Screening for drinking water contaminants of concern using an automated exposure-focused workflow

BACKGROUND

The number of chemicals present in the environment exceeds the capacity of government bodies to characterize risk. Therefore, data-informed and reproducible processes are needed for identifying chemicals for further assessment. The Minnesota Department of Health (MDH), under its Contaminants of Emerging Concern (CEC) initiative, uses a standardized process to screen potential drinking water contaminants based on toxicity and exposure potential.

OBJECTIVE

Recently, MDH partnered with the U.S. Environmental Protection Agency (EPA) Office of Research and Development (ORD) to accelerate the screening process via development of an automated workflow accessing relevant exposure data, including exposure new approach methodologies (NAMs) from ORD's ExpoCast project.

METHODS

The workflow incorporated information from 27 data sources related to persistence and fate, release potential, water occurrence, and exposure potential, making use of ORD tools for harmonization of chemical names and identifiers. The workflow also incorporated data and criteria specific to Minnesota and MDH's regulatory authority. The collected data were used to score chemicals using quantitative algorithms developed by MDH. The workflow was applied to 1867 case study chemicals, including 82 chemicals that were previously manually evaluated by MDH.

RESULTS

Evaluation of the automated and manual results for these 82 chemicals indicated reasonable agreement between the scores although agreement depended on data availability; automated scores were lower than manual scores for chemicals with fewer available data. Case study chemicals with high exposure scores included disinfection by-products, pharmaceuticals, consumer product chemicals, per- and polyfluoroalkyl substances, pesticides, and metals. Scores were integrated with in vitro bioactivity data to assess the feasibility of using NAMs for further risk prioritization.

SIGNIFICANCE

This workflow will allow MDH to accelerate exposure screening and expand the number of chemicals examined, freeing resources for in-depth assessments. The workflow will be useful in screening large libraries of chemicals for candidates for the CEC program.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

BACKGROUND

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.

OBJECTIVE

This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.

METHODS

The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.

RESULTS

A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.

SIGNIFICANCE

Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.

IMPACT STATEMENT

Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

BACKGROUND

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.

OBJECTIVE

This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.

METHODS

The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.

RESULTS

A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.

SIGNIFICANCE

Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.

IMPACT STATEMENT

Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

The chemical landscape of high-throughput new approach methodologies for exposure

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

A harmonized chemical monitoring database for support of exposure assessments

Direct monitoring of chemical concentrations in different environmental and biological media is critical to understanding the mechanisms by which human and ecological receptors are exposed to exogenous chemicals. Monitoring data provides evidence of chemical occurrence in different media and can be used to inform exposure assessments. Monitoring data provide required information for parameterization and evaluation of predictive models based on chemical uses, fate and transport, and release or emission processes. Finally, these data are useful in supporting regulatory chemical assessment and decision-making. There are a wide variety of public monitoring data available from existing government programs, historical efforts, public data repositories, and peer-reviewed literature databases. However, these data are difficult to access and analyze in a coordinated manner. Here, data from 20 individual public monitoring data sources were extracted, curated for chemical and medium, and harmonized into a sustainable machine-readable data format for support of exposure assessments.

Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures

BACKGROUND

Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge.

OBJECTIVES

We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products.

METHODS

We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products.

RESULTS

We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways.

DISCUSSION

Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. https://doi.org/10.1289/EHP8610.

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence

Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. We propose that this framework, and models based on it, can generate longitudinal human behavior data for use in exposure assessments.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Household fuel use and biomarkers of inflammation and respiratory illness among rural South African Women

Though literature suggests a positive association between use of biomass fuel for cooking and inflammation, few studies among women in rural South Africa exist. We included 415 women from the South African Study of Women and Babies (SOWB), recruited from 2010 to 2011. We obtained demographics, general medical history and usual source of cooking fuel (wood, electricity) via baseline questionnaire. A nurse obtained height, weight, blood pressure, and blood samples. We measured plasma concentrations of a suite of inflammatory markers (e.g., interleukins, tumor necrosis factor-α, C-reactive protein). We assessed associations between cooking fuel and biomarkers of inflammation and respiratory symptoms/illness using crude and adjusted linear and logistic regression models. We found little evidence of an association between fuel-use and biomarkers of inflammation, pre-hypertension/hypertension, or respiratory illnesses. Though imprecise, we found 41% (95% confidence interval (CI) = 0.72-2.77) higher odds of self-reported wheezing/chest tightness among wood-users compared with electricity-users. Though studies among other populations report positive findings between biomass fuel use and inflammation, it is possible that women in the present study experience lower exposures to household air pollution given the cleaner burning nature of wood compared with other biomass fuels (e.g., coal, dung).

The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products

Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on more than 75,000 chemicals and more than 15,000 consumer products. The data's primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.

Consumer product chemical weight fractions from ingredient lists

Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.

Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter

BACKGROUND

Household air pollution from solid fuel burning is a leading contributor to disease burden globally. Fine particulate matter (PM_2.5) is thought to be responsible for many of these health impacts. A co-pollutant, carbon monoxide (CO) has been widely used as a surrogate measure of PM_2.5 in studies of household air pollution.

OBJECTIVE

The goal was to evaluate the validity of exposure to CO as a surrogate of exposure to PM_2.5 in studies of household air pollution and the consistency of the PM_2.5-CO relationship across different study settings and conditions.

METHODS

We conducted a systematic review of studies with exposure and/or cooking area PM_2.5 and CO measurements and assembled 2,048 PM_2.5 and CO measurements from a subset of studies (18 cooking area studies and 9 personal exposure studies) retained in the systematic review. We conducted pooled multivariate analyses of PM_2.5-CO associations, evaluating fuels, urbanicity, season, study, and CO methods as covariates and effect modifiers.

RESULTS

We retained 61 of 70 studies for review, representing 27 countries. Reported PM_2.5-CO correlations (r) were lower for personal exposure (range: 0.22-0.97; median=0.57) than for cooking areas (range: 0.10-0.96; median=0.71). In the pooled analyses of personal exposure and cooking area concentrations, the variation in ln(CO) explained 13% and 48% of the variation in ln(PM_2.5), respectively.

CONCLUSIONS

Our results suggest that exposure to CO is not a consistently valid surrogate measure of exposure to PM_2.5. Studies measuring CO exposure as a surrogate measure of PM exposure should conduct local validation studies for different stove/fuel types and seasons. https://doi.org/10.1289/EHP767.

Characterizing the impact of projected changes in climate and air quality on human exposures to ozone

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O₃) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O₃ exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O₃ exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O₃ exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O₃ are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O₃ exposure as a result of changes in climate that could impact human health.

Maternal exposure to carbon monoxide and fine particulate matter during pregnancy in an urban Tanzanian cohort

Low birthweight contributes to as many as 60% of all neonatal deaths; exposure during pregnancy to household air pollution has been implicated as a risk factor. Between 2011 and 2013, we measured personal exposures to carbon monoxide (CO) and fine particulate matter (PM_2.5 ) in 239 pregnant women in Dar es Salaam, Tanzania. CO and PM_2.5 exposures during pregnancy were moderately high (geometric means 2.0 ppm and 40.5 μg/m³ ); 87% of PM_2.5 measurements exceeded WHO air quality guidelines. Median and high (75th centile) CO exposures were increased for those cooking with charcoal and kerosene versus kerosene alone in quantile regression. High PM_2.5 exposures were increased with charcoal use. Outdoor cooking reduced median PM_2.5 exposures. For PM_2.5 , we observed a 0.15 kg reduction in birthweight per interquartile increase in exposure (23.0 μg/m³ ) in multivariable linear regression; this finding was of borderline statistical significance (95% confidence interval 0.30, 0.00 kg; P = 0.05). PM_2.5 was not significantly associated with birth length or head circumference nor were CO exposures associated with newborn anthropometrics. Our findings contribute to the evidence that exposure to household air pollution, and specifically fine particulate matter, may adversely affect birthweight.

High-throughput screening of chemicals as functional substitutes using structure-based classification models

Identifying chemicals that provide a specific function within a product, yet have minimal impact on the human body or environment, is the goal of most formulation chemists and engineers practicing green chemistry. We present a methodology to identify potential chemical functional substitutes from large libraries of chemicals using machine learning based models. We collect and analyze publicly available information on the function of chemicals in consumer products or industrial processes to identify a suite of harmonized function categories suitable for modeling. We use structural and physicochemical descriptors for these chemicals to build 41 quantitative structure-use relationship (QSUR) models for harmonized function categories using random forest classification. We apply these models to screen a library of nearly 6400 chemicals with available structure information for potential functional substitutes. Using our Functional Use database (FUse), we could identify uses for 3121 chemicals; 4412 predicted functional uses had a probability of 80% or greater. We demonstrate the potential application of the models to high-throughput (HT) screening for "candidate alternatives" by merging the valid functional substitute classifications with hazard metrics developed from HT screening assays for bioactivity. A descriptor set could be obtained for 6356 Tox21 chemicals that have undergone a battery of HT in vitro bioactivity screening assays. By applying QSURs, we were able to identify over 1600 candidate chemical alternatives. These QSURs can be rapidly applied to thousands of additional chemicals to generate HT functional use information for combination with complementary HT toxicity information for screening for greener chemical alternatives.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models

BACKGROUND

Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.

METHODS

ZIP-code level estimates of exposure for six pollutants (CO, NO_x, EC, PM_2.5, SO₄, O₃) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.

RESULTS

Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO_x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate.

CONCLUSIONS

The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals

Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.

Childhood pneumonia and crowding, bed-sharing and nutrition: a case-control study from The Gambia

SETTING

Greater Banjul and Upper River Regions, The Gambia.

OBJECTIVE

To investigate tractable social, environmental and nutritional risk factors for childhood pneumonia.

DESIGN

A case-control study examining the association of crowding, household air pollution (HAP) and nutritional factors with pneumonia was undertaken in children aged 2-59 months: 458 children with severe pneumonia, defined according to the modified WHO criteria, were compared with 322 children with non-severe pneumonia, and these groups were compared to 801 neighbourhood controls. Controls were matched by age, sex, area and season.

RESULTS

Strong evidence was found of an association between bed-sharing with someone with a cough and severe pneumonia (adjusted OR [aOR] 5.1, 95%CI 3.2-8.2, P < 0.001) and non-severe pneumonia (aOR 7.3, 95%CI 4.1-13.1, P < 0.001), with 18% of severe cases estimated to be attributable to this risk factor. Malnutrition and pneumonia had clear evidence of association, which was strongest between severe malnutrition and severe pneumonia (aOR 8.7, 95%CI 4.2-17.8, P < 0.001). No association was found between pneumonia and individual carbon monoxide exposure as a measure of HAP.

CONCLUSION

Bed-sharing with someone with a cough is an important risk factor for severe pneumonia, and potentially tractable to intervention, while malnutrition remains an important tractable determinant.

Personal particulate matter exposures and locations of students in four neighborhoods in Accra, Ghana

Air pollution exposure and places where the exposures occur may differ in cities in the developing world compared with high-income countries. Our aim was to measure personal fine particulate matter (PM2.5) exposure of students in neighborhoods of varying socioeconomic status in Accra, Ghana, and to quantify the main predictors of exposure. We measured 24-hour PM2.5 exposure of 56 students from eight schools in four neighborhoods. PM2.5 was measured both gravimetrically and continuously, with time-matched global positioning system coordinates. We collected data on determinants of exposure, such as distances of homes and schools from main roads and fuel used for cooking at their home or in the area of residence/school. The association of PM2.5 exposure with sources was estimated using linear mixed-effects models. Personal PM2.5 exposures ranged from less than 10 μg/m(3) to more than 150 μg/m(3) (mean 56 μg/m(3)). Girls had higher exposure than boys (67 vs 44 μg/m(3); P-value=0.001). Exposure was inversely associated with distance of home or school to main roads, but the associations were not statistically significant in the multivariate model. Use of biomass fuels in the area where the school was located was also associated with higher exposure, as was household's own biomass use. Paved schoolyard surface was associated with lower exposure. School locations in relation to major roads, materials of school ground surfaces, and biomass use in the area around schools may be important determinants of air pollution exposure.

Exploring consumer exposure pathways and patterns of use for chemicals in the environment

•

To assign use-related information to chemicals to help prioritize which will be given more scrutiny relative to human exposure potential.

•

Categorical chemical use and functional information are presented through the Chemical/Product Categories Database (CPCat).

•

CPCat contains information on >43,000 unique chemicals mapped to ∼800 terms categorizing their usage or function.

•

The CPCat database is useful for modeling and prioritizing human chemical exposures.

Humans are exposed to thousands of chemicals in the workplace, home, and via air, water, food, and soil. A major challenge in estimating chemical exposures is to understand which chemicals are present in these media and microenvironments. Here we describe the Chemical/Product Categories Database (CPCat), a new, publically available (http://actor.epa.gov/cpcat) database of information on chemicals mapped to “use categories” describing the usage or function of the chemical. CPCat was created by combining multiple and diverse sources of data on consumer- and industrial-process based chemical uses from regulatory agencies, manufacturers, and retailers in various countries. The database uses a controlled vocabulary of 833 terms and a novel nomenclature to capture and streamline descriptors of chemical use for 43,596 chemicals from the various sources. Examples of potential applications of CPCat are provided, including identifying chemicals to which children may be exposed and to support prioritization of chemicals for toxicity screening. CPCat is expected to be a valuable resource for regulators, risk assessors, and exposure scientists to identify potential sources of human exposures and exposure pathways, particularly for use in high-throughput chemical exposure assessment.

High throughput heuristics for prioritizing human exposure to environmental chemicals

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.

An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models

BACKGROUND

Using multipollutant models to understand combined health effects of exposure to multiple pollutants is becoming more common. However, complex relationships between pollutants and differing degrees of exposure error across pollutants can make health effect estimates from multipollutant models difficult to interpret.

OBJECTIVES

We aimed to quantify relationships between multiple pollutants and their associated exposure errors across metrics of exposure and to use empirical values to evaluate potential attenuation of coefficients in epidemiologic models.

METHODS

We used three daily exposure metrics (central-site measurements, air quality model estimates, and population exposure model estimates) for 193 ZIP codes in the Atlanta, Georgia, metropolitan area from 1999 through 2002 for PM2.5 and its components (EC and SO4), as well as O3, CO, and NOx, to construct three types of exposure error: δspatial (comparing air quality model estimates to central-site measurements), δpopulation (comparing population exposure model estimates to air quality model estimates), and δtotal (comparing population exposure model estimates to central-site measurements). We compared exposure metrics and exposure errors within and across pollutants and derived attenuation factors (ratio of observed to true coefficient for pollutant of interest) for single- and bipollutant model coefficients.

RESULTS

Pollutant concentrations and their exposure errors were moderately to highly correlated (typically, > 0.5), especially for CO, NOx, and EC (i.e., "local" pollutants); correlations differed across exposure metrics and types of exposure error. Spatial variability was evident, with variance of exposure error for local pollutants ranging from 0.25 to 0.83 for δspatial and δtotal. The attenuation of model coefficients in single- and bipollutant epidemiologic models relative to the true value differed across types of exposure error, pollutants, and space.

CONCLUSIONS

Under a classical exposure-error framework, attenuation may be substantial for local pollutants as a result of δspatial and δtotal with true coefficients reduced by a factor typically < 0.6 (results varied for δpopulation and regional pollutants).

Chemical characterization and source apportionment of household fine particulate matter in rural, peri-urban, and urban West Africa

Household air pollution in sub-Saharan Africa and other developing regions is an important cause of disease burden. Little is known about the chemical composition and sources of household air pollution in sub-Saharan Africa, and how they differ between rural and urban homes. We analyzed the chemical composition and sources of fine particles (PM2.5) in household cooking areas of multiple neighborhoods in Accra, Ghana, and in peri-urban (Banjul) and rural (Basse) areas in The Gambia. In Accra, biomass burning accounted for 39-62% of total PM2.5 mass in the cooking area in different neighborhoods; the absolute contributions were 10-45 μg/m(3). Road dust and vehicle emissions comprised 12-33% of PM2.5 mass. Solid waste burning was also a significant contributor to household PM2.5 in a low-income neighborhood but not for those living in better-off areas. In Banjul and Basse, biomass burning was the single dominant source of cooking-area PM2.5, accounting for 74-87% of its total mass; the relative and absolute contributions of biomass smoke to PM2.5 mass were larger in households that used firewood than in those using charcoal, reaching as high as 463 μg/m(3) in Basse homes that used firewood for cooking. Our findings demonstrate the need for policies that enhance access to cleaner fuels in both rural and urban areas, and for controlling traffic emissions in cities in sub-Saharan Africa.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.

Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia

Measurements from central site (CS) monitors are often used as estimates of exposure in air pollution epidemiological studies. As these measurements are typically limited in their spatiotemporal resolution, true exposure variability within a population is often obscured, leading to potential measurement errors. To fully examine this limitation, we developed a set of alternative daily exposure metrics for each of the 169 ZIP codes in the Atlanta, GA, metropolitan area, from 1999 to 2002, for PM(2.5) and its components (elemental carbon (EC), SO(4)), O(3), carbon monoxide (CO), and nitrogen oxides (NOx). Metrics were applied in a study investigating the respiratory health effects of these pollutants. The metrics included: (i) CS measurements (one CS per pollutant); (ii) air quality model results for regional background pollution; (iii) local-scale AERMOD air quality model results; (iv) hybrid air quality model estimates (a combination of (ii) and (iii)); and (iv) population exposure model predictions (SHEDS and APEX). Differences in estimated spatial and temporal variability were compared by exposure metric and pollutant. Comparisons showed that: (i) both hybrid and exposure model estimates exhibited high spatial variability for traffic-related pollutants (CO, NO(x), and EC), but little spatial variability among ZIP code centroids for regional pollutants (PM(2.5), SO(4), and O(3)); (ii) for all pollutants except NO(x), temporal variability was consistent across metrics; (iii) daily hybrid-to-exposure model correlations were strong (r>0.82) for all pollutants, suggesting that when temporal variability of pollutant concentrations is of main interest in an epidemiological application, the use of estimates from either model may yield similar results; (iv) exposure models incorporating infiltration parameters, time-location-activity budgets, and other exposure factors affect the magnitude and spatiotemporal distribution of exposure, especially for local pollutants. The results of this analysis can inform the development of more appropriate exposure metrics for future epidemiological studies of the short-term effects of particulate and gaseous ambient pollutant exposure in a community.

Air pollution exposure prediction approaches used in air pollution epidemiology studies

Epidemiological studies of the health effects of outdoor air pollution have traditionally relied upon surrogates of personal exposures, most commonly ambient concentration measurements from central-site monitors. However, this approach may introduce exposure prediction errors and misclassification of exposures for pollutants that are spatially heterogeneous, such as those associated with traffic emissions (e.g., carbon monoxide, elemental carbon, nitrogen oxides, and particulate matter). We review alternative air quality and human exposure metrics applied in recent air pollution health effect studies discussed during the International Society of Exposure Science 2011 conference in Baltimore, MD. Symposium presenters considered various alternative exposure metrics, including: central site or interpolated monitoring data, regional pollution levels predicted using the national scale Community Multiscale Air Quality model or from measurements combined with local-scale (AERMOD) air quality models, hybrid models that include satellite data, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and population-based human exposure model (Stochastic Human Exposure and Dose Simulation, and Air Pollutants Exposure models) predictions. These alternative exposure metrics were applied in epidemiological applications to health outcomes, including daily mortality and respiratory hospital admissions, daily hospital emergency department visits, daily myocardial infarctions, and daily adverse birth outcomes. This paper summarizes the research projects presented during the symposium, with full details of the work presented in individual papers in this journal issue.

Spatial and temporal patterns of particulate matter sources and pollution in four communities in Accra, Ghana

Sources of air pollution in developing country cities include transportation and industrial pollution, biomass fuel use, and re-suspended dust from unpaved roads. We examined the spatial patterns of particulate matter (PM) and its sources in four neighborhoods of varying socioeconomic status (SES) in Accra. PM data were from 1 week of morning and afternoon mobile and stationary air pollution measurements in each of the study neighborhoods. PM(2.5) and PM(10) were measured continuously, with matched GPS coordinates. Data on biomass fuel use were from the Ghana 2000 population and housing census and from a census of wood and charcoal stoves along the mobile monitoring paths. We analyzed the associations of PM with sources using a mixed-effects regression model accounting for temporal and spatial autocorrelation. After adjusting for other factors, the density of wood stoves, fish smoking, and trash burning along the mobile monitoring path as well as road capacity and surface were associated with higher PM(2.5). Road capacity and road surface variables were also associated with PM(10), but the association with biomass sources was weak or absent. While wood stoves and fish smoking were significant sources of air pollution, addressing them would require financial and physical access to alternative fuels for low-income households and communities.

Household concentrations and exposure of children to particulate matter from biomass fuels in The Gambia

Particulate matter (PM) is an important metric for studying the health effects of household air pollution. There are limited data on PM exposure for children in homes that use biomass fuels, and no previous study has used direct measurement of personal exposure in children younger than 5 years of age. We estimated PM(2.5) exposure for 1266 children in The Gambia by applying the cookhouse PM(2.5)-CO relationship to the child's CO exposure. Using this indirect method, mean PM(2.5) exposure for all subjects was 135 ± 38 μg/m(3); 25% of children had exposures of 151 μg/m(3) or higher. Indirectly estimated exposure was highest among children who lived in homes that used firewood (collected or purchased) as their main fuel (144 μg/m(3)) compared to those who used charcoal (85 μg/m(3)). To validate the indirect method, we also directly measured PM(2.5) exposure on 31 children. Mean exposure for this validation data set was 65 ± 41 μg/m(3) using actual measurement and 125 ± 54 μg/m(3) using the indirect method based on simultaneously-measured CO exposure. The correlation coefficient between direct measurements and indirect estimates was 0.01. Children in The Gambia have relatively high PM(2.5) exposure. There is a need for simple methods that can directly measure PM(2.5) exposure in field studies.

The exposure of infants and children to carbon monoxide from biomass fuels in The Gambia: a measurement and modeling study

Smoke from biomass fuels is a risk factor for pneumonia, the leading cause of child death worldwide. Although particulate matter (PM) is the metric of choice for studying the health effects of biomass smoke, measuring children's PM exposure is difficult. Carbon monoxide (CO), which is easier to measure, can be used as a proxy for PM exposure. We measured the exposure of children ≤ 5 years of age in The Gambia to CO using small, passive, color stain diffusion tubes. We conducted multiple CO measurements on a subset of children to measure day-to-day exposure variability. Usual CO exposure was modeled using a mixed effects model, which also included individual and household level exposure predictors. Mean measured CO exposure for 1181 children (n=2263 measurements) was 1.04 ± 1.46 p.p.m., indicating that the Gambian children in this study on average have a relatively low CO exposure. However, 25% of children had exposures of 1.3 p.p.m. or higher. CO exposure was higher during the rainy months (1.33 ± 1.62 p.p.m.). Burning insect coils, using charcoal, and measurement done in the rainy season were associated with higher exposure. A parsimonious model with fuel, season, and other PM sources as covariates explained 39% of between-child variation in exposure and helped remove within-child variability.

Within-neighborhood patterns and sources of particle pollution: mobile monitoring and geographic information system analysis in four communities in Accra, Ghana

BACKGROUND

Sources of air pollution in developing country cities include transportation and industrial pollution, biomass and coal fuel use, and resuspended dust from unpaved roads.

OBJECTIVES

Our goal was to understand within-neighborhood spatial variability of particulate matter (PM) in communities of varying socioeconomic status (SES) in Accra, Ghana, and to quantify the effects of nearby sources on local PM concentration.

METHODS

We conducted 1 week of morning and afternoon mobile and stationary air pollution measurements in four study neighborhoods. PM with aerodynamic diameters <or= 2.5 microm (PM2.5) and <or= 10 microm (PM10) was measured continuously, with matched global positioning system coordinates; detailed data on local sources were collected at periodic stops. The effects of nearby sources on local PM were estimated using linear mixed-effects models.

RESULTS

In our measurement campaign, the geometric means of PM2.5 and PM10 along the mobile monitoring path were 21 and 49 microg/m3, respectively, in the neighborhood with highest SES and 39 and 96 microg/m3, respectively, in the neighborhood with lowest SES and highest population density. PM2.5 and PM10 were as high as 200 and 400 microg/m3, respectively, in some segments of the path. After adjusting for other factors, the factors that had the largest effects on local PM pollution were nearby wood and charcoal stoves, congested and heavy traffic, loose dirt road surface, and trash burning.

CONCLUSIONS

Biomass fuels, transportation, and unpaved roads may be important determinants of local PM variation in Accra neighborhoods. If confirmed by additional or supporting data, the results demonstrate the need for effective and equitable interventions and policies that reduce the impacts of traffic and biomass pollution.

Air pollution in Accra neighborhoods: spatial, socioeconomic, and temporal patterns

This study examined the spatial, socioeconomic status (SES), and temporal patterns of ambient air pollution in Accra, Ghana. Over 22 months, integrated and continuous rooftop particulate matter (PM) monitors were placed at a total of 11 residential or roadside monitoring sites in four neighborhoods of varying SES and biomass fuel use. PM concentrations were highest in late December and January, due to dust blown from the Sahara. Excluding this period, annual PM(2.5) ranged from 39 to 53 microg/m(3) at roadside sites and 30 to 70 microg/m(3) at residential sites; mean annual PM(10) ranged from 80 to 108 microg/m(3) at roadside sites and 57 to 106 microg/m(3) at residential sites. The low-income and densely populated neighborhood of Jamestown/Ushertown had the single highest residential PM concentration. There was less difference across traffic sites. Daily PM increased at all sites at daybreak, followed by a mid-day peak at some sites, and a more spread-out evening peak at all sites. Average carbon monoxide concentrations at different sites and seasons ranged from 7 to 55 ppm, and were generally lower at residential sites than at traffic sites. The results show that PM in these four neighborhoods is substantially higher than the WHO Air Quality Guidelines and in some cases even higher than the WHO Interim Target 1, with the highest pollution in the poorest neighborhood.

Characterizing air pollution in two low-income neighborhoods in Accra, Ghana

Sub-Saharan Africa has the highest rate of urban population growth in the world, with a large number of urban residents living in low-income "slum" neighborhoods. We conducted a study for an initial assessment of the levels and spatial and/or temporal patterns of multiple pollutants in the ambient air in two low-income neighborhoods in Accra, Ghana. Over a 3-week period we measured (i) 24-hour integrated PM(10) and PM(2.5) mass at four roof-top fixed sites, also used for particle speciation; (ii) continuous PM(10) and PM(2.5) at one fixed site; and (iii) 96-hour integrated concentration of sulfur dioxide (SO(2)) and nitrogen dioxide (NO(2)) at 30 fixed sites. We also conducted seven consecutive days of mobile monitoring of PM(10) and PM(2.5) mass and submicron particle count. PM(10) ranged from 57.9 to 93.6 microg/m(3) at the four sites, with a weighted average of 71.8 microg/m(3) and PM(2.5) from 22.3 to 40.2 microg/m(3), with an average of 27.4 microg/m(3). PM(2.5)/PM(10) ratio at the four fixed sites ranged from 0.33 to 0.43. Elemental carbon (EC) was 10-11% of PM(2.5) mass at all four measurement sites; organic matter (OM) formed slightly less than 50% of PM(2.5) mass. Cl, K, and S had the largest elemental contributions to PM(2.5) mass, and Cl, Si, Ca, Fe, and Al to coarse particles. SO(2) and NO(2) concentrations were almost universally lower than the US-EPA National Ambient Air Quality Standards (NAAQS), with virtually no variation across sites. There is evidence for the contributions from biomass and traffic sources, and from geological and marine non-combustion sources to particle pollution. The implications of the results for future urban air pollution monitoring and measurement in developing countries are discussed.

Measuring the exposure of infants and children to indoor air pollution from biomass fuels in The Gambia

Indoor air pollution (IAP) from biomass fuels contains high concentrations of health damaging pollutants and is associated with an increased risk of childhood pneumonia. We aimed to design an exposure measurement component for a matched case-control study of IAP as a risk factor for pneumonia and severe pneumonia in infants and children in The Gambia. We conducted co-located simultaneous area measurement of carbon monoxide (CO) and particles with aerodynamic diameter <2.5 microm (PM(2.5)) in 13 households for 48 h each. CO was measured using a passive integrated monitor and PM(2.5) using a continuous monitor. In three of the 13 households, we also measured continuous PM(2.5) concentration for 2 weeks in the cooking, sleeping, and playing areas. We used gravimetric PM(2.5) samples as the reference to correct the continuous PM(2.5) for instrument measurement error. Forty-eight hour CO and PM(2.5) concentrations in the cooking area had a correlation coefficient of 0.80. Average 48-h CO and PM(2.5) concentrations in the cooking area were 3.8 +/- 3.9 ppm and 361 +/- 312 microg/m3, respectively. The average 48-h CO exposure was 1.5 +/- 1.6 ppm for children and 2.4 +/- 1.9 ppm for mothers. PM(2.5) exposure was an estimated 219 microg/m3 for children and 275 microg/m3 for their mothers. The continuous PM(2.5) concentration had peaks in all households representing the morning, midday, and evening cooking periods, with the largest peak corresponding to midday. The results are used to provide specific recommendations for measuring the exposure of infants and children in an epidemiological study.

PRACTICAL IMPLICATIONS

Measuring personal particulate matter (PM) exposure of young children in epidemiological studies is hindered by the absence of small personal monitors. Simultaneous measurement of PM and carbon monoxide suggests that a combination of methods may be needed for measuring children's PM exposure in areas where household biomass combustion is the primary source of indoor air pollution. Children's PM exposure in biomass burning homes in The Gambia is substantially higher than concentrations in the world's most polluted cities.