Josephine T. Bates

Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects.

Exposure to atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality, but the mechanisms are not well understood. We assess the hypothesis that PM2.5 induces oxidative stress in the body via catalytic generation of reactive oxygen species (ROS). A dithiothreitol (DTT) assay was used to measure the ROS-generation potential of water-soluble PM2.5. Source apportionment on ambient (Atlanta, GA) PM2.5 was performed using the chemical mass balance method with ensemble-averaged source impact profiles. Linear regression analysis was used to relate PM2.5 emission sources to ROS-generation potential and to estimate historical levels of DTT activity for use in an epidemiologic analysis for the period of 1998-2009. Light-duty gasoline vehicles (LDGV) exhibited the highest intrinsic DTT activity, followed by biomass burning (BURN) and heavy-duty diesel vehicles (HDDV) (0.11 ± 0.02, 0.069 ± 0.02, and 0.052 ± 0.01 nmol min(-1) μg(-1)source, respectively). BURN contributed the largest fraction to total DTT activity over the study period, followed by LDGV and HDDV (45, 20, and 14%, respectively). DTT activity was more strongly associated with emergency department visits for asthma/wheezing and congestive heart failure than PM2.5. This work provides further epidemiologic evidence of a biologically plausible mechanism, that of oxidative stress, for associations of adverse health outcomes with PM2.5 mass and supports continued assessment of the utility of the DTT activity assay as a measure of ROS-generating potential of particles.

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

Exposure to mobile source air pollution in early life and childhood asthma incidence: The Kaiser Air Pollution and Pediatric Asthma Study

Background: Early-life exposure to traffic-related air pollution exacerbates childhood asthma, but it is unclear what role it plays in asthma development. Methods: The association between exposure to primary mobile source pollutants during pregnancy and during infancy and asthma incidence by ages 2 through 6 was examined in the Kaiser Air Pollution and Pediatric Asthma Study, a racially diverse birth cohort of 24,608 children born between 2000 and 2010 and insured by Kaiser Permanente Georgia. We estimated concentrations of mobile source fine particulate matter (PM2.5, µg/m3), nitrogen oxides (NOX, ppb), and carbon monoxide (CO, ppm) at the maternal and child residence using a Research LINE source dispersion model for near-surface releases. Asthma was defined using diagnoses and medication dispensings from medical records. We used binomial generalized linear regression to model the impact of exposure continuously and by quintiles on asthma risk. Results: Controlling for covariates and modeling log-transformed exposure, a 2.7-fold increase in first year of life PM2.5 was associated with an absolute 4.1% (95% confidence interval, 1.6%, 6.6%) increase in risk of asthma by age 5. Quintile analysis showed an increase in risk from the first to second quintile, but similar risk across quintiles 2–5. Risk differences increased with follow-up age. Results were similar for NOX and CO and for exposure during pregnancy and the first year of life owing to high correlation. Conclusions: Results provide limited evidence for an association of early-life mobile source air pollution with childhood asthma incidence with a steeper concentration–response relationship observed at lower levels of exposure.

Application and evaluation of two model fusion approaches to obtain ambient air pollutant concentrations at a fine spatial resolution (250m) in Atlanta

Abstract Epidemiologic studies rely on accurately characterizing spatiotemporal variation in air pollutant concentrations. This work presents two model fusion approaches that use publicly available chemical transport simulations, dispersion model simulations, and observations to estimate air pollutant concentrations at a neighborhood-level spatial resolution while incorporating comprehensive chemistry and emissions sources. The first method is additive and the alternative method is multiplicative. These approaches are applied to Atlanta, GA at a 250 m grid resolution to obtain daily 24-hr averaged PM2.5 and 1-hr max CO and NOx concentrations during the years 2003–2008 for use in health studies. The modeled concentrations provide comprehensive estimates with steep spatial gradients near roadways, secondary formation and loss, and effects of regional sources that can influence daily variation in ambient pollutant concentrations. Results show high temporal and spatial correlation and low biases across monitors, providing accurate pollutant concentration estimates for epidemiologic analyses.

Source Impacts on and Cardiorespiratory Effects of Reactive Oxygen Species Generated by Water-Soluble PM2.5 Across the Eastern United States

It is hypothesized that PM2.5 with high oxidative potential (OP) can catalytically generate reactive oxygen species (ROS) in excess of the body’s antioxidant capacity, leading to oxidative stress. Therefore, two advanced methods for conducting source apportionment, along with field experiments characterizing air quality, are used to identify the sources of PM2.5 with high OP and relate them to acute health effects. The field study measured OP of ambient water-soluble PM2.5 using a dithiothreitol (DTT) assay at four sites across the Southeastern United States from June 2012 to June 2013. Source apportionment was performed on collocated speciated PM2.5 samples using the Chemical Mass Balance Method with ensemble-trained profiles in Atlanta, GA and CMAQ-DDM for Atlanta and all other measurement sites (Yorkville, GA, Centerville, AL, and Birmingham, AL). Source-OP relationships were investigated using least squares linear regression. The model for Atlanta, GA was applied to PM2.5 source impacts from 1998–2010 to estimate long-term trends in ambient PM2.5 OP for use in population-level acute epidemiologic studies. Biomass burning contributes the largest fraction to total historical OP in Atlanta, followed by light-duty gasoline vehicles and heavy-duty diesel vehicles (43, 22 and 17%, respectively). Results find significant associations between estimated OP and emergency department visits related to congestive heart failure and asthma/wheezing attacks, supporting the hypothesis that PM2.5 health effects are, in part, due to oxidative stress and that OP is a useful indicator of PM2.5 health impacts. Finally, controlling PM2.5 sources with high OP, like biomass burning, may help prevent acute health effects.

Air Quality Model-Based Methods for Estimating Human Exposures: A Review and Comparison

Determining estimates of human exposure is increasingly relying on the use of air quality models and satellite observations to provide spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive as they capture the emissions and meteorological linkages. Additionally they can provide source impact information and concentration fields for a range of species not currently provided from satellite-based observations (e.g., MODIS and MAIAC), and are not subject to cloud interference. Multiple methods based on air quality modeling (including using CMAQ and/or RLINE) with and without data fusion, have been developed and are being used in health studies as part of the EPA-funded Southeastern Center for Air Pollution and Epidemiology Clean Air Research Center. The methods include CMAQ-Data Fusion where concentrations fields are blended with observations to provide spatially and temporally complete pollutant concentrations fields of PM2.5, EC, CO, and NO2. To improve the spatial resolution, this method was extended to include RLINE fields for fine scale (250 m) exposure assessment. Another method was developed to estimate spatial exposure estimates of emissions source categories using CMAQ-derived source impacts for 16–32 sources, along with observations of individual PM species. Each of these approaches have individual strengths and weaknesses. The methods that use a data fusion approach to blend observations and air quality model fields are found to best capture the spatiotemporal trends in the observations, reducing the standard error in the exposure estimates. In the past, such methods were limited by the availability of air quality model fields over long periods, but such fields are becoming more routinely available from air quality forecasting activities.