Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA.

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH.

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration.

A typical challenge in air pollution epidemiology is to perform detailed exposure assessment for individuals for which health data are available. To address this problem, in the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at fine spatial and temporal scales (daily, usually) with complete coverage. However, it is not clear how much the predicted exposures yielded by the various methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance. Using PM2.5 in year 2011 over the continental U.S. as a case study, we generate national maps of ambient PM2.5 concentration using: (i) ordinary least squares and inverse distance weighting; (ii) kriging; (iii) statistical downscaling models, that is, spatial statistical models that use the information contained in air quality model outputs; (iv) land use regression, that is, linear regression modeling approaches that leverage the information in Geographical Information System (GIS) covariates; and (v) machine learning methods, such as neural networks, random forests and support vector regression. We examine the various methods' predictive performance via cross-validation using Root Mean Squared Error, Mean Absolute Deviation, Pearson correlation, and Mean Spatial Pearson Correlation. Additionally, we evaluated whether factors such as, season, urbanicty, and levels of PM2.5 concentration (low, medium or high) affected the performance of the different methods. Overall, statistical methods that explicitly modeled the spatial correlation, e.g. universal kriging and the downscaler model, outperform all the other exposure assessment approaches regardless of season, urbanicity and PM2.5 concentration level. We posit that the better predictive performance of spatial statistical models over machine learning methods is due to the fact that they explicitly account for spatial dependence, thus borrowing information from neighboring observations. In light of our findings, we suggest that future exposure assessment methods for regional PM2.5 incorporate information from neighboring sites when deriving predictions at unsampled locations or attempt to account for spatial dependence.

Fine Particle Iron in Soils and Road Dust Is Modulated by Coal-Fired Power Plant Sulfur.

Transition metal ions, such as water-soluble iron (WS-Fe), are toxic components of fine particles (PM2.5). In Atlanta, from 1998 to 2013, a previous study found that WS-Fe was the PM2.5 species most associated with adverse cardiovascular outcomes. We examined this data set to investigate the sources of WS-Fe and the effects of air quality regulations on ambient levels of WS-Fe. We find that insoluble forms of iron in mineral and road dust combined with sulfate from coal-fired electrical generating units were converted into soluble forms by sulfate-driven acid dissolution. Sulfate produced both the highly acidic aerosol (summer pH 1.5-2) and liquid water required for the aqueous phase acid dissolution, but variability in WS-Fe was mainly driven by particle liquid water. These processes were more pronounced in summer when particles were most acidic, whereas in winter the relative importance of WS-Fe from combustion emissions increased. Although WS-Fe constituted a minute fraction of PM2.5 mass (0.15%), the WS-Fe-PM2.5 mass correlation was high (r = 0.67) and may be explained by these formation routes, which, in part, could account for observed associations between PM2.5 mass and adverse health seen in past studies. Similar processes are expected in many regions, implying that these unexpected benefits from coal-burning reduction may be widespread.

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014.

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3-, NH4+, EC, OC, SO42-) over the contiguous United States on a daily basis for a period of ten years (2005-2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84-0.98 across pollutants) and the spatial variation less well (R2 values 0.42-0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.

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.

Associations Between Ambient Air Pollutant Concentrations and Birth Weight: A Quantile Regression Analysis.

INTRODUCTION: We investigated the extent to which associations of ambient air pollutant concentrations and birth weight varied across birth weight quantiles. METHODS: We analyzed singleton births ≥27 weeks of gestation from 20-county metropolitan Atlanta with conception dates between January 1, 2002 and February 28, 2006 (N = 273,711). Trimester-specific and total pregnancy average concentrations for 10 pollutants, obtained from ground observations that were interpolated using 12-km Community Multiscale Air Quality model outputs, were assigned using maternal residence at delivery. We estimated associations between interquartile range width (IQRw) increases in pollutant concentrations and changes in birth weight using quantile regression. RESULTS: Gestational age-adjusted associations were of greater magnitude at higher percentiles of the birth weight distribution. Pollutants with large vehicle source contributions (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon, and total PM2.5 mass), as well as PM2.5 sulfate and PM2.5 ammonium, were associated with birth weight decreases for the higher birth weight percentiles. For example, whereas the decrease in mean birthweight per IQRw increase in PM2.5 averaged over pregnancy was -7.8 g (95% confidence interval = -13.6, -2.0 g), the quantile-specific associations were: 10th percentile -2.4 g (-11.5, 6.7 g); 50th percentile -8.9 g (-15.7, -2.0g); and 90th percentile -19.3 g (-30.6, -7.9 g). Associations for the intermediate and high birth weight quantiles were not sensitive to gestational age adjustment. For some pollutants, we saw associations at the lowest quantile (10th percentile) when not adjusting for gestational age. CONCLUSIONS: Associations between air pollution and reduced birth weight were of greater magnitude for newborns at relatively heavy birth weights.

The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA.

Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.

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.

Spatial PM2.5 mobile source impacts using a calibrated indicator method.

Motor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NOx estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley.

Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

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.

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.

Responses in Ozone and Its Production Efficiency Attributable to Recent and Future Emissions Changes in the Eastern United States.

Ozone production efficiency (OPE), a measure of the number of ozone (O3) molecules produced per emitted NOX (NO + NO2) molecule, helps establish the relationship between NOX emissions and O3 formation. We estimate long-term OPE variability across the eastern United States using two novel approaches: an observation-based empirical method and a chemical transport model (CTM) method. The CTM approach explicitly controls for differing O3 and NOX reaction product (NOZ) deposition rates and separately estimates OPEs from on-road mobile and electricity generating unit sources across a broad spatial scale. We find lower OPEs in urban areas and that average July OPE increased over the eastern United States domain between 2001 and 2011 from 11 to 14. CTM and empirical approaches agree at low NOZ concentrations, but CTM OPEs are greater than empirical OPEs at high NOZ. Our results support that NOX emissions reductions become more effective at reducing O3 at lower NOZ concentrations. Electricity generating unit OPEs are higher than mobile OPEs except near emissions locations, meaning further utility NOX emissions reductions will have greater per unit impacts on O3 regionally.

Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach.

BACKGROUND: Ground-level ozone is a potent airway irritant and a determinant of respiratory morbidity. Susceptibility to the health effects of ambient ozone may be influenced by both intrinsic and extrinsic factors, such as neighborhood socioeconomic status (SES). Questions remain regarding the manner and extent that factors such as SES influence ozone-related health effects, particularly across different study areas. METHODS: Using a 2-stage modeling approach we evaluated neighborhood SES as a modifier of ozone-related pediatric respiratory morbidity in Atlanta, Dallas, & St. Louis. We acquired multi-year data on emergency department (ED) visits among 5-18 year olds with a primary diagnosis of respiratory disease in each city. Daily concentrations of 8-h maximum ambient ozone were estimated for all ZIP Code Tabulation Areas (ZCTA) in each city by fusing observed concentration data from available network monitors with simulations from an emissions-based chemical transport model. In the first stage, we used conditional logistic regression to estimate ZCTA-specific odds ratios (OR) between ozone and respiratory ED visits, controlling for temporal trends and meteorology. In the second stage, we combined ZCTA-level estimates in a Bayesian hierarchical model to assess overall associations and effect modification by neighborhood SES considering categorical and continuous SES indicators (e.g., ZCTA-specific levels of poverty). We estimated ORs and 95% posterior intervals (PI) for a 25 ppb increase in ozone. RESULTS: The hierarchical model combined effect estimates from 179 ZCTAs in Atlanta, 205 ZCTAs in Dallas, and 151 ZCTAs in St. Louis. The strongest overall association of ozone and pediatric respiratory disease was in Atlanta (OR = 1.08, 95% PI: 1.06, 1.11), followed by Dallas (OR = 1.04, 95% PI: 1.01, 1.07) and St. Louis (OR = 1.03, 95% PI: 0.99, 1.07). Patterns of association across levels of neighborhood SES in each city suggested stronger ORs in low compared to high SES areas, with some evidence of non-linear effect modification. CONCLUSIONS: Results suggest that ozone is associated with pediatric respiratory morbidity in multiple US cities; neighborhood SES may modify this association in a non-linear manner. In each city, children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying rates of respiratory morbidity.