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Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009–2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.

The relationship between traffic-related air pollution (TRAP) and risk factors for cardiovascular disease needs to be better understood in order to address the adverse impact of air pollution on human health. We examined associations between roadway proximity and traffic exposure zones, as markers of TRAP exposure, and metabolic biomarkers for cardiovascular disease risk in a cohort of patients undergoing cardiac catheterization. We performed a cross-sectional study of 2,124 individuals residing in North Carolina (USA). Roadway proximity was assessed via distance to primary and secondary roadways, and we used residence in traffic exposure zones (TEZs) as a proxy for TRAP. Two categories of metabolic outcomes were studied: measures associated with glucose control, and measures associated with lipid metabolism. Statistical models were adjusted for race, sex, smoking, body mass index, and socioeconomic status (SES). An interquartile-range (990 m) decrease in distance to roadways was associated with higher fasting plasma glucose (β = 2.17 mg/dL; 95% CI: -0.24, 4.59), and the association appeared to be limited to women (β = 5.16 mg/dL; 95% CI: 1.48, 8.84 compared with β = 0.14 mg/dL; 95% CI: -3.04, 3.33 in men). Residence in TEZ 5 (high-speed traffic) and TEZ 6 (stop-and-go traffic), the two traffic zones assumed to have the highest levels of TRAP, was positively associated with high-density lipoprotein cholesterol (HDL-C; β = 8.36; 95% CI: -0.15, 16.9 and β = 5.98; 95% CI: -3.96, 15.9, for TEZ 5 and 6, respectively). Proxy measures of TRAP exposure were associated with intermediate metabolic traits associated with cardiovascular disease, including fasting plasma glucose and possibly HDL-C.

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.

Two deterministic models (US EPA's Office of Pesticide Programs Residential Standard Operating Procedures (OPP Residential SOPs) and Draft Protocol for Measuring Children's Non-Occupational Exposure to Pesticides by all Relevant Pathways ( Draft Protocol )) and four probabilistic models (CARES ® , Calendex™, ConsExpo, and SHEDS) were used to estimate aggregate residential exposures to pesticides. The route-specific exposure estimates for young children (2–5 years) generated by each model were compared to evaluate data inputs, algorithms, and underlying assumptions. Three indoor exposure scenarios were considered: crack and crevice, fogger, and flying insect killer. Dermal exposure estimates from the OPP Residential SOPs and the Draft Protocol were 4.75 and 2.37 mg/kg/day (crack and crevice scenario) and 0.73 and 0.36 mg/kg/day (fogger), respectively. The dermal exposure estimates (99th percentile) for the crack and crevice scenario were 16.52, 12.82, 3.57, and 3.30 mg/kg/day for CARES, Calendex, SHEDS, and ConsExpo, respectively. Dermal exposure estimates for the fogger scenario from CARES and Calendex (1.50 and 1.47 mg/kg/day, respectively) were slightly higher than those from SHEDS and ConsExpo (0.74 and 0.55 mg/kg/day, respectively). The ConsExpo derived non-dietary ingestion estimates (99th percentile) under these two scenarios were higher than those from SHEDS, CARES, and Calendex. All models produced extremely low exposure estimates for the flying insect killer scenario. Using similar data inputs, the model estimates by route for these scenarios were consistent and comparable. Most of the models predicted exposures within a factor of 5 at the 50th and 99th percentiles. The differences identified are explained by activity assumptions, input distributions, and exposure algorithms.

Gaseous oxidized mercury (GOM) dry deposition measurements using aerodynamic surrogate surface passive samplers were collected in central and eastern Texas and eastern Oklahoma, from September 2011 to September 2012. The purpose of this study was to provide an initial characterization of the magnitude and spatial extent of ambient GOM dry deposition in central and eastern Texas for a 12-month period which contained statistically average annual results for precipitation totals, temperature, and wind speed. The research objective was to investigate GOM dry deposition in areas of Texas impacted by emissions from coal-fired utility boilers and compare it with GOM dry deposition measurements previously observed in eastern Oklahoma and the Four Corners area. Annual GOM dry deposition rate estimates were relatively low in Texas, ranging from 0.1 to 0.3 ng/m2h at the four Texas monitoring sites, similar to the 0.2 ng/m2h annual GOM dry deposition rate estimate recorded at the eastern Oklahoma monitoring site. The Texas and eastern Oklahoma annual GOM dry deposition rate estimates were at least four times lower than the highest annual GOM dry deposition rate estimate previously measured in the more arid bordering western states of New Mexico and Colorado in the Four Corners area.

In the summer of 2003, ambient air concentrations of volatile organic compounds (VOCs) were measured at 12 sites within a 3-km radius in Deer Park, Texas near Houston. The purpose of the study was to assess local spatial influence of traffic and other urban sources and was part of a larger investigation of VOC spatial and temporal heterogeneity influences in selected areas of Houston. Seventy 2-h samples were collected using passive organic vapor monitors. Most measurements of 13 VOC species were greater than the method detection limits. Samplers were located at 10 residential sites, a regulatory air monitoring station, and a site located at the centroid of the census tract in which the regulatory station was located. For residential sites, sampler placement locations (e. g., covered porch vs. house eaves) had no effect on concentration with the exception of methyl tertiary-butyl ether (MTBE). Relatively high correlations (Pearson r > 0.8) were found between toluene, ethylbenzene, and o,m,p-xylenes suggesting petroleum-related influence. Chloroform was not correlated with these species or benzene (Pearson r < 0.35) suggesting a different source influence, possibly from process-related activities. As shown in other spatial studies, wind direction relative to source location had an effect on VOC concentrations.

This study evaluates the performance of Model 3300 Ogawa Passive Nitrogen Dioxide (NO 2 ) Samplers and 3M 3520 Organic Vapor Monitors (OVMs) by comparing integrated passive sampling concentrations to averaged hourly NO 2 and volatile organic compound (VOC) measurements at two sites in El Paso, TX. Sampling periods were three time intervals (3-day weekend, 4-day weekday, and 7-day weekly) for three consecutive weeks. OVM concentrations were corrected for ambient pressure to account for higher elevation. Precise results (<5% relative standard deviation, RSD) were found for NO 2 measurements from collocated Ogawa samplers. Reproducibility was lower from duplicate OVMs for BTEX (benzene, toluene, ethylbenzene, and xylene isomers) VOCs (≥7% RSD for 2-day samples) with better precision for longer sampling periods. Comparison of Ogawa NO 2 samplers with chemiluminescence measurements averaged over the same time period suggested potential calibration problems with the chemiluminescence analyzer. For BTEX species, generally good agreement was obtained between OVMs and automated-gas chromatograph (auto-GC) measurements. The OVMs successfully tracked increasing levels of VOCs recorded by the auto-GCs.

Appropriate prediction of residential air exchange rate (AER) is important for estimating human exposures in the residential microenvironment, as AER drives the infiltration of outdoor-generated air pollutants indoors. AER differences among homes may result from a number of factors, including housing characteristics and meteorological conditions. Residential AER data collected in the Detroit Exposure and Aerosol Research Study (DEARS) and the Relationships of Indoor, Outdoor and Personal Air (RIOPA) study were analyzed to determine whether the influence of a number of housing and meteorological conditions on AER were consistent across four cities in different regions of the United States (Detroit MI, Elizabeth NJ, Houston TX, Los Angeles, CA). Influential factors were identified and used as binning variables for deriving final AER distributions for the use in exposure modeling. In addition, both between-home and within-home variance in AER in DEARS were quantified with the goal of identifying reasonable AER resampling frequencies for use in longitudinal exposure modeling efforts. The results of this analysis indicate that residential AER is depended on ambient temperature, the presence (or not) of central air conditioning, and the age of the home. Furthermore, between-home variability in AER accounted for the majority (67%) of the total variance in AER for Detroit homes, indicating lower within-home variability. These findings are compared with other previously published AER distributions, and the implications for exposure modeling are discussed.

Reliable, evaluated human exposure and dose models are important for understanding the health risks from chemicals. A case study focusing on permethrin was conducted because of this insecticide's widespread use and potential health effects. SHEDS-Multimedia was applied to estimate US population permethrin exposures for 3- to 5-year-old children from residential, dietary, and combined exposure routes, using available dietary consumption data, food residue data, residential concentrations, and exposure factors. Sensitivity and uncertainty analyses were conducted to identify key factors, pathways, and research needs. Model evaluation was conducted using duplicate diet data and biomonitoring data from multiple field studies, and comparison to other models. Key exposure variables were consumption of spinach, lettuce, and cabbage; surface-to-skin transfer efficiency; hand mouthing frequency; fraction of hand mouthed; saliva removal efficiency; fraction of house treated; and usage frequency. For children in households using residential permethrin, the non-dietary exposure route was most important, and when all households were included, dietary exposure dominated. SHEDS-Multimedia model estimates compared well to real-world measurements data; this exposure assessment tool can enhance human health risk assessments and inform children's health research. The case study provides insights into children's aggregate exposures to permethrin and lays the foundation for a future cumulative pyrethroid pesticides risk assessment.

A critical aspect of air pollution exposure assessments is estimation of the air exchange rate (AER) for various buildings where people spend their time. The AER, which is the rate of exchange of indoor air with outdoor air, is an important determinant for entry of outdoor air pollutants and for removal of indoor-emitted air pollutants. This paper presents an overview and critical analysis of the scientific literature on empirical and physically based AER models for residential and commercial buildings; the models highlighted here are feasible for exposure assessments as extensive inputs are not required. Models are included for the three types of airflows that can occur across building envelopes: leakage, natural ventilation, and mechanical ventilation. Guidance is provided to select the preferable AER model based on available data, desired temporal resolution, types of airflows, and types of buildings included in the exposure assessment. For exposure assessments with some limited building leakage or AER measurements, strategies are described to reduce AER model uncertainty. This review will facilitate the selection of AER models in support of air pollution exposure assessments.