations Industrial Development Organization. 1999. Pollution Prevention Cd chapter, p. 212
and Abatement Handbook 1998: Toward Cleaner Production. Washington,
DC: World Bank.
Source: World Bank compilation.

LEAD (Pb)

Sources
Lead occurs naturally in the environment in ores and has many uses, including
automobile batteries, leaded gasoline (at one time), lead alloys, use in soldering
materials, shielding for X-ray machines, in the manufacture of corrosion-
resistant and acid-resistant materials used in the building industry, and a variety
of dyes and pigments. Prior to World War II, Pb was used extensively in pesti-
cides. The amount of Pb contained in pipes and plumbing fittings has decreased
substantially, but many areas still have public water-distribution systems con-
taining Pb. Other sources of Pb exposure include lead glazing on pottery. Pb has
also been found as an additive in nonpharmaceutical health remedies and spices.

Health outcomes
The primary health outcome associated with exposure to Pb is cognitive deficits
in children exposed prenatally and throughout childhood.
Pb alters the hematological system by inhibiting the activities of several
enzymes involved in heme biosynthesis, particularly δ-aminolevulinic acid
dehydratase (ALAD), leading to clinical anemia. Population studies suggest an
association between bone-lead levels (measured by XRF) and elevated blood
pressure, which may lead to other cardiovascular-health outcomes. Pb is also
associated with renal effects, including kidney function, such as glomerular-
filtration rate.
58 | Recycling of Used Lead-Acid Batteries

TABLE A.3 Additional information and detailed profiles on lead
SOURCE DESCRIPTION
ATSDR (US Agency for Toxic Substances and Disease Registry). Toxicological profile, community information, environmental
2020. “Toxicological Profile for Lead.” Atlanta: ATSDR. health and medical education; many resources for health
professionals
Health Canada. 2013. Final Human Health State of the Toxicological profile, Canadian regulatory perspective
Science Report on Lead. Ottawa: Health Canada.
IPCS (International Programme on Chemical Safety). 1995. Detailed toxicological profile
“Inorganic Lead. Environmental Health Criteria 165.” EHC
document. Geneva: IPCS, World Health Organization, and
United Nations Environment Programme.
Nawrot, T. S., L. Thijs, E. M. Den Hond, H. A. Roels, and Meta-analysis of potential cardiovascular effects
J. A. Staessen. 2002. “An Epidemiological Re-Appraisal of the
Association between Blood Pressure and Blood Lead: A
Meta-Analysis.” Journal of Human Hypertension 16: 123–31.
Source: World Bank compilation.
APPENDIX B

Guidelines for Designing and
Conducting Home Surveys

A home survey questionnaire will be used to obtain information on relevant
demographics, housing characteristics, behaviors, activity patterns, intake
rates, other site- or population-specific exposure factors, and basic health
information from participants at used lead-acid battery (ULAB) sites. This
appendix provides general guidelines for the types of questions that should
be included in the household survey and recommended response categories.
Important factors that will need to be considered on a site-by-site basis are
also noted. The final (formatted) survey questionnaire to be administered in
the field should be developed by the in-field research team based on the
resources provided here. It is recommended that the questionnaire responses
be entered into a portable computer in real time, if possible, to avoid hard-
copy losses or subsequent data-entry errors and to facilitate the data-analysis
process. It is essential that the household survey be administered to the same
individual household members for whom the environmental (chapter 4),
biomonitoring (chapter 5), and health-outcome (chapter 6) data are col-
lected. The same home survey instrument should be administered to house-
holds in both the identified ULAB sites and matched control sites. Note that
any necessary ethical clearances will need to be obtained prior to sample
collection by the in-field research team.

GUIDANCE FOR POWER CALCULATIONS, OPTIMAL SAMPLE
SIZES, AND HEALTH SURVEY DESIGN

Aday, L. A., and L. J. Cornelius. 2006. Designing and Conducting Health Surveys: A Comprehensive
Guide, 3rd ed. San Francisco: Jossey-Bass, an imprint of John Wiley & Sons.

This reference is a key resource for designing and conducting health surveys,
providing details for designing health surveys using high-quality, effective, and
efficient statistical and methodological practices as well as providing optimal
sample designs. It is also important that subsequent applications of estimation
strategies to the survey data, as well as analytical techniques and interpretations
of resultant research findings, are guided by well-grounded statistical theory,
and this reference provides these details.

59
60 | Recycling of Used Lead-Acid Batteries

Adcock, C. J. 1997. “Sample Size Determination: A Review.” Journal of the Royal Statistical
Society: Series D (The Statistician) 46 (2): 261–83.
This article provides a review of estimating appropriate sample sizes using both
frequentist and Bayesian methods.
Greenland, S. 1993. “Methods for Epidemiologic Analyses of Multiple Exposures: A Review and
Comparative Study of Maximum-Likelihood, Preliminary-Testing, and Empirical-Bayes
Regression.” Statistics in Medicine 12 (8): 717–36.

Many epidemiologic investigations are designed to study the effects of multiple
exposures. Most of these studies are analyzed either by fitting a risk-regression
model with all exposures forced in the model, or by using a preliminary-testing
algorithm, such as stepwise regression, to produce a smaller model. Research
indicates that hierarchical-modeling methods can outperform these conven-
tional approaches, as discussed in this review.
Lubin, J. H., M. H. Gail, and A. G. Ershow. 1988. “Sample Size and Power for Case-Control
Studies When Exposures Are Continuous.” Statistics in Medicine 7 (3): 363–76.

Environmental exposures are continuous, and dichotomization may result in a
“not exposed” category that has little practical meaning. In addition, if risks vary
monotonically with exposure, then dichotomization will obscure risk effects and
require a greater number of subjects to detect differences in the exposure distri-
butions among cases and referents. Starting from the usual score statistic to
detect differences in exposure, this paper develops sample-size formulae for
case-control studies with arbitrary exposure distributions; this includes both
continuous and dichotomous exposure measurements as special cases.
Lui, K.-J. 1993. “Sample Size Determination for Multiple Continuous Risk Factors in Case-
Control Studies.” Biometrics 49 (3): 873–76.

For a desired power of detecting the association between a disease and several
potential risk factors in case-control studies, it is difficult to choose appropriate
values for each parameter in the alternative hypothesis. A proposed statistical
strategy is discussed.
Lwanga, Stephen Kaggwa, Stanley Lemeshow, and World Health Organization. 1991. Sample
Size Determination in Health Studies: A Practical Manual. Geneva: World Health
Organization. https://apps.who.int/iris/handle/10665/40062

This manual provides the practical and statistical information needed to help
investigators decide how large a sample to select from a population targeted for
a health study or survey. Designed to perform a “cookbook function,” the book
uses explanatory text and abundant tabular calculations to vastly simplify the
task of determining the minimum sample size needed to obtain statistically valid
results given a set of simple hypotheses.
Thomas, D. C., J. Siemiatycki, R. Dewar, J. Robins, M. Goldberg, and B. G. Armstrong. 1985. “The
Problem of Multiple Inference in Studies Designed to Generate Hypotheses.” American
Journal of Epidemiology 122 (6): 1080–95.

Epidemiologic research often involves the simultaneous assessment of
associations between many risk factors and several disease outcomes. In such

situations, often designed to generate hypotheses, multiple univariate

hypothesis testing is not an appropriate basis for inference. This paper
discusses an approach in which all associations in the data are reported,

whether significant or not, followed by a ranking in order of priority for inves-
tigation using empirical Bayesian techniques.
Guidelines for Designing and Conducting Home Surveys | 61

EXPOSURE HISTORY RESOURCES

The US Agency for Toxic Substances and Disease Registry (ATSDR) provides
educational and resource materials for taking exposure histories in adults and
children, and shows how this information can be linked to potential health
outcomes.

ANNOTATED REFERENCES ON FOOD FREQUENCY
QUESTIONNAIRES

Several food-frequency questionnaire (FFQ) templates are available to use as
guides for developing a specific FFQ in the context of exposures in low- and
middle-income countries (LMICs). For the research proposed here, an import-
ant aspect of the FFQ is to identify the amount and frequency of consumption of
locally produced agricultural products and locally caught fish and shellfish that
may be affected by contaminants of concern (CoCs) originating from ULAB
activities.
The US National Cancer Institute (US NCI) has developed guidance on FFQs
to support assessments of dietary and nutritional supplement intake.
The Women’s Health Initiative (WHI) is a long-term national health study
with both observational and clinical components involving over 40 health
centers. The original WHI study included 161,808 postmenopausal women

enrolled between 1993 and 1998. The Fred Hutchinson Cancer Research
Center in Seattle serves as the WHI Clinical Coordinating Center for data col-
lection, management, and analysis of the WHI. One aspect of the WHI involves
application of a detailed dietary assessment including several food-frequency
questionnaires.
WHO, UNEP, and IOMC (World Health Organization, United Nations Environment Programme,
and Inter-Organization Programme for the Sound Management of Chemicals). 2008.
“Guidance for Identifying Populations at Risk from Mercury Exposure.” Reference
document. Geneva: WHO and UNEP. https://www.who.int/foodsafety/publications/chem

/mercuryexposure.pdf.

This publication contains examples of an FFQ, health-assessment questionnaire,
and socioeconomic questionnaire, as well as sample collection guidelines for
urine, blood, and hair.

ADDITIONAL FFQ REFERENCES

Boynton, P. M., and T. Greenhalgh. “Hands-On Guide to Questionnaire Research: Selecting,
Designing, and Developing Your Questionnaire.” BMJ 328 (7451): 1312–15.
Cade, J., R. Thompson, V. Burley, and D. Warm. 2002. “Development, Validation and Utilisation
of Food-Frequency Questionnaires—A Review.” Public Health Nutrition 5 (4): 567–87.
Matthys, C., I. Pynaert, W. De Keyzer, and S. De Henauw. 2007. “Validity and Reproducibility of
an Adolescent Web-Based Food Frequency Questionnaire.” Journal of the American Dietetic
Association 107 (4): 605–10.
Shim, J.-S., K. Oh, and H. C. Kim. 2014. “Dietary Assessment Methods in Epidemiologic Studies.”
Epidemiology and Health 36: e2014009.
62 | Recycling of Used Lead-Acid Batteries

ADDITIONAL LMIC-SPECIFIC RESOURCES

Population-based surveys, repeated approximately every five years, are now
available for more than 100 LMICs, providing information on nutritional status,
health-related behaviors, morbidity, and mortality. These include Demographic
and Health Surveys under the auspices of the US Agency for International
Development (USAID) and Multiple Indicator Cluster Surveys conducted by the
United Nations Children’s Fund (UNICEF).

SAMPLE HOME SURVEY QUESTIONNAIRE

Each survey question should have a predetermined list of responses (that is,
check-box categories) to ensure uniformity in response options across partici-
pants. Open-ended questions should generally be avoided. Note that an adult
will need to provide the answer for some (or all) questions on behalf of sampled
children.

1. Demographics (provide information on age, sex, length of residence, edu-
cation, income, household size, and composition)
1.1 How old are you? (check box for category of age ranges; for example,
6–10 years, 11–15 years)
1.2 What is your gender identity? (check box)
1.3 How long have you lived here? (check box for date ranges; for exam-
ple, 1–2 years, 3–5 years)
1.4 Where did you live previously? (used to determine whether previous
residence was in a similar exposure zone) (*need to determine list of
possible neighborhoods, cities, regions in advance)
1.5 How long did you live there? (check box for date ranges; for example,
1–2 years, 3–5 years)
1.6 What is your highest level of education? (check box for education
ranges; for example, grade school, secondary school)
1.7 What is your income level? (check box for income ranges) (*need to
determine appropriate ranges and $ units in advance)
1.8 What is the size of your current household? (check box for house-
hold ranges; for example, 1–2 people, 3–4 people)
1.9 Who (and how many) are the other family members? (check all that
apply, for example, brother [#], sister [#], mother, father,
grandmother)
2. Occupation/School (seeking information on possible workplace, off-site,
or take-home exposures)
2.1 Do you work or attend school outside the home? [if y → 2.2; if n → 3]
2.2 What do you do? (*need to determine possible industry sectors or
schools in advance)
2.3 Where is that located? (*need to identify possible zone or sector
relative to exposure source in advance; map-based)

2.4 How long have you worked / attended school there? (check box for
date ranges)
2.5 How much time do you spend at your occupation?
2.6 Do you work with or handle chemicals in any way? [If y → 2.6a; if
n → 2.7] (define “chemicals” in advance)
Guidelines for Designing and Conducting Home Surveys | 63

2.6a What chemicals do you work with or handle? (*need to identify
possible list of chemicals or materials/products that contain chemi-

cals in advance)
2.6b Do you wear protective equipment? (*need to define personal

protective equipment and give list of options; for example, gloves,
clothing, dust mask, respirator)
2.6c Do you get any of the chemicals on your skin, hair, or clothing?
2.6d Do you wash off before coming home?
2.6e Do you wash off when you get home or remove clothes?
2.6f Who washes clothing? (check box for possible options; for example,
self, spouse, child)
2.6g Where are clothes washed? (*need to identify possible option in
advance)
2.7 If attend school, do you play outdoors or in soil? (check box for
either/both) [if y → 2.7a; if n → 3.0]
2.7a How often? (check box for frequency ranges; for example, 1–2 days
per week, 3–4 days per week)
2.7b How long? (check box for duration ranges; for example, 0.5–1 hour/
day, 2–3 hours/day)
3. Time-Activity Patterns and Lifestyle/Housing Characteristics (seeking
information on exposure factors and lifestyle/housing details, including
other possible sources of exposure)
3.1 Is there a wood-burning stove and/or fireplace in the home? [if y →
3.1a if n → 3.2]
3.1a What type of wood (or other material) is burned?
3.1b How often?
3.1c Where (for example, kitchen)?
3.2 How close is the home to the road/traffic? (check box for distance
ranges; for example, 10–100 yards)
3.3 What kind of traffic (car, bike, horse, foot)? Is the road dusty?
3.4 Do you use pesticides or chemicals inside or outside the home (for
example, pest control, gardening, and so forth)? [if y → 3.4.a; if n → 3.5]
3.4a What kinds of chemicals/pesticides do you use? (checkoff list)
3.4b How much do you use? (quantity choices)
3.5 How much time do you spend
• Outside the home (for example, work, school, other activities)
(check boxes for time spent at each location; for example, 0.5–1
hour/day) (*Need questions re: whether [how often/how long]
spend time playing in soil at home [yard, garden] or other
locations)
• Inside the home (approximate time in areas of the home [for
example, communal space, kitchen, sleeping, and so forth])
Do you have a dirt floor? [if y → 3.5.a; if n → 3.6]
3.5a How do you (or other family member) clean the floor? (check all
cleaning options that apply; for example, sweeping, mopping—may
need to identify options in advance)
3.5b How often do you (or other family member) clean the floor? (check
box for frequency options; for example, 1–2 times per week, 3–4
times per week)
3.6 What is the floor material? (*need to identify possible materials in
advance)
64 | Recycling of Used Lead-Acid Batteries

3.7 What is the building material of the home? (*need to identify possi-
ble materials in advance)
3.8 What type of roof? (*need to identify possible materials in advance)
3.9 Are there windows? How many? Which rooms? Covered or open?
(some findings may be based on personal observations while admin-
istering survey)
3.10 How many doors? What material? Gaps around door frame? (some
findings may be based on personal observations while administering
survey)
3.11 What is ventilation/air exchange like in the home? (*need to identify
possible descriptors in advance; may also be based on personal
observations)
3.12 Is the home dusty? (questions plus observation) How often cleaned?
How cleaned?
3.13 How many floors is home (single versus multistory)?
How is house arranged—bedrooms, kitchen, living area, and so forth
(draw map?)
3.14 Do any animals stay inside home? What kind? How many? Where?
Contact with household members?
3.15 Do you wear shoes? Track dirt in home?
3.16 Ask questions related to frequency and duration of hand-to-mouth
and object-to-mouth activities
3.17 Ask questions related to showering/bathing—where done? Source of
water? Frequency and duration?
3.18 Ask questions related to aluminum cookware (contains lead)
4. Health Status (seeking information on current health and possible comor-
bidities; note this chapter may be superseded in the event of a more formal
medical examination—see chapter 6)
4.1 Do you have any diagnosed chronic illnesses? (diabetes, cancer, list
and check off with a write-in option?)
4.2 Do you have any acute or chronic health issues? (for example, per-
sistent cough, tremors, skin rashes, and so forth; develop a master list
and check off symptoms as appropriate)
4.3 Do you smoke? [If y → 4.3a; if n → 4.4]
4.3a How many cigarettes (or cigars?) do you smoke in a day?
4.3b How long have you been smoking (for example, years)?
4.3c What brand cigarettes? Filtered or unfiltered?
4.4 Do you drink alcohol? [if y → 4.4.a; if n → 4.5]
4.4a How much alcohol do you drink in a typical week?
4.5 Are you physically active?
4.6 Height (cm)
4.7 Weight (kg)
4.8 Body mass index (calculated)
4.9 Ask questions related to malaria, dengue fever, diarrhea, or other
region-specific infectious or other issues
4.10 Ask questions (here or below) that get at nutritional or vitamin-
deficiency issues
4.11 Use of traditional medicines (contain metals)
Unique to ULAB neurological checklist:
4.12 Do you work at any ULAB-related activities?
4.13 Do you ever experience a metallic taste in your mouth?
Guidelines for Designing and Conducting Home Surveys | 65

4.14 Do you ever have excessive salivation?
4.15 Do you have tremors or do your hands shake?
4.16 Do you have trouble falling or staying asleep?
5. Dietary Information (seeking information on intake rates)
5.1 In general, how would you describe your diet?
5.2 Do you eat vegetables/meat/dairy from your own or nearby
gardens?
5.3 Have you eaten seafood in the last 72 hours (important for biomoni-
toring of As)
5.4 How much water do you drink in a day? (liters)
5.5 Where does your water come from? (location, well, tap)
5.6 Questions related to local fishing, such as types of fish, and so forth
FFQ? YES—need questions related to food intake (amount and frequency
per day or week)
6. Cost of Illness Economics-Related Information (all questions are based on
the previous year)
6.1 How many days during the last year have you missed work because of
illness?
6.2 How much income did you lose in the last year because of illness?
6.3 How many times did you visit the emergency room or health center?
6.4 How many nights did you spend in a hospital or health center?
6.5 How much did you spend on health care in the last year?
APPENDIX C

Key References and Resource Guides
for Environmental Sampling

GUIDELINES FOR SITE CHARACTERIZATION AND
DEVELOPING SAMPLING PLANS

When developing a site-specific characterization and sampling plan,
multiple resources are available for consultation, as listed and described in

table C.1.

TABLE C.1 Site-characterization resources
SOURCE DESCRIPTION
Canadian Council of Ministers of the Environment. Guidance for site characterization, risk assessment, and general
Guidance Manual for Environmental Site contaminated-site assessment
Characterization in Support of Environmental and
Human Health Risk Assessment. https://ccme.ca/en/res
/guidancemanual-environmentalsitecharacterization_vol
_1e.pdf.
Demetriades, A., and M. Birke. 2015. Urban Geochemical Guidelines provided by EuroGeoSurveys out of Brussels; provides
Mapping Manual: Sampling, Sample Preparation, detailed information on mapping and site characterization from a
Laboratory Analysis, Quality Control Check, Statistical European perspective
Processing and Map Plotting. Brussels: EuroGeoSurveys.
ESDAC (European Soil Data Centre) website: https://esdac The European Soil Data Centre (ESDAC) of the European Commission’s
.jrc.ec.europa.eu/. Joint Research Centre is the thematic center for soil-related data in
Europe. The goal is to be the single reference point for and to host all
relevant soil data and information at the European level. It contains a
number of resources: datasets, services or applications, maps,
documents, events, projects, and external links.
EPA (US Environmental Protection Agency). 1989. “Interim Provides guidance on site characterization and sampling strategies
Final RCRA Facility Investigation (RFI) Guidance, Volume II of
IV: Soil, Ground Water and Subsurface Gas Releases.” EPA
530/SW-89-031. Washington, DC: EPA.
EPA (US Environmental Protection Agency). 2014. “Sampling This Sampling and Analysis Plan (SAP) guidance and template is
and Analysis Plan—Guidance and Template: Version 4, intended to assist organizations in documenting procedural and
General Projects.” R9QA/009.1. Washington, DC: EPA. analytical requirements for projects involving the collection of water,
soil, sediment, or other samples taken to characterize areas of
potential environmental contamination.
continued

67
68 | Recycling of Used Lead-Acid Batteries

TABLE C.1, continued

SOURCE DESCRIPTION
ISO (International Organization for Standardization). 2017. Establishes general principles for packing, preservation, transport, and
ISO 18400-105:2017, Soil quality—Sampling—Part 105: delivery of soil samples and related materials; requirements for
Packaging, transport, storage and preservation of samples. chemical analysis of samples
Olusola, O. I., and O. K. Aisha. 2007. “Towards “…Proposes…procedure…for comparable, representative and cost
Standardization of Sampling Methodology for Evaluation of effective, soil sampling; …explores…policy issues regarding
Soil Pollution in Nigeria.” Journal of Applied Sciences and standardization of sampling activities and analytical process as it
Environmental Management 1 1 (3): 81–85. relates to soil pollution in Nigeria” (Olusola and Aisha 2007, 81).
Source: World Bank compilation.

TABLE C.2 Criteria for analytical-method selection
CRITERION a
RATIONALE
Gold standard Method demonstrates contaminant and matrix specificity; widely used in epidemiological studies
Broadly applicable Applies to more than just one contaminant
Used in LMIC studies Documented use in the literature
Feasibility In field versus send to lab and correlation between field and laboratory results
Cost To be determined
Detection level Detection level relative to expected concentrations
Capability Laboratory likely to have calibrated method
Local capability In consultation with local expertise
Source: World Bank.
Note: a. Criteria given in order of importance. LMIC = low- to middle-income country.

LABORATORY METHODS FOR ENVIRONMENTAL SAMPLING

The US-based ASTM International (https://www.astm.org/) (formerly known
as the American Society for Testing and Materials) and the International
Standards Organization (ISO, https://www.iso.org/) are the preeminent organi-
zations providing guidelines and standards for collecting and analyzing envi-
ronmental samples across different matrices. The specific methods chosen to
analyze metals in water, soil, dust, agricultural products, fish, and other
matrices—such as sludge, fertilizer, and solid waste—will depend on many fac-

tors, some of which can be determined a priori and some of which will require
additional collaboration by the accredited laboratory performing the analyses
and the in-field research team. The general criteria for method evaluation are
given in table C.2.
Table C.3 provides a nonexhaustive list of resources most often used interna-
tionally in selecting analytical methods for contaminated-site assessments.
Relevant US Environmental Protection Agency (EPA) laboratory methods for
environmental sampling are shown in table C.4.
Key References and Resource Guides for Environmental Sampling | 69

TABLE C.3 Sources of analytical guidelines for contaminated-site assessments
SOURCE DESCRIPTION
ASTM International website: https://www.astm.org/. Internationally recognized as the authority on guidance and
guidelines for laboratory testing, collecting samples, metals
analysis, and many other standards. Available for purchase
individually or by subscription by topic.
EA (Environment Agency). 2006. “The Determination of Metals in Guidance from the UK Environment Agency on laboratory
Solid Environmental Samples: Methods for the Examination of methods for metals in solid matrices.
Waters and Associated Materials.” Booklet. Bristol, UK: EA.
EPA (US Environmental Protection Agency). n.d. “Collection of EPA offices and laboratories, and outside organizations, have
Methods.” Environment Measurements and Modeling, EPA developed approved methods for measuring contaminant
website: https://www.epa.gov/measurements-modeling concentrations. Contains extensive links to many laboratory
/collection-methods. resources and a complete listing of approved methods.
EPA (US Environmental Protection Agency). n.d. “The SW-486 US EPA’s SW-846 Compendium provides a complete listing and
Compendium.” https://www.epa.gov/hw-sw846/sw-846 guidance of all US EPA-approved laboratory methods. Most
-compendium. methods are intended as guidance.
European Union Reference Laboratory for Heavy Metals in Feed Determination of As, Cd, Hg, and Pb in food and feed products
and Food (EURL-HM), European Commission. https://www including pet food; validated a method for the determination of
.feedsafety.org/activities/eurl/eurl-heavy-metals/ MeHg in seafood; determination of iAs in food of vegetable origin.
Hageman, P. L. 2007. “Determination of Mercury in Aqueous and Discussion of updated CVAAS methods for determining total Hg
Geologic Materials by Continuous Flow–Cold Vapor–Atomic in geologic materials and dissolved Hg in aqueous samples;
Fluorescence Spectrometry (CVAFS).” In U.S. Geological Survey replaces the methods in use prior to 2006.
Techniques and Methods, Book 5, Chapter 2. Reston, VA: United
States Geological Survey.
ISO (International Organization for Standardization). 2013. ISO Microwave digestion of sludge, treated biowaste, and soil using
16729:2013, Soil quality—Digestion of nitric acid soluble nitric acid suitable for all metals.
fractions of elements.
ISO (International Organization for Standardization). 2013. ISO/TS Specifies a method for determining metals in aqua regia or nitric
16965:2013, Soil quality—Determination of trace elements using acid digests or other extraction solutions of sludge, treated
inductively coupled plasma mass spectrometry (ICP-MS). biowaste, and soil.
Source: World Bank compilation.

TABLE C.4 US EPA laboratory methods
METHOD # TITLE TYPE ANALYTE TECHNIQUE MEDIA/MATRIX DATE
3005A Acid Digestion of Waters for Sample Multi-metal Acid digestion Surface water, July 1992
Total Recoverable or Dissolved preparation screen; As, groundwater
Metals for Analysis by FLAA or Pb
ICP Spectroscopy. https://www
.epa.gov/sites/default/files/2015
-12/documents/3005a.pdf
3010A Acid Digestion of Aqueous Sample Multi-metal Acid digestion Aqueous July 1992
Samples and Extracts for Total preparation screen; As, samples,
Metals for Analysis by FLAA or Pb extracts,
ICP Spectroscopy. https://www wastes with
.epa.gov/sites/default/files/2015 suspended
-12/documents/3010a.pdf solids
3015A Microwave Assisted Acid Sample Multi-metal Microwave-assisted Aqueous Feb. 2007
Digestion of Aqueous Samples preparation screen; As, acid digestion samples,
and Extracts. https://www.epa Pb drinking water,
.gov/sites/default/files/2015-12 extracts,
/documents/3015a.pdf wastes with
suspended
solids
continued
70 | Recycling of Used Lead-Acid Batteries

TABLE C.4, continued

METHOD # TITLE TYPE ANALYTE TECHNIQUE MEDIA/MATRIX DATE
3020A Acid Digestion of Aqueous Sample Pb Acid digestion Aqueous July 1992
Samples and Extracts for Total preparation samples,
Metals for Analysis by GFAA extracts,
Spectroscopy. https://www.epa wastes with
.gov/hw-sw846/sw-846-test suspended
-method-3020a-acid-digestion solids
-aqueous-samples-and-extracts
-total-metals-analysis
3031 Acid Digestion of Oils for Metals Sample Multi-metal Acid digestion Oils, oil Dec. 1996
Analysis by Atomic Absorption preparation screen; As, sludges, tars,
or ICP Spectrometry. Pb waxes, paints,
https://19january2017snapshot paint sludges,
.epa.gov/sites/production/files other viscous
/2015-07/documents/epa-3031 petroleum
.pdf products
3040A Dissolution Procedure for Oils, Sample Multi-metal Solvent dissolution Oils, greases, Dec. 1996
Greases, or Waxes. https://www preparation screen; As, waxes
.epa.gov/sites/default/files/2015 Pb
-12/documents/3040a.pdf
3050B Acid Digestion of Sediments, Sample Multi-metal Acid digestion Sediments, Dec. 1996
Sludges, and Soils. https://www preparation screen; As, sludges, soils,
.epa.gov/sites/default/files/2015 Pb and oils
-06/documents/epa-3050b.pdf
3051A Microwave Assisted Acid Sample Multi-metal Microwave-assisted Sediments, Feb. 2007
Digestion of Sediments, preparation screen; As, acid digestion sludges, soils,
Sludges, Soils, and Oils. https:// Pb and oils
www.epa.gov/sites/default/files
/2015-06/documents/epa-3050b
.pdf
7010 Graphite Furnace Atomic Determinative Multi-metal Graphite furnace Groundwater, Feb. 2007
Absorption Spectrophotometry. screen; As, atomic absorption domestic
https://www.epa.gov/sites Pb spectrophotometry wastes,
/default/files/2015-07 (GFAA or GFAAS) industrial
/documents/epa-7010.pdf wastes,
extracts, soils,
sludges,
sediments
7000B Flame Atomic Absorption Determinative Pb Flame atomic Groundwater, Feb. 2007
Spectrophotometry. https:// absorption aqueous
www.epa.gov/sites/default/files spectrophotometry samples,
/2015-12/documents/7000b.pdf (FLAA or FAAS) extracts,
industrial
waste, soils,
sludges,
sediments
6800 Elemental and Molecular Determinative Pb Isotope dilution Water samples, July 2014
Speciated Isotope Dilution Mass mass spectrometry solid samples,
Spectrometry. https://www.epa (IDMS), molecular extracts,
.gov/sites/default/files/2015-12 speciated isotope digests, blood,
/documents/6800.pdf dilution mass foods
spectrometry
(SIDMS)
continued
Key References and Resource Guides for Environmental Sampling | 71

TABLE C.4, continued

METHOD # TITLE TYPE ANALYTE TECHNIQUE MEDIA/MATRIX DATE
6200 Field Portable X-Ray Determinative Multi-metal X-ray fluorescence Soils, sediment Feb. 2007
Fluorescence Spectrometry for screen; As,
the Determination of Elemental Pb
Concentrations in Soil and
Sediment. https://www.epa.gov
/hw-sw846/sw-846-test-method
-6200-field-portable-x-ray
-fluorescence-spectrometry
-determination
6020B Inductively Coupled Plasma- Determinative Multi-metal Inductively coupled Water samples, July 2014
Mass Spectrometry. https:// screen; As, plasma-mass waste extracts,
www.epa.gov/sites/default/files Pb spectrometry digests
/2015-12/documents/6020b.pdf (ICP-MS)
6010D Inductively Coupled Plasma- Determinative Multi-metal Inductively coupled Groundwater, July 2014
Optical Emissions Spectrometry. screen; As, plasma-atomic (or digested
https://www.epa.gov/hw-sw846 Pb optical) emission aqueous and
/sw-846-test-method-6010d spectrometry solid matrices
-inductively-coupled-plasma (ICP-AES or ICP-OES)
-optical-emission-spectrometry
-icp-oes
3052 Microwave Assisted Acid Sample Multi-metal Microwave-assisted Siliceous Dec. 1996
Digestion of Siliceous and preparation screen; As, acid digestion matrices,
Organically Based Matrices. Pb organic
https://19january2017snapshot matrices, and
.epa.gov/hw-sw846/sw-846 other complex
-test-method-3052-microwave matrices
-assisted-acid-digestion
-siliceous-and-organically
-based_.html
7472 Mercury in Aqueous Samples Determinative Hg Anodic stripping Drinking water, Dec. 1996
and Extracts by Anodic voltammetry (ASV) natural surface
Stripping Voltammetry (ASV). water,
https://www.epa.gov/sites seawater,
/default/files/2015-12 domestic or
/documents/7472.pdf industrial
wastewater,
soil extracts
7473 Mercury in Solids and Solutions Determinative Hg Thermal Solids, Feb. 2007
by Thermal Decomposition, decomposition and aqueous
Amalgamation, and Atomic atomic absorption samples,
Absorption Spectrophotometry. spectrophotometry digested
https://www.epa.gov/hw-sw846 (AAS) solutions
/sw-846-test-method-7473
-mercury-solids-and-solutions
-thermal-decomposition
-amalgamation-and
7474 Mercury in Sediment and Tissue Determinative Hg Atomic fluorescence Sediment, Feb. 2007
Samples by Atomic Fluorescence spectrometry (AFS) tissue
Spectrometry. https://www.epa
.gov/hw-sw846/sw-846-test
-method-7474-mercury
-sediment-and-tissue-samples
-atomic-fluorescence
Source: World Bank compilation.
Note: As = arsenic; EPA = US Environmental Protection Agency; FLAA = lead analysis by flame atomic absorption; GFAA = graphite furnace atomic
absorption spectroscopy; Hg = mercury; ICP = inductively coupled plasma analysis; Pb = lead.
72 | Recycling of Used Lead-Acid Batteries

BIOACCESSIBILITY AND BIOAVAILABILITY OF LEAD AND
ARSENIC: US EPA GUIDANCE

EPA (US Environmental Protection Agency). 2007. “Guidance for Evaluating the Oral
Bioavailability of Metals in Soils for Use in Human Health Risk Assessment.” OSWER
9285.7-80. Washington, DC: EPA.
EPA (US Environmental Protection Agency). 2015. “Guidance for Sample Collection for In Vitro
Bioaccessibility Assay for Lead (Pb) in Soil.” OSWER 9200.3-100. Washington, DC: EPA.
EPA (US Environmental Protection Agency). 2017. “Method 1340: In Vitro Bioaccessibility
Assay for Lead in Soil.” SW-846 Update VI. Washington, DC: EPA.
EPA (US Environmental Protection Agency). 2017. “Release of Standard Operating Procedure for
an In Vitro Bioaccessibility Assay for Lead and Arsenic in Soil and ‘Validation Assessment of
In Vitro Arsenic Bioaccessibility Assay for Predicting Relative Bioavailability of Arsenic in
Soils and Soil-like Materials at Superfund Sites.’” OLEM 9355.4-29, April 20. Washington, DC:
EPA. https://clu-in.org/download/contaminantfocus/arsenic/arsenic-OLEM-9355.4-29.pdf.

DUST SAMPLE COLLECTION

ASTM International. 2018. “ASTM D6966—18, Standard Practice for Collection of Settled
Dust Samples Using Wipe Sampling Methods for Subsequent Determination of Metals.”
West Conshohocken, PA: ASTM International. https://www.astm.org/Standards
/D6966.htm.
ASTM International. 2020. “ASTM E1728—20, Standard Practice for Collection of Settled Dust
Samples Using Wipe Sampling Methods for Subsequent Lead Determination.” West
Conshohocken, PA: ASTM International. https://www.astm.org/Standards/E1728.htm.
EPA (US Environmental Protection Agency). 1966. “Analysis of Composite Wipe Samples for
Lead Content.” EPA 747-R-96-003. Washington, DC: EPA.
Friederich, N. J., M. Karin, K. M. Bauer, B. D. Schultz, and T. S. Holderman. 1999. “The Use of
Composite Dust Wipe Samples as a Means of Assessing Lead Exposure.” American Industrial
Hygiene Association Journal 60 (3): 326–33. doi:10.1080/00028899908984449.
HUD (US Department of Housing and Urban Development). 2012. “Wipe Sampling of Settled
Dust for Lead Determination.” In Guidelines for the Evaluation and Control of Lead-Based
Paint Hazards in Housing, 2nd ed., Appendix 13.1. Washington, DC: HUD. https://www.hud
.gov/sites/documents/LBPH-40.PDF.
APPENDIX D

Biomonitoring Resources

Biomonitoring and biological-sample collection should be conducted under the
supervision of a trained professional, and most institutional review boards and
ethics-review organizations will make that a prerequisite for data collection.
This appendix provides links to accepted methods for sample collection across
biological matrices, as well as information on efforts worldwide to coordinate

biomonitoring programs.
Table D.1 provides an overview of biomonitoring studies conducted in low-
and middle-income countries (LMICs).

TABLE D.1 Selected biomonitoring studies for lead and metals with application to LMICs
BIOLOGICAL ANALYTICAL ANALYTICAL
REFERENCE LOCATION INDUSTRY POLLUTANT MATRIX METHOD LAB NOTES
Lead
Baghurst Port Pirie, Lead Lead Capillary Electrothermal Department Cited prior study
et al. (1992) South smelter blood atomization atomic of Chemical showing close
Australia absorption Pathology at correlation (r = 0.97)
spectrometry Adelaide b/w capillary and
Centre for venous sampling
Women’s and (Calder et al. 1986)
Children’s
Health
Malcoe Northeastern Lead and Lead Venous Graphite furnace Samples shipped to Oklahoma State
et al. (2002) Oklahoma zinc mining blood atomic absorption Department of Health laboratory
spectrometry
Jones et al. Senegal Lead-acid Lead (1) Venous (1) Graphite (1) Samples “HI” LeadCare
(2011) (Thiaroye Sur battery blood furnace atomic shipped to readings sent to lab
Mer) disposal absorption Pasteur
(2) Capillary
spectrometry Cerba-
blood
certified lab
(2) LeadCare
(France)
portable test kits
(2) In field
continued

73
74 | Recycling of Used Lead-Acid Batteries

TABLE D.1, continued

BIOLOGICAL ANALYTICAL ANALYTICAL
REFERENCE LOCATION INDUSTRY POLLUTANT MATRIX METHOD LAB NOTES
Lo et al. Zamfara Gold-ore Lead Venous LeadCare II Samples were Product lots of all
(2012) State, Nigeria processing blood portable analyzer analyzed at blood collection
the Blood supplies were
Lead and prescreened for lead
Inorganic contamination by
Metals Lab CDC labs, and
(Gusau, supplies were stored
Zamfara) in plastic bags before
collection to prevent
in-field contamina-
tion
Caravanos Kabwe, Lead Lead Capillary LeadCare II In field
et al. (2014) Zambia mining and blood portable analyzer
smelting
Gao et al. Wuxi City, n.a. Lead Capillary Graphite furnace Shipped to School of Public Health,
(2001) China blood atomic absorption Beijing Medical University
spectrometry
Riddell et al. Central n.a. Lead Venous LeadCare analyzer; Samples were Cited previous field
(2007); Philippines blood subset analyzed analyzed at a work demonstrating
Solon et al. using atomic central good correlation
(2008) absorption laboratory in (r = 0.829) between
spectroscopy Manila LeadCare device and
atomic absorption
spectrometry
(Counter et al. 1998);
study also measured
hemoglobin
(HemoCue Blood
Hemoglobin
Photometer) and red
blood cell folate
(Architect system)
Xie et al. China n.a. Lead Capillary BH2100 tungsten n.a. QA/QC program for
(2013) (16 cities) blood atomizer absorp- blood lead levels
tion spectropho- higher than 10 ug/dL
tometer (used double test
method)
Daniell et al. Hung Yen Battery Lead Capillary LeadCare II In field Children only;
(2015) Province, recycling blood portable analyzer confirmatory venous
northern sampling for high
Vietnam field levels; extensive
soil, survey, medical
data also collected
Grigoryan Northern Metal Lead Capillary LeadCare II In field Blood samples
et al. (2016) Armenia mining and blood portable analyzer collected following
smelting CDC recommended
finger-stick method;
cites results of CLIA
waiver clinical field
trials that found good
correlation (r = 0.979)
between this device
and graphite furnace
atomic absorption
spectrometry
(GFAAS)
continued
Biomonitoring Resources | 75

TABLE D.1, continued

BIOLOGICAL ANALYTICAL ANALYTICAL
REFERENCE LOCATION INDUSTRY POLLUTANT MATRIX METHOD LAB NOTES
Metals
Were et al. Nairobi, School-age Lead, Fingernails Atomic absorption Kenyatta University Research
(2008) Kenya children in cadmium, spectrometer with Laboratory and Mines and Geology
industrial calcium, acid digestion Analytical Research Department,
areas zinc, and Nairobi
iron
Qu et al. Jiangsu Lead-zinc Metals Hair Inductively — Ag, Cd, Cr, Cu, Ni, Pb,
(2012) Province, mining coupled argon Se, Ti, Zn, Hg; notes
China plasma mass that hair useful for
spectrometry assessing long-term
(USEPA 6020A) for exposure and for
metals certain metals (Pb,
Hg) but not others
Thermal decompo-
(Zn, Cu, Cd)
sition, amalgama-
tion, and atomic
absorption
spectrophotometry
(USEPA 7473) for
Hg
Thakur et al. Punjab, India Wastewater Metals, Blood Urine Community-based — Urine (Hg, Cd, Pb,
(2010) drains pesticides Human milk interviews of As, Se)
women and Blood/milk
children; clinical (pesticides)
examination and
records review by
medical doctors of
selected cases.
Röllin et al. South Africa Multiple Metals Venous Element 2 mass Samples Cd, Hg, Pb, Mn, CO,
(2009) (for blood spectrometer shipped to Cu, Zn, As, Se
example, (before University of
industrial delivery) Tromso,
and mining Norway, and
Umbilical
sites) analyzed at
cord blood
National
Institute for
Occupational
Health
Banza et al. Congo, Dem. Metal Metals Urine (spot) Inductively Samples Al, Sb, As, Cd, Cr, Co,
(2009) Rep. mining and coupled argon analyzed in Cu, Pb, Mn, Mo, Ni,
smelting plasma mass Laboratory of Se, Te, Sn, U, V, Zn;
spectrometry Industrial creatinine adjusted
Toxicology
and
Occupational
Medicine Unit
(Belgium)
Ibeto and Enugu State, n.a. Metals Venous GBC atomic University of Ni, Mn, Cr
Okoye Nigeria blood absorption Nigeria
(2010) spectrophotometer Nsukka, Enugu
State
Alatise and Nigeria, Africa n.a. Metals Blood Inductively — Cu, Zn, Pb, Se, Cd,
Schrauzer (fasting) coupled plasma Hg, As, Mn, Sr, Ca,
(2010) mass spectrometry Mg, Li, Co, Zn/Cu,
Hair (scalp)
Ca/Mg; notes various
Breast interactions (for
biopsy example, Pb interacts
with Se and iodine
in vivo)
continued
76 | Recycling of Used Lead-Acid Batteries

TABLE D.1, continued

BIOLOGICAL ANALYTICAL ANALYTICAL
REFERENCE LOCATION INDUSTRY POLLUTANT MATRIX METHOD LAB NOTES
Caravanos Ghana, West E-waste Metals Urine (first Graphite furnace Ghana Ba, Cd, Co, Mn, Cr,
et al. (2013) Africa dumping void) atomic absorption Standards Cu, Fe, Hg, Pb, Se,
and spectrometry; Board Forensic Zn; Sample collection
Venous
recycling whole blood spun Lab in Accra equipment and
blood
to isolate cells to containers were
(serum)
produce serum prescreened or
soaked in trace
metal-grade nitric
acid; analytical flaw
in using blood serum
because lead resides
in erythrocyte
Obiri et al. Tarkwa Mining Metals Whole blood Neutron activation Ghana Atomic As, Cd, Hg, Cu, Pb,
(2016) Nsuaem analysis Energy Zn, Mn; also
Venous
Municipality Commission administered a health
blood
and the questionnaire.
(serum)
Fasting sample
Prestea Huni
Valley District,
Ghana
Sanders Red River Smelting Metals Capillary LeadCare II In-field In whole blood and
et al. (2014) Delta, (automo- blood portable analyzer serum
toenail
Vietnam bile Toenail Toenails extracted samples
batteries) using modified shipped to RTI
Method 3050B International
(Research
Triangle
Park, NC)
Jasso- Villa de la Paz, Mining Metals Venous Atomic absorption Universidad Blood (lead)
Pineda et al. Mexico blood spectrometry Autónoma de Urine (spot)
(2007) San Luis
Urine (first
Potosı, Mexico
void)
Uriah et al. Zamfara Artisanal Lead,
(2013) State, Nigeria gold mercury
mining
Source: World Bank compilation.
Note: Bibliographic information for references is listed below by topic. n.a. = not applicable. Al = aluminum; As = arsenic; Ba = barium; Ca = calcium; Cd =
cadmium; Co = cobalt; Cr = chromium; Cu = copper; Fe = iron; Hg = mercury; Li = lithium; MeHg = methylmercury; Mg = magnesium; Mn = manganese;
Mo = molybdenum; Ni = nickel; Pb = lead; Sb = antimony; Se = Selenium; Sn = tin; Sr = strontium; Te = tellurium; Ti = titanium; U = uranium;
V = vanadium; Zn = zinc.
Biomonitoring Resources | 77

SAMPLE-COLLECTION GUIDELINES FOR TRACE ELEMENTS
IN BLOOD AND URINE

APHL (Association of Public Health Laboratories). n.d. “Biomonitoring” online resource page
includes the National Biomonitoring Network (NBN) of federal, regional, state, and local
laboratories that conduct biomonitoring for use in public health practice. APHL, Silver
Spring, MD. https://www.aphl.org/programs/environmental_health/nbn/Pages/default
.aspx.
ATSDR (US Agency for Toxic Substances and Disease Registry). 2020. “Analytical Methods.”
Discussion of measuring lead in biological matrices in chapter 7 of “Toxicological Profile for
Lead,” ATSDR, Atlanta. https://www.ncbi.nlm.nih.gov/books/NBK158761/.
CDC (US Centers for Disease Control and Prevention). 2006. “CDC Specimen-Collection
Protocol for a Chemical-Exposure Event.” Infographic, CDC, Atlanta. https://www.health
.ny.gov/guidance/oph/wadsworth/chemspecimencollection.pdf.
CDC (US Centers for Disease Control and Prevention). 2013. “Guidelines for Measuring Lead in
Blood Using Point of Care Instruments.” Guidance from the Advisory Committee on
Childhood Lead Poisoning Prevention of the CDC, Atlanta. https://www.cdc.gov/nceh
/lead/acclpp/20131024_pocguidelines_final.pdf.
CLSI (Clinical Laboratory Standards Institute). 2013. Measurement Procedures for the
Determination of Lead Concentrations in Blood and Urine, 2nd Ed. CLSI document C40-A2.
Wayne, PA: CLSI. https://clsi.org/standards/products/clinical-chemistry-and-toxicology
/documents/c40/.
Cornelis, R., B. Heinzow, R. F. M. Herber, J. M. Christensen, O. M. Poulsen, E. Sabbioni,
D. M. Templeton, Y. Thomassen, M. Vahter, and O. Vesterberg. 1995. “Sample Collection
Guidelines for Trace Elements in Blood and Urine.” Pure and Applied Chemistry 67 (8–9):
1575–1608. http://publications.iupac.org/pac-2007/1995/pdf/6708x1575.pdf.
Cornelis, R., B. Heinzow, R. F. M. Herber, J. M. Christensen, O. M. Poulsen, E. Sabbioni,
D. M. Templeton, Y. Thomassen, M. Vahter, and O. Vesterberg. 1996. “Sample Collection
Guidelines for Trace Elements in Blood and Urine.” Journal of Trace Elements in Medicine
and Biology 10 (2): 103–27.
EPA (US Environmental Protection Agency). 2019. “Guidelines for Human Exposure
Assessment.” EPA/100/B-19/001, Risk Assessment Forum. Washington, DC: EPA. https://
www.epa.gov/sites/default/files/2020-01/documents/guidelines_for_human_exposure
_assessment_final2019.pdf.
FDA and NIH (US Food and Drug Administration and National Institutes of Health). 2016.
“BEST (Biomarkers, EndpointS, and other Tools) Resource.” Glossary copublished by the
FDA, Silver Spring, MD; and NIH, Bethesda, MD. https://www.ncbi.nlm.nih.gov/books
/NBK326791/pdf/Bookshelf_NBK326791.pdf.
Heppner, Claudia. 2011. “Biomarkers in Risk Assessment: Application for Chemical
Contaminants.” PowerPoint presentation at the EU Decision Makers Meeting, “Use of
Human Biomonitoring for Policy Making,” Munich, May 4. http://www.eu-hbm.info
/cophes/4_Biomarkersinriskassessment.pdf.
IPCS (International Programme on Chemical Safety). 1993. Biomarkers and Risk Assessment:
Concepts and Principles. Environmental Health Criteria (EHC) 155. Geneva: World Health
Organization. http://apps.who.int/iris/bitstream/handle/10665/39037/9241571551-eng
.pdf;jsessionid=77D78AFFF865ABE58A666BB4941A9330?sequence=1.
MEASURE Evaluation. 2000. “Biological and Clinical Data Collection in Population Surveys in
Less Developed Countries.” Summary of MEASURE Evaluation meeting, National Academy
of Sciences, Washington, DC, January 24–25. https://www.who.int/hiv/pub/surveillance
/en/biomarkers.pdf?ua=1.
WHO (World Health Organization). 2010. WHO Guidelines on Drawing Blood: Best Practices
in Phlebotomy . Geneva: WHO. https://www.euro.who.int/__data/assets/pdf_file
/0 005/268790/WHO-guidelines-on-drawing-blood-best-practices-in-phlebotomy
-Eng.pdf.
WHO (World Health Organization). 2011. Brief Guide to Analytical Methods for Measuring
Lead in Blood. Geneva: WHO. http://www.who.int/ipcs/assessment/public_health/lead
_blood.pdf.
78 | Recycling of Used Lead-Acid Batteries

Dried blood spots
Crimmins, E. M., J. D. Faul, J. K. Kim, and D. R. Weir. 2017. “Documentation of Blood-Based
Biomarkers in the 2014 Health and Retirement Study.” Report, Survey Research Center,
Institute for Social Research, University of Michigan, Ann Arbor.
Crimmins, E., J. K. Kim, H. McCreath, J. Faul, D. Weir, and T. Seeman. 2014. “Validation of
Blood-Based Assays Using Dried Blood Spots for Use in Large Population Studies.”
Biodemography and Social Biology 60 (1): 38–48. doi:10.1080/19485565.2014.901885.
Delahaye, L., B. Janssens, and C. Stove. 2017. “Alternative Sampling Strategies for the Assessment
of Biomarkers of Exposure.” Current Opinion in Toxicology 4: 43–51.
Freeman, J. D., L. M. Rosman, J. D. Ratcliff, P. T. Strickland, D. R. Graham, and E. K. Silbergeld.
2017. “State of the Science in Dried Blood Spots.” Clinical Chemistry 64 (4): 656–79.
Funk, W. E., J. D. Pleil, D. J. Sauter, T. McDade, and J. L. Holl. 2015. “Use of Dried Blood Spots for
Estimating Children’s Exposures to Heavy Metals in Epidemiological Research.” Journal of
Environmental & Analytical Toxicology 7 (2): 1–9.
Wagner, M., D. Tonoli, E. Varesio, and G. Hopfgartner. 2016. “The Use of Mass Spectrometry to
Analyze Dried Blood Spots.” Mass Spectrometry Reviews 35 (3): 361–438.

Cardiovascular (C-reactive protein)
Ahn, J. S., S. Choi, S. H. Jang, H. J. Chang, J. H. Kim, K. B. Nahm, S. W. Oh, and E. Y. Choi. 2003.
“Development of a Point-of-Care Assay System for High-Sensitivity C-Reactive Protein in
Whole Blood.” Clinica Chimica Acta 332 (1–2): 51–59.
Brindle, E., M. Fujita, J. Shofer, and K. A. O’Connor. 2010. “Serum, Plasma, and Dried Blood Spot
High-Sensitivity C-Reactive Protein Enzyme Immunoassay for Population Research.”
Journal of Immunological Methods 362 (1–2): 112–20.
McDade, T. W., J. Burhop, and J. Dohnal. 2004. “High-Sensitivity Enzyme Immunoassay for
C-Reactive Protein in Dried Blood Spots.” Clinical Chemistry 50 (3): 652–54.
Ochoa-Martínez, Á. C., E. D. Cardona-Lozano, L. Carrizales-Yáñez, and I. N. Pérez-Maldonado.
2018. “Serum Concentrations of New Predictive Cardiovascular Disease Biomarkers in
Mexican Women Exposed to Lead.” Archives of Environmental Contamination and
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Peña, M. S. B., and A. Rollins. 2017. “Environmental Exposures and Cardiovascular Disease:
A Challenge for Health and Development in Low- and Middle-Income Countries.”
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Roberts, W. L., L. Moulton, T. C. Law, G. Farrow, M. Cooper-Anderson, J. Savory, and N. Rifai.
2001. “Evaluation of Nine Automated High-Sensitivity C-Reactive Protein Methods:
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Roberts, W. L., E. L. Schwarz, S. Ayanian, and N. Rifai. 2001, “Performance Characteristics of a
Point of Care C-Reactive Protein Assay.” Clinica Chimica Acta 314 (1–2): 255–59.
Roberts W. L., R. Sedrick, L. Moulton, A. Spencer, and N. Rifai. 2000. “Evaluation of Four
Automated High-Sensitivity C-Reactive Protein Methods: Implications for Clinical and
Epidemiological Applications.” Clinical Chemistry 46 (4): 461–68.

Point-of-care (POC) and in-field diagnostic assays and
methods
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Diagnostics for Low-Resource Point-of-Care Settings.” Bioanalysis 5 (22): 2821–36.
Drain, P. K., E. P. Hyle, F. Noubary, K. A. Freedberg, D. Wilson, W. R. Bishai, W. Rodriguez, and
I. V. Bassett. 2014. “Diagnostic Point-of-Care Tests in Resource-Limited Settings.” Lancet
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Gubala, V., L. F. Harris, A. J. Ricco, M. X .Tan, and D. E. Williams. 2011. “Point of Care Diagnostics:
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103 (2): 236–47.

MODELING TOOLS

A variety of modeling approaches are available for quantifying and predicting
contaminant fate, transport, and external and internal exposures from source to
outcome as presented in the conceptual site model (CSM). Fate and transport
models are used to quantify the movement of contaminants through environ-
mental media to the point of exposure. For example, air-quality models predict
wet and dry deposition of airborne contaminants from a variety of sources based
on local estimates of wind speed, rainfall, and other parameters. Similarly,
groundwater models predict expected concentrations in groundwater from
leaching in soils or other mechanisms. These models could be used together
with measured soil concentrations (chapter 3) and site-specific parameters to
predict groundwater concentrations, which could then be verified using mea-
sured groundwater measurements (chapter 3).
There are many different models that could be applied along the continuum
from contaminant source to health outcome, and they vary in complexity and
required inputs. This appendix provides links to resources to consult in decid-
ing which models to use and identifies a limited set of specific models relevant
to assessing exposure to metals in low- and middle-income countries (LMICs).
For example, the integrated exposure uptake biokinetic model for lead in chil-
dren (IEUBK) is a model developed by the US Environmental Protection
Agency (EPA) to predict expected blood lead levels in children from measured
concentrations in soil. This model, together with LMIC-specific exposure fac-
tors (appendix B), could be combined to predict the biomonitoring data (chap-
ter 4). Similarly, several physiologically based pharmacokinetic (PBPK) models
exist to link external exposure concentrations (chapter 3) to internal concen-
trations in target organs, tissues, and blood, which can then be verified in a
limited way (for example, blood, urine, hair) using the biomonitoring data
(chapter 4). This may allow for less data collection in the future or achieve
other goals.
Depending on the model’s complexity, some degree of training and experi-
ence with specific models is generally required to gain proficiency with their
use. Models generally require site-specific calibration and verification to effec-
tively support decision-making.
80 | Recycling of Used Lead-Acid Batteries

EPA (US Environmental Protection Agency). 2021. “Integrated Exposure
Uptake Biokinetic Model for Lead in Children, Windows® version (IEUBKwin
v2) (May 2021) 32/64-bit version.” Software, EPA, Washington, DC.
The Integrated Exposure Uptake Biokinetic (IEUBK) Model for Lead in
Children is stand-alone, Windows-based software developed by the US EPA.
The model predicts the distribution of expected blood lead concentrations for a
hypothetical child or population of children based on measured or assumed
concentrations of Pb in the environment, particularly soil and drinking water

(chapter 3). From this distribution, the model calculates the probability that pre-
dicted blood lead concentrations will exceed a user-defined level of concern
(default 10 μg/dL). The user can then explore an array of possible changes in
exposure media that would reduce the probability that blood lead concentra-
tions would be above this level of concern. Beginning in 1990, the model has
undergone many iterations and review cycles, and has been well vetted in the
literature and elsewhere.
The model is optimized for children less than seven years old who are
exposed to environmental Pb from many sources. The model can also be used to
predict cleanup levels for various media assuming residential land use. Studies
show the model is most sensitive to the amount of soil and dust ingested per day.
In decreasing order of sensitivity, predicted Pb uptake is moderately sensitive to
the assumed absorption fraction for soil/dust and diet, the soil Pb concentra-
tion, the indoor dust Pb concentration, dietary-lead concentration, contribution
of soil lead to indoor dust lead, and the half-saturation absorbable intake (based
on output-input ratio). Finally, the predicted probability of exceeding a speci-
fied level of concern is highly sensitive to changes in the geometric standard
deviation (GSD). The GSD is a measure of the variability among individuals who
have contact with a fixed lead concentration and is based on analyses of data
from neighborhoods having paired sets of environmental concentration and
blood Pb data from high-income countries (HICs). This value likely differs for
LMICs.
EPA (US Environmental Protection Agency). n.d. Lead at Superfund Sites:
Frequent Questions from Risk Assessors on the Adult Lead Model Methodology.
Questions, input variables, and application, EPA, Washington, DC.
While the IEUBK model is designed for children, the Adult Lead Model
(ALM) focuses on adults. The required inputs are similar, but the ALM model is
designed for adult populations.

Physiologically based pharmacokinetic (PBPK) models
for metals
PBPK models are contaminant-specific and typically used to evaluate contami-
nant disposition in the human body following exposure. The models are generally
based on studies in which animals, often rodents, are exposed to known quanti-
ties of contaminants via specific exposure routes and the animals are sacrificed at
various time points and organ-specific contaminant concentrations assessed.
The animal data relate to humans through a comparison of physiological-rate
constants (for example, breathing rate, blood volume, and so on).
Kenyon, E. M., and H. J. Clewell III. 2015. “Toxicokinetics and Pharmacokinetic Modeling of
Arsenic.” In Arsenic: Exposure Sources, Health Risks, and Mechanisms of Toxicity, edited by
J. C. States, 495–510. Hoboken, NJ: John Wiley & Sons. https://onlinelibrary.wiley.com/doi
/book/10.1002/9781118876992.
Biomonitoring Resources | 81

This book illustrates the chemistry, toxicology, and health effects of As using
novel modeling techniques, case studies, experimental data, and future
perspectives. Chapter 22 in
particular focuses on PBPK modeling for As.
Liao, C. M., T. L. Lin, and S. C. Chen. 2008. “A Weibull-PBPK Model for Assessing Risk of
Arsenic-Induced Skin Lesions in Children.” Science of the Total Environment 392 (2–3):
203–17.
Mumtaz, M., J. Fisher, B. Blount, and P. Ruiz. 2012. “Application of Physiologically Based
Pharmacokinetic Models in Chemical Risk Assessment.” Journal of Toxicology. https://
www.ncbi.nlm.nih.gov/pmc/articles/PMC3317240/.
Ruiz, P., B. A. Fowler, J. D. Osterloh, J. Fisher, and M. Mumtaz. 2010. “Physiologically Based
Pharmacokinetic (PBPK) Tool Kit for Environmental Pollutants–Metals.” SAR and QSAR in
Environmental Research 21 (7–8): 603–18.
Ruiz, P., M. Ray, J. Fisher, and M. Mumtaz. 2011. “Development of a Human Physiologically
Based Pharmacokinetic (PBPK) Toolkit for Environmental Pollutants.” International
Journal of Molecular Sciences 12 (11): 7469–80.

Bioaccumulation models
Alatise, Olusegun I., and Gerhard N. Schrauzer. 2010. “Lead Exposure: A Contributing Cause of
the Current Breast Cancer Epidemic in Nigerian Women.” Biological Trace Element Research
136: 127–39.
Baghurst, Peter A., Anthony J. McMichael, Neil R. Wigg, Graham V. Vimpani, Evelyn F.
Robertson, Russell J. Roberts, and Shi-Lu Tong. 1992. “Environmental Exposure to Lead and
Children’s Intelligence at the Age of Seven Years: The Port Pirie Cohort Study.” NEJM 327:
1279–84.
Calder, Ian C., David M. Roder, Adrian J. Esterman, Milton J. Lewis, Malcolm C. Harrison, and
Robert K. Oldfield. 1986. “Blood Lead Levels in Children in the North-West of Adelaide.”
Medical Journal of Australia 144 (10): 509–12.
Caravanos, Jack, Edith E. Clarke, Carl S. Osei, and Yaw Amoyaw-Osei. 2013. “Exploratory
Health Assessment of Chemical Exposures at E-Waste Recycling and Scrapyard Facility in
/2156-9614
Ghana.” Journal of Health and Pollution 3 (4): 11–22. https://doi.org/10.5696
-3.4.11.
Caravanos, Jack, Russell Dowling, Martha María Téllez-Rojo Dra, Alejandra Cantoral, Roni
Kobrosly, Daniel Estrada, Manuela Orjuela, Sandra Gualtero, Bret Ericson, Anthony Rivera,
and Richard Fuller. 2014. “Blood Lead Levels in Mexico and Pediatric Burden of Disease
Implications.” Annals of Global Health 80 (4): 269–77.
Counter, S. A., L. H. Buchanan, G. Laurell, and F. Ortega. 1998. “Field Screening of Blood Lead
Levels in Remote Andean Villages.” Neurotoxicology 19 (6): 871–77.
Daniell, William E., Lo Van Tung, Ryan M. Wallace, Deborah J. Havens, Catherine J. Karr,
Nguyen Bich Diep, Gerry A. Croteau, Nancy J. Beaudet, and Nguyen Duy Bao. 2015.
“Childhood Lead Exposure from Battery Recycling in Vietnam.” BioMed Research
International 2015: 193715. http://dx.doi.org/10.1155/2015/193715.
Gao, Wanzhen, Zhu Lia, Rachel B. Kaufmann, Robert L. Jones, Zhengang Wang, Yafen Chen,
Xiuqin Zhao, and Naifen Wang. 2001. “Blood Lead Levels among Children Aged 1 to 5 Years
in Wuxi City, China.” Environmental Research 87 (1): 11–19.
Grigoryan, Ruzanna, Varduhi Petrosyan, Dzovinar Melkom Melkomian, Vahe Khachadourian,
Andrew McCartor, and Byron Crape. 2016. “Risk Factors for Children’s Blood Lead Levels
in Metal Mining and Smelting Communities in Armenia: A Cross-Sectional Study.” BMC
Public Health 16: 945. doi:10.1186/s12889-016-3613-9.
Ibeto, C. N., and C. O. B. Okoye. 2010. “High Levels of Heavy Metals in Blood of the Urban
Population in Nigeria.” Research Journal of Environmental Sciences 4 (4): 371– 82.
Jasso-Pineda, Yolanda, Guillermo Espinosa-Reyes, Donají González-Mille, Israel Razo-Soto,
Leticia Carrizales, Arturo Torres-Dosal, Jesús Mejía-Saavedra, Marcos Monroy, Ana Irina
Ize, Mario Yarto, and Fernando Díaz-Barriga. 2007. “An Integrated Health Risk Assessment
82 | Recycling of Used Lead-Acid Batteries

Approach to the Study of Mining Sites Contaminated with Arsenic and Lead.” Integrated
Environmental Assessment and Management 3 (3): 344–50.
Jones, Donald E., Assane Diop, Meredith Block, Alexander Smith-Jones, and Andrea Smith-
Jones 2011. “Assessment and Remediation of Lead Contamination in Senegal.” Blacksmith
Institute Journal of Health & Pollution 1 (2): 37–47.
Lo, Yi-Chun, Carrie A. Dooyema, Antonio Neri, James Durant, Taran Jefferies, Andrew Medina-
Marino, Lori de Ravello, Douglas Thoroughman, Lora Davis, Raymond S. Dankoli, Matthias
Y. Samson, Luka M. Ibrahim, Ossai Okechukwu, Nasir T. Umar-Tsafe, Alhassan H. Dama,
and Mary Jean Brown. 2012. “Childhood Lead Poisoning Associated with Gold Ore
Processing: A Village-Level Investigation—Zamfara State, Nigeria, October–November
2010.” Environmental Health Perspectives 120 (10): 1450–55.
Malcoe, Lorraine Halinka, Robert A. Lynch, Michelle Crozier Keger, and Valerie J. Skaggs.
2002. “Lead Sources, Behaviors, and Socioeconomic Factors in Relation to Blood Lead of
Native American and White Children: A Community-Based Assessment of a Former Mining
Area.” Environmental Health Perspectives 110 (Supplement 2): 221–31.
Qu, Chang-Sheng, Zong-Wei Ma, Jin Yang, Yang Liu, Jun Bi, and Lei Huang. 2012. “Human
Exposure Pathways of Heavy Metals in a Lead-Zinc Mining Area, Jiangsu Province, China.”
PLOS ONE 7 (11) e46793.
Riddell, Travis J, Orville Solon, Stella A. Quimbo, Cheryl May C. Tan, Elizabeth Butrick, and
John W. Peabody. 2007. “Elevated Blood-Lead Levels among Children Living in the Rural
Philippines.” Bulletin of the World Health Organization 85 (9): 674–82.
Röllin, Halina B., Cibele V. C. Rudge, Yngvar Thomassen, Angela Mathee, and Jon Ø. Odland.
2009. “Levels of Toxic and Essential Metals in Maternal and Umbilical Cord Blood from
Selected Areas of South Africa—Results of a Pilot Study.” J. Environ. Monit. 11: 618–27.
Sanders, Alison P., Sloane K. Miller, Viet Nguyen, Jonathan B. Kotch, and Rebecca C. Fry. 2014.
“Toxic Metal Levels in Children Residing in a Smelting Craft Village in Vietnam: A Pilot
Biomonitoring Study.” BMC Public Health 14: 114.
Solon, Orville, Travis J. Riddell, Stella A. Quimbo, Elizabeth Butrick, Glen P. Aylward, Marife
Lou Bacate, and John W. Peabody. 2008. “Associations between Cognitive Function, Blood
Lead Concentration, and Nutrition among Children in the Central Philippines.” Journal of
Pediatrics 152 (2): 237–43.
Suvd, Duvjir Suvd, Rendoo Davaadorj, Dayanjav Baatartsol, Surenjav Unursaikhan, Myagmar
Tsengelmaa, Tsogbayar Oyu, Sonom Yunden, Ana M. Hagan-Rogers, and Stephan Böse-
O’Reilly. 2015. “Toxicity Assessment in Artisanal Miners from Low-Level Mercury Exposure
in Bornuur and Jargalant Soums of Mongolia.” Procedia Environmental Sciences 30: 97–102.
Thakur, Jarnail Singh, Shankar Prinja, Dalbir Singh, Arvind Rajwanshi, Rajendra Prasad,
Harjinder Kaur Parwana, and Rajesh Kumar. 2010. “Adverse Reproductive and Child Health
Outcomes among People Living near Highly Toxic Waste Water Drains in Punjab, India.”
J. Epidemiol. Community Health 64: 148e–154. doi:10.1136/jech.2008.078568.
Were, Faridah Hussein, Wilson Njue, Jane Murung, and Ruth Wanjau. 2008. “Use of Human
Nails as Bio-Indicators of Heavy Metals Environmental Exposure among School Age
Children in Kenya.” Science of the Total Environment 393 (2–3): 376–84.
Xie, Xiao-hua, Zang-wen Tan, Ni Jia, Zhao-yang Fan, Shuai-ming Zhang, Yan-yu Lü, Li Chen,
and Yao-hua Dai. 2013. “Blood Lead Levels among Children Aged 0 to 6 Years in 16 Cities of
China, 2004-2008.” Chinese Medical Journal 126 (12): 2291–95. doi:10.3760
/cma.j.issn.0366-6999.20122327.
APPENDIX E

Resources for Health-Outcomes
Assessment

This appendix provides links to resources for evaluating methods for assessing
specific health outcomes (see the Bickley reference below and table E.1). Note
that intermediate health outcomes evaluated using biomarkers are discussed in
chapter 4 and appendix D, which focus on sampling in biological matrixes for
biomarkers of exposure, effect, or both exposure and effect.

Bickley, L. S. 2012. Bates’ Guide to Examination and History Interviewing and obtaining a health history
Taking, 11th ed. Philadelphia: Lippincott Williams & Wilkins, an
imprint of Wolters Kluwer.

TABLE E.1 Links to resources for health-outcomes assessment
REFERENCE HEALTH OUTCOME
Pulmonary function testing
“Pulmonary Function Testing,” American Thoracic Society website, https://www Recommended guidelines for pulmonary-
.thoracic.org/ function testing (PFT)
Ranu, H., M. Wilde, and B. Madden. 2011. “Pulmonary Function Tests.” Ulster Medical Overview of PFTs, including references to
Journal 80 (2): 84–90. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3229853/ European and US reference and guidance
manuals
Higashimoto, Y., T. Iwata, M. Okada, H. Satoh, K. Fukuda, and Y. Tohda. 2009. “Serum Discussion of the utility of C-reactive protein
Biomarkers as Predictors of Lung Function Decline in Chronic Obstructive Pulmonary (CRP) for predicting lung function
Disease.” Respiratory Medicine 103 (8): 1231–38. https://www.resmedjournal.com
/article/S0954-6111(09)00037-7/fulltext
Renal outcomes in children and adults
National Kidney Foundation website, https://www.kidney.org/ Official guidance from the National Kidney
Foundation (US) on standard renal-panel
testing and interpretation in urine samples
continued

83
84 | Recycling of Used Lead-Acid Batteries

TABLE E.1, continued

REFERENCE HEALTH OUTCOME
Swedish Council on Health Technology Assessment (SBU). 2013. “Methods to Estimate Creatinine-based equations from the
and Measure Renal Function (Glomerular Filtration Rate): A Systematic Review.” Yellow Modification of Diet in Renal Disease Study
Report No. 214, SBU, Stockholm. https://www.ncbi.nlm.nih.gov/books/NBK285322 (MDRD), the Chronic Kidney Disease
/pdf/Bookshelf_NBK285322.pdf Epidemiology Collaboration (CKD-EPI), and
the revised Lund-Malmö equation (LM-rev)
are all accurate (P30 ≥ 75%) for estimating
kidney function in adults, except in patients
with GFR < 30 mL/min/1.73 m2 or BMI < 20
kg/m2. Cockcroft-Gault (CG) should not be
used.
Argyropoulos, C. P., S. S. Chen, Y. H. Ng, M. E Roumelioti, K. Shaffi, P. P. Singh, and Background and justification for use of
A. H. Tzamaloukas. 2017. “Rediscovering Beta-2 Microglobulin as a Biomarker Across beta-2-microglobulin as a sensitive marker of
the Spectrum of Kidney Diseases.” Frontiers in Medicine 4: 73. https://www kidney damage. This is the preferred
.frontiersin.org/articles/10.3389/fmed.2017.00073/full biomarker of cadmium (Cd) effects
recommended by the European Food Safety
Authority (EFSA).
Complete blood count
Keng, T. B., B. De La Salle, G. Bourner, A. Merino, J.-Y. Han, Y. Kawai, M. T. Peng, Recommendation for standardization of
R. McCafferty, and International Council for Standardization in Haematology (ICSH). hematology-reporting units used for
2016. “Standardization of Haematology Critical Results Management in Adults: An complete blood count (CBC)
International Council for Standardization in Haematology, ICSH, Survey and
Recommendations.” Int j Lab Hematl 38 (5): 457–71. https://doi.org/10.1111/ijlh.12526
Neurodevelopmental outcomes in children
Fernald, L. C. H., E. Prado, P. Kariger, and A. Raikes. 2017. “A Toolkit for Measuring Early Report comes with an Excel spreadsheet to
Childhood Development in Low- and Middle-Income Countries.” World Bank, use to help guide selection of appropriate
Washington, DC. https://openknowledge.worldbank.org/handle/10986/29000 instrument out of 147 possible instruments.
UNESCO (United Nations Educational, Scientific and Cultural Organization). Measuring Collaborative effort of the MELQO core team,
Early Learning Quality and Outcomes (MELQO). https://www.brookings.edu/wp technical advisory groups, and steering
-content/uploads/2017/06/melqo-measuring-early-learning-quality-outcomes.pdf committee, describing modules and where
they were piloted
Anderson, K., and R. Sayre. 2016. “Measuring Early Learning Quality and Outcomes in Application of the MELQO modules and
Tanzania.” Report, Center for Universal Education at Brookings, Washington, DC. approach in consultation with Ministry of
https://www.brookings.edu/wp-content/uploads/2017/06/melqo-measuring-early Education in Tanzania, published by the
-learning-quality-outcomes-in-tanzania_2016oct.pdf Brookings Institution
Brookings Institution. 2017. “Measuring Early Learning Quality and Outcomes Further background and reports on the
(MELQO).” https://www.brookings.edu/research/measuring-early-learning-quality MELQO effort
-and-outcomes-in-tanzania/
World Bank. 2016. “Measuring Early Learning Quality and Outcomes (MELQO) Resource guide for applying the MELQO
Modules: Quick Guide to Content and Use.” https://www.worldbank.org/en/topic modules
/education/brief/ecd-resources
Abubakar, A., P. Holding, A. Van Baar, C. R. Newton, and F. J. van de Vijver. 2008. Overview of a psychomotor-testing
“Monitoring Psychomotor Development in a Resource Limited Setting: An Evaluation instrument for use in limited-resource
of the Kilifi Developmental Inventory.” Annals of Tropical Paediatrics 28 (3): 217–26. settings
https://ora.ox.ac.uk/objects/uuid:b6a43d8c-0d7e-4e01-a2ba-521e4ae33c55
Ballot, D. E., T. Ramdin, D. Rakotsoane, F. Agaba, V. A. Davies, T. Chirwa, and P. A. Example of an adaptation of a standardized
Cooper. 2017. “Use of the Bayley Scales of Infant and Toddler Development, Third instrument, the Bayley Scales, to South Africa
Edition, to Assess Developmental Outcome in Infants and Young Children in an Urban
Setting in South Africa.” International Scholarly Research Notices 2017 (2): 1631760.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5556991/
Dramé, C., and C. J. Ferguson. 2019. “Measurements of Intelligence in Sub-Saharan Discussion of neurodevelopmental testing
Africa: Perspectives Gathered from Research in Mali.” Current Psychology 38: 110–15. approaches used in Mali
https://link.springer.com/article/10.1007/s12144-017-9591-y
Ertem, I. O., D. G. Dogan, C. G. Gok, S. U. Kizilates, A. Caliskan, G. Atay, N. Vatandas, Overview of testing instruments that have
T. Karaaslan, S. G. Baskan, and D. V. Cicchetti. 2008. “A Guide for Monitoring Child been adapted to low-resource settings
Development in Low- and Middle-Income Countries.” Pediatrics 121 (3): e581–89.
https://pediatrics.aappublications.org/content/121/3/e581.short
continued
Resources for Health-Outcomes Assessment | 85

TABLE E.1, continued

REFERENCE HEALTH OUTCOME
Gladstone, M. J., G. A. Lancaster, A. P. Jones, K. Maleta, E. Mtitimila, P. Ashorn, and Discussion of potential instruments
R. L. Smyth. 2008. “Can Western Developmental Screening Tools be Modified for Use
in a Rural Malawian Setting?” Archives of Disease in Childhood 93 (1): 23–29. https://
adc.bmj.com/content/archdischild/93/1/23.full.pdf?with-ds=yes
Gladstone, M., G. A. Lancaster, E. Umar, M. Nyirenda, E. Kayira, N. R. van den Broek, Development and application of a testing
and R. L. Smyth. 2010. “The Malawi Developmental Assessment Tool (MDAT): The instrument in Malawi; could be adapted to
Creation, Validation, and Reliability of a Tool to Assess Child Development in Rural other locations
African Settings.” PLoS Medicine 7 (5): e1000273. https://journals.plos.org
/plosmedicine/article?id=10.1371/journal.pmed.1000273
Holding, P. A., H. G. Taylor, S. D. Kazungu, T. Mkala, J. Gona, B. Mwamuye, L. Mbonani, Experience in Kenya developing an
and J. Stevenson. 2004. “Assessing Cognitive Outcomes in a Rural African Population: instrument for assessing cognitive
Development of a Neuropsychological Battery in Kilifi District, Kenya.” Journal of the development
International Neuropsychological Society 10 (2): 246–60. https://www.cambridge
.org/core/journals/journal-of-the-international-neuropsychological-society/article/abs
/assessing-cognitive-outcomes-in-a-rural-african-population-development-of-a
-neuropsychological-battery-in-kilifi-district-kenya/7B19180497EA3C84AD41C9C1D
F476F77
Janus, M., and D. R. Offord. 2007. “Development and Psychometric Properties of the Although developed in a Western context,
Early Development Instrument (EDI): A Measure of Children’s School Readiness.” the Early Development Instrument (EDI) may
Canadian Journal of Behavioural Science 39 (1): 1–22. https://psycnet.apa.org be adaptable to low-resource settings
/buy/2007-04967-001
McCoy, D. C., M. M. Black, B. Daelmans, and T. Dua. 2016. “Measuring Development in Further background on the ECDI instrument
Children from Birth to Age 3 at Population Level.” Early Childhood Matters 125:
34–39. https://bernardvanleer.org/app/uploads/2016/07/Early-Childhood-Matters
-2016_6.pdf
McCoy, D. C, E. D. Peet, M. Ezzati, G. Danaei, M. M. Black, C. R. Sudfeld, W. Fawzi, and Early Childhood Development Instrument
G. Fink. 2016. “Early Childhood Developmental Status in Low- and Middle-Income (ECDI) suitable for young children; well
Countries: National, Regional, and Global Prevalence Estimates Using Predictive validated in low-resource settings
Modeling.” PLoS Medicine 4 (1): e1002233. https://journals.plos.org/plosmedicine
/article?id=10.1371/journal.pmed.1002034
McCoy, D. C., C. R. Sudfeld, D. C. Bellinger, A. Muhihi, G. Ashery, T. E. Weary, W. Fawzi, Detailed evaluation of ECDI
and G. Fink. 2017. “Development and Validation of an Early Childhood Development
Scale for Use in Low-Resourced Settings.” Population Health Metrics 15 (1): 3. https://
pophealthmetrics.biomedcentral.com/articles/10.1186/s12963-017-0122-8
Oppong, S. 2017. “Contextualizing Psychological Testing in Ghana.” Psychology and Its Discussion of the types of tests used in
Context 8 (1): 3–17. https://www.researchgate.net/publication/327536906 Ghana and the challenge associated with the
_Contextualizing_psychological_testing_in_Ghana current state of psychological testing in
Ghana
Sabanathan, S., B. Wills, and M. Gladstone. 2015. “Child Development Assessment Issues and criteria for application of
Tools in Low-Income and Middle-Income Countries: How Can We Use Them More neurodevelopmental assessment instruments
Appropriately?” Archives of Disease in Childhood 100 (5): 482–88. https://www.ncbi in low-resource settings
.nlm.nih.gov/pmc/articles/PMC4413834/pdf/archdischild-2014-308114.pdf
Semrud-Clikeman, M., R. A. Romero, E. L. Prado, E. G. Shapiro, P. Bangirana, and Issues and criteria for application of
C. C. John. 2017. “Selecting Measures for the Neurodevelopmental Assessment of neurodevelopmental assessment instruments
Children in Low- and Middle-Income Countries.” Child Neuropsychology 23 (7): in low-resource settings
761–802. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5690490/
Cardiovascular disease
Cosselman, K. E., A. Navas-Acien, and J. D. Kaufman. 2015. “Environmental Factors in Discussion of lead (Pb), cadmium (Cd),
Cardiovascular Disease.” Nature Reviews Cardiology 12 (11): 627–42. https://www arsenic (As), and cardiovascular disease
.nature.com/articles/nrcardio.2015.152
Mordukhovich, I., R. O. Wright, H. Hu, C. Amarasiriwardena, A. Baccarelli, A. Litonjua, Observed associations between blood
D. Sparrow, P. Vokonas, and J. Schwartz. 2012. “Associations of Toenail Arsenic, pressure and arsenic (As) and manganese but
Cadmium, Mercury, Manganese, and Lead with Blood Pressure in the Normative not the other metals
Aging Study.” Environmental Health Perspectives 120 (1): 98–104. https://www.ncbi
.nlm.nih.gov/pmc/articles/PMC3261928/
Source: World Bank compilation.
APPENDIX F

Bibliography

The “Health Outcomes” section of this bibliography lists studies of exposures to
metals in relation to health outcomes as well as studies that discuss measure-
ment of outcomes. Subsequent sections focus on studies relevant to used lead-
acid batteries (ULAB) and peer-reviewed literature on measurement methods
and analysis of metals, particularly in terms of bioaccessibility and
bioavailability.

HEALTH OUTCOMES

Metals exposure
Arsenic
Abdul, K. S., S. S. Jayasinghe, E. P. Chandana, C. Jayasumana, and P. M. De Silva. 2015. “Arsenic
and Human Health Effects: A Review.” Environmental Toxicology and Pharmacology 40 (3):
828–46.
Adonis, M., V. Martinez, P. Marin, and L. Gil. 2005. “CYP1A1 and GSTM1 Genetic Polymorphisms
in Lung Cancer Populations Exposed to Arsenic in Drinking Water.” Xenobiotica 35 (5):
519–30.
Ahir, B. K., A. P. Sanders, J. E. Rager, and R. C. Fry. 2013. “Systems Biology and Birth Defects
Prevention: Blockade of the Glucocorticoid Receptor Prevents Arsenic-Induced Birth
Defects.” Environmental Health Perspectives 121 (3): 332–38.
Alamolhodaei, N. S., K. Shirani, and G. Karimi. 2015. “Arsenic Cardiotoxicity: An Overview.”
Environmental Toxicology and Pharmacology 40 (3): 1005–14.
Andrade, V. M., M. L. Mateus, M. C. Batoréu, M. Aschner, and A. M. Dos Santos. 2015. “Lead,
Arsenic, and Manganese Metal Mixture Exposures: Focus on Biomarkers of Effect.”
Biological Trace Element Research 166 (1): 13–23.
Arita, A., and M. Costa. 2009. “Epigenetics in Metal Carcinogenesis: Nickel, Arsenic, Chromium
and Cadmium.” Metallomics 1 (3): 222–28.
Arslan, B., M. B. Djamgoz, and E. Akün. 2016. “ARSENIC: A Review on Exposure Pathways,
Accumulation, Mobility and Transmission into the Human Food Chain.” In Reviews of
Environmental Contamination and Toxicology 243: 27–51.
Bailey, K., and R. C. Fry. 2014. “Long-Term Health Consequences of Prenatal Arsenic Exposure:
Links to the Genome and the Epigenome.” Reviews on Environmental Health 29 (1–2): 9–12.

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Bailey, K. A., A. H. Smith, E. J. Tokar, J. H. Graziano, K.-W. Kim, P. Navasumrit, M. Ruchirawat,
A. Thiantanawat, W. A. Suk, and R. C. Fry. 2016. “Mechanisms Underlying Latent Disease
Risk Associated with Early-Life Arsenic Exposure: Current Research Trends and Scientific
Gaps.” Environmental Health Perspectives 124 (2): 170–75.
Brender, J. D., L. Suarez, M. Felkner, Z. Gilani, D. Stinchcomb, K. Moody, J. Henry, and
K. Hendricks. 2006. “Maternal Exposure to Arsenic, Cadmium, Lead, and Mercury and
Neural Tube Defects in Offspring.” Environmental Research 101 (1): 132–29.
Cubadda, F., B. P. Jackson, K. L. Cottingham, Y. O. Van Horne, and M. Kurzius-Spencer. 2017.
“Human Exposure to Dietary Inorganic Arsenic and Other Arsenic Species: State of
Knowledge, Gaps and Uncertainties.” Science of the Total Environment 579: 1228–39.
de Burbure, C. D., J.-P. Buchet, A. Bernard, A. Leroyer, C. Nisse, J.-M. Haguenoer, E. Bergamaschi,
and A. Mutti. 2003. “Biomarkers of Renal Effects in Children and Adults with Low
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Mercury, Manganese, and Lead with Blood Pressure in the Normative Aging Study.”
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2018. “Serum Concentrations of New Predictive Cardiovascular Disease Biomarkers in
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94 | Recycling of Used Lead-Acid Batteries

Epigenetics
Arita, A., and M. Costa. 2009. “Epigenetics in Metal Carcinogenesis: Nickel, Arsenic, Chromium
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Cheng, T. F., S. Choudhuri, and K. Muldoon-Jacobs. 2012. “Epigenetic Targets of Some
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Ercal, N., H. Gurer-Orhan, and N. Aykin-Burns. 2001. “Toxic Metals and Oxidative Stress Part I:
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Martinez-Zamudio, R., and H. C. Ha. 2011. “Environmental Epigenetics in Metal Exposure.”
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Ryu, H.-W., D. H. Lee, H.-R. Won, K. H. Kim, Y. J. Seong, and S. H. Kwon. 2015. “Influence of
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31 (1): 1–9.

Fetal growth
Sabra, S., E. Malmqvist, A. Saborit, E. Gratacós, and M. D. Roig. 2017. “Heavy Metals Exposure
Levels and their Correlation with Different Clinical Forms of Fetal Growth Restriction.”
PLoS One 12 (10): e0185645.

Anemia
Balarajan, Y., U. Ramakrishnan, E. Özaltin, A. H. Shankar, and S. V. Subramanian. 2012. “Anaemia
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Sanchis-Gomar, F., J. Cortell-Ballester, H. Pareja-Galeano, G. Banfi, and G. Lippi. 2013.
“Hemoglobin Point-of-Care Testing: the HemoCue System.” Journal of Laboratory
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Renal outcomes
D’Amico, G., and C. Bazzi. 2003. “Urinary Protein and Enzyme Excretion as Markers of Tubular
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Lung and lung function
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General
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ULAB AND OTHER RELEVANT STUDIES

Abdulkareem, J. H., A. Abdulkadir, and N. Abdu. 2015. “Vertical Distribution of Lead (Pb) in
Farmlands Around Contaminated Goldmine in Zamfara State, Northern Nigeria.” African
Journal of Agricultural Research 10 (53): 4975–89.
Dowling, R., B. Ericson, J. Caravanos, P. Grigsby, and Y. Amoyaw-Osei. 2015. “Spatial Associations
Between Contaminated Land and Socio Demographics in Ghana.” International Journal of
Environmental Research and Public Health 12 (10): 13587–601.
Dzomba, P., S. Nyoni, and N. Mudavanhu. 2012. “Heavy Metal Contamination Risk Through
Consumption of Traditional Food Plants Growing Around Bindura Town, Zimbabwe.”
Journal of Toxicology and Environmental Health Sciences 4 (5): 92–95.
Ericson, B., J. Caravanos, K. Chatham-Stephens, P. Landrigan, and R. Fuller. 2013. “Approaches
to Systematic Assessment of Environmental Exposures Posed at Hazardous Waste Sites in
the Developing World: The Toxic Sites Identification Program.” Environmental Monitoring
and Assessment 185 (2): 1755–66.
Glorennec, P., J. P. Lucas, C. Mandin, and B. Le Bot. 2012. “French Children’s Exposure to Metals
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Environment International 45: 129–34.
Islam, M. S., M. K. Ahmed, and M. Habibullah-Al-Mamun. 2015. “Metal Speciation in Soil and
Health Risk Due to Vegetables Consumption in Bangladesh.” Environmental Monitoring and
Assessment 187 (5): 288.
Khan, M. U., R. N. Malik, and S. Muhammad. 2013. “Human Health Risk from Heavy Metal Via
Food Crops Consumption with Wastewater Irrigation Practices in Pakistan.” Chemosphere
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Mbilu, Z. J., and M. E. Lyimo. 2015. “Heavy Metals Contamination in Soils and Selected Edible
Parts of Free-Range Local Chicken.” International Journal of Environmental Science and
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98 | Recycling of Used Lead-Acid Batteries

Muhanji, G., R. L. Roothaert, C. Webo, and M. Stanley. 2011. “African Indigenous Vegetable
Enterprises and Market Access for Small-Scale Farmers in East Africa.” International
Journal of Agricultural Sustainability 9 (1): 194–202.
Oguri, T., G. Suzuki, H. Matsukami, N. Uchida, N. M. Tue, P. H. Viet, S. Takahashi, S. Tanabe, and
H. Takigami. 2017. “Exposure Assessment of Heavy Metals in an E-Waste Processing Area in
Northern Vietnam.” Science of The Total Environment 621: 1115–23. doi:10.1016/j
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Okoye, C. O., C. N. Ibeto, and J. N. Ihedioha. 2011. “Assessment of Heavy Metals in Chicken
Feeds Sold in South Eastern, Nigeria.” Advances in Applied Science Research 2 (3):
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Secretariat of the Basel Convention and the United National Environment Programme. 2003.
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Yang. 2013. “Heavy Metals in Food, House Dust, and Water from an E-Waste Recycling Area
in South China and the Potential Risk to Human Health.” Ecotoxicology and Environmental
Safety 96: 205–12.

METHODS

The sources listed here cover only the peer-reviewed literature (as opposed to
guidance); bioaccessibility / bioavailability. For a complete official methods
compilation, see “Hazardous Waste Methods / SW-846” (https://www.epa.gov
/ hw-sw846) and “Collection of Methods” (https://www.epa.gov
/measurements-modeling/collection-methods) on the US Environmental
Protection Agency (EPA) website.

Brent, R. N., H. Wines, J. Luther, N. Irving, J. Collins, and D. L. Drake. 2017. “Validation of
Handheld X-Ray Fluorescence for In Situ Measurement of Mercury in Soils.” Journal of
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by L. I. Simeonov, M. V. Kochubovski, and B. G. Simeonova, 129–43. Dordrecht, The
Netherlands: Springer.
Cornelis, R., B. Heinzow, R. F. Herber, J. M. Christensen, O. M. Poulsen, E. Sabbioni, D. M.
Templeton, Y. Thomassen, M. Vahter, and O. Vesterberg. 1995. “Sample Collection Guidelines
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Cornelis, R., B. Heinzow, R. F. Herber, J. M. Christensen, O. M. Poulsen, E. Sabbioni, D. M.
Templeton, Y. Thomassen, M. Vahter, and O. Vesterberg. 1996. “Sample Collection Guidelines
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Ibanez, Y., B. Le Bot, and P. Glorennec. 2010. “House-Dust Metal Content and Bioaccessibility:
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Le Bot, B., J.-P. Lucas, F. Lacroix, and P. Glorennec. 2016. “Exposure of Children to Metals via
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in France.” Environment International 94: 500–507.
Li, J., K. Li, X.-Y. Cui, N. T. Basta, L.-P. Li, H.-B. Li, and L. Q. Ma. 2015. “In Vitro Bioaccessibility
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Ljung, K., A. Oomen, M. Duits, O. Selinus, and M. Berglund. 2007. “Bioaccessibility of Metals in
Urban Playground Soils.” Journal of Environmental Science and Health Part A 42 (9):
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Van Wijnen. 2002. “Comparison of Five In Vitro Digestion Models to Study the
Bioaccessibility of Soil Contaminants.” Environmental Science & Technology 36 (15):
3326–34.
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Peijnenburg, W. J., M. Zablotskaja, and M. G. Vijver. 2007. “Monitoring Metals in Terrestrial
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Sampling design
EPA (US Environmental Protection Agency). 2002. “Guidance on Choosing a Sampling Design
for Environmental Data Collection for Use in Developing a Quality Assurance Plan.”
Guidance document, EPA, Washington, DC. https://www.epa.gov/sites/production
/files/2015-06/documents/g5s-final.pdf.

Systematic grid sampling versus stratified random sampling
Gilbert, R. O., and B. A. Pulsipher. 2005. “Role of Sampling Designs in Obtaining Representative
Data.” Environmental Forensics 6 (1): 27–33.
100 | Recycling of Used Lead-Acid Batteries

Mattuck, R., R. Blanchet, and A. D. Wait. 2005. “Data Representativeness for Risk Assessment.”
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T his document includes a pragmatic framework for designing representative
studies and developing uniform sampling guidelines to support estimates of
morbidity that are explicitly linked to exposure to land-based contaminants from
used lead acid battery recycling (ULAB) activities. A primary goal is to support
environmental burden of disease evaluations, which attempt to attribute health
outcomes to specific sources of pollution. The guidelines provide recommendations
on the most appropriate and cost-effective sampling and analysis methods to ensure
the collection of representative population-level data, sample size recommendations
for each contaminant and environmental media, biological sampling data, household
survey data, and health outcome data.
These guidelines focus on small-scale ULABs that are known to generate
significant amounts of lead waste through the smelting process, as well as other
metals including arsenic and cadmium. A primary concern with lead exposure
is the documented association with neurodevelopmental outcomes in children
as demonstrated by statistically significant reduced performance on a variety of
cognitive tests. These associations are evident even in the youngest children, and
toxicological and epidemiologic data indicate these effects have no threshold.
Other potential exposures include arsenic and cadmium, and exposure to these
contaminants is also associated with neurodevelopmental outcomes in children, as
well as arsenicosis; bladder, lung, and skin cancers; and renal outcomes.
The primary objective of this document is to guide research to assess the
relationship between environmental contamination, exposures, and health outcomes
related to a subset of contaminants originating from ULAB activities for particularly
vulnerable populations (such as children) and the general population within a
single household in the vicinity of ULAB sites in low- and middle-income countries.
To achieve this objective, biomonitoring and health outcome data are linked to
household survey and environmental data (for example, soil, dust, water, and
agricultural products) at the individual level from an exposed population compared
to individuals from an unexposed (reference) population. Data on exposures and
health outcomes in the same individual, across a representative set of individuals,
is required to support an understanding of the potential impact of ULAB activities
on local populations. The guidelines can also assist in building local capacity to
conduct environmental assessments following a consistent methodology to facilitate
comparability across ULAB sites in different geographic areas. Sampling strategies
and methods are prioritized given information needs, resource availability, and
other constraints or considerations. The document includes a number of supporting
appendixes that provide additional resources and references on relevant topics.
Data obtained following these recommendations can be used to support
consistent, comparable, and standardized community risk and health impact
assessments at contaminated sites in low- and middle-income countries. These
data can also be used to support economic analyses and risk management
decision-making for evaluating site cleanup and risk mitigation options in the most
cost-effective and efficient manner. Following these recommendations will facilitate
comparisons and meta-analyses across studies by standardizing data collection
efforts at the community level.

ISBN 978-1-4648-1820-2

SKU 211820