Grantee Research Project Results
2016 Progress Report: Multi-Scale Assessment of Health Effects of Air Pollution Mixtures Using Novel Measurements and Models
EPA Grant Number: R834799Center: The Southeastern Center for Air Pollution and Epidemiology: Multiscale Measurements and Modeling of Mixtures
Center Director: Tolbert, Paige
Title: Multi-Scale Assessment of Health Effects of Air Pollution Mixtures Using Novel Measurements and Models
Investigators: Tolbert, Paige , Sarnat, Stefanie Ebelt , Strickland, Matthew J , Weber, Rodney J. , Odman, Mehmet Talat , Winquist, Andrea , Russell, Armistead G. , Nenes, Athanasios , Flanders, Dana , Diaz-Sanchez, David , Talbott, Evelynn , Chang, Howard , Mulholland, James , Sarnat, Jeremy , Waller, Lance , Darrow, Lyndsey , Bergin, Michael , Klein, Mitchel , Guensler, Randy , Bilonick, Richard , Greenwald, Roby , Barry, Vaughn , Liu, Yang , Hu, Yongtao
Institution: Emory University , Georgia Institute of Technology , Duke University
Current Institution: Emory University , Duke University , Georgia Institute of Technology
EPA Project Officer: Chung, Serena
Project Period: January 1, 2011 through December 31, 2016
Project Period Covered by this Report: August 1, 2015 through July 31,2016
Project Amount: $7,999,779
RFA: Clean Air Research Centers (2009) RFA Text | Recipients Lists
Research Category: Climate Change , Air Quality and Air Toxics , Air
Objective:
Project 1: Development and Deployment of an Instrumentation Suite for Comprehensive Air Quality Characterization, Including Aerosol Reactive Oxygen Species (ROS)
Develop method(s) for measuring ROS online and semi-continuously, acquire instrumentation and organize measurement program. Undertake an extensive measurement campaign that will characterize spatial distributions of key air quality parameters to inform the Southeastern Center for Air Pollution and Epidemiology (SCAPE) modeling and health studies.
Project 2: Examining In-Vehicle Pollution and Oxidative Stress in a Cohort of Daily Commuters
Examine the effects of exposure to particulate mixtures occurring during automobile commuting and within indoor, non-commuting microenvironments and corresponding measures of oxidative stress-mediated response.
Project 3: Novel Estimates of Pollutant Mixtures and Pediatric Health in Two Birth Cohorts
Explore the interplay between certain early life events, characterizations of air pollutant mixtures developed as part of the Center's Mixtures Characterization Toolkit (MC Toolkit), and a range of pediatric health outcomes using two large, population-based birth cohorts.
Project 4: A Multi-City Time-Series Study of Pollutant Mixtures and Acute Morbidity
Conduct a multi-city time-series study to clarify the impacts of air quality on acute cardiorespiratory morbidity in five U.S. cities using novel mixture characterization metrics.
Core B: Air Quality Core
Provide SCAPE researchers with the methods and data to comprehensively characterize air pollutants relevant to the four projects and other cores. Project activities are supported by collecting and managing atmospheric data, developing the MC Toolkit for further analyses specific to the projects, and providing the expertise and resources to facilitate the application of the various components of the toolkit. Comprehensive characterization of air pollutants is developed by analyses of the detailed chemical and physical measurements conducted by the Center, along with those available from ambient air quality monitoring networks and special field campaigns. Spatial and temporal characterization of the air pollutant mixtures and emission sources are determined by using extended receptor-oriented models, chemical transport models, regression approaches, hybrid methods and remote sensing applied over multiple scales.
Core C: Biostatistics Core
Provide statistical support to the Center and to the associated Projects.
Collaborative Project 1: Characterization of Primary and Secondary Traffic-Related Particles (Collaborators: Harvard and SCAPE)
The objective of this collaborative project is to characterize the composition of tunnel primary, secondary and aged primary plus secondary aerosols generated for exposures in Harvard's toxicology study (Project 1). Dr. Sally Ng from Georgia Institute of Technology (Georgia Tech) and doctoral student Matt Kollman collaborated with Harvard Clean Air Research Center (CLARC) researchers on this effort, using an Aerosol Chemical Speciation Monitor (ACSM) provided by Aerodyne Inc. The ACSM provides quantitative measurement of non-refractory submicron aerosol composition, including mass spectra, with a time resolution on the order of 15–30 minutes. The ACSM measures organics, nitrate, sulfate, ammonium and chloride. The extent of oxidation of chamber aerosols can also be determined semi-continuously from the mass fraction m/z 44 (CO2+), allowing measurement of the evolution of O/C over the course of each experiment.
Collaborative Project 2: Mobile and Fixed-Site Characterization of Vehicle Emission Impacts in Atlanta, Georgia (Collaborators: SCAPE, Center for Clean Air Research [CCAR], and the U.S. Environmental Protection Agency [EPA])
The goals of this collaborative project are to compare instruments and methods for characterizing vehicle emissions, personal exposures and spatial distributions by deploying the CCAR measurement platform and sampling protocols in Atlanta for a 16-day period and to compare a limited set of spatially intensive mobile and fixed site measurements of selected pollutants with downscaled Community Multiscale Air Quality (CMAQ) predictions in Atlanta.
Collaborative Project 3: Inter-Comparison of Ambient Fine Particulate Matter (PM2.5) Estimation Models in North Carolina (Collaborators: SCAPE, Harvard, CCAR, and EPA)
The goal of this effort is to summarize the strengths and limitations of current satellite-driven PM2.5 exposure models and CMAQ PM2.5 simulations, and to identify directions for future model development and applications in various population-based health effects studies. There are five candidate models to be evaluated: (1) Koutrakis group's mixed effects model, (2) Chang's spatial downscaler,(3) Liu group's mixed effects model, (4) University of Washington (UW)/CCAR group's spatiotemporal model, and (5) Russell group's CMAQ PM2.5 simulation.
Collaborative Project 4: Measurement Error for Air Pollution Cohort Studies: Application and Comparison of Several Statistical Methods to Georgia Birth Cohort Data (Collaborators: CCAR and SCAPE)
The project considered three statistical approaches to account for measurement error arising from spatiotemporal exposure prediction models. These statistical approaches were developed and applied to examine linear associations between ambient PM2.5 concentrations and birth weight among full-term births using Georgia statewide geocoded birth records.
Progress Summary:
Project 1: Development and Deployment of an Instrumentation Suite for Comprehensive Air Quality Characterization, Including Aerosol ROS
- We published five papers; two more are in preparation.
- We presented research results at conferences (nine presentations).
- We measured size distributions of dithiothreitol (DTT) and ascorbic acid (AA).
- We investigated DTT activity of approximately 40 different forms of isoprene secondary organic aerosols (SOA) from chamber experiments.
- We developed a method for measurement of total DTT (water soluble + water insoluble).
- We continued work on the development of an online DTT system.
- We performed experiments to quantify the stability of DTT on archived filters.
Project 2: Examining In-Vehicle Pollution and Oxidative Stress in a Cohort of Daily Commuters
- We continued analyses of data collected as part of the Atlanta Commuters Exposure Study Projects 1 and 2 (ACE-1 and ACE-2) commuter studies.
- Seven manuscripts are currently in preparation, to be submitted during the next 6 months.
- Central findings include demonstration of clear differences in pollutant levels/composition by commute exposure scenario and higher levels of DTT in highway commutes than surface street commutes.
- Epidemiologic analyses indicated post-commute responses in measured biomarkers consistent with both local and systemic inflammation and autonomic response.
- We found in-vehicle pollutant levels to be associated with markers of acute phase inflammatory response (increase exhaled nitric oxide, C-reactive protein and breath malondialdehyde) and deceased measures of heart rate variability (SDNN) and lung function (FEV1, FVC).
- We observed additional evidence of modification by asthma control status. Individuals with poorly controlled asthma exhibited more pronounced decrements in lung function and increased exhaled nitric oxide compared to non-asthmatics and individuals with well-controlled asthma.
Project 3: Novel Estimates of Pollutant Mixtures and Pediatric Health in Two Birth Cohorts
- We published associations of fused CMAQ estimates of air pollution with preterm birth in Georgia (Hao, et al., 2016, Environ Health Perspect).
- We published associations of satellite-derived PM2.5 with emergency department (ED) visits for pediatric respiratory disease in Georgia (Strickland, et al., 2016, Environ Health Perspect).
- We published a paper on methods for defining asthma in the Kaiser cohort (Pennington, et al., 2016, Pediatr Allergy Immunol).
- We published a method for partial correction of residual confounding in time-series studies (Flanders, et al., in press, Am J Epidemiol).
- We submitted a paper on exposure measurement error due to maternal residential mobility (Pennington, et al., in review).
- We submitted a paper on fused CMAQ estimates of air pollution with ED visits for pediatric respiratory disease in Georgia (Xiao, et al., in review).
- The Kaiser study abstracts on associations of PM2.5 and traffic-related PM2.5 with pediatric asthma and pediatric pneumonia accepted at 2016 International Society for Environmental Epidemiology (ISEE) conference. Two manuscripts detailing these results are being prepared.
Project 4: A Multi-City Time-Series Study of Pollutant Mixtures and Acute Morbidity
- We continued work on methods for detecting and analyzing air pollution mixtures using multi-pollutant monitoring data, including self-organizing maps (Pearce, et al., in press), joint effects (Sarnat, et al., ISEE 2016; Ye, et al., in press), PM2.5 oxidative potential (Bates, et al., 2015; Fang, et al., 2016; Abrams, et al., ISEE 2016; Bates, et al., AWMA 2016; Bates, et al., ITM 2016), source apportionment (Krall, et al., in press) and assessment of PM2.5 metals (Ye, et al., ISEE 2016).
- We continued efforts on assessing exposure assignment approaches for large study areas (Friberg, et al., 2016; Chang, et al., ICSA 2016).
- We conducted multi-city analyses examining the shape of concentration-response functions (Barry, et al., ISEE 2016).
- We continued our assessment of potential factors conferring susceptibility and vulnerability to ambient air pollution, including age, sex, race (Alhanti, et al., 2016) and neighborhood socioeconomic conditions (O’Lenick, et al., in press; O’Lenick, et al., submitted; O’Lenick, et al., ISEE 2016; O’Lenick, et al., APHA 2016).
- In conjunction with funding from the National Institutes of Health, we conducted an investigation of the impacts of high temperatures and heat waves on ED visits (Winquist, et al., 2016; Chen, et al., submitted; O’Lenick, et al., ISEE 2016; O’Lenick, et al., APHA 2016).
- We also assessed modification of the acute respiratory effects of ambient air pollution by ambient temperature (Darrow, et al., ISEE 2016) and by residential air exchange rate estimates (Liang, et al., ISEE 2016) across multiple cities.
Core B: Air Quality Core
- We are leading the preparation of a manuscript covering the various spatiotemporal pollutant modeling methods used by the CLARCs as part of their research.
- The uniquely large data set of ROS, as measured using a DTT assay and advanced as part of Project 1, was modeled using the ensemble-trained chemical mass balance method to develop source-ROS relationships. Those relationships were used to simulate historical trends in ambient PM2.5 DTT activity. The modeled historical trend in ROS was then used in an epidemiological analysis in Atlanta using a long-term ED record to assess the association between ROS (as measured using the DTT assay) and various health outcomes.
- An analysis similar to the above was carried out using ROS measured using the AA assay, and the results were provided to Emory as part of Project 4.
- The OBS-CMAQ data fusion method (described last year) has been applied to the five Project 4 cities. The method was written up in manuscript form and was submitted for publication.
- The same data fusion methodology was applied to North Carolina for comparison as part of the Harvard-Georgia Tech satellite method intercomparison (Huang, et al., ITM 2016; Huang, et al., CMAS 2016).
- Working with the Atlanta Regional Council (ARC) and the Environmental Protection Division (EPD) of the Georgia Department of Natural Resources, we applied the Research Line Model (RLINE) to 250 m resolution grids and explored various methods to scale the results, including using our CMB mobile source impacts at monitors in Atlanta. We compared our OBS-CMAQ data fusion with Integrated Mobile Source Indicator (IMSI) to the RLINE results. Our methods have been used by ARC and are shown on their website, have been presented at a national conference (Zhai, et al., CMAS 2015) and are now written up in a publication for submission in July.
- Using the rescaled ARC/EPD RLINE results mapped to 250 m resolution grids, we explored various methods to provide researchers in Project 3 with 250 m resolution PM2.5, CO, and NOx fields for 2002–2011. Methods examined include: (1) use of Emory's statistical downscaler using satellite observations or CMAQ model results and the RLINE results and combining, and (2) directly combining RLINE and the OBS-CMAQ fields. A manuscript was submitted to Atmospheric Environment.
- We applied the CMAQ-DDM (Decoupled Direct Method) model, described last year in the progress report and in a publication, to additional years to determine the source impact for 40 PM2.5 species from 20 source categories in the 36-km continental United States (CONUS) domain (Ivey, et al., GMD 2015).
- We developed a method to address biases in CMAQ-modeled secondary aerosol concentrations. This secondary correction involves a weighted bias distribution across source impacts, and the bias is determined by incorporating observed concentrations. After applying this method for 2006, source impacts from 20 sources on sulfate, nitrate, ammonium and secondary organic carbon were estimated for the 36-km CONUS domain (Ivey, et al., FESE 2016).
- We developed a method to assimilate observations and source impacts to improve PM2.5 source profiles for 20 source categories. This inverse modeling approach is applied over CSN monitors for the year 2006. The new source profiles take into account local variability, whereas traditional methods use one static profile for all locations (Ivey, et al., ITM 2015).
- We developed multipollutant CMAQ fields and RLINE fields for the 2013 period when CCAR (UW) was conducting their collaborative measurements in Atlanta. The CMAQ fields were provided to CCAR. Both teams have compared CCAR’s observations to our modeled results, as well as to our fixed site measurements (Zhai, et al., AAAR 2015).
Core C: Biostatistics Core
As part of and in addition to their participation in SCAPE research projects, members of the Biostatistics Core participated in a number of peer-reviewed publications and presentations relating to development and application of advanced epidemiologic and statistical methods. Core contributions include—
- Twelve new peer-reviewed publications were published, and four additional were accepted for publication; multiple presentations were given at national conferences (both presented by or supported by members of the Biostatistics Core).
- New analytic collaborations with the Air Quality Core regarding backcasting levels of DTT assay measures of air quality to extend the available study period for associations with health outcomes into the past to assess temporal trends in associations.
Collaborative Project 1: Characterization of Primary and Secondary Traffic-Related Particles (Collaborators: Harvard and SCAPE)
For exposures conducted during the collaboration, the ACSM was operated continuously. The goal was to provide complementary chemical data, including near real-time determination of the contribution of primary and secondary aerosols, as well as the extent of oxidation over the course of each experiment. An additional goal of the collaboration was to provide information about the atmosphere inside the photochemical chamber and how the secondary products relate to those found in the atmosphere. To do this, the ACSM was operated through the normal startup procedures of the photochemical chamber during the primary plus secondary organic aerosol (P+SOA) and SOA-only atmospheres, as well as with the primary tunnel particles (P), to evaluate the changes observed in aerosol evolution as the photochemical chamber output stabilized prior to exposures. During this portion of the study, additional measurements collocated with the ACSM were made, including integrated particle mass, EC/OC, and trace elemental concentrations, as well as continuous particle size distribution using a Scanning Mobility Particle Sizer (SMPS).
During animal exposures, measurements were made at the point of exposure, including integrated particle mass, EC/OC, and trace elemental concentrations, as well as continuous particle size distribution using a Scanning Mobility Particle Sizer (SMPS). During the hours where animals were not being exposed, a collocated SMPS along with the ACSM were operated, but the remaining chamber output was utilized for collecting samples for analysis for reactive oxygen species.
The data analysis has been completed. We are preparing a manuscript. Below is a summary of the main findings from this study:
- There are some variations in the organic aerosol loading in each exposure environment. The loadings are fairly constant for P only (9.4 µg/m3) and P+SOA (2.1 µg/m3). However, for the SOA-only atmosphere, there is a clear diurnal cycle in the organic aerosol loading (highest around midnight). The average organic loading for the SOA-only system is also the highest among the three systems, at 13 µg/m3.
- Results from Positive Matrix Factorization (PMF) analysis of each system reveal the presence of highly oxygenated organic aerosols (OOA) in all exposure environments. Surprisingly, even the P-only system contains a fair amount of OOA. This could result from the mixing of ambient air with the tunnel air, prior to entering the chamber. We resolve an aromatic-type OOA factor, which has not been resolved in prior studies. The high levels of aromatic hydrocarbons under these exposure environments and the lack of biogenic influences likely make the aromatic OOA more distinctive in this study. Hydrocarbon-like OA (HOA) is only resolved in the P-only system and P+SOA system, which is expected.
- The total DTT activity of the aerosols increases with the OC content of the sample.
- The intrinsic DTT activity of the aerosols formed in the three test environments are comparable. While the SOA system has the highest intrinsic DTT activity, it also has the largest variation, which could be a result of the more diverse source of SOA.
- The intrinsic DTT activity of traffic aerosols in this study is comparable to ambient PM and oxidized OOA in Atlanta.
- Results from this study clearly demonstrate that organic aerosols alone can generate reactive oxygen species. Many prior studies attributed DTT activity of ambient aerosols to metals. In this study, the SOA system does not contain any metals but still exhibits a similar level of DTT activity compared to the other two systems.
Collaborative Project 2: Mobile and Fixed-Site Characterization of Vehicle Emission Impacts in Atlanta (Collaborators: SCAPE, CCAR, and EPA)
We have been working with our CCAR collaborators to evaluate how well the ambient monitoring network and emissions-based regional scale (CMAQ chemical transport model) and local scale (RLINE dispersion model) compare with mobile platform measurements of air pollutant concentration gradients in metropolitan Atlanta. The CCAR mobile platform measurements taken in September 2013 included “fuzzy point” characterizations and three trips originating and ending at an Atlanta central monitor location (Jefferson Street, SEARCH monitor). One trip included a southeastern segment of the Atlanta perimeter highway from the airport to a CSN monitoring site. A second route included taking the I-75/I-85 connector to a large highway intersection (“spaghetti junction”) in northeastern Atlanta. A third route extended 60 km west to the rural SEARCH monitor at Yorkville.
Comparison of trip measurements with RLINE predictions and with roadside stationary measurements shows that RLINE estimates higher concentrations and near-road gradients, demonstrating the need for adjustment in the RLINE estimates. A poster presentation was given at the AAAR 2015 Annual Conference in Minneapolis, Minnesota in October 2015.
Collaborative Project 3: Inter-Comparison of Ambient PM2.5 Estimation Models in North Carolina (Collaborators: SCAPE, Harvard, CCAR, and EPA)
We have designed a modeling domain centered in North Carolina for the 2006–2008 time period. To facilitate model cross-comparison, a common input dataset was compiled by Liu group, including MODIS total Aerosol Optical Depth (AOD) values, derived meteorological parameters and, from the UW database, GIS-based spatial covariates. This was distributed to all participating research teams. A common master modeling grid at 10-km resolution was developed by the Liu group and shared by all teams. A set of common procedures and statistics will be jointly developed by all participating teams to evaluate model performance. After preliminary results are generated, each team will document their model development in sufficient detail for other teams to reproduce their results. The estimated deliverable of this project will be a manuscript to report evaluation results.
Given the latest method development reported in the literature, we decided not to pursue the cross-comparison paper based on the originally proposed models, as they are being replaced with more sophisticated models with better performance. Instead, we are working on a national Bayesian downscaler model to provide daily PM2.5 estimates across the CONUS. As of May 2016, model development has completed and a manuscript is in progress.
As of July 2016, the Georgia Tech team has applied the OBS-CMAQ fusion approach to North Carolina for comparison to the satellite retrieval-based results. Georgia Tech developed daily concentration fields of PM2.5 total mass, five major particulate species (OC, EC, SO42-, NO3-, and NH4+), and three gaseous pollutants (CO, NOx, NO2) over North Carolina for the years 2006–2008. The results show improvements in capturing spatial and temporal variability compared to observations, which is important for health and planning studies. Comparisons of PM2.5 total mass concentration estimates using the OBS-CMAQ fusion approach and MODIS-retrieved aerosol optical depth (AOD) show that the data-fusion fields have slightly less overall error (Huang, et al., ITM 2016; Huang, et al., CMAS 2016 abstract submitted). Georgia Tech also applied the IMSI method to the study area, which shows that the data fusion fields can be used to estimate mobile source impacts
Collaborative Project 4: Measurement Error for Air Pollution Cohort Studies: Application and Comparison of Several Statistical Methods to Georgia Birth Cohort Data (Collaborators: CCAR and SCAPE)
Emory investigators have successfully applied UW's spatiotemporal exposure model to predict ambient PM2.5 concentrations across the state of Georgia. The exposures were linked to individual birth records at the Census blockgroup level. Preliminary health analyses were conducted, and the results were discussed among collaborators across CLARC centers during the Annual Meeting. UW has replicated Emory's analysis and has applied the parameter bootstrap and the non-parametric bootstrap to assess bias and/or inflated standard errors from measurement error. A paper reporting these results was accepted for publication at Epidemiology (Keller, at al., 2016).
Future Activities:
Project 1
- Continue to analyze data, complete manuscripts currently in progress, and present results at meetings.
- Develop and verify method for measurement of total DTT on filter samples.
- Refine and re-test method for online DTT measurements.
Project 2
- Complete and submit draft manuscripts currently in preparation.
Project 3
- Finalize and publish the air quality models and epidemiologic results for the Kaiser Permanente cohort.
- Finalize and publish the Georgia-wide birth weight quantile regression paper.
Project 4
- Ensure currently submitted manuscripts are published.
- Submit 3-5 manuscripts currently in preparation and planned.
- Continue several analyses focused on assessment of mixtures and effect modification in the single- and multi-city context.
Core B: Air Quality Core
- Complete application of the CMAQ-CMB hybrid method to the entire 10 year period (2002-2012) to provide source-specific impact fields for the CONUS.
- Continue exploring the use of the CMAQ-CMB hybrid method for ozone source apportionment.
- Use the CMAQ-Hybrid results and the ROS observations, both using the DTT and AA assays from Project 1, to develop source specific ROS impacts. The resulting associations would then be useful to develop long term, national trends in ROS for use in an acute health association analysis.
- Finish the application of the OBS-CMAQ fusion approach to the NC area as part of the Harvard-GIT-EPA collaboration. Apply to PM2.5 total mass, five major particulate species (OC, EC, SO42-, NO3-, and NH4+), and three gaseous pollutants (CO, NOx, NO2). Use the EC, CO and NO2 results to estimate mobile source impacts using the IMSI approach. Conduct more thorough evaluation of the method and further comparison to the satellite-based approaches.
- Provide further support to Project 3 in terms of using CMAQ, satellite observations and RLINE to develop fine scale fields.
- Provide further support to Project 4 to estimate source impacts at the five SCAPE cities.
- Provide further support to Project 1 to analyze their observational results.
- Continue working with CCAR on the collaborative. We will apply RLINE at a fine scale to provide more detailed multipollutant data for comparison to their observations and to CMAQ.
Core C: Biostatistics Core
- Continue collaborations with investigators across SCAPE as the next generation of research projects builds on SCAPE success.
- Submit multiple publications building on SCAPE success that are currently in development.
- Continue current collaborations relating to environmental epidemiology and methodological research that form the basis of related projects, proposed next steps currently under review, and multiple projects in the formative proposal stage.
Collaborative Project 2
Data from 28 passive badges deployed during the intensive sampling will be evaluated to assess pollutant gradients and compare to RLINE estimated near-road gradients. The passive badge data were continuously collected from September 5, 2013, 11 am local time to September 18, 2013, 9 pm. The two-week measurements passive badge data will be used to help evaluate RLINE estimates.
Journal Articles: 136 Displayed | Download in RIS Format
| Other center views: | All 338 publications | 139 publications in selected types | All 135 journal articles |
|---|
| Type | Citation | ||
|---|---|---|---|
|
|
Abrams JY, Weber RJ, Klein M, Samat SE, Chang HH, Strickland MJ, Verma V, Fang T, Bates JT, Mulholland JA, Russell AG, Tolbert PE. Associations between ambient fine particulate oxidative potential and cardiorespiratory emergency department visits. Environmental Health Perspectives 2017;125(10):107008. |
R834799 (Final) |
|
|
|
Alhanti BA, Chang HH, Winquist A, Mulholland JA, Darrow LA, Sarnat SE. Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age. Journal of Exposure Science & Environmental Epidemiology 2016;26(2):180-188. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2015) R834799C004 (Final) R829213 (Final) |
Exit |
|
|
Balachandran S, Pachon JE, Hu Y, Lee D, Mulholland JA, Russell AG. Ensemble-trained source apportionment of fine particulate matter and method uncertainty analysis. Atmospheric Environment 2012;61:387-394. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R833866 (2012) R833866 (Final) |
Exit Exit Exit |
|
|
Balachandran S, Chang HH, Pachon JE, Holmes HA, Mulholland JA, Russell AG. Bayesian-based ensemble source apportionment of PM2.5. Environmental Science & Technology 2013;47(23):13511-13518. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R833866 (Final) |
Exit Exit Exit |
|
|
Balachandran S, Pachon JE, Lee S, Oakes MM, Rastogi N, Shi W, Tagaris E, Yan B, Davis A, Zhang X, Weber RJ, Mulholland JA, Bergin MH, Zheng M, Russell AG. Particulate and gas sampling of prescribed fires in South Georgia, USA. Atmospheric Environment 2013;81:125-135. |
R834799 (2015) R834799 (2016) R834799 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
Bates JT, Weber RJ, Abrams J, Verma V, Fang T, Klein M, Strickland MJ, Sarnat SE, Chang HH, Mulholland JA, Tolbert PE, Russell AG. Reactive oxygen species generation linked to sources of atmospheric particulate matter and cardiorespiratory effects. Environmental Science & Technology 2015;49(22):13605-13612. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (Final) R834799C003 (Final) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Baxter LK, Dionisio KL, Burke J, Sarnat SE, Sarnat JA, Hodas N, Rich DQ, Turpin BJ, Jones RR, Mannshardt E, Kumar N, Beevers SD, Ozkaynak H. Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):654-659. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R833865 (Final) |
Exit Exit Exit |
|
|
Bergin MH, Tripathi SN, Jai Devi J, Gupta T, Mckenzie M, Rana KS, Shafer MM, Villalobos AM, Schauer JJ. The discoloration of the Taj Mahal due to particulate carbon and dust deposition. Environmental Science & Technology 2015;49(2):808-812. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C002 (2015) R834799C002 (Final) R835039 (Final) |
Exit Exit Exit |
|
|
Boyd CM, Sanchez J, Xu L, Eugene AJ, Nah T, Tuet WY, Guzman MI, Ng NL. Secondary organic aerosol formation from the β-pinene+NO3 system: effect of humidity and peroxy radical fate. Atmospheric Chemistry and Physics 2015;15(13):7497-7522. |
R834799 (Final) R835403 (2014) R835403 (2015) R835403 (Final) |
Exit Exit Exit |
|
|
Brock CA, Wagner NL, Anderson BE, Attwood AR, Beyersdorf A, Campuzano-Jost P, Carlton AG, Day DA, Diskin GS, Gordon TD, Jimenez JL, Lack DA, Liao J, Markovic MZ, Middlebrook AM, Ng NL, Perring AE, Richardson MS, Schwarz JP, Washenfelder RA, Welti A, Xu L, Ziemba LD, Murphy DM. Aerosol optical properties in the southeastern United States in summer--Part 1: Hygroscopic growth. Atmospheric Chemistry and Physics 2016;16(8):4987-5007. |
R834799 (Final) |
Exit Exit |
|
|
Brown MS, Sarnat SE, DeMuth KA, Brown LAS, Whitlock DR, Brown SW, Tolbert PE, Fitzpatrick AM. Residential proximity to a major roadway is associated with features of asthma control in children. PLoS ONE 2012;7(5):e37044 ( pp.). |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C002 (2013) R834799C002 (2014) R834799C002 (2015) R834799C002 (Final) R834799C004 (2012) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit |
|
|
Budisulistiorini SH, Canagaratna MR, Croteau PL, Baumann K, Edgerton ES, Kollman MS, Ng NL, Verma V, Shaw SL, Knipping EM, Worsnop DR, Jayne JT, Weber RJ, Surratt JD. Intercomparison of an Aerosol Chemical Speciation Monitor (ACSM) with ambient fine aerosol measurements in downtown Atlanta, Georgia. Atmospheric Measurement Techniques 2014;7(7):1929-1941. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Chang HH, Reich BJ, Miranda ML. A spatial time-to-event approach for estimating associations between air pollution and preterm birth. Journal of the Royal Statistical Society--Series C (Applied Statistics) 2013;62(2):167-179. |
R834799 (2014) R834799 (2016) R834799 (Final) R834799C002 (2014) R834799C003 (2013) R834799C003 (2014) R833293 (2011) R833293 (2012) R833293 (Final) R833293C001 (2011) R833293C001 (Final) R833293C002 (2011) R833293C002 (Final) |
Exit Exit |
|
|
Chang HH, Hu X, Liu Y. Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling. Journal of Exposure Science & Environmental Epidemiology 2014;24(4):398-404. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Chang HH, Hao H, Sarnat SE. A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmospheric Environment 2014;89:290-297. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Chang HH, Warren JL, Darrow LA, Reich BJ, Waller LA. Assessment of critical exposure and outcome windows in time-to-event analysis with application to air pollution and preterm birth study. Biostatistics 2015;16(3):509-521. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Chen T, Sarnat SE, Grundstein AJ, Winquist A, Chang HH. Time-series analysis of heat waves and emergency department visits in Atlanta, 1993 to 2012. Environmental Health Perspectives 2017;125(5):057009 (9 pp.). |
R834799 (2016) R834799 (Final) R834799C004 (Final) R829213 (Final) |
|
|
|
Darrow LA, Hess J, Rogers CA, Tolbert PE, Klein M, Sarnat SE. Ambient pollen concentrations and emergency department visits for asthma and wheeze. Journal of Allergy and Clinical Immunology 2012;130(3):630-638. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2012) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Darrow LA, Klein M, Flanders WD, Mulholland JA, Tolbert PE, Strickland MJ. Air pollution and acute respiratory infections among children 0-4 years: an 18-year time-series study. American Journal of Epidemiology 2014;180(10):968-977. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Dionisio KL, Isakov V, Baxter LK, Sarnat JA, Sarnat SE, Burke J, Rosenbaum A, Graham SE, Cook R, Mulholland J, Ozkaynak H. Development and evaluation of alternative approaches for exposure assessment of multiple air pollutants in Atlanta, Georgia. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):581-592. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Dionisio KL, Baxter LK, Chang HH. An empirical assessment of exposure measurement error and effect attenuation in bipollutant epidemiologic models. Environmental Health Perspectives 2014;122(11):1216-1224. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
|
|
|
Dionisio KL, Chang HH, Baxter LK. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models. Environmental Health 2016;15(1):114 (10 pp.). |
R834799 (Final) |
Exit Exit |
|
|
Fang T, Verma V, Guo H, King LE, Edgerton ES, Weber RJ. A semi-automated system for quantifying the oxidative potential of ambient particles in aqueous extracts using the dithiothreitol (DTT) assay: results from the Southeastern Center for Air Pollution and Epidemiology (SCAPE). Atmospheric Measurement Techniques 2015;8(1):471-482. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Fang T, Guo H, Verma V, Peltier RE, Weber RJ. PM2.5 water-soluble elements in the southeastern United States: automated analytical method development, spatiotemporal distributions, source apportionment, and implications for heath studies. Atmospheric Chemistry and Physics 2015;15(20):11667-11682. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (Final) |
Exit Exit Exit |
|
|
Fang T, Verma V, Bates JT, Abrams J, Klein M, Strickland MJ, Sarnat SE, Chang HH, Mulholland JA, Tolbert PE, Russell AG, Weber RJ. Oxidative potential of ambient water-soluble PM2.5 in the southeastern United States: contrasts in sources and health associations between ascorbic acid (AA) and dithiothreitol (DTT) assays. Atmospheric Chemistry and Physics 2016;16(6):3865-3879. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (Final) R834799C003 (Final) R834799C004 (Final) |
Exit Exit Exit |
|
|
Fang T, Zeng L, Gao D, Verma V, Stefaniak AB, Weber RJ. Ambient size distributions and lung deposition of aerosol dithiothreitol-measured oxidative potential: Contrast between soluble and insoluble particles. Environmental Science & Technology 2017;51(12):6802-6811. |
R834799 (Final) |
Exit |
|
|
Fang T, Guo H, Zeng L, Verma V, Nenes A, Weber RJ. Highly acidic ambient particles, soluble metals, and oxidative potential: A link between sulfate and aerosol toxicity. Environmental Science & Technology 2017;51(5):2611-2620. |
R834799 (Final) |
Exit |
|
|
Flanders WD, Klein M, Darrow LA, Strickland MJ, Sarnat SE, Sarnat JA, Waller LA, Winquist A, Tolbert PE. A method for detection of residual confounding in time-series and other observational studies. Epidemiology 2011;22(1):59-67. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R833626 (Final) |
Exit |
|
|
Flanders WD, Klein M, Darrow LA, Strickland MJ, Sarnat SE, Sarnat JA, Waller LA, Winquist A, Tolbert PE. A method to detect residual confounding in spatial and other observational studies. Epidemiology 2011;22(6):823-826. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R833626 (Final) |
Exit |
|
|
Flanders WD, Klein M. A general, multivariate definition of causal effects in epidemiology. Epidemiology 2015;26(4):481-489. |
R834799 (2016) R834799 (Final) |
Exit |
|
|
Flanders WD, Klein M. Rejoinder. Epidemiology 2015;26(4):496-497. |
R834799 (2016) R834799 (Final) |
Exit |
|
|
Flanders WD, Klein M, Mirabelli MC. Conditions for valid estimation of causal effects on prevalence in cross-sectional and other studies. Annals of Epidemiology 2016;26(6):389-394.e2. |
R834799 (2016) R834799 (Final) |
Exit |
|
|
Flanders WD, Strickland MJ, Klein M. A new method for partial correction of residual confounding in time-series and other observational studies. American Journal of Epidemiology 2017;185(10):941-949. |
R834799 (2016) R834799 (Final) R834799C003 (Final) |
Exit |
|
|
Friberg MD, Zhai X, Holmes HA, Chang HH, Strickland MJ, Sarnat SE, Tolbert PE, Russell AG, Mulholland JA. Method for fusing observational data and chemical transport model simulations to estimate spatiotemporally resolved ambient air pollution. Environmental Science & Technology 2016;50(7):3695-3705. |
R834799 (2016) R834799 (Final) R834799C003 (Final) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Friberg MD, Kahn RA, Holmes HA, Chang HH, Sarnat SE, Tolbert PE, Russell AG, Mulholland JA. Daily ambient air pollution metrics for five cities: evaluation of data-fusion-based estimates and uncertainties. Atmospheric Environment 2017;158:36-50. |
R834799 (2016) R834799 (Final) R834799C004 (Final) |
Exit Exit Exit |
|
|
Gao D, Fang T, Verma V, Zeng L, Weber RJ. A method for measuring total aerosol oxidative potential (OP) with the dithiothreitol (DTT) assay and comparisons between an urban and roadside site of water-soluble and total OP. Atmospheric Measurement Techniques 2017;10(8):2821-2835. |
R834799 (Final) |
Exit Exit |
|
|
Gass K, Klein M, Chang HH, Flanders WD, Strickland MJ. Classification and regression trees for epidemiologic research: an air pollution example. Environmental Health 2014;13(1):17 (10 pp.). |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Gass K, Balachandran S, Chang HH, Russell AG, Strickland MJ. Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma. American Journal of Epidemiology 2015;181(7):504-512. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) R833866 (Final) |
Exit Exit |
|
|
Gass K, Klein M, Sarnat SE, Winquist A, Darrow LA, Flanders WD, Chang HH, Mulholland JA, Tolbert PE, Strickland MJ. Associations between ambient air pollutant mixtures and pediatric asthma emergency department visits in three cities: a classification and regression tree approach. Environmental Health 2015;14:58 (14 pp.). |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Golan R, Ladva C, Greenwald R, Krall JR, Raysoni AU, Kewada P, Winquist A, Flanders WD, Liang D-H, Sarnat JA. Acute pulmonary and inflammatory response in young adults following a scripted car commute. Air Quality, Atmosphere & Health 2018;11(2):123-136. |
R834799 (Final) |
Exit |
|
|
Goldman GT, Mulholland JA, Russell AG, Strickland MJ, Klein M, Waller LA, Tolbert PE. Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies. Environmental Health 2011;10:61 (11 pp.). |
R834799 (2011) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2011) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R829213 (Final) R833866 (Final) |
Exit Exit |
|
|
Goldman GT, Mulholland JA, Russell AG, Gass K, Strickland MJ, Tolbert PE. Characterization of ambient air pollution measurement error in a time-series health study using a geostatistical simulation approach. Atmospheric Environment 2012;57:101-108. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2012) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R829213 (Final) R833626 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
Greenwald R, Bergin MH, Yip F, Boehmer T, Kewada P, Shafer MM, Schauer JJ, Sarnat JA. On-roadway in-cabin exposure to particulate matter: measurement results using both continuous and time-integrated sampling approaches. Aerosol Science and Technology 2014;48(6):664-675. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C002 (2014) R834799C002 (2015) R834799C002 (Final) |
Exit Exit Exit |
|
|
Guo H, Xu L, Bougiatioti A, Cerully KM, Capps SL, Hite Jr. JR, Carlton AG, Lee S-H, Bergin MH, Ng NL, Nenes A, Weber RJ. Fine-particle water and pH in the southeastern United States. Atmospheric Chemistry and Physics 2015;15(9):5211-5228. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2015) R834799C001 (Final) R835041 (2015) R835041 (Final) R835410 (2013) R835410 (2014) R835410 (2015) R835410 (Final) |
Exit Exit Exit |
|
|
Hao H, Chang HH, Holmes HA, Mulholland JA, Klein M, Darrow LA, Strickland MJ. Air pollution and preterm birth in the U.S. state of Georgia (2002-2006): associations with concentrations of 11 ambient air pollutants estimated by combining Community Multiscale Air Quality Model (CMAQ) simulations with stationary monitor measurements. Environmental Health Perspectives 2016;124(6):875-880. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) |
|
|
|
Heidari L, Winquist A, Klein M, O’Lenick CR, Grundstein A, Sarnat SE. Susceptibility to heat-related fluid and electrolyte imbalance emergency department visits in Atlanta, Georgia, USA. International Journal of Environmental Research and Public Health 2016;13(10):982 (17 pp.). |
R834799 (2016) R834799 (Final) R834799C004 (Final) R829213 (Final) |
Exit Exit Exit |
|
|
Henneman LRF, Holmes HA, Mulholland JA, Russell AG. Meteorological detrending of primary and secondary pollutant concentrations: Method application and evaluation using long-term (2000–2012) data in Atlanta. Atmospheric Environment 2015;119(Suppl C):201-210. |
R834799 (Final) |
Exit Exit |
|
|
Hu X, Waller LA, Al-Hamdan MZ, Crosson WL, Estes Jr. MG, Estes SM, Quattrochi DA, Sarnat JA, Liu Y. Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression. Environmental Research 2013;121:1-10. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Hu X, Waller LA, Lyapustin A, Wang Y, Al-Hamdan MZ, Crosson WL, Estes Jr. MG, Estes SM, Quattrochi DA, Puttaswamy SJ, Liu Y. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sensing of Environment 2014;140:220-232. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Hu X, Waller LA, Lyapustin A, Wang Y, Liu Y. 10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data. Atmospheric Chemistry and Physics 2014;14(12):6301-6314. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit |
|
|
Hu X, Waller LA, Lyapustin A, Wang Y, Liu Y. Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S. Journal of Geophysical Research: Atmospheres 2014;119(19):11375-11386. |
R834799 (2015) R834799 (2016) R834799 (Final) |
Exit Exit |
|
|
Hu Y, Balachandran S, Pachon JE, Baek J, Ivey C, Holmes H, Odman MT, Mulholland JA, Russell AG. Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach. Atmospheric Chemistry and Physics 2014;14(11):5415-5431. |
R834799 (2015) R834799 (2016) R834799 (Final) |
Exit Exit |
|
|
Hu Y, Odman MT, Chang ME, Russell AG. Operational forecasting of source impacts for dynamic air quality management. Atmospheric Environment 2015;116:320-322. |
R834799 (2015) R834799 (2016) R834799 (Final) R833866 (Final) R835217 (2014) R835217 (Final) |
Exit Exit Exit |
|
|
Huang R, Zhai X, Ivey CE, Friberg MD, Hu X, Liu Y, Qian Di, Schwartz J, Mulholland JA, Russel AG. Air pollutant exposure field modeling using air quality model-data fusion methods and comparison with satellite AOD-derived fields: application over North Carolina, USA. Air Quality, Atmosphere & Health 2018;11(1):11-22. |
R834799 (Final) |
Exit |
|
|
Ivey CE, Holmes HA, Hu YT, Mulholland JA, Russell AG. Development of PM2.5 source impact spatial fields using a hybrid source apportionment air quality model. Geoscientific Model Development 2015;8(7):2153-2165. |
R834799 (2015) R834799 (2016) R834799 (Final) |
Exit Exit |
|
|
Ivey CE, Holmes HA, Hu Y, Mulholland JA, Russell AG. A method for quantifying bias in modeled concentrations and source impacts for secondary particulate matter. Frontiers of Environmental Science & Engineering 2016;10:14. |
R834799 (2016) R834799 (Final) |
Exit Exit |
|
|
Ivey C, Holmes H, Shi G, Balachandran S, Hu Y, Russell AG. Development of PM2.5 source profiles using a hybrid chemical transport-receptor modeling approach. Environmental Science & Technology 2017;51(23):13788-13796. |
R834799 (Final) R833626 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
Keller JP, Chang HH, Strickland MJ, Szpiro AA. Measurement error correction for predicted spatiotemporal air pollution exposures. Epidemiology 2017;28(3):338-345. |
R834799 (2016) R834799 (Final) R834799C003 (Final) R834796 (2016) R834796 (Final) |
Exit Exit |
|
|
Keller JP, Chang HH, Strickland MJ, Szpiro AA. Measurement error correction for predicted spatiotemporal air pollution exposures. Epidemiology 2017;28(3):338-345. |
R834799 (Final) R834796 (Final) |
Exit Exit |
|
|
Keller JP, Chang HH, Strickland MJ, Szpiro AA. Measurement error correction for predicted spatiotemporal air pollution exposures. Epidemiology 2017;28(3):338-345. |
R834799 (2016) R834799 (Final) R834799C003 (Final) R834796 (2016) R834796 (Final) |
Exit Exit |
|
|
King LE, Weber RJ. Development and testing of an online method to measure ambient fine particulate Reactive Oxygen Species (ROS) based on the 2’,7’-dichlorofluorescin (DCFH) assay. Atmospheric Measurement Techniques 2013;6(7):1647-1658. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2013) R834799C001 (2014) R834799C001 (2015) R834799C001 (Final) |
Exit Exit |
|
|
Krall JR, Chang HH, Sarnat SE, Peng RD, Waller LA. Current methods and challenges for epidemiological studies of the associations between chemical constituents of particulate matter and health. Current Environmental Health Reports 2015;2(4):388-398. |
R834799 (2016) R834799 (Final) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environmental Health Perspectives 2017;125(1):97-103. |
R834799 (2016) R834799 (Final) R834799C004 (2015) R834799C004 (Final) R829213 (Final) R833866 (Final) |
|
|
|
Krall Jenna R, Chandresh N, Ladva Armistead G, Russell Rachel Golan, Xing Peng, Guoliang Shi, Roby Greenwald, Amit U. Raysoni, Lance A. Waller, and Jeremy A. Sarnat. “Source-Specific Pollution Exposure and Associations with Pulmonary Response in the Atlanta Commuters Exposure Studies.” Journal of Exposure Science & Environmental Epidemiology 28, no. 4 (June 2018):337–47. |
R834799 (Final) |
Exit Exit |
|
|
Ladva CN, Golan R, Greenwald R, Yu T, Sarnat SE, Flanders WD, Uppal K, Walker DI, Tran V, Liang D, Jones DP, Sarnat JA. Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection. Journal of Breath Research 2017;12(1):016008. |
R834799 (Final) |
Exit |
|
|
Lawson AB. Commentary: Assessment of chance should be central in investigation of cancer clusters. International Journal of Epidemiology 2013;42(2):448-449. |
R834799 (Final) |
Exit Exit |
|
|
Li W, Xu L, Liu X, Zhang J, Lin Y, Yao X, Gao H, Zhang D, Chen J, Wang W, Harrison RM, Zhang X, Shao L, Fu P, Nenes A, Shi Z. Air pollution–aerosol interactions produce more bioavailable iron for ocean ecosystems. Science Advances 2017;3(3):e1601749 (7 pp.). |
R834799 (Final) |
Exit Exit |
|
|
Liang, Donghai, Rachel Golan, Jennifer L. Moutinho, Howard H. Chang, Roby Greenwald, Stefanie E. Sarnat, Armistead G. Russell, and Jeremy A. Sarnat. “Errors Associated with the Use of Roadside Monitoring in the Estimation of Acute Traffic Pollutant-Related Health Effects.” Environmental Research 165 (August 1, 2018):210–19. |
R834799 (Final) |
Exit Exit Exit |
|
|
Liu J, Bergin M, Guo H, King L, Kotra N, Edgerton E, Weber RJ. Size-resolved measurements of brown carbon in water and methanol extracts and estimates of their contribution to ambient fine-particle light absorption. Atmospheric Chemistry and Physics 2013;13(24):12389-12404. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2013) R834799C001 (2014) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Liu J, Scheuer E, Dibb J, Ziemba LD, Thornhill KL, Anderson BE, Wisthaler A, Mikoviny T, Devi JJ, Bergin M, Weber RJ. Brown carbon in the continental troposphere. Geophysical Research Letters 2014;41(6):2191-2195. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2014) R834799C001 (2015) R834799C001 (Final) R835039 (Final) |
Exit Exit Exit |
|
|
Lv B, Hu Y, Chang HH, Russell AG, Bai Y. Improving the accuracy of daily PM2.5 distributions derived from the fusion of ground-level measurements with aerosol optical depth observations, a case study in North China. Environmental Science & Technology 2016;50(9):4752-4759. |
R834799 (Final) R833866 (Final) R835217 (Final) |
Exit Exit Exit |
|
|
Ma Z, Hu X, Huang L, Bi J, Liu Y. Estimating ground-level PM2.5 in China using satellite remote sensing. Environmental Science & Technology 2014;48(13):7436-7444. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004-2013. Environmental Health Perspectives 2016;124(2):184-192. |
R834799 (2015) R834799 (2016) R834799 (Final) |
|
|
|
Maier ML, Balachandran S, Sarnat SE, Turner JR, Mulholland JA, Russell AG. Application of an ensemble-trained source apportionment approach at a site impacted by multiple point sources. Environmental Science & Technology 2013;47(8):3743-3751. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R833626 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
McGuinn LA, Ward-Caviness C, Neas LM, Schneider A, Di Q, Chudnovsky A, Schwartz J, Koutrakis P, Russell AG, Garcia V, Kraus WE, Hauser ER, Cascio W, Diaz-Sanchez D, Devlin RB. Fine particulate matter and cardiovascular disease: comparison of assessment methods for long-term exposure. Environmental Research 2017;159:16-23. |
R834799 (Final) R835872 (2016) |
Exit Exit Exit |
|
|
Mirabelli MC, Golan R, Greenwald R, Raysoni AU, Holguin F, Kewada P, Winquist A, Flanders WD, Sarnat JA. Modification of traffic-related respiratory response by asthma control in a population of car commuters. Epidemiology 2015;26(4):546-555. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C002 (2015) R834799C002 (Final) |
Exit |
|
|
Neelon B, Chang HH, Ling Q, Hastings NS. Spatiotemporal hurdle models for zero-inflated count data: exploring trends in emergency department visits. Statistical Methods in Medical Research 2014 [Epub ahead of print]. |
R834799 (2014) R834799 (2015) R834799 (2016) |
Exit |
|
|
Neelon B, Chang HH, Ling Q, Hastings NS. Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits. Statistical Methods in Medical Research 2016;25(6):2558-2576. |
R834799 (Final) |
Exit |
|
|
O'Lenick CR, Winquist A, Mulholland JA, Friberg MD, Chang HH, Kramer MR, Darrow LA, Sarnat SE. Assessment of neighbourhood-level socioeconomic status as a modifier of air pollution-asthma associations among children in Atlanta. Journal of Epidemiology and Community Health 2017;71(2):129-136. |
R834799 (2016) R834799 (Final) R834799C004 (Final) R829213 (Final) |
Exit |
|
|
O'Lenick CR, Winquist A, Chang HH, Kramer MR, Mulholland JA, Grundstein A, Sarnat SE. Evaluation of individual and area-level factors as modifiers of the association between warm-season temperature and pediatric asthma morbidity in Atlanta, GA. Environmental Research 2017;156:132-144. |
R834799 (2016) R834799 (Final) R834799C004 (Final) |
Exit Exit Exit |
|
|
O’Lenick CR, Chang HH, Kramer MR, Winquist A, Mulholland JA, Friberg MD, Sarnat SE. Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach. Environmental Health 2017;16(1):36 (15 pp.). |
R834799 (2016) R834799 (Final) R834799C004 (Final) R829213 (Final) |
Exit Exit Exit |
|
|
Pachon JE, Balachandran S, Hu Y, Mulholland JA, Darrow LA, Sarnat JA, Tolbert PE, Russell AG. Development of outcome-based, multipollutant mobile source indicators. Journal of the Air & Waste Management Association 2012;62(4):431-442. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R833626 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
Pachon JE, Weber RJ, Zhang X, Mulholland JA, Russell AG. Revising the use of potassium (K) in the source apportionment of PM2.5. Atmospheric Pollution Research 2013;4(1):14-21. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R833626 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
Park S-K, Russell AG. Regional adjustment of emission strengths via four dimensional data assimilation. Asia-Pacific Journal of Atmospheric Sciences 2013;49(3):361-374. |
R834799 (2015) R834799 (2016) R834799 (Final) R831076 (Final) |
Exit |
|
|
Park S-K, Marmur A, Russell AG. Environmental risk assessment: comparison of receptor and air quality models for source apportionment. Human and Ecological Risk Assessment 2013;19(5):1385-1403. |
R834799 (2015) R834799 (2016) R834799 (Final) R831076 (Final) |
Exit |
|
|
Pearce JL, Waller LA, Chang HH, Klein M, Mulholland JA, Sarnat JA, Sarnat SE, Strickland MJ, Tolbert PE. Using self-organizing maps to develop ambient air quality classifications: a time series example. Environmental Health 2014;13:56. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Pearce JL, Waller LA, Mulholland JA, Sarnat SE, Strickland MJ, Chang HH, Tolbert PE. Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications. Environmental Health 2015;14:55 (12 pp.). |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Pearce JL, Waller LA, Sarnat SE, Chang HH, Klein M, Mulholland JA, Tolbert PE. Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia. Spatial and Spatio-temporal Epidemiology 2016;18:13-23. |
R834799 (2016) R834799 (Final) R834799C004 (Final) |
Exit Exit Exit |
|
|
Pennington AF, Strickland MJ, Freedle KA, Klein M, Drews-Botsch C, Hansen C, Darrow LA. Evaluating early-life asthma definitions as a marker for subsequent asthma in an electronic medical record setting. Pediatric Allergy and Immunology 2016;27(6):591-596. |
R834799 (2016) R834799 (Final) |
Exit |
|
|
Pennington AF, Strickland MJ, Klein M, Zhai X, Russell AG, Hansen C, Darrow LA. Measurement error in mobile source air pollution exposure estimates due to residential mobility during pregnancy. Journal of Exposure Science and Environmental Epidemiology 2017;27(5):513-520. |
R834799 (2016) R834799 (Final) R834799C003 (Final) |
Exit |
|
|
Pennington AF, Strickland MJ, Klein M, Zhai X, Bates JT, Drews-Botsch C, Hansen C, Russell AG, Tolbert PE, Darrow LA. Exposure to mobile source air pollution in early-life and childhood asthma incidence: the Kaiser Air Pollution and Pediatric Asthma Study. Epidemiology 2018;29(1):22-30. |
R834799 (Final) |
Exit |
|
|
Pennington A, Strickland M, Gass K, Klein M, Sarnat S, Tolbert P, Balachandran S, Change H, Russel A, Mulholland J, Darrow L. Source-Apportioned PM2.5 and Cardiorespiratory Emergency Department Visits Accounting for Source Contribution Uncertainty. EPIDEMIOLOGY 2019;30(6):789-798. |
R834799 (Final) R833866 (Final) |
Exit |
|
|
Qin M, Hu Y, Wang X, Vasilakos P, Boyd CM, Xu L, Song Y, Ng NL, Nenes A, Russell AG. Modeling biogenic secondary organic aerosol (BSOA) formation from monoterpene reactions with NO3:a case study of the SOAS campaign using CMAQ. Atmospheric Environment 2018;184:146-155. |
R834799 (Final) R835403 (Final) |
Exit Exit Exit |
|
|
Redman JD, Holmes HA, Balachandran S, Maier ML, Zhai X, Ivey C, Digby K, Mulholland JA, Russell AG. Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment. Atmospheric Environment 2016;140:529-538. |
R834799 (2016) R834799 (Final) R833626 (Final) R833866 (Final) |
Exit Exit Exit |
|
|
Reich BJ, Chang HH, Strickland MJ. Spatial health effects analysis with uncertain residential locations. Statistical Methods in Medical Research 2014;23(2):156-168. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2012) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R833863 (2011) |
Exit Exit Exit |
|
|
Reich BJ, Chang HH, Foley KM. A spectral method for spatial downscaling. Biometrics 2014;70(4):932-942. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R835228 (2013) R835228 (2014) R835228 (Final) |
Exit Exit Exit |
|
|
Sarnat JA, Sarnat SE, Flanders WD, Chang HH, Mulholland J, Baxter L, Isakov V, Ozkaynak H. Spatiotemporally resolved air exchange rate as a modifier of acute air pollution-related morbidity in Atlanta. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):606-615. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C002 (2013) R834799C002 (2014) R834799C002 (2015) R834799C002 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit |
|
|
Sarnat JA, Golan R, Greenwald R, Raysoni AU, Kewada P, Winquist A, Sarnat SE, Flanders WD, Mirabelli MC, Zora JE, Bergin MH, Yip F. Exposure to traffic pollution, acute inflammation and autonomic response in a panel of car commuters. Environmental Research 2014;133:66-76. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C002 (2014) R834799C002 (2015) R834799C002 (Final) |
Exit Exit Exit |
|
|
Sarnat SE, Sarnat JA, Mulholland J, Isakov V, Ozkaynak H, Chang HH, Klein M, Tolbert PE. Application of alternative spatiotemporal metrics of ambient air pollution exposure in a time-series epidemiological study in Atlanta. Journal of Exposure Science & Environmental Epidemiology 2013;23(6):593-605. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit |
|
|
Sarnat SE, Winquist A, Schauer JJ, Turner JR, Sarnat JA. Fine particulate matter components and emergency department visits for cardiovascular and respiratory diseases in the St. Louis, Missouri-Illinois, metropolitan area. Environmental Health Perspectives 2015;123(5):437-444. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
|
|
|
Sarnat SE, Chang HH, Weber RJ. Ambient PM2.5 and health: does PM2.5 oxidative potential play a role? American Journal of Respiratory and Critical Care Medicine 2016;194(5):530-531. |
R834799 (2016) R834799 (Final) R834799C001 (Final) R834799C004 (Final) |
Exit |
|
|
Shi G, Xu J, Peng X, Xiao Z, Chen K, Tian Y, Guan X, Feng Y, Yu H, Nenes A, Russell AG. pH of aerosols in a polluted atmosphere:source contributions to highly acidic Aerosol. Environmental Science & Technology 2017;51(8):4289-4296. |
R834799 (Final) |
Exit |
|
|
Shi G, Peng X, Huangfu Y, Wang W, Xu J, Tian Y, Feng Y, Ivey CE, Russell AG. Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data. Atmospheric Environment 2017;160:89-96. |
R834799 (Final) |
Exit Exit |
|
|
Strickland MJ, Darrow LA, Mulholland JA, Klein M, Flanders WD, Winquist A, Tolbert PE. Implications of different approaches for characterizing ambient air pollutant concentrations within the urban airshed for time-series studies and health benefits analyses. Environmental Health 2011;10:36 (9 pp.). |
R834799 (2011) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2011) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) R829213 (Final) |
Exit Exit |
|
|
Strickland MJ, Marsh CA, Darrow LA. Gestational age-specific associations between infantile acute bronchiolitis and asthma after age five. Pediatric and Perinatal Epidemiology 2014;28(6):521-526. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) |
Exit |
|
|
Strickland MJ, Klein M, Flanders WD, Chang HH, Mulholland JA, Tolbert PE, Darrow LA. Modification of the effect of ambient air pollution on pediatric asthma emergency visits: susceptible subpopulations. Epidemiology 2014;25(6):843-850. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit |
|
|
Strickland MJ, Gass KM, Goldman GT, Mulholland JA. Effects of ambient air pollution measurement error on health effect estimates in time-series studies: a simulation-based analysis. Journal of Exposure Science & Environmental Epidemiology 2015;25(2):160-166. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit |
|
|
Strickland MJ, Hao H, Hu X, Chang HH, Darrow LA, Liu Y. Pediatric emergency visits and short-term changes in PM2.5 concentrations in the U.S. state of Georgia. Environmental Health Perspectives 2016;124(5):690-696. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2015) R834799C003 (Final) |
|
|
|
Strickland MJ, Lin Y, Darrow LA, Warren JL, Mulholland JA, Chang HH. Associations between ambient air pollutant concentrations and birth weight:A quantile regression analysis.Epidemiology 2019;30:624. |
R834799 (Final) |
Exit |
|
|
Tuet WY, Fok S, Verma V, Tagle Rodriguez MS, Grosberg A, Champion JA, Ng NL. Dose-dependent intracellular reactive oxygen and nitrogen species (ROS/RNS) production from particulate matter exposure: comparison to oxidative potential and chemical composition. Atmospheric Environment 2016;144:335-344. |
R834799 (Final) |
Exit Exit |
|
|
Verma V, Rico-Martinez R, Kotra N, King L, Liu J, Snell TW, Weber RJ. Contribution of water-soluble and insoluble components and their hydrophobic/hydrophilic subfractions to the reactive oxygen species-generating potential of fine ambient aerosols. Environmental Science & Technology 2012;46(20):11384-11392. |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2012) R834799C001 (2013) R834799C001 (2014) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Verma V, Rico-Martinez R, Kotra N, Rennolds C, Liu J, Snell TW, Weber RJ. Estimating the toxicity of ambient fine aerosols using freshwater rotifer Brachionus calyciflorus (Rotifera: Monogononta). Environmental Pollution 2013;182:379-384. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2013) R834799C001 (2014) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Verma V, Fang T, Guo H, King L, Bates JT, Peltier RE, Edgerton E, Russell AG, Weber RJ. Reactive oxygen species associated with water-soluble PM2.5 in the southeastern United States: spatiotemporal trends and source apportionment. Atmospheric Chemistry and Physics 2014;14(23):12915-12930. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Verma V, Fang T, Xu L, Peltier RE, Russell AG, Ng NL, Weber RJ. Organic aerosols associated with the generation of reactive oxygen species (ROS) by water-soluble PM2.5. Environmental Science & Technology 2015;49(7):4646-4656. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2015) R834799C001 (Final) |
Exit Exit Exit |
|
|
Verma V, Wang Y, El-Afifi R, Fang T, Rowland J, Russell AG, Weber RJ. Fractionating ambient humic-like substances (HULIS) for their reactive oxygen species activity—assessing the importance of quinones and atmospheric aging. Atmospheric Environment 2015;120:351-359. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (Final) |
Exit Exit Exit |
|
|
Vreeland H, Weber R, Bergin M, Greenwald R, Golan R, Russell AG, Verma V, Sarnat JA. Oxidative potential of PM2.5 during Atlanta rush hour: Measurements of in-vehicle dithiothreitol (DTT) activity. Atmospheric Environment 2017;165:169-178. |
R834799 (Final) |
Exit Exit |
|
|
Waller LA. Commentary: regarding assessments of chance in investigations of ‘cluster series.’ International Journal of Epidemiology 2013;42(2):449-452. |
R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2013) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Weber RJ, Guo H, Russell AG, Nenes A. High aerosol acidity despite declining atmospheric sulfate concentrations over the past 15 years. Nature Geoscience 2016;9:282-285. |
R834799 (2016) R834799 (Final) R834799C001 (Final) R835410 (2013) R835410 (2014) R835410 (2015) R835410 (Final) |
Exit Exit |
|
|
Winquist A, Klein M, Tolbert P, Sarnat SE. Power estimation using simulations for air pollution time-series studies. Environmental Health 2012;11:68 (12 pp.). |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2012) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Winquist A, Klein M, Tolbert P, Flanders WD, Hess J, Sarnat SE. Comparison of emergency department and hospital admissions data for air pollution time-series studies. Environmental Health 2012;11:70 (14 pp.). |
R834799 (2012) R834799 (2013) R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2012) R834799C004 (2013) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit Exit |
|
|
Winquist A, Kirrane E, Klein M, Strickland M, Darrow LA, Sarnat SE, Gass K, Mulholland J, Russell A, Tolbert P. Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004. Epidemiology 2014;25(5):666-673. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit |
|
|
Winquist A, Schauer JJ, Turner JR, Klein M, Sarnat SE. Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity. Journal of Exposure Science & Environmental Epidemiology 2015;25(2):215-221. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C004 (2014) R834799C004 (2015) R834799C004 (Final) |
Exit Exit |
|
|
Winquist A, Grundstein A, Chang HH, Hess J, Sarnat SE. Warm season temperature and emergency department visits in Atlanta, Georgia. Environmental Research 2016;147:314-323. |
R834799 (2016) R834799 (Final) R834799C004 (Final) |
Exit Exit Exit |
|
|
Xiao Q, Ma Z, Li S, Liu Y. The impact of winter heating on air pollution in China. PLoS One 2015;10(1):e0117311 (11 pp.). |
R834799 (Final) |
Exit Exit |
|
|
Xiao Q, Liu Y, Mulholland JA, Russell AG, Darrow LA, Tolbert PE, Strickland MJ. Pediatric emergency department visits and ambient air pollution in the U.S. state of Georgia: a case-crossover study. Environmental Health 2016;15(1):115. |
R834799 (2016) R834799 (Final) R834799C003 (Final) |
Exit |
|
|
Xu L, Guo H, Boyd CM, Klein M, Bougiatioti A, Cerully KM, Hite JR, Isaacman-VanWertz G, Kreisberg NM, Knote C, Olson K, Koss A, Goldstein AH, Hering SV, de Gouw JA, Baumann K, Lee S-H, Nenes A, Weber RJ, Ng NL. Effects of anthropogenic emissions on aerosol formation from isoprene and monoterpenes in the southeastern United States. Proceedings of the National Academy of Sciences of the United States of America 2015;112(1):37-42. |
R834799 (2015) R834799 (2016) R834799 (Final) R834799C001 (2015) R834799C001 (Final) R835403 (2014) R835403 (2015) R835403 (Final) R835410 (2013) R835410 (2014) R835410 (2015) R835410 (Final) |
Exit Exit Exit |
|
|
Xu L, Suresh S, Guo H, Weber RJ, Ng NL. Aerosol characterization over the southeastern United States using high resolution aerosol mass spectrometry: spatial and seasonal variation of aerosol composition, sources, and organic nitrates. Atmospheric Chemistry and Physics 2015;15(15):7307-7336. |
R834799 (2015) R834799 (2016) R834799 (Final) |
Exit Exit Exit |
|
|
Ye D, Klein M, Chang HH, Sarnat JA, Mulholland JA, Edgerton ES, Winquist A, Tolbert PE, Sarnat SE. Estimating acute cardiorespiratory effects of ambient volatile organic compounds. Epidemiology 2017;28(2):197-206. |
R834799 (2016) R834799 (Final) R834799C004 (Final) |
Exit |
|
|
Ye D, Klein M, Mulholland JA, Russell AG, Weber R, Edgerton ES, Chang HH, Sarnat JA, Tolbert PE, Sarnat SE. Estimating acute cardiovascular effects of ambient PM2.5 metals. Environmental Health Perspectives 2018;126:027007 (10 pp.). |
R834799 (Final) R829213 (Final) |
|
|
|
Yu C, Di Girolamo L, Chen L, Zhang X, Liu Y. Statistical evaluation of the feasibility of satellite-retrieved cloud parameters as indicators of PM2.5 levels. Journal of Exposure Science & Environmental Epidemiology 2015;25(5):457-466. |
R834799 (2014) R834799 (2015) R834799 (2016) R834799 (Final) R834799C003 (2014) R834799C003 (2015) R834799C003 (Final) |
Exit Exit Exit |
|
|
Yu W, Liu Y, Ma Z, Bi J. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting. Scientific Reports 2017;7(1):7048 (9 pp.). |
R834799 (Final) |
Exit Exit |
|
|
Zhai X, Russell AG, Sampath P, Mulholland JA, Kim B-U, Kim Y, D'Onofrio D. Calibrating R-LINE model results with observational data to develop annual mobile source air pollutant fields at fine spatial resolution: Application in Atlanta. Atmospheric Environment 2016;147:446-457. |
R834799 (Final) |
Exit Exit |
|
|
Zhai X, Mulholland JA, Russell AG, Holmes HA. Spatial and temporal source apportionment of PM2.5 in Georgia, 2002 to 2013. Atmospheric Environment 2017;161:112-121. |
R834799 (Final) |
Exit Exit |
|
|
Zhang T, Gong W, Wang W, Ji Y, Zhu Z, Huang Y. Ground level PM(2.5) estimates over China using satellite-based geographically weighted regression (GWR) models are improved by including NO(2) and enhanced vegetation index (EVI). International Journal of Environmental Research and Public Health 2016;13(12):E1215 (12 pp). |
R834799 (Final) |
Exit Exit |
|
|
Zhang W, Trail MA, Hu Y, Nenes A, Russell AG. Use of high-order sensitivity analysis and reduced-form modeling to quantify uncertainty in particulate matter simulations in the presence of uncertain emissions rates: A case study in Houston. Atmospheric Environment 2015;122:103-113. |
R834799 (Final) |
Exit Exit |
|
|
Zhang Z, Manjourides J, Cohen T, Hu Y, Jiang Q. Spatial measurement errors in the field of spatial epidemiology. International Journal of Health Geographics 2016;15(1):21 (12 pp.). |
R834799 (Final) |
Exit Exit |
Supplemental Keywords:
aerosol, air quality, air quality modeling, ambient air, atmosphere, biostatistics, chemical transport modeling, children, confounder control, cumulative effects, data analysis, dose-response, elemental carbon, epidemiology, exposure, exposure measurement error, GA, Georgia, health effects, human health, infants, inflammation, measurement methods, metals, mobile sources, monitoring, organics, oxidants, oxidative potential, oxidative stress, ozone, PAH, PAHs, particulates, PM2.5, public policy, reactive oxygen species, receptor modeling, risk, ROS, sensitive populations, source characterization, Southeast, study design, sulfates, susceptibility, vulnerability, Health, Scientific Discipline, Health Risk Assessment, Risk Assessments, Biochemistry, children's health, particulate matter, ambient air monitoring, climate change, air pollution, airshed modeling, ambient particle health effects, human health riskRelevant Websites:
Southeastern Center for Air Pollution & Epidemiology Emory/Georgia Tech EPA Clean Air Research Center Exit
Progress and Final Reports:
Original Abstract Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R834799C001 Development and Deployment of an Instrumentation Suite for Comprehensive Air Quality Characterization Including Aerosol ROS
R834799C002 Examining In-Vehicle Pollution and Oxidative Stress in a Cohort of Daily Commuters
R834799C003 Novel Estimates of Pollutant Mixtures and Pediatric Health in Two Birth Cohorts
R834799C004 A Multi-City Time-Series Study of Pollutant Mixtures and Acute Morbidity
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
- Final Report
- 2015 Progress Report
- 2014 Progress Report
- 2013 Progress Report
- 2012 Progress Report
- 2011 Progress Report
- Original Abstract
135 journal articles for this center