Near-road Vehicle Emissions Air Quality Monitoring for Exposure Modeling.

Exposure to vehicular emissions has been linked to numerous adverse health effects. In response to the arising concerns, near-road monitoring is conducted to better characterize the impact of mobile source emissions on air quality and exposure in the near-road environment. An intensive measurement campaign measured traffic-related air pollutants (TRAPs) and related data (e.g., meteorology, traffic, regional air pollutant levels) in Atlanta, along one of the busiest highway corridors in the US. Given the complexity of the near-road environment, the study aimed to compare two near-road monitors, located in close proximity to each other, to assess how observed similarities and differences between measurements at these two sites inform the siting of other near-road monitoring stations. TRAP measurements, including carbon monoxide (CO) and nitrogen dioxide (NO2), are analyzed at two roadside monitors in Atlanta, GA located within 325m of each other. Both meteorological and traffic conditions were monitored to assess the temporal impact of these factors on traffic-related pollutant concentrations. The meteorological factors drove the diurnal variability of primary pollutant concentration more than traffic count. In spite of their proximity, while the CO and NO2 concentrations were correlated with similar diurnal variations, pollutant concentrations at the two closely sited monitors differed, likely due to the differences in the siting characteristics reducing the dispersion of the primary emissions out of the near-road environment. Overall, the near-road TRAP concentrations at all sites were not as elevated as seen in prior studies, supporting that decreased vehicle emissions have led to significant reductions in TRAP levels, even along major interstates. Further, the differences in the observed levels show that use of single near-road observations will not capture pollutant levels representative of the local near-road environment and that additional approaches (e.g., air quality models) are needed to characterize exposures.

INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH.

Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.

Precipitation and Salmonellosis Incidence in Georgia, USA: Interactions between Extreme Rainfall Events and Antecedent Rainfall Conditions.

BACKGROUND: The southeastern United States consistently has high salmonellosis incidence, but disease drivers remain unknown. Salmonella is regularly detected in this region's natural environment, leading to numerous exposure opportunities. Rainfall patterns may impact the survival/transport of environmental Salmonella in ways that can affect disease transmission. OBJECTIVES: This study investigated associations between short-term precipitation (extreme rainfall events) and longer-term precipitation (rainfall conditions antecedent to these extreme events) on salmonellosis counts in the state of Georgia in the United States. METHODS: For the period 1997-2016, negative binomial models estimated associations between weekly county-level extreme rainfall events (≥90th percentile of daily rainfall) and antecedent conditions (8-week precipitation sums, categorized into tertiles) and weekly county-level salmonellosis counts. RESULTS: In Georgia's Coastal Plain counties, extreme and antecedent rainfall were associated with significant differences in salmonellosis counts. In these counties, extreme rainfall was associated with a 5% increase in salmonellosis risk (95% CI: 1%, 10%) compared with weeks with no extreme rainfall. Antecedent dry periods were associated with a 9% risk decrease (95% CI: 5%, 12%), whereas wet periods were associated with a 5% increase (95% CI: 1%, 9%), compared with periods of moderate rainfall. In models considering the interaction between extreme and antecedent rainfall conditions, wet periods were associated with a 13% risk increase (95% CI: 6%, 19%), whereas wet periods followed by extreme events were associated with an 11% increase (95% CI: 5%, 18%). Associations were substantially magnified when analyses were restricted to cases attributed to serovars commonly isolated from wildlife/environment (e.g., Javiana). For example, wet periods followed by extreme rainfall were associated with a 34% risk increase (95% CI: 20%, 49%) in environmental serovar infection. CONCLUSIONS: Given the associations of short-term extreme rainfall events and longer-term rainfall conditions on salmonellosis incidence, our findings suggest that avoiding contact with environmental reservoirs of Salmonella following heavy rainfall events, especially during the rainy season, may reduce the risk of salmonellosis. https://doi.org/10.1289/EHP4621.

Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits: Accounting for Source Contribution Uncertainty.

BACKGROUND: Despite evidence suggesting that air pollution-related health effects differ by emissions source, epidemiologic studies on fine particulate matter (PM2.5) infrequently differentiate between particles from different sources. Those that do rarely account for the uncertainty of source apportionment methods. METHODS: For each day in a 12-year period (1998-2010) in Atlanta, GA, we estimated daily PM2.5 source contributions from a Bayesian ensemble model that combined four source apportionment methods including chemical transport and receptor-based models. We fit Poisson generalized linear models to estimate associations between source-specific PM2.5 concentrations and cardiorespiratory emergency department visits (n = 1,598,117). We propagated uncertainty in the source contribution estimates through analyses using multiple imputation. RESULTS: Respiratory emergency department visits were positively associated with biomass burning and secondary organic carbon. For a 1 µg/m increase in PM2.5 from biomass burning during the past 3 days, the rate of visits for all respiratory outcomes increased by 0.4% (95% CI 0.0%, 0.7%). There was less evidence for associations between PM2.5 sources and cardiovascular outcomes, with the exception of ischemic stroke, which was positively associated with most PM2.5 sources. Accounting for the uncertainty of source apportionment estimates resulted, on average, in an 18% increase in the standard error for rate ratio estimates for all respiratory and cardiovascular emergency department visits, but inflation varied across specific sources and outcomes, ranging from 2% to 39%. CONCLUSIONS: This study provides evidence of associations between PM2.5 sources and some cardiorespiratory outcomes and quantifies the impact of accounting for variability in source apportionment approaches.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies.

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Perturbations of the arginine metabolome following exposures to traffic-related air pollution in a panel of commuters with and without asthma.

BACKGROUND: Mechanisms underlying the effects of traffic-related air pollution on people with asthma remain largely unknown, despite the abundance of observational and controlled studies reporting associations between traffic sources and asthma exacerbation and hospitalizations. OBJECTIVES: To identify molecular pathways perturbed following traffic pollution exposures, we analyzed data as part of the Atlanta Commuters Exposure (ACE-2) study, a crossover panel of commuters with and without asthma. METHODS: We measured 27 air pollutants and conducted high-resolution metabolomics profiling on blood samples from 45 commuters before and after each exposure session. We evaluated metabolite and metabolic pathway perturbations using an untargeted metabolome-wide association study framework with pathway analyses and chemical annotation. RESULTS: Most of the measured pollutants were elevated in highway commutes (p < 0.05). From both negative and positive ionization modes, 17,586 and 9087 metabolic features were extracted from plasma, respectively. 494 and 220 unique features were associated with at least 3 of the 27 exposures, respectively (p < 0.05), after controlling confounders and false discovery rates. Pathway analysis indicated alteration of several inflammatory and oxidative stress related metabolic pathways, including leukotriene, vitamin E, cytochrome P450, and tryptophan metabolism. We identified and annotated 45 unique metabolites enriched in these pathways, including arginine, histidine, and methionine. Most of these metabolites were not only associated with multiple pollutants, but also differentially expressed between participants with and without asthma. The analysis indicated that these metabolites collectively participated in an interrelated molecular network centering on arginine metabolism, underlying the impact of traffic-related pollutants on individuals with asthma. CONCLUSIONS: We detected numerous significant metabolic perturbations associated with in-vehicle exposures during commuting and validated metabolites that were closely linked to several inflammatory and redox pathways, elucidating the potential molecular mechanisms of traffic-related air pollution toxicity. These results support future studies of metabolic markers of traffic exposures and the corresponding molecular mechanisms.

Impact of air pollution control policies on cardiorespiratory emergency department visits, Atlanta, GA, 1999-2013.

BACKGROUND: Air pollution control policies resulting from the 1990 Clean Air Act Amendments were aimed at reducing pollutant emissions, ambient concentrations, and ultimately adverse health outcomes. OBJECTIVES: As part of a comprehensive air pollution accountability study, we used a counterfactual study design to estimate the impact of mobile source and electricity generation control policies on health outcomes in the Atlanta, GA, metropolitan area from 1999 to 2013. METHODS: We identified nine sets of pollution control policies, estimated changes in emissions in the absence of these policies, and employed those changes to estimate counterfactual daily ambient pollutant concentrations at a central monitoring location. Using a multipollutant Poisson time-series model, we estimated associations between observed pollutant levels and daily counts of cardiorespiratory emergency department (ED) visits at Atlanta hospitals. These associations were then used to estimate the number of ED visits prevented due to control policies, comparing observed to counterfactual daily concentrations. RESULTS: Pollution control policies were estimated to substantially reduce ambient concentrations of the nine pollutants examined for the period 1999-2013. We estimated that pollutant concentration reductions resulting from the control policies led to the avoidance of over 55,000 cardiorespiratory disease ED visits in the five-county metropolitan Atlanta area, with greater proportions of visits prevented in later years as effects of policies became more fully realized. During the final two years of the study period, 2012-2013, the policies were estimated to prevent 16.5% of ED visits due to asthma (95% interval estimate: 7.5%, 25.1%), 5.9% (95% interval estimate: -0.4%, 12.3%) of respiratory ED visits, and 2.3% (95% interval estimate: -1.8%, 6.2%) of cardiovascular disease ED visits. DISCUSSION: Pollution control policies resulting from the 1990 Clean Air Act Amendments led to substantial estimated reductions in ambient pollutant concentrations and cardiorespiratory ED visits in the Atlanta area.

Review of Acellular Assays of Ambient Particulate Matter Oxidative Potential: Methods and Relationships with Composition, Sources, and Health Effects.

Oxidative stress is a potential mechanism of action for particulate matter (PM) toxicity and can occur when the body's antioxidant capacity cannot counteract or detoxify harmful effects of reactive oxygen species (ROS) due to an excess presence of ROS. ROS are introduced to the body via inhalation of PM with these species present on and/or within the particles (particle-bound ROS) and/or through catalytic generation of ROS in vivo after inhaling redox-active PM species (oxidative potential, OP). The recent development of acellular OP measurement techniques has led to a surge in research across the globe. In this review, particle-bound ROS techniques are discussed briefly while OP measurements are the focus due to an increasing number of epidemiologic studies using OP measurements showing associations with adverse health effects in some studies. The most common OP measurement techniques, including the dithiothreitol assay, glutathione assay, and ascorbic acid assay, are discussed along with evidence for utility of OP measurements in epidemiologic studies and PM characteristics that drive different responses between assay types (such as species composition, emission source, and photochemistry). Overall, most OP assays respond to metals like copper than can be found in emission sources like vehicles. Some OP assays respond to organics, especially photochemically aged organics, from sources like biomass burning. Select OP measurements have significant associations with certain cardiorespiratory end points, such as asthma, congestive heart disease, and lung cancer. In fact, multiple studies have found that exposure to OP measured using the dithiothreitol and glutathione assays drives higher risk ratios for certain cardiorespiratory outcomes than PM mass, suggesting OP measurements may be integrating the health-relevant fraction of PM and will be useful tools for future health analyses. The compositional impacts, including species and emission sources, on OP could have serious implications for health-relevant PM exposure. Though more work is needed, OP assays show promise for health studies as they integrate the impacts of PM species and properties on catalytic redox reactions into one measurement, and current work highlights the importance of metals, organic carbon, vehicles, and biomass burning emissions to PM exposures that could impact health.

Characterization of the concentration-response curve for ambient ozone and acute respiratory morbidity in 5 US cities.

Although short-term exposure to ambient ozone (O3) can cause poor respiratory health outcomes, the shape of the concentration-response (C-R) between O3 and respiratory morbidity has not been widely investigated. We estimated the effect of daily O3 on emergency department (ED) visits for selected respiratory outcomes in 5 US cities under various model assumptions and assessed model fit. Population-weighted average 8-h maximum O3 concentrations were estimated in each city. Individual-level data on ED visits were obtained from hospitals or hospital associations. Poisson log-linear models were used to estimate city-specific associations between the daily number of respiratory ED visits and 3-day moving average O3 levels controlling for long-term trends and meteorology. Linear, linear-threshold, quadratic, cubic, categorical, and cubic spline O3 C-R models were considered. Using linear C-R models, O3 was significantly and positively associated with respiratory ED visits in each city with rate ratios of 1.02-1.07 per 25 ppb. Models suggested that O3-ED C-R shapes were linear until O3 concentrations of roughly 60 ppb at which point risk continued to increase linearly in some cities for certain outcomes while risk flattened in others. Assessing C-R shape is necessary to identify the most appropriate form of the exposure for each given study setting.

Toxicological Risk by Inhalation Exposure of Air Pollution Emitted from China's Municipal Solid Waste Incineration.

Municipal solid waste (MSW) incineration has developed rapidly in China. However, the air pollution-related health risks attributable to MSW incinerators are still far from known. In this context, an MSW incineration emission inventory was compiled using plant-level activity data and localized emission factors. Subsequently, Gaussian Plume Model and Risk Quotients Model were utilized to calculate the spatialized hazard index (HI) and carcinogenic risk (CR). Altogether, 76449 tons (t) of NO X, 25725 t of SO2, 16937 t of CO, 9279 t of HCl, 5629 t of particulate matter, 33 t of Cr, 33 t of Pb, 20 t of Hg, 6 t of Cd, 4 t of Ni, 0.4 t of As, and 94 g-toxic equivalent quantity of polychlorinated dibenzo- p-dioxins and polychlorinated dibenzofurans were emitted in 2015. The national average HI was 1.88 × 10-2, which was far lower than the acceptable level (HI ≤ 1). However, the national average CR was 5.71 × 10-6, which was approximately five times higher than the acceptable level (CR ≤ 1 × 10-6). The spatial heterogeneity of health risks was observed. The results enrich spatial dimensions of prior estimates and provide policy implications from the aspects of accelerating technology upgrades, strengthening emission standards, optimizing site selection and enhancing risk communication.

Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution.

BACKGROUND: High-resolution metabolomics (HRM) is emerging as a sensitive tool for measuring environmental exposures and biological responses. The aim of this analysis is to assess the ability of high-resolution metabolomics (HRM) to reflect internal exposures to complex traffic-related air pollution mixtures. METHODS: We used untargeted HRM profiling to characterize plasma and saliva collected from participants in the Dorm Room Inhalation to Vehicle Emission (DRIVE) study to identify metabolic pathways associated with traffic emission exposures. We measured a suite of traffic-related pollutants at multiple ambient and indoor sites at varying distances from a major highway artery for 12 weeks in 2014. In parallel, 54 students living in dormitories near (20 m) or far (1.4 km) from the highway contributed plasma and saliva samples. Untargeted HRM profiling was completed for both plasma and saliva samples; metabolite and metabolic pathway alternations were evaluated using a metabolome-wide association study (MWAS) framework with pathway analyses. RESULTS: Weekly levels of traffic pollutants were significantly higher at the near dorm when compared to the far dorm (p < 0.05 for all pollutants). In total, 20,766 metabolic features were extracted from plasma samples and 29,013 from saliva samples. 45% of features were detected and shared in both plasma and saliva samples. 1291 unique metabolic features were significantly associated with at least one or more traffic indicator, including black carbon, carbon monoxide, nitrogen oxides and fine particulate matter (p < 0.05 for all significant features), after controlling for confounding and false discovery rate. Pathway analysis of metabolic features associated with traffic exposure indicated elicitation of inflammatory and oxidative stress related pathways, including leukotriene and vitamin E metabolism. We confirmed the chemical identities of 10 metabolites associated with traffic pollutants, including arginine, histidine, γ­linolenic acid, and hypoxanthine. CONCLUSIONS: Using HRM, we identified and verified biological perturbations associated with primary traffic pollutant in panel-based setting with repeated measurement. Observed response was consistent with endogenous metabolic signaling related to oxidative stress, inflammation, and nucleic acid damage and repair. Collectively, the current findings provide support for the use of untargeted HRM in the development of metabolic biomarkers of traffic pollution exposure and response.

A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities.

Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.

Time-Series Analysis of Air Pollution and Health Accounting for Covariate-Dependent Overdispersion.

Time-series studies are routinely used to estimate associations between adverse health outcomes and short-term exposures to ambient air pollutants. Use of the Poisson log-linear model with the assumption of constant overdispersion is the most common approach, particularly when estimating associations between daily air pollution concentrations and aggregated counts of adverse health events throughout a geographical region. We examined how the assumption of constant overdispersion plays a role in estimation of air pollution effects by comparing estimates derived from the standard approach with those estimated from covariate-dependent Bayesian generalized Poisson and negative binomial models that accounted for potential time-varying overdispersion. Through simulation studies, we found that while there was negligible bias in effect estimates, the standard quasi-Poisson approach can result in a larger standard error when the constant overdispersion assumption is violated. This was also observed in a time-series study of daily emergency department visits for respiratory diseases and ozone concentration in Atlanta, Georgia (1999-2009). Allowing for covariate-dependent overdispersion resulted in a reduction in the ozone effect standard error, while the ozone-associated relative risk remained robust to different model specifications. Our findings suggest that improved characterization of overdispersion in time-series modeling can result in more precise health effect estimates in studies of short-term environmental exposures.

Errors associated with the use of roadside monitoring in the estimation of acute traffic pollutant-related health effects.

Near-road monitoring creates opportunities to provide direct measurement on traffic-related air pollutants and to better understand the changing near-road environment. However, how such observations represent traffic-related air pollution exposures for estimating adverse health effect in epidemiologic studies remains unknown. A better understanding of potential exposure measurement error when utilizing near-road measurement is needed for the design and interpretation of the many observational studies linking traffic pollution and adverse health. The Dorm Room Inhalation to Vehicle Emission (DRIVE) study conducted near-road measurements of several single traffic indicators at six indoor and outdoor sites ranging from 0.01 to 2.3 km away from a heavily-trafficked (average annual daily traffic over 350,000) highway artery between September 2014 to January 2015. We examined spatiotemporal variability trends and assessed the potential for bias and errors when using a roadside monitor as a primary traffic pollution exposure surrogate, in lieu of more spatially-refined, proximal exposure indicators. Pollutant levels measured during DRIVE showed a low impact of this highway hotspot source. Primary pollutant species, including NO, CO, and BC declined to near background levels by 20-30 m from the highway source. Patterns of correlation among the sites also varied by pollutant and time of day. NO2, specifically, exhibited spatial trends that differed from other single-pollutant primary traffic indicators. This finding provides some indication of limitations in the use of NO2 as a primary traffic exposure indicator in panel-based health effect studies. Interestingly, roadside monitoring of NO, CO, and BC tended to be more strongly correlated with sites, both near and far from the road, during morning rush hour periods, and more weakly correlated during other periods of the day. We found pronounced attenuation of observed changes in health effects when using measured pollutant from the near-road monitor as a surrogate for true exposure, and the magnitude varied substantially over the course of the day. Caution should be taken when using near-road monitoring network observations, alone, to investigate health effects of traffic pollutants.

Estimating Acute Cardiovascular Effects of Ambient PM2.5 Metals.

BACKGROUND: Few epidemiologic studies have investigated health effects of water-soluble fractions of PM2.5 metals, the more biologically accessible fractions of metals, in their attempt to identify health-relevant components of ambient PM2.5. OBJECTIVES: In this study, we estimated acute cardiovascular effects of PM2.5 components in an urban population, including a suite of water-soluble metals that are not routinely measured at the ambient level. METHODS: Ambient concentrations of criteria gases, PM2.5, and PM2.5 components were measured at a central monitor in Atlanta, Georgia, during 1998-2013, with some PM2.5 components only measured during 2008-2013. In a time-series framework using Poisson regression, we estimated associations between these pollutants and daily counts of emergency department (ED) visits for cardiovascular diseases in the five-county Atlanta area. RESULTS: Among the PM2.5 components we examined during 1998-2013, water-soluble iron had the strongest estimated effect on cardiovascular outcomes [RÍ¡R=1.012 (95% CI: 1.005, 1.019), per interquartile range increase (20.46ng/m3)]. The associations for PM2.5 and other PM2.5 components were consistent with the null when controlling for water-soluble iron. Among PM2.5 components that were only measured during 2008-2013, water-soluble vanadium was associated with cardiovascular ED visits [RÍ¡R=1.012 (95% CI: 1.000, 1.025), per interquartile range increase (0.19ng/m3)]. CONCLUSIONS: Our study suggests cardiovascular effects of certain water-soluble metals, particularly water-soluble iron. The observed associations with water-soluble iron may also point to certain aspects of traffic pollution, when processed by acidifying sulfate, as a mixture harmful for cardiovascular health. https://doi.org/10.1289/EHP2182.

Erratum: Associations between Ambient Fine Particulate Oxidative Potential and Cardiorespiratory Emergency Department Visits.

[This corrects the article DOI: 10.1289/EHP1545.].

Associations between Ambient Fine Particulate Oxidative Potential and Cardiorespiratory Emergency Department Visits.

BACKGROUND: Oxidative potential (OP) has been proposed as a measure of toxicity of ambient particulate matter (PM). OBJECTIVES: Our goal was to address an important research gap by using daily OP measurements to conduct population-level analysis of the health effects of measured ambient OP. METHODS: A semi-automated dithiothreitol (DTT) analytical system was used to measure daily average OP (OPDTT) in water-soluble fine PM at a central monitor site in Atlanta, Georgia, over eight sampling periods (a total of 196 d) during June 2012-April 2013. Data on emergency department (ED) visits for selected cardiorespiratory outcomes were obtained for the five-county Atlanta metropolitan area. Poisson log-linear regression models controlling for temporal confounders were used to conduct time-series analyses of the relationship between daily counts of ED visits and either the 3-d moving average (lag 0-2) of OPDTT or same-day OPDTT. Bipollutant regression models were run to estimate the health associations of OPDTT while controlling for other pollutants. RESULTS: OPDTT was measured for 196 d (mean=0.32 nmol/min/m3, interquartile range=0.21). Lag 0-2 OPDTT was associated with ED visits for respiratory disease (RR=1.03, 95% confidence interval (CI): 1.00, 1.05 per interquartile range increase in OPDTT), asthma (RR=1.12, 95% CI: 1.03, 1.22), and ischemic heart disease (RR=1.19, 95% CI: 1.03, 1.38). Same-day OPDTT was not associated with ED visits for any outcome. Lag 0-2 OPDTT remained a significant predictor of asthma and ischemic heart disease in most bipollutant models. CONCLUSIONS: Lag 0-2 OPDTT was associated with ED visits for multiple cardiorespiratory outcomes, providing support for the utility of OPDTT as a measure of fine particle toxicity. https://doi.org/10.1289/EHP1545.

Evaluation of VOC concentrations in indoor and outdoor microenvironments at near-road schools.

A 14-week air quality study, characterizing the indoor and outdoor concentrations of 18 VOCs at four El Paso, Texas elementary schools, was conducted in Spring 2010. Three schools were in an area of high traffic density and the fourth school, considered as a background school, was situated in an area affected minimally by stationary and mobile sources of air pollution. Passive samplers were deployed for monitoring and analyzed by GC/MS. Differences in the concentration profiles of the BTEX species between the high and low traffic density schools confirmed the pre-defined exposure patterns. Toluene was the predominant compound within the BTEX group and the 96-hr average outdoor concentrations varied from 1.16 to 4.25 µg/m3 across the four schools. Outdoor BTEX species were strongly correlated with each other (0.63 < r < 1.00, p < 0.05) suggesting a common source: vehicular traffic emissions. As expected, the strength of the associations between these compounds was more intense at each of the three high-exposure schools in contrast to the low-exposure school. This was further corroborated by the results obtained from the BTEX inter-species ratios (toluene: benzene and m, p- xylenes: ethylbenzene). Certain episodic events during the study period resulted in very elevated concentrations of some VOCs such as n-pentane. Indoor concentration of compounds with known indoor sources such as α -pinene, d-limonene, p-dichlorobenzene, and chloroform were generally higher than their corresponding outdoor concentrations. Cleaning agents, furniture polishes, materials used in arts and crafts activities, hot-water usage, and deodorizing cakes used in urinal pots were the likely major sources for these high indoor concentrations. Finally, retrospective assessment of average ambient BTEX concentrations over the last twenty years suggest a gradual decrement in this border region.

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection.

INTRODUCTION: Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics. METHODS: Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform. RESULTS: The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics. CONCLUSIONS: Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.