Advancing Methodologies Used in Trace Element-Based Mass Balance Studies to Separately Estimate Soil and Dust Ingestion Rates for Children.

Historically, soil ingestion rate estimates were based on trace element-based mass balance (MB) study results. These were used in assessing exposures and health risks for children residing in Superfund or chemically contaminated communities. However, soil and dust can have considerable differences with respect to their sources, chemical, physical, and toxicological characteristics. Unfortunately, the MB approach is incapable of disentangling dust ingestion rates from soil ingestion rates. Alternative methods, such as activity pattern and biokinetic modeling techniques, have also been used to predict soil and dust ingestion rates. The results from these studies differed from those obtained from the MB studies. This research evaluated the MB methodology and formulated a physical model which characterized the environmental and behavioral determinants of soil and dust ingestion exposures by children. This new approach explicitly separates outdoor soil exposures from the indoor tracked-in soil portion of the dust and total dust exposures by utilizing information from five key MB studies along with new information derived from the SHEDS-Soil/Dust time-activity pattern-based modeling runs. Application of this new hybrid methodology showed that the predicted mean soil ingestion rates are 30%-70% less than the "total soil" ingestion rates obtained from the selected MB studies. In contrast, most of the predicted dust ingestion rate estimates were typically greater than the predicted soil ingestion rates. Moreover, the predicted total soil plus dust ingestion rates were found to be mostly higher (by ≤ 60%) than the MB-based "total soil" ingestion rates. Except for one study these results were higher than the results produced by the stand-alone SHEDS-Soil/Dust model runs. Across the MB studies analyzed, predicted outdoor soil ingestion rate contributions to "total soil" ingestion rates varied between 29% and 70% while the tracked-in soil portion of the indoor dust ingestion rates varied between 30% and 71%.

Model-based predictions of soil and dust ingestion rates for U.S. adults using the stochastic human exposure and dose simulation soil and dust model.

BACKGROUND: Adults can be exposed to chemicals through incidental ingestion of soil and dust, either through hobbies, occupations, or behaviors that increase contact with soil or dust (e.g., cleaning or renovating). However, few data describing these ingestion rates are available. OBJECTIVE: Our objective was to use the Stochastic Human Exposure and Dose Simulation Soil and Dust (SHEDS-Soil/Dust) model to estimate distributions of soil and dust ingestion rates for adults (≥21 years old) with varying degrees of soil and dust contact. METHODS: We parameterized SHEDS-Soil/Dust to estimate soil and dust ingestion rates for several categories of adults: adults in the general population; adults with moderate (higher) soil exposure (represented by hobbyists, such as gardeners, with increased soil contact); adults with high soil exposure (represented by occupationally exposed individuals, such as landscapers); and individuals who have high dust exposure (e.g., are in contact with very dusty indoor environments). RESULTS: Total soil plus dust ingestion for adults in the general population was 7 mg/day. Hobbyists or adults with moderate soil exposure averaged 33 mg/day and occupationally exposed individuals averaged 123 mg/day. Total soil plus dust ingestion for adults in the high dust exposure scenario was 25 mg/day. Results were driven by time spent in contact with soil and, thus, warmer seasons (e.g., summer) were associated with higher ingestion rates than colder seasons (e.g., winter). SIGNIFICANCE: These results provide modeled estimates of soil and dust ingestion rates for adults for use in decision making using real-world exposure considerations. These modeled estimates suggest that soil and dust ingestion is a potential concern for adults who spend a higher amount of time interacting with either soil or dusty environments. IMPACT STATEMENT: The parameterization of real-world scenarios within the application of SHEDS-Soil/Dust model to predict soil and dust ingestion rates for adults provides estimates of soil and dust ingestion rates useful for refining population-based risk assessments. These data illuminate drivers of exposure useful for both risk management decisions and designing future studies to improve existing tracer methodologies.

Model based prediction of age-specific soil and dust ingestion rates for children.

BACKGROUND: Soil and dust ingestion can be a primary route of environmental exposures. Studies have shown that young children are more vulnerable to incidental soil and dust ingestion. However, available data to develop soil and dust ingestion rates for some child-specific age groups are either lacking or uncertain. OBJECTIVE: Our objective was to use the Stochastic Human Exposure and Dose Simulation Soil and Dust (SHEDS-Soil/Dust) model to estimate distributions of soil and dust ingestion rates for ten age ranges from infancy to late adolescents (birth to 21 years). METHODS: We developed approaches for modeling age groups previously not studied, including a new exposure scenario for infants to capture exposures to indoor dust via pacifier use and accounting for use of blankets that act as a barrier to soil and dust exposure. RESULTS: Overall mean soil and dust ingestion rates ranged from ~35 mg/day (infants, 0-<6 m) to ~60 mg/day (toddlers and young children, 6m-<11 yr) and were considerably lower (about 20 mg/day) for teenagers and late adolescents (16-<21 y). The pacifier use scenario contributed about 20 mg/day to the median dust ingestion rate for young infants. Except for the infant age groups, seasonal analysis showed that the modeled estimates of average summer mean daily total soil plus dust ingestion rates were about 50% higher than the values predicted for the winter months. Pacifier use factors and carpet dust loading values were drivers of exposure for infants and younger children. For older children, influential variables included carpet dust loading, soil adherence, and factors that capture the frequency and intensity of hand-to-mouth behaviors. SIGNIFICANCE: These results provide modeled estimates of children's soil and dust ingestion rates for use in decision making using real-world exposure considerations. IMPACT STATEMENT: The parameterization of scenarios to capture infant soil and dust ingestion and the application of SHEDS-Soil/Dust to a broader age range of children provides additional estimates of soil and dust ingestion rates that are useful in refining population-based risk assessments. These data illuminate drivers of exposure that are useful to both risk management applications and for designing future studies that improve upon existing tracer methodologies.

Estimation of Soil and Dust Ingestion Rates from the Stochastic Human Exposure and Dose Simulation Soil and Dust Model for Children in Taiwan.

This study focuses on estimating the probabilistic soil and dust ingestion rates for children under 3 years old by the Stochastic Human Exposure and Dose Simulation Soil and Dust (SHEDS-S/D) model developed by the U.S. Environmental Protection Agency. The health risk of children's exposure to heavy metals through soil and dust ingestion and dermal absorption was then assessed in three exposure scenarios. In the exposure scenario of direct contact with soil, the average soil and dust ingestion rates for children aged 24 to 36 months were 90.7 and 29.8 mg day-1 in the sand and clay groups, respectively. Hand-to-mouth soil ingestion was identified as the main contributor to soil and dust ingestion rates, followed by hand-to-mouth dust ingestion and object-to-mouth dust ingestion. The soil-to-skin adherence factor was the most influential factor increasing the soil and dust ingestion rate based on a sensitivity analysis in the SHEDS-S/D model. Furthermore, the modeled soil and dust ingestion rates based on the SHEDS-S/D model were coincident with results calculated by the tracer element method. Our estimates highlight the soil ingestion rate as the key parameter increasing the risk for children, while a higher frequency of hand washing could potentially reduce the risk.

Soil-to-skin adherence during different activities for children in Taiwan.

Children may be exposed to environmental contaminants through incidental ingestion of soil resulting from hand-to-mouth contact. We measured soil adherence to the skin among 86 children from four kindergartens and one elementary school in Taiwan. Rinse water samples were collected from the hands, forearms, feet and lower legs of children after they had engaged in assigned activity groups (pre-activity, indirect contact and direct contact) from two different soil textures groups: sand and clay. We found that the soil loadings significantly differed between the different soil textures, body parts, activities, and clothing groups. Measured soil loadings for hands of pre-activity, indirect contact activity, and direct contact activity groups were 0.0069, 0.0307 and 0.153 mg cm-2, respectively, for the group playing on sand and 0.0061, 0.0116 and 0.0942 mg cm-2, respectively, for the group playing on clay. To facilitate the use of soil adherence data in exposure assessments, we provided a new and simple way to group activities based on the intensity of children's interactions with soil. The adherence data from this study can help enhance existing information based on soil-to-skin adherence factors used to assess children's exposure to soil contaminants during their play activities.

Determination of hand soil loading, soil transfer, and particle size variations after hand-pressing and hand-mouthing activities.

Hand-pressing trials and hand-to-mouth soil transfer experiments were conducted to better understand soil loadings, soil transfer ratios for three mouthing activities, and variations in particle size distributions under various conditions. Results indicated that sand caused higher soil loadings on the hand than clay. When the moisture level of clay soil exceeded its liquid limit, soil loadings also increased. Greater pressing pressures also led to larger clay loadings. Clay with a moisture content close to its plastic limit caused the smallest soil loadings due to strong soil cohesion. Particle sizes of the transferred clay were larger than that of the original clay, indicating that hand-pressing and the pressure exerted may have enhanced clay particles of larger sizes adhering onto the hand. Nevertheless, the sizes of most particles that adhered to the hand were still smaller than 150 µm. Higher pressing pressures and greater moisture contents resulted in larger soil loadings on the hand, and transfer ratios became smaller. Transfer ratios from palm-licking with clay particles were smaller than those from finger-mouthing, which may have been due to finer particles that more readily adhered to the skin of the palm and that were transferred from the hand to the mouth with greater difficulty.

Mouthing activity data for children age 3 to <6 years old and fraction of hand area mouthed for children age <6 years old in Taiwan.

Non-dietary ingestion is an important exposure pathway for children owing to their frequent hand-to-mouth and object-to-mouth activities involving soil and dust contacts. We used videotaping and the computer-based translating methods to quantify the mouthing activity information for 24 children ages 3 to <6 years old living in Taiwan. We also reviewed the entire mouthing activity data collected during the project to determine the lesser studied information on hand surface areas mouthed by children ages <6 years old. The median indoor hand-to-mouth and object-to-mouth frequencies were found to be 10 and 4.3 contacts/h, respectively. Hand-to-mouth and object-to-mouth contact frequencies used in exposure assessments for children ages 3 to <6 years old in this study were similar to the recommended values reported in United States. Exposure Factors Handbook for comparable age US children. The average fractions of the hand area mouthed for children 6 to <12 months, 1 to <2 years, 2 to <3 years, and 3 to <6 years old were 0.12, 0.12, 0.13, and 0.09, respectively. The fraction of hand area mouthed by children was found to be significantly and negatively correlated with their age. About half of the total hand-to-mouth contact events involved immersion of part of a hand or a finger into the mouth. The findings from this study extend the available mouthing activity information for 3 to <6 years old children and also provide new data for an Asian country, allowing comparison of results with western values collected mostly in the United States.

Soil ingestion rates for children under 3 years old in Taiwan.

Soil and dust ingestion rates by children are among the most critical exposure factors in determining risks to children from exposures to environmental contaminants in soil and dust. We believe this is the first published soil ingestion study for children in Taiwan using tracer element methodology. In this study, 66 children under 3 years of age were enrolled from Taiwan. Three days of fecal samples and a 24-h duplicate food sample were collected. The soil and household dust samples were also collected from children's homes. Soil ingestion rates were estimated based on silicon (Si) and titanium (Ti). The average soil ingestion rates were 9.6±19.2 mg/day based on Si as a tracer. The estimated soil ingestion rates based on Si did not have statistically significant differences by children's age and gender, although the average soil ingestion rates clearly increased as a function of children's age category. The estimated soil ingestion rates based on Si was significantly and positively correlated with the sum of indoor and outdoor hand-to-mouth frequency rates. The average soil ingestion rates based on Si were generally lower than the results from previous studies for the US children. Ti may not be a suitable tracer for estimating soil ingestion rates in Taiwan because the Ti dioxide is a common additive in food. To the best of our knowledge, this is the first study that investigated the correlations between soil ingestion rates and mouthing behaviors in Taiwan or other parts of Asia. It is also the first study that could compare available soil ingestion data from different countries and/or different cultures. The hand-to-mouth frequency and health habits are important to estimate the soil ingestion exposure for children. The results in this study are particularly important when assessing children's exposure and potential health risk from nearby contaminated soils in Taiwan.

Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment.

BACKGROUND: Computational exposure science represents a frontier of environmental science that is emerging and quickly evolving. OBJECTIVES: In this commentary, we define this burgeoning discipline, describe a framework for implementation, and review some key ongoing research elements that are advancing the science with respect to exposure to chemicals in consumer products. DISCUSSION: The fundamental elements of computational exposure science include the development of reliable, computationally efficient predictive exposure models; the identification, acquisition, and application of data to support and evaluate these models; and generation of improved methods for extrapolating across chemicals. We describe our efforts in each of these areas and provide examples that demonstrate both progress and potential. CONCLUSIONS: Computational exposure science, linked with comparable efforts in toxicology, is ushering in a new era of risk assessment that greatly expands our ability to evaluate chemical safety and sustainability and to protect public health. CITATION: Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith M-R, Wambaugh JF, Judson RS, Buckley TJ. 2016. Computational exposure science: an emerging discipline to support 21st-century risk assessment. Environ Health Perspect 124:697-702; http://dx.doi.org/10.1289/ehp.1509748.

Respiratory hospitalizations in association with fine PM and its components in New York State.

UNLABELLED: Despite observed geographic and temporal variation in particulate matter (PM)-related health morbidities, only a small number of epidemiologic studies have evaluated the relation between PM2.5 chemical constituents and respiratory disease. Most assessments are limited by inadequate spatial and temporal resolution of ambient PM measurements and/or by their approaches to examine the role of specific PM components on health outcomes. In a case-crossover analysis using daily average ambient PM2.5 total mass and species estimates derived from the Community Multiscale Air Quality (CMAQ) model and available observations, we examined the association between the chemical components of PM (including elemental and organic carbon, sulfate, nitrate, ammonium, and other remaining) and respiratory hospitalizations in New York State. We evaluated relationships between levels (low, medium, high) of PM constituent mass fractions, and assessed modification of the PM2.5-hospitalization association via models stratified by mass fractions of both primary and secondary PM components. In our results, average daily PM2.5 concentrations in New York State were generally lower than the 24-hr average National Ambient Air Quality Standard (NAAQS). Year-round analyses showed statistically significant positive associations between respiratory hospitalizations and PM2.5 total mass, sulfate, nitrate, and ammonium concentrations at multiple exposure lags (0.5-2.0% per interquartile range [IQR] increase). Primarily in the summer months, the greatest associations with respiratory hospitalizations were observed per IQR increase in the secondary species sulfate and ammonium concentrations at lags of 1-4 days (1.0-2.0%). Although there were subtle differences in associations observed between mass fraction tertiles, there was no strong evidence to support modification of the PM2.5-respiratory disease association by a particular constituent. We conclude that ambient concentrations of PM2.5 and secondary aerosols including sulfate, ammonium, and nitrate were positively associated with respiratory hospitalizations, although patterns varied by season. Exposure to specific fine PM constituents is a plausible risk factor for respiratory hospitalization in New York State. IMPLICATIONS: The association between ambient concentrations of PM2.5 components has been evaluated in only a small number of epidemiologic studies with refined spatial and temporal scale data. In New York State, fine PM and several of its constituents, including sulfate, ammonium, and nitrate, were positively associated with respiratory hospitalizations. Results suggest that PM species relationships and their influence on respiratory endpoints are complex and season dependent. Additional work is needed to better understand the relative toxicity of PM species, and to further explore the role of co-pollutant relationships and exposure prediction error on observed PM-respiratory disease associations.

Mouthing activity data for children aged 7 to 35 months in Taiwan.

Young children's mouthing activities thought to be among the most important exposure pathways. Unfortunately, mouthing activity studies have only been conducted in a few countries. In the current study, we used videotaping and computer-based translating method to obtain mouthing activity data for 66 children aged 7-35 months in Taiwan. The median indoor hand-to-mouth and object-to-mouth frequencies were 8.91 and 11.39 contacts/h, respectively. The median indoor hand-to-mouth and object-to-mouth hourly contact durations were 0.34 and 0.46 min/h, respectively. The indoor object-to-mouth activities were significantly and negatively correlated with age. Children aged 12 to <24 months in the current study had lower indoor hand-to-mouth and object-to-mouth frequencies than children of same age group in the United States. We also found that indoor mouthing duration with pacifier was significantly and negatively correlated with indoor mouthing duration with other non-dietary objects. The results of the current study indicate that the mouthing behaviors may be different between different countries or populations with different ethnic or lifestyle characteristics. We conclude that using hand-to-mouth frequency values from the current literature may not be most reliable for estimating non-dietary exposures of young children living in Taiwan or even in other similar Asian countries.

SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources.

United States Environmental Protection Agency (USEPA) researchers are developing a strategy for high-throughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologically relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Based on probabilistic methods and algorithms developed for The Stochastic Human Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM), a new mechanistic modeling approach has been developed to accommodate high-throughput (HT) assessment of exposure potential. In this SHEDS-HT model, the residential and dietary modules of SHEDS-MM have been operationally modified to reduce the user burden, input data demands, and run times of the higher-tier model, while maintaining critical features and inputs that influence exposure. The model has been implemented in R; the modeling framework links chemicals to consumer product categories or food groups (and thus exposure scenarios) to predict HT exposures and intake doses. Initially, SHEDS-HT has been applied to 2507 organic chemicals associated with consumer products and agricultural pesticides. These evaluations employ data from recent USEPA efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. In modeling indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based module to estimate concentrations in indoor environmental media. The concentration estimates, along with relevant exposure factors and human activity data, are then used by the model to rapidly generate probabilistic population distributions of near-field indirect exposures via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific estimates of near-field direct exposures from consumer products are also modeled. Population dietary exposures for a variety of chemicals found in foods are combined with the corresponding chemical-specific near-field exposure predictions to produce aggregate population exposure estimates. The estimated intake dose rates (mg/kg/day) for the 2507 chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully reproduced the pathway-specific exposure results of the higher-tier SHEDS-MM for a case-study pesticide and produced median intake doses significantly correlated (p<0.0001, R2=0.39) with medians inferred using biomonitoring data for 39 chemicals from the National Health and Nutrition Examination Survey (NHANES). Based on the favorable performance of SHEDS-HT with respect to these initial evaluations, we believe this new tool will be useful for HT prediction of chemical exposure potential.

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.

Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta.

Exposure error in studies of ambient air pollution and health that use city-wide measures of exposure may be substantial for pollutants that exhibit spatiotemporal variability. Alternative spatiotemporal metrics of exposure for traffic-related and regional pollutants were applied in a time-series study of ambient air pollution and cardiorespiratory emergency department visits in Atlanta, GA, USA. Exposure metrics included daily central site monitoring for particles and gases; daily spatially refined ambient concentrations obtained from regional background monitors, local-scale dispersion, and hybrid air quality models; and spatially refined ambient exposures from population exposure models. Health risk estimates from Poisson models using the different exposure metrics were compared. We observed stronger associations, particularly for traffic-related pollutants, when using spatially refined ambient concentrations compared with a conventional central site exposure assignment approach. For some relationships, estimates of spatially refined ambient population exposures showed slightly stronger associations than corresponding spatially refined ambient concentrations. Using spatially refined pollutant metrics, we identified socioeconomic disparities in concentration-response functions that were not observed when using central site data. In some cases, spatially refined pollutant metrics identified associations with health that were not observed using measurements from the central site. Complexity and challenges in incorporating modeled pollutant estimates in time-series studies are discussed.

Associations between summertime ambient pollutants and respiratory morbidity in New York City: comparison of results using ambient concentrations versus predicted exposures.

Epidemiological analyses of air quality often estimate human exposure from ambient monitoring data, potentially leading to exposure misclassification and subsequent bias in estimated health risks. To investigate this, we conducted a case-crossover study of summertime ambient ozone and fine particulate matter (PM(2.5)) levels and daily respiratory hospitalizations in New York City during 2001-2005. Comparisons were made between associations estimated using two pollutant exposure metrics: observed concentrations and predicted exposures from the EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) model. Small, positive associations between interquartile range mean ozone concentrations and hospitalizations were observed and were strongest for 0-day lags (hazard ratio (HR)=1.013, 95% confidence interval (CI): 0.998, 1.029) and 3-day lags (HR=1.006, 95% CI: 0.991, 1.021); applying mean predicted ozone exposures yielded similar results. PM(2.5) was also associated with admissions, strongest at 2- and 4-day lags, with few differences between exposure metrics. Subgroup analyses support recognized sociodemographic differences in concentration-related hospitalization risk, whereas few inter-stratum variations were observed in relation to SHEDS exposures. Predicted exposures for these spatially homogenous pollutants were similar across sociodemographic strata, therefore SHEDS predictions coupled with the case-crossover design may have masked observable heterogeneity in risks. However, significant effect modification was found for subjects in the top exposure-to-concentration ratio tertiles, suggesting risks may increase as a consequence of infiltration or greater exposure to outdoor air.

The triggering of myocardial infarction by fine particles is enhanced when particles are enriched in secondary species.

Previous studies have reported an increased risk of myocardial infarction (MI) associated with acute increases in PM concentration. Recently, we reported that MI/fine particle (PM2.5) associations may be limited to transmural infarctions. In this study, we retained data on hospital discharges with a primary diagnosis of acute myocardial infarction (using International Classification of Diseases ninth Revision [ICD-9] codes), for those admitted January 1, 2004 to December 31, 2006, who were ≥ 18 years of age, and were residents of New Jersey at the time of their MI. We excluded MI with a diagnosis of a previous MI and MI coded as a subendocardial infarction, leaving n = 1563 transmural infarctions available for analysis. We coupled these health data with PM2.5 species concentrations predicted by the Community Multiscale Air Quality chemical transport model, ambient PM2.5 concentrations, and used the same case-crossover methods to evaluate whether the relative odds of transmural MI associated with increased PM2.5 concentration is modified by the PM2.5 composition/mixture (i.e., mass fractions of sulfate, nitrate, elemental carbon, organic carbon, and ammonium). We found the largest relative odds estimates on the days with the highest tertile of sulfate mass fraction (OR = 1.13; 95% CI = 1.00, 1.27), nitrate mass fraction (OR = 1.18; 95% CI = 0.98, 1.35), and ammonium mass fraction (OR = 1.13; 95% CI = 1.00 1.28), and the lowest tertile of EC mass fraction (OR = 1.17; 95% CI = 1.03, 1.34). Air pollution mixtures on these days were enhanced in pollutants formed through atmospheric chemistry (i.e., secondary PM2.5) and depleted in primary pollutants (e.g., EC). When mixtures were laden with secondary PM species (sulfate, nitrate, and/or organics), we observed larger relative odds of myocardial infarction associated with increased PM2.5 concentrations. Further work is needed to confirm these findings and examine which secondary PM2.5 component(s) is/are responsible for an acute MI response.

Spatiotemporally resolved air exchange rate as a modifier of acute air pollution-related morbidity in Atlanta.

Epidemiological studies frequently use central site concentrations as surrogates of exposure to air pollutants. Variability in air pollutant infiltration due to differential air exchange rates (AERs) is potentially a major factor affecting the relationship between central site concentrations and actual exposure, and may thus influence observed health risk estimates. In this analysis, we examined AER as an effect modifier of associations between several urban air pollutants and corresponding emergency department (ED) visits for asthma and wheeze during a 4-year study period (January 1999-December 2002) for a 186 ZIP code area in metro Atlanta. We found positive associations for the interaction between AER and pollution on asthma ED visits for both carbon monoxide (CO) and nitrogen oxides (NO(x)), indicating significant or near-significant effect modification by AER on the pollutant risk-ratio estimates. In contrast, the interaction term between particulate matter (PM)(2.5) and AER on asthma ED visits was negative and significant. However, alternative distributional tertile analyses showed PM(2.5) and AER epidemiological model results to be similar to those found for NOx and CO (namely, increasing risk ratios (RRs) with increasing AERs when ambient PM(2.5) concentrations were below the highest tertile of their distribution). Despite the fact that ozone (O(3)) was a strong independent predictor of asthma ED visits in our main analysis, we found no O(3)-AER effect modification. To our knowledge, our findings for CO, NOx, and PM(2.5) are the first to provide an indication of short-term (i.e., daily) effect modification of multiple air pollution-related risk associations with daily changes in AER. Although limited to one outcome category in a single large urban locale, the findings suggest that the use of relatively simple and easy-to-derive AER surrogates may reflect intraurban differences in short-term exposures to pollutants of ambient origin.

Refined ambient PM2.5 exposure surrogates and the risk of myocardial infarction.

Using a case-crossover study design and conditional logistic regression, we compared the relative odds of transmural (full-wall) myocardial infarction (MI) calculated using exposure surrogates that account for human activity patterns and the indoor transport of ambient PM(2.5) with those calculated using central-site PM(2.5) concentrations to estimate exposure to PM(2.5) of outdoor origin (exposure to ambient PM(2.5)). Because variability in human activity and indoor PM(2.5) transport contributes exposure error in epidemiologic analyses when central-site concentrations are used as exposure surrogates, we refer to surrogates that account for this variability as "refined" surrogates. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM(2.5) is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM(2.5) that penetrates and persists indoors. Use of refined exposure surrogates did not result in larger health effect estimates (ORs=1.10-1.11 with each interquartile range (IQR) increase), narrower confidence intervals, or better model fits compared with the analysis that used central-site PM(2.5). We did observe evidence for heterogeneity in the relative odds of transmural MI with residential AER (effect-modification), with residents of homes with higher AERs having larger ORs than homes in lower AER tertiles. For the level of exposure-estimate refinement considered here, our findings add support to the use of central-site PM(2.5) concentrations for epidemiological studies that use similar case-crossover study designs. In such designs, each subject serves as his or her own matched control. Thus, exposure error related to factors that vary spatially or across subjects should only minimally impact effect estimates. These findings also illustrate that variability in factors that influence the fraction of ambient PM(2.5) in indoor air (e.g., AER) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.