2019 Progress Report: Regional Air Pollution Mixtures: The past and future impacts of emissions controls and climate change on air quality and health

EPA Grant Number: R835872
Center: Regional Air Pollution Mixtures
Center Director: Koutrakis, Petros
Title: Regional Air Pollution Mixtures: The past and future impacts of emissions controls and climate change on air quality and health
Investigators: Koutrakis, Petros , Coull, Brent , Jacob, Daniel J. , Mickley, Loretta J. , Schwartz, Joel , Zanobetti, Antonella , Zigler, Corwin , Dominici, Francesca , Barrett, Steven , Selin, Noelle Eckley , Solomon, Scott , Reilly, Joseph
Current Investigators: Koutrakis, Petros , Schwartz, Joel , Coull, Brent , Dominici, Francesca , Selin, Noelle Eckley , Mickley, Loretta J. , Zanobetti, Antonella , Barrett, Steven , Solomon, Susan , Reilly, John , Zigler, Cory
Institution: Harvard University , Massachusetts Institute of Technology
Current Institution: Harvard University , Massachusetts Institute of Technology , The University of Texas at Austin
EPA Project Officer:
Project Period: December 1, 2015 through November 30, 2020 (Extended to November 30, 2022)
Project Period Covered by this Report: December 1, 2018 through November 30,2019
Project Amount: $10,000,000
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text |  Recipients Lists
Research Category: Air , Climate Change , Air Quality and Air Toxics , Social Science , Airborne Particulate Matter Health Effects , Air Toxics , Human Health

Objective:

Project 1: Regional Air Pollution: Mixtures Characterization, Emission Inventories, Pollutant Trends, and Climate Impacts

The overall objective of Project 1 is to apply new approaches to characterize and analyze both historical and projected regional air pollution mixtures and emissions across the continental U.S. Project 1 characterizes temporal and spatial patterns of pollutant mixtures within and across regions. In addition, this project investigates factors influencing regional pollutant mixtures and predicts the impact of climate change on future air quality. Project 1 has four specific objectives.

Objective 1 is to compile comprehensive air pollution, weather, emissions, and GIS datasets for the entire continental US for the period 2000-2015. We will estimate gas and particle concentrations at a high spatial resolution by assimilating data from monitoring networks (compiled in collaboration with the Air Pollution Core), satellite platforms, air pollution models, and spatiotemporal statistical models. Objective 2 is to develop and make publically available a national PM2.5 emission inventory database of high spatial resolution (1 km) for 2000-2015. This will be achieved through the application of a novel methodology we developed that predicts point and area source emissions using aerosol optical thickness measured by satellite remote sensors. Objective 3 is to characterize spatial and temporal trends of pollutant mixtures. We will perform cluster analysis to group areas that exhibit distinct pollutant profiles or mixtures, referred to as “Air Pollution Regions,” then analyze their spatial patterns and temporal trends to investigate the impact of regulations, climate change, and modifiable factors on regional mixtures. Objective 4 is to forecast the impact of regional climate change on air quality for 2016-2040 using an ensemble of climate models. We will project the potential impact of climate change on regional pollutant mixtures and predict future regional air quality assuming no changes in anthropogenic emissions. We have added a fifth research objective to this Project, as previously explained in the Year 3 ACE Center Annual Report. Objective 5 is to investigate particle radioactivity exposures and their health effects. We will compile a large database on PM radioactivity exposures, including gross beta, gamma, and radon exposures by zip code, along with other parameters influencing exposures to PM radioactivity. We will also develop analysis techniques to measure gross alpha and beta activities from retrospective Teflon filter samples. Using this exposure database, we will investigate the health effects of PM radioactivity with existing outcome data from previous studies and ongoing cohorts.

 

Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate, and Particle-induced Mortality

The objective of Project 2 is to characterize historical air pollution in Eastern Massachusetts at a high spatial resolution and identify modifiable factors responsible for observed changes in PM2.5 mass, emissions, elemental profiles, and ground air temperature. Project 2 investigates within-region variability of pollutant mixtures; examines the impact of modifiable factors on air quality; and evaluates the effectiveness of source control policies. Project 2 has four specific objectives.

Objective 1 is to use a novel, multi-resolution spatial analysis based on wavelet decomposition of high-resolution (1x1 km) remote sensing data on PM2.5 mass and ground air temperature to identify daily regional, sub-regional (urban background) and locally generated variation in these fields. Objective 2 is to develop and apply spatiotemporal regression models to (a) quantify the impact of modifiable factors, including transportation, heating fuel use, energy, urban planning, PM2.5 emissions, population statistics, and policy interventions, on (i) sub-regional and local variation in PM2.5 mass and ground air temperature and (ii) high resolution local estimates of PM2.5 emissions; (b) identify locations in which these impacts are greatest; and (c) identify lag times between implementation of a given control strategy and decreases in PM2.5 emissions and mass. Objective 3 is to implement a novel multi-resolution correlation analysis to identify PM2.5 elemental profiles that vary at regional, sub-regional, and local scales, and apply spatiotemporal regression models to these profiles to identify modifiable factors driving urban background and local variability in PM2.5 composition. And Objective 4 is to use the spatial scale-specific (regional, sub-regional, and local) temporal variability in PM2.5 mass and the PM2.5 elemental profiles to identify source types (regional, urban background, or local) and the composition of their emissions driving pollution-induced mortality in Eastern Massachusetts. This project relies on existing remote-sensing satellite data, ambient monitoring data collected from numerous sampling campaigns (including the HSPH Boston Supersite daily samples collected since 1998 and samples from 600 locations), as well as new data collected from 2015-2018 in Eastern Massachusetts.

 

Project 3: Causal Estimates of Effects of Regional and National Pollution Mixtures on Health: Providing Tools for Policy Makers

The objective of Project 3 is to estimate the causal impact of changes in pollution concentrations and mixtures (annual averages and daily patterns), how they vary by modifiable factors, the causal impacts of AQI triggers, how climate change that occurred in the last 20 years has increased mortality due to pollution, how temperature modifies the effects of pollution mixtures, and how these effects change for exposures less than the ambient standards for PM2.5. Project 3 provides region-specific causal estimates of effects of pollution mixtures; provides causal estimates of the impact of modifiable factors; assesses the impact of climate change on mortality from air pollution using historic data, avoiding any dependence on the accuracy of climate models; and provides causal estimates of how changes in particular components of mixtures affect mortality, to guide region-specific policy decisions on air pollution. Project 3 has five specific objectives.

Objective 1 is to identify and estimate the causal effects of air pollution and mixtures on human health. We will use methods of causal inference to a) identify the causal effects of regional annual air pollution concentration fluctuations and temperature fluctuations during the last 16 years on human health; b) identify the causal effects of regional air pollution trends during the last 16 years; c) identify the causal effects of pollution mixtures, sources, and emissions on health; d) identify differences in these effects by modifiable factors; e) conduct a national risk assessment on the causal impact of past pollution on mortality, including the regional differences in concentration-response; and f) investigate the causal impact of AQI thresholds for PM2.5 and O3 due to behavioral adaptation. Objective 2 is to analyze relative acute toxicity of pollution mixtures. We will a) examine spatial (across regions) and temporal heterogeneity in the acute toxicity of pollution mixtures and emissions to understand which source types, atmospheric processes, and exposure factors influence the toxicity of regional mixtures and b) use causal mediation analysis to determine how much of the temperature effect on mortality is mediated by its effects of pollution concentrations, and how that varies regionally. This will allow us to obtain local- and region specific estimates of future health effects and the benefits of changes in modifiable factors and adaptation. Objective 3 is to estimate the excess deaths resulting from air pollutant concentration changes due to weather changes in the last 20 years. We will demonstrate the extent to which public health impacts of climate change through pollution have already occurred, by using causal estimates of C-R relationships. Regional health impacts will be assessed using region-specific mortality risks estimates from Objective 1. Objective 4 is to estimate the causal health effects of low-level air pollution exposure. Specifically, we will examine whether the observed effects at low pollutant levels are due to the synergistic effect of multiple pollutants (mixtures) present at low levels. And Objective 5 is to investigate air pollution-related health effects at high and low temperatures. We will examine this by region and determine whether populations, especially those that include sensitive individuals, adapt to abrupt temperature changes.

 

Project 4: A Causal Inference Framework to Support Policy Decisions by Evaluating the Effectiveness of Past Air Pollution Control Strategies for the Entire United States

The overall objective of Project 4 is to develop a new methodological framework rooted in principles of causal inference to investigate the effectiveness of specific control strategies on impacting the largest power-generating units in the United States. In Project 4, we combine state-of-the-art atmospheric modeling, causal inference methods, and national data sets to conduct accountability research; that is, research that characterizes causal effects of well-defined regulatory actions at power plants on: 1) emissions; 2) air quality across distant locations in accordance with atmospheric fate, transport, and other factors; and 3) health outcomes. Project 4 has 4 specific objectives.

Objective 1 is to develop a national database on emissions control technologies employed at a large number of power-generating units in the US linked with:  continuous emissions monitoring, ambient air quality monitoring, weather, population demographics, and Medicare hospitalization and mortality outcomes for the period 1995 to 2015. Objective 2 is to estimate and compare the causal effects of past control strategies implemented at the largest power-generating facilities on SO2, NOx, CO2, and PM2.5 emissions and population exposure to criteria pollutants (PM2.5 and O3) for the entire US for the period 2000 to 2015. This requires integrating new statistical methods for causal inference with atmospheric chemistry models of how changes in emissions impact ambient exposures across distant locations in accordance with atmospheric fate, transport, and other factors. Objective 3 is to estimate the causal effects of past control strategies implemented at the largest power-generating facilities on mortality and morbidity in the entire US both locally and nationally, and compare the differential health impact of different control strategies. And Objective 4 is to develop approaches for mediation analysis that will quantify the extent to which causal effects of regulatory actions on health outcomes can be attributable to changes in targeted modifiable factors (e.g., emissions, targeted pollutants), as opposed to being driven by co-benefits to other factors.

 

Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts

Project 5 investigates future changes in regional air pollution characteristics as a result of technological and societal changes. We will quantify the future implications of technologies and efficiency improvements in the energy and transportation sectors on regional differences in air pollution impacts. Selected case studies assess, inter alia, the environmental and health benefits of choices in state and regional carbon policy implementation relevant to recently proposed carbon dioxide emission reductions from the energy sector. We will examine the health-related benefits of reducing concentrations of ozone and particulate matter, as well as changing regional air pollution mixtures including air toxics.

Progress Summary:

Project 1: Regional Air Pollution: Mixtures Characterization, Emission Inventories, Pollutant Trends, and Climate Impacts

Due to the difficulties encountered and reported in the Year 3 Project report, we have suspended work on Objective 2. These difficulties, including effect of wind direction fields, surface reflection, the spatial uncertainty of grid locations, and lack of success in validating the PEIRS result with NEI point data, were found to be insurmountable.

During Year 4, we finalized and published several papers supporting Objective 3. Preliminary results from some of these studies were included in Year 3’s annual report. In Silvern et al. (2019), we showed that the steady 2005-2017 decrease in NOx emissions reported by the NEI is consistent with (1) observed network trends of surface NO2 and ozone concentrations and (2) satellite-derived trends of NO2 over urban areas. GEOS-Chem reproduced these results, confirming our understanding of the role of emissions in driving NOx and ozone. The NEI reports a steady decrease in U.S. NOx emissions over the 2005–2017 period at a rate of 0.1 Tg N a-1 (53 % decrease over the period), reflecting sustained efforts to improve air quality. Tropospheric NO2 columns observed by the satellite-based Ozone Monitoring Instrument (OMI) over the U.S. show a steady decrease until 2009 but a flattening afterward, which has been attributed to a flattening of NOx emissions, contradicting the NEI. We show here that the steady 2005–2017 decrease in NOx emissions reported by the NEI is in fact largely consistent with observed network trends of surface NO2 and ozone concentrations. The OMI NO2 trend is instead similar to that observed for nitrate wet deposition fluxes, which is weaker than that for anthropogenic NOx emissions, due to a large and increasing relative contribution of non-anthropogenic background sources of NOx (mainly lightning and soils). This is confirmed by contrasting OMI NO2 trends in urban winter, where the background is low and OMI NO2 shows a 2005–2017 decrease consistent with the NEI, and rural summer, where the background is high and OMI NO2 shows no significant 2005–2017 trend. A GEOS-Chem model simulation driven by NEI emission trends for the 2005–2017 period reproduces these different trends, except for the post-2009 flattening of OMI NO2, which we attribute to a model underestimate of free tropospheric NO2. Better understanding is needed of the factors controlling free tropospheric NO2 in order to relate satellite observations of tropospheric NO2 columns to the underlying NOx emissions and their trends. Focusing on urban winter conditions in the satellite data minimizes the effect of this free tropospheric background.

The next was an investigation of spatial differences in air pollution mixtures across the U.S. (Requia 2019d). The study described an innovative approach to classify regions in the U.S. based on the clusters of air pollutant mixtures. There was a strong influence of the relationship between regional and local pollution in clustering air pollution mixture. Sites that were close spatially were assigned to different clusters. When categorized based on land use, sites do not necessarily have the same land use class within a cluster. These results may be related to whether the cluster profile was influenced by regional pollution versus local pollution. The concentration measured at specific air pollution monitoring stations have differing amounts of measurement error, depending on the spatial heterogeneity of a given pollutant across the study region. This approach can benefit researchers, policy makers, and local communities to create future strategies related to air pollution and environmental health. It supports more targeted and regional air quality management practices by minimizing within-region variability and maximizing between-regional variability of regional mixture profiles. This is based on the concept that regions with similar pollutant mixtures are impacted by similar air pollution sources and atmospheric processes. We expect that further investigations can use our findings to analyze the relationship between areas that exhibit distinct pollutant mixtures and the impact of regulations, climate change, and health effects in the U.S.

Another paper (Requia 2019f) contributes to the understanding on the impacts of weather on PM2.5 composition. Ambient PM2.5 components are strongly linked to weather variables such as temperature, relative humidity, and wind conditions. Weather changes over the last 30 years were associated with increased ambient concentration of most PM2.5 components in the U.S. This impact was defined in our study as weather penalty. The direction and the magnitude of the weather penalty varied considerably over the region and season. During the warm season, we found an overall national positive weather penalty on EC, OC, nitrate, sulfate, ammonium, and silicon and a negative weather penalty for sodium during the study period. In the cold season, the estimated overall national penalty compared to the warm season penalty was the same for EC, OC, and sodium (negative); increased for nitrate and silicon; significantly decreased for sulfate; and negative for ammonium. Taken together, the weather penalty on some of the more toxic particulate pollutants highlight potential future challenges of regulating land use and air pollution sources. The evidence of historical weather penalty should be of interest to policy makers to devise future strategies related to environmental health and climate change, given that the weather trends observed in our study period are projected by climate prediction models to continue in the future.

Weather trends-related air pollution at regional scale may underestimate or overestimate the weather impact on air pollution, given that the regional scale does not capture the site-to-site variation of air pollution and weather. To addresses this gap, we quantified weather-associated changes in air pollution at site location (air pollution sites) in the U.S. between 1988 and 2018 (Requia et al. 2020). We quantified the past weather-related increases CO, NO2, O3, nitrate, organic carbon, silicon, sodium, sulfate, and SO2 concentration using Generalized Additive Models (GAMs) using a framework that derives “penalties” (weather penalty, in µg/m3, ppm or ppb per year) for each season (warm and cold). Our findings show significant spatiotemporal variation of climate impact on CO, NO2, and O3, but not for nitrate, OC, silicon, sodium, sulfate and SO2. In the warm season we estimated a total penalty over the study period for the sites with the highest penalty on CO (site in Boise, Idaho), NO2 (site in New York City), and O3 (site in Tucson, Arizona) of 6.18 (95%CI: 0.30; 12.0) ppm, 182.04 (95%CI: 39.33; 324.72) ppb, and 0.09 (95%CI: 0.030; 0.150) ppm, respectively. In the cold season, the estimated total penalty for the sites with the highest penalty on CO (site in Los Angeles, California), NO2 (site in Washington, Pennsylvania), and O3 (site in Decatur, Illinois) was 12.01 (95%CI: 1.50; 22.50) ppm, 285.03 (95%CI: 14.37; 555.69) ppb, and -0.066 (95%CI: -0.120; -0.030) ppm, respectively. Climate models typically show large variation in projections of the key variables influencing pollution, and our model based on local scale allowed us to identify statistically robust results. Our results should be of interest to policy makers to create future strategies related to environmental health and climate change.

As anthropogenic PM2.5 concentrations decline over much of the United States, smoke from wildfires becomes relatively more important as a contributor to pollution. Estimates of wildfire smoke emissions, however, vary greatly in magnitude. In Liu et al. (accepted), we closely examined reasons for the differences among inventories and made recommendations for the choice of inventory for different regions. Many global inventories use satellite measurements of active fires and/or burned area from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, differences across inventories in the interpretation of satellite imagery, the emissions factors assumed for different components of smoke, and the adjustments made for small and obscured fires can result in large regional differences in fire emissions estimates across inventories. Using Google Earth Engine, we leverage 15 years (2003-2017) of MODIS observations and 6 years (2012-2017) of observations from the higher spatial resolution Visible Imaging Infrared Radiometer Suite (VIIRS) sensor to develop metrics to quantify five major sources of spatial bias or uncertainty in the inventories: (1) primary reliance on active fires versus burned area, (2) cloud/haze burden on the ability of satellites to “see” fires, (3) fragmentation of burned area, (4) roughness in topography, and (5) small fires, which are challenging to detect. Based on all these uncertainties, we devise comprehensive “relative fire confidence scores,” mapped globally at 0.25° x 0.25° spatial resolution over 2003-2017.  Finally, we developed an online app called FIRECAM for end-users of global fire emissions inventories. The app diagnoses differences in emissions among the five inventories and gauges the relative uncertainty associated with satellite-observed fires on a regional basis.

During Year 4, our work on Objective 4 has continued. Our previous studies for EPA-ACE mainly focused on the impacts of a changing climate on ozone and anthropogenic PM. Relatively few studies, however, have focused on the potentially profound effects of climate change on emissions of dust and wildfire smoke, especially in the western United States.  A robust result across climate models is a shift toward drier, more arid conditions across the West in coming decades, and such a trend could increase the incidence and severity of dust storms in this region. In addition, the frequency and extent of wildfires are projected to increase in future decades, with consequences for smoke air quality. In year 4 of EPA-ACE, we began work linking a dynamic vegetation model to GEOS-Chem to gauge the effects of climate change on vegetation, dust, and wildfire in the western U.S. The vegetation model, Lund-Potsdam-Jena-Lausanne-Mainz (LPJ-LMfire), calculates the response of land cover and area burned to changing climate over the 2000-2100 timeframe for two Representative Concentration Pathways (RCPs). We feed these projections into GEOS-Chem, and then calculate dust and smoke emissions for three 5-year time windows across the 21st century. Preliminary results show increasing dust concentrations in the desert Southwest by 2100, with implications for human health. We also find increasing wildfire smoke across the West, especially in the heavily forested regions in the worst-case climate scenario, RCP8.5.

Our research on Objective 5, particle radioactivity exposures and their health effects, continued to grow during Year 4. This is an emerging field that has involved investigators from Project 1 and other center projects. Our productivity is reflected by the 8 accepted papers on this topic we have included in our publication list. In addition, we have submitted several additional manuscripts for publication. More importantly, we have compiled a large database on gross beta, gamma, PM radioactivity, radon exposures by zip code, along with other parameters influencing exposures to PM radioactivity. These data have become available to our ACE Center investigators and other groups outside our university and our Center. Finally, we have developed sampling and analysis techniques to measure gross alpha and beta activities from retrospective and prospective Teflon filter samples. 

Furthermore, we have compiled a large database which encompasses information on conventional and unconventional gas and oil wells, particle radioactivity and speciation data linked to well locations. This made possible to examine the impacts of fracking on particle gross beta activities and speciation.

 

Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate, and Particle-induced Mortality

This past year we finalized and published several manuscripts analyzing data PM2.5 composition in Eastern Massachusetts. These include:

Understanding the factors that affect spatial differences in PM2.5 composition is crucial for implementing emissions control and health policies. Although previous studies have explored modeling of spatial patterns as a tool to improve human exposure assessment, little work has employed a multivariate clustering approach to identify spatial patterns in particle composition. In this study, we used this approach to assess the spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts in the United States.  We considered air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 elemental components of ambient PM2.5.  The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in: 2 clusters for Fe, Zn, V, and Ni; 3 clusters; and for 12 clusters for K. Overall, our findings suggest substantial variation of clusters among PM2.5 components. In addition, land use, population density, and daily traffic were used as variables to more effectively characterize clusters of sites. This study improved the ability to model both the between- and within-area variability of source emissions and pollution regime, using concentrations of PM2.5 components. This work appeared in Environmental Pollution.

In a second paper, we attempted to systematically compare exposure estimates from different PM2.5 constituent models. We compared the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The forest model presented the best performance, with R2 values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. The results presented here can be useful for the environmental health community to more accurately estimate PM2.5 constituents over space. This work was published in Environmental Research.

In a third paper in this series, we proposed that areas representing clusters of PM2.5 elements are potential predictor variables to be included in spatial models for particle composition. The inclusion of these clusters may minimize the exposure misclassification. We evaluated the influence of spatial patterning on modeling of 10 components of ambient PM2.5, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. This study was performed in three stages. First, we applied a hybrid approach (combination of Empirical Bayesian Kriging and land use regression) to estimate spatial variability for each one of the 10 components of ambient PM2.5. In the second stage, we applied the same hybrid approach adding clusters of each PM2.5 component to the set of predictor variables.  Finally, in the last stage, we compared the estimates obtained from the model without clusters (first stage) and the model with clusters (second stage). Overall, our findings suggest significant influence of spatial clusters on modeling some PM2.5 components. We observed that the clusters may affect the error of the prediction values and especially the proportion of explained variance for most of the PM2.5 constituents evaluated in this study.  This work appeared in Science in the Total Environment.

Several other papers were published on related methodological issues of Project 2. These include:

A common study design for analyzing data on the short-term effects of ambient particulate matter (PM) on the risk of acute events is the case-crossover design, in which exposure at a time when a patient experiences an event is compared to exposure at times when the patient did not experience an event within a case-control paradigm.  However, the time of true event onset may precede hospitalization by hours or days, which can yield attenuated effect estimates.  We developed and compared several methods that correct for this bias. All considered approaches require validation data on the distribution of the delay times.   We compared the performance of the approaches in realistic scenarios via simulation, and apply the methods to analyze data from a Boston-area study of the association between ambient air pollution and acute stroke onset.  We concluded that a two-stage regression calibration estimator is an effective method for correcting bias in health effect estimates arising from misclassification of event onset times in a case-crossover study. This work appeared in the biostatistics journal Biometrics.

In the last few years, several research teams have developed distinct spatio-temporal models (exposure models) to predict ambient air pollution exposures of study participants even in areas where air pollution monitors are sparse. A significant limitation, however, is that these health effect estimates and their statistical uncertainty are based on the very strong assumption that a single exposure model is correct.   We developed novel methodology to (1) integrate information across existing air pollution prediction models in an ensemble that weighs each model by its predictive accuracy, differently across space and time; (2) for the first time, comprehensively quantify both intra- and inter-model uncertainty associated with ambient air pollution exposures; and (3) propagate the estimated uncertainty into health effect estimates in nationwide studies. The approach relies on a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty. We show that our method achieves accurate uncertainty estimates under complex observational noise, and illustrate its real-world utility in terms of uncertainty decomposition and model bias detection for an ensemble in predict air pollution exposures in Eastern Massachusetts, USA. This work appeared in Advances in Neural Information Processing Systems (NeurIPS), a highly regarded, competitive peer-reviewed computer science proceedings.

Interest remains high on estimating health risks associated with air pollution mixtures.  However, there are very few methods to estimate critical windows of prenatal exposure for a mixture of air pollutants in children’s health research. Project 2 investigators developed a multi-pollutant distributed lag model. The approach allows for estimation of the health risks of an entire air pollution mixture, and how this varies across pregnancy in pre-birth cohorts.  We applied the data to estimate the association between estimated weekly residential nitrate, OC, EC, and sulfate and birthweight in the Boston-area ACCESS pre-birth cohort. We submitted a manuscript (available at https://arxiv.org/abs/1904.12417) that has been invited for revision at the Annals of Applied Statistics.   

In a related set of papers, we applied another distributed lag method in children’s pre-birth cohort studies previously developed by Project investigators (Wilson et al. Biostatistics 2017) that estimates effect modification of a distributed lag function by subject characteristics in pre-birth cohort studies.  Rosa et al. (2019) applied the methods in the PROGRESS cohort in Mexico City to estimate effect modification of prenatal air pollution exposure on telomere length in cord blood by fetal sex. Lee et al. (2020, revision invited to Environmental Research) evaluated the same outcome in the Boston- and New York-area PRISM cohort, and identified effect modification of prenatal air pollution exposure by fetal sex and maternal antioxidant intake. 

On-going work currently being written up for publication:

We used a spatial decomposition method previously developed by Project investigators (Antonelli et al. 2017) to decompose daily surfaces of PM2.5 to identify which scales of pollution are most associated with adverse health outcomes. We used these spatial scale-specific decompositions of PM2.5 mass that we developed in Objective 1 to identify source types (regional, urban background, or local) and how these pollution source types are associated with mortality, both in terms of chronic and acute effects, in New England.  We have applied the decomposition methods developed in the work conducted as part of Objective 1 of this project to daily 1x1km grid values of PM2.5 from 2000-2015, merged the resulting spatially decomposed values to mortality data from the New England region from the same time period, and ran Poisson log-linear models to quantify the association between each daily and yearly exposures to regional and local PM2.5 contributions and zip-code level mortality counts.  We are currently writing up a manuscript describing the results to be submitted for publication.

The National Academies of Science concluded that a multi-pollutant regulatory approach that takes into account the joint effects of multiple pollution constituents is likely to be more protective of human health. Unfortunately, the large majority of existing research focuses on health effects of air pollution either one pollutant at a time or on the effects of one pollutant while controlling for the independent effects of a small number of co-pollutants. Limitations in existing statistical methodology is at least partially responsible for this gap in the literature, particularly in large, nation-wide studies. We developed algorithmitic advances to speed up one popular approach for quantifying multi-pollutant effects, Bayesian Kernel Machine Regression (BKMR), allowing us to expand the scope of this approach from small cohort studies to big administrative data and ultimately, national studies on particle effects.  We have shown through simulations that the proposed sketching algorithm reduces the computational complexity of BKMR hundred-fold.  We then used it to examine multiple pollutants and birthweight in over 900,000 births in Massachusetts and found independent significant linear decreases in birthweight with PM2.5 and black carbon, and increases with greenness. No effect was seen for sulfate or nitrate. This work is being written up for publication.

 

Project 3: Causal Estimates of Effects of Regional and National Pollution Mixtures on Health: Providing Tools for Policy Makers

We have used causal modeling to explore the acute effects of air pollution on hospital admissions for acute myocardial infarction, congestive heart failure, and ischemic stroke in the Medicare population of New England. Unlike traditional time series, this analysis looked at exposures on the zip code level across cities, towns, and rural areas, not just in major cities. We fit distributed lag models, and for each lag, fit a propensity score that controlled for all other lags of that pollutant and all lags of the other pollutant (we studied PM2.5 and O3) as well as nonlinear temperature terms, humidity, and controlled for season and day of the week by matching in a case-crossover design. This provides unambiguous estimates of the effects at each lag, unlike constrained distributed lag models. We found significant causal effects of PM2.5 on all three outcomes, but no effect of ozone on these cardiovascular outcomes. Blacks, diabetics, women, and people with COPD were more susceptible to the PM2.5 effects (Qui, 2020).

  • In Shtein et al. (2020) we extended our ensemble averaging approach to include other learners in the ensemble and used it to fit a model predicting PM2.5 and PM10 on a 1km grid over Italy. This demonstrates the generalizability of the approach.  It will be used to fit causal models using Italian data.

  • In Wei et al. (2020) we used an agnostic approach to determine whether PM2.5 was associated with admissions for outcome not traditionally examined. We looked at 214 mutually exclusive diagnostic groups defined by CDC, corrected for multiple comparisons, and found that PM2.5 was associated with several additional outcomes, including septicemia, fluid and electrolyte disorders, and renal failure.

  • In Rhee et al. (2019) we examined modifiers of PM2.5 effects on birthweight and found that the association of PM2.5 during the second trimester with reduced birthweight was stronger among non-Hispanic Black mothers.

  • In Di et al. (2019) we expanded our model for PM2.5 in the contiguous U.S. to incorporate three machine learners, additional variables, and additional years. We improved out of sample R2 (CV R2 for annual means =0.89).

  • In Di et al. (2020) we fit a similar model nationwide for daily NO2 concentrations across the U.S. for 2000-2019. This model is now being used in our epidemiology analyses.

  • In Fong 2019 we demonstrated that the association of PM2.5 with low birthweight was stronger at the tail of the distribution of birthweights.

  • In Danesh-Yazdi et al. (2019) we examined the effects of annual average exposure to PM2.5 and NO2 on hospital admissions for MI, Stroke, Pneumonia, CHF, COPD, and Lung Cancer in the Southeastern US, and found significant associations.

  • In Lee et al. (2019) we reported an association between long term exposure to PM2.5 and dementia hospital admissions.

  • In Gaskins et al. (2019) we demonstrated that increases in PM2.5, NO2, and BC were associated with increased odds of failed IVF treatment. In a follow-up study (also 2019) we reported that folate intake provided protection against the effects of NO2, but not PM2.5 or BC. 

  • In Yitshak-Sade et al. (2019) we applied a causal modeling approach, differences in differences, to examine PM2.5 and mortality in the Northeast and Mid-Atlantic states, and found a significant effect.

  • In Wei et al. (2019) we found that seasonal average temperature was associated with risk of hospital admissions of persons with dementia.

  • In Rice et al. (2019), we reported that EC, but not PM2.5 or O3, was associated with interstitial lung abnormalities and progression of interstitial lung disease.  

In addition, we have examined the novel hypothesis that particle radiation may be responsible for adverse health outcomes. This has included a study of geomagnetic storms and mortality in U.S. cities by Vieira et al. (2019), an association of particle radioactivity and mortality among Medicare participants reported in Yitshak-Sade et al. (2019), and an association of particle radioactivity and renal function in the Normative Aging Study reported in Gao et al. (2019). In Nyhan et al. (2019) we reported an association between particle radioactivity and lung function in the elderly.

 

Project 4: A Causal Inference Framework to Support Policy Decisions by Evaluating the Effectiveness of Past Air Pollution Control Strategies for the Entire United States

Year 4 showed continued success towards three objectives that span Objectives 2, 3, and 4 of the project. First, following the final publication of papers in Atmospheric Environment and Epidemiology related to the use of our HyADS reduced-complexity dispersion model, we have pursued several publications documenting a variety of use cases related to modeling changes in population exposure to point source power plant emissions, including disentangling emissions impacts from wind field impacts following energy transitions and comparison against more complex atmospheric models (e.g., GEOS-Chem adjoint).  Second, we have published several papers on causal inference methodology, in particular some that relate to causal inference with interference and causal mediation analysis, all in the context of evaluating impacts of emissions from or interventions on power plants. Signature publications on bipartite causal inference with interference and mediation analysis have appeared in Statistical Science and Journal of the Royal Statistical Society.  Third, we have conducted several epidemiological studies using newly developed methods and the HyADS reduced-complexity model, notably one that is under revision at Nature Energy that evaluates a natural experiment related to coal power plant retirements and scrubber installation near Louisville, KY. 

Project 4 investigators have also used work on this project to support publications pertaining specifically to recent regulatory review processes at EPA, including commentaries in Science and an op-ed in Bloomberg.  All efforts are augmented by more general epidemiological studies and statistical methods development (focusing on causal inference) relating to the analysis of air pollution exposure and health.

 

Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts

Work on Project 5 during the reporting year has continued in pursuit of four main goals: two carried over from the previous year, and two new. The first has been to wrap up the cross-state air pollution project under project Objectives 2 and 4. This work quantified health impacts resulting from pollution which crosses state boundaries, and has now (as of February 12th 2020) been published in the journal Nature (Dedoussi et al, 2020). We have also worked to disseminate this research, including through several presentations.

Data produced from this work has now been used in two follow-on studies. In the first, we have quantified air quality impacts from the energy sector per tonne of CO2 emitted, providing the co-pollutant cost of carbon (CPCC). This work has been published in the journal Environmental Research Letters (Dedoussi et al. 2019). A second study, this time in collaboration with Inês Azevedo (an investigator in the EPA Center for Air, Climate, and Energy Solutions), quantifies air quality trade-offs associated with the implementation of carbon pricing. All simulation work for this study has now been completed and the manuscript is in preparation.

Broader work has continued to quantify the role that changes in the mix and magnitude of anthropogenic emissions has affected and will continue to affect local air quality. This work was published in the journal Environmental Research Letters (Dimanchev et al. 2019).

The second goal of year 4 was to advance work on quantifying the time-of-emergence of air quality impacts over the 21st century (Objectives 1 and 5). This work relies on a combination of two models. First is the Community Atmospheric Model (CAM) v3.1, to produce meteorological data. This data is then fed to the GEOS-Chem High Performance (GCHP) chemistry-transport model, enabling simulations of air quality over a 120-year period.

Work in project year 4 was focused on completing the CAM simulation ensemble, and verifying that GCHP simulations driven by CAM provided realistic results. A comprehensive “benchmarking” process comparing results from “GCHP-CAM” to results from running GCHP with input data from the NASA MERRA-2 reanalysis dataset has now been completed, showing reasonable agreement between the two models. However, this process took longer than expected due to computational difficulties which arose on the “svante” computing cluster at MIT. Updates in the GCHP core architecture (the NASA MAPL infrastructure) made the existing multi-node (OpenMPI v3) libraries on svante obsolete, but the newer (OpenMPI v4) libraries were not supported by MAPL. Diagnosing this issue, and incorporating fixes to MAPL which enabled GCHP to function again on svante, involved significant diagnosis and debugging, delaying the project for several months.

With GCHP-CAM now once again operational, we have been able to re-start simulations of 21st century air quality. Preliminary results were disseminated in an oral presentation at the 100th American Meteorological Society Meeting in Boston in early 2020. This has included initial application of the methodology (identified during year 3) for quantifying the “time of emergence” of air quality impacts due to meteorological factors (Objective 5).

A third goal of this year has been to leverage previous work on offsets of energy emissions by wind power. We have used data collected earlier in the project to estimate the effects of renewable policies on air quality and human health in U.S, using a state-of-the-art chemical transport model (GEOS-Chem). Through this we were able to quantify the relative effectiveness of renewable policy as an approach to reducing air pollution. We found that wind power has significantly decreased emissions of CO2, sulfur dioxide, and nitrogen oxides, resulting in an estimated $867 million in avoided air quality damages and $891 million in avoided climate damages. We presented these results at the 100th American Meteorological Society Meeting in Boston in early 2020, and a manuscript is now in preparation. The long-term goal is to provide insights into the question of whether existing and planned renewable policy is targeting the optimal set of power plants in terms of abatement costs and environmental benefits (Objectives 2 and 3).

Finally, we have started work to quantify air quality tradeoffs and benefits associated with recent state and federal environmental legislation. Our project quantifying air quality tradeoffs and benefits associated with recent state and federal environmental legislation is focused specifically on nuclear power plant shutdowns in the United States. Our identified policies are a theoretical limit of immediate shutdown of nuclear power plants, slow phase-outs that are in line with license expirations, and extension of licenses to continue beyond operation time. In order to implement these scenarios, we have collaborated with UC Davis on the development of an energy grid optimization model which can estimate the emission inventories for each scenario. The optimization model is in its final stage of validation, comparing results to those provided in the EPA’s National Emissions Inventory. Electrification of long-haul freight was also identified as an underexplored area of research, but one with the potential for significant impacts on regional air pollution. We have used an efficient grid dispatch model implemented in Python to estimate how different electrification scenarios could change in emissions of CO2 and pollutant precursors under current and future grids. We have also begun work on identifying plausible electrification options for a limited number of major freight corridors, including the possibility of both conventional electric vehicles and/or “catenary” approaches.

Future Activities:

Project 1: Regional Air Pollution: Mixtures Characterization, Emission Inventories, Pollutant Trends, and Climate Impacts

For Objective 4, we will continue the work recently begun with Irene Dedoussi at MIT, which is the effect of climate change on the concentrations of soil particles (traced as Si, Al, Ca, Fe etc.). Vegetation influences air quality through its impact on emissions, transport and fate of pollutants. We will examine the relationship between vegetation and fine particulate matter (PM2.5) trace element concentrations in the Southwest U.S., by combining air quality station data and remotely sensed (satellite) vegetation data for the period 2000-2015. Generalized additive models (GAMs) will be applied to quantify the effect of long-term vegetation trends on trace element concentrations.

During Year 5, we will also continue our efforts to investigate the influence that climate change will exert on surface levels of particulate matter (PM), with consequences for human health. Previously, we have focused on the impacts of a changing climate on ozone and anthropogenic PM (e.g., Shen et al., 2017a, b). During the next years of the Center, we will focus on the potentially profound effects of climate change on emissions of dust and wildfire smoke during the period of 2000-2100, especially in the western U.S. A robust result across climate models is a shift toward drier, more arid conditions across the West in coming decades, and such a trend could increase the incidence and severity of dust storms in this region. In addition, the frequency and extent of wildfires are projected to increase in future decades, with consequences for smoke air quality. In Year 5 of the ACE Center, we will continue work linking a dynamic vegetation model to GEOS-Chem to gauge the effects of climate change on vegetation, dust, and wildfire in the western U.S. The vegetation model, Lund-Potsdam-Jena-Lausanne-Mainz (LPJ-LMfire), calculates the response of land cover and area burned to changing climate over the 2000-2100 timeframe for two Representative Concentration Pathways (RCPs). We will feed these projections into GEOS-Chem, and then calculate dust and smoke emissions for three 5-year time windows across the 21st century. Preliminary results show increasing dust concentrations in the desert Southwest by 2100, with implications for human health. We also find increasing wildfire smoke across the West, especially in the heavily forested regions in the worst-case climate scenario, RCP8.5.

During the next year, we will also continue our work on all facets of Objective 5. This will include:

  • Continuing to measure particle gross alpha and beta activities from already collected filters sample from our Harvard supersite and indoor studies. This will make it possible to examine spatiotemporal patterns of ambient particle radioactivity levels and indoor outdoor relationships.

  • Developing spatiotemporal models using data learning machines that incorporate RadNet concentrations, meteorological parameters and air pollution data to assess PM radioactivity exposures in Massachusetts. These predictions will be used by other Center projects to assess the effects of PM radioactivity exposures on mortality and perinatal, cardiovascular and respiratory health. 

  • Investigating and acquiring additional exposure data. For example, we have received over half a million indoor radon measurements conducted in the basements of Massachusetts homes. We will use the large data set to identify predictors of radon indoor exposures. In addition, we will make available these data to investigate the effects of radon as we presented above for particle radioactivity.

 

Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate, and Particle-induced Mortality

We will continue to focus on the areas of research most strongly recommended by the Center’s Scientific Advisory Council last May: that of further improving characterization of uncertainty associated with predictions from model ensembles, and how to propagate this uncertainty through to health effect estimates. 

We will continue to focus on the papers that are in preparation and in revision for submission to journals in statistics and environmental health sciences: the paper describing the mortality analysis of Objective 4, and the fast Bayesian Kernel Machine Regression for nation-wide health effects analyses of administrative data.

 

Project 3: Causal Estimates of Effects of Regional and National Pollution Mixtures on Health: Providing Tools for Policy Makers

We plan on continuing our modeling efforts to include ozone, where a manuscript is in preparation, and particle components, and extending our causal modeling approaches to include national quasi-experimental designs and propensity score-based approaches, including the novel doubly robust additive hazard model we developed earlier under this grant. This will include both hospital admissions and mortality. We also plan on examining asthma and air pollution in the Medicaid population. We will continue examination of the effects of radioactivity on particles on health.

Project 4: A Causal Inference Framework to Support Policy Decisions by Evaluating the Effectiveness of Past Air Pollution Control Strategies for the Entire United States

During year 5 we will continue development along all lines described above.  One major objective related to HyADS is pursuing improved ways to re-scale HyADS output to physical, interpretable, units.  This is being explored with both traditional linear regression methods and machine learning methods (e.g., gradient boosted machines) to calibrate HyADS output to output such as that derived from a CMAQ-DDM hybrid product.  This is expected to improve HyADS in its own right as a reduced complexity model for coal-emissions-derived PM, but is also expected to be important for its deployment in epidemiological studies.

We expect to continue collaborating with researchers in Project 5 of the center to investigate the effectiveness of state-level climate and energy policies that focus on the power generating sector, with Dr. Zigler now serving on the dissertation committee of one of Dr. Selin’s Ph.D. students. Statistical methods development for bipartite causal inference with interference has been to date confined to rough approximations of atmospheric transport relying, for example, on mathematical simplifications related to clusters of power plants.  Year 5 will continue to pursue more realistic interference structures, both specified with tools such as HyADS or estimated with statistical and machine learning methods based on, for example, community detection.  Efforts to improve the open access and usability of our databases and the HyADS reduced-complexity model are ongoing, including continued iterations of publicly-available software packages. 

 

Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts

Our first goal for project year 5 is to complete our long-planned ensemble of air quality simulations using the “GCHP-CAM” modeling chain. This project has suffered from multiple setbacks due to computational issues, resulting in significant delays. However, we have now demonstrated (and presented at AMS) prototype results, showing that the system can be used to estimate the “time of emergence” of a climate change-driven ozone signal. We therefore expect to use the coming year to complete our first full air quality simulation ensemble for the 1980-2100 period, followed by analysis of the time of emergence for air quality impacts using the methodology established in year 3 and tested in year 4 (Objectives 1 and 5). This includes not only the time of emergence for ozone, which we have already begun quantifying, but also particulate matter, which GEOS-Chem is well adapted for. A key component of this research activity will be to modify the computational approach to ensure consistent treatment of methane. The same ensemble will be used to quantify changes in anticipated exposure over the 21st century, including quantification of uncertainty within and between climate scenarios (Objectives 4 and 5). We will also aim to perform a statistical assessment which determines whether a hypothetical monitoring station at different locations of the planet could demonstrate an earlier “time of emergence” for climate-driven ozone change if changes by regressing observed ozone trends against those for specific humidity, or other relevant meteorological parameters.

A second goal is to continue to quantify air quality tradeoffs and benefits associated with modifiable strategies. In one related project, we will produce a first assessment of the impacts of nuclear power retirement on air quality in the US. Once validation through the GEOS-Chem nested model is completed, we plan to implement our emission inventories in the updated GEOS-Chem high performance model, enabling shorter simulation times. We will perform sensitivity studies to determine the impact of small changes in emissions, and will identify which areas are most negatively affected. We will complete both the modeling and assessment of air quality impacts by monetizing and comparing them to expected economic costs and benefits of the legislation. This will be completed at a ~30km scale across the contiguous United States, and therefore we may focus in on certain regions if they have particularly unique or large changes in air quality. Work during this year will focus on the short term, with a maximum five-year outlook, while longer term projections will be assessed later on. Over the coming year we also expect to estimate grid and on-road emissions associated with freight corridor electrification scenarios. This will include an estimate of emissions under a “no-electrification” scenario. Initial work will focus on the current-day electricity grid, but we also aim to expand the scope to include projections of future electricity grids. These emissions will be generated at a one-hour temporal resolution and allocated to a fine (~20-50 km resolution) spatial grid across the contiguous United States. Once available, we will simulate the effects of these emissions scenarios on US air quality using the GEOS-Chem regional air quality model. Changes in air quality and climate-relevant pollutants will be calculated in terms of avoided or incurred economic impacts, to enable quantitative analysis of tradeoffs. Although not a focus of the task, we will also perform a low-fidelity estimate of infrastructure development costs for each scenario in order to give appropriate context.

A third goal which remains from last year is to intensify our collaboration with project 1. This was delayed due to the aforementioned computational issues, but should be viable in the coming year. We will first compare estimates for 2000-2015 using CAM meteorology with estimates from project 1’s simulations using the high-resolution MERRA-2 reanalysis meteorology. Since the MERRA-2 reanalysis dataset assimilates observed meteorological data, this comparison will allow us to quantify errors in temporal and spatial variability present in the CAM dataset, and how these will affect our long-term estimates of spatial and temporal variability over the full 100-year simulation period. Simulated PM2.5 concentrations for the same period will also be compared with the monitor data collected under project 1. This will allow bias quantification for the estimates of future PM concentration under the existing CAM scenarios. These cross-project collaborations will improve outcomes for Objectives 1, 4, and 5.

Finally, we expect to complete and submit for review manuscripts based on the project investigating air quality and CO2 trade-offs of carbon taxation. We anticipate no significant additional timeline adjustments.


Journal Articles: 202 Displayed | Download in RIS Format

Other center views: All 223 publications 203 publications in selected types All 202 journal articles
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Journal Article Abu Awad Y, Koutrakis P, Coull BA, Schwartz J. A spatio-temporal prediction model based on support vector machine regression: ambient black carbon in three New England states. Environmental Research 2017;159:427-434. R835872 (2016)
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  • Journal Article Achilleos S, Kioumourtzoglou M-A, Wu C-D, Schwartz JD, Koutrakis P, Papatheodorou SI. Acute effects of fine particulate matter constituents on mortality: a systematic review and meta-regression analysis. Environment International 2017;109:89-100. R835872 (2016)
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  • Journal Article Antonelli J, Zigler C, Dominici F. Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research. Biostatistics 2017;18(3):553-568. R835872 (2016)
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  • Journal Article Antonelli, J., Schwartz, J., Kloog, I., & Coull, B. A. Spatial multiresolution analysis of the effect of PM2.5 on birth weights. The Annals of Applied Statistics. 2017: 11(2);792-807. R835872 (2017)
    R835872C002 (2016)
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  • Journal Article Awad YA, Di Q, Wang Y, Choirat C, Coull BA, Zanobetti A, Schwartz J. Change in PM2.5 exposure and mortality among Medicare recipients:combining a semi-randomized approach and inverse probability weights in a low exposure population. Environmental Epidemiology 2019;3(4):e054. R835872 (2019)
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  • Journal Article Bobb JF, Ho KKL, Yeh RW, Harrington L, Zai A, Liao KP, Dominici F. Time-Course of Cause-Specific Hospital Admissions During Snowstorms: An Analysis of Electronic Medical Records From Major Hospitals in Boston, Massachusetts. American Journal of Epidemiology 2017;185(4):283-294. R835872 (2016)
    R835872C005 (2016)
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  • Journal Article Braun D, Gorfine M, Parmigiani G, Arvold ND, Dominici F, Zigler C.Propensity scores with misclassified treatment assignment: a likelihood-based adjustment.Biostatistics2017;18(4):695-710. R835872 (2016)
    R835872C004 (2016)
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  • Journal Article Brunst KJ, Sanchez-Guerra M, Chiu YH, Wilson A, Coull BA, Kloog I, Schwartz J, Brennan KJ, Enlow MB, Wright RO, Baccarelli AA. Prenatal particulate matter exposure and mitochondrial dysfunction at the maternal-fetal interface:effect modification by maternal lifetime trauma and child sex. Environment international 2018;112:49-58. R835872 (2017)
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  • Journal Article Cefalu M, Dominici F, Arvold N, Parmigiani G. Model averaged double robust estimation. Biometrics 2017;73(2):410-421. R835872 (2016)
    R834798 (Final)
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  • Journal Article Chen YH, Mukherjee B, Adar SD, Berrocal VJ, Coull BA. Robust distributed lag models using data adaptive shrinkage. Biostatistics 2017;19(4):461-478. R835872 (2017)
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  • Journal Article Colicino E, Wilson A, Frisardi MC, Prada D, Power MC, Hoxha M, Dioni L, Spiro III A, Vokonas PS, Weisskopf MG, Schwartz JD, Baccarelli AA. Telomere length, long-term black carbon exposure, and cognitive function in a cohort of older men:the VA Normative Aging Study. Environmental Health Perspectives 2017;125(1):76-81. R835872 (2016)
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  • Journal Article Dai L, Mehta A, Mordukhovich I, Just AC, Shen J, Hou L, Koutrakis P, Sparrow D, Vokonas PS, Baccarelli AA, Schwartz JD. Differential DNA methylation and PM2.5 species in a 450K epigenome-wide association study. Epigenetics 2017;12(2):139-148. R835872 (2016)
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  • Journal Article Dey T and Dominici F. COVID-19, Air Pollution, and Racial Inequity:Connecting the Dots. Chem Res Toxicol 2021; 34(3):669-671. R835872 (2020)
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  • Journal Article Di Q, Dai L, Wang Y, Zanobetti A, Choirat C, Schwartz JD, Dominici F. Association of short-term exposure to air pollution with mortality in older adults. JAMA 2017;318(24):2446-2456. R835872 (2016)
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  • Journal Article Di Q, Rowland S, Koutrakis P, Schwartz J. A hybrid model for spatially and temporally resolved ozone exposures in the continental United States. Journal of the Air & Waste Management Association 2017;67(1):39-52. R835872 (2016)
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  • Journal Article Di Q, Wang Y, Zanobetti A, Wang Y, Koutrakis P, Choirat C, Dominici F, Schwartz JD. Air pollution and mortality in the Medicare population. New England Journal of Medicine 2017;376(26):2513-2522. R835872 (2016)
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  • Journal Article Dominici F, Zigler C. Best practices for gauging evidence of causality in air pollution epidemiology. American Journal of Epidemiology 2017;186(12):1303-1309. R835872 (2016)
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  • Journal Article Dorans KS, Wilker EH, Li W, Rice MB, Ljungman PL, Schwartz J, Coull BA, Kloog I, Koutrakis P, D’Agostino RB, Massaro JM, Hoffmann U, O'Donnell J, Mittleman MA. Residential proximity to major roads, exposure to fine particulate matter, and coronary artery calcium: the Framingham Heart Study. Arteriosclerosis, Thrombosis, and Vascular Biology 2016;36(8):1679-1685. R835872 (2016)
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  • Journal Article Fong KC, Hart JE, James P. A review of epidemiologic studies on greenness and health:updated literature through 2017. Current Environmental Health Reports 2018;5(1):77-87. R835872 (2020)
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  • Journal Article Fong KC, Kosheleva A, Kloog I, Koutrakis P, Laden F, Coull BA, Schwartz JD. Fine particulate air pollution and birthweight:differences in associations along the birthweight distribution. Epidemiology 2019;30(5):617-623. R835872 (2019)
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  • Journal Article Grady ST, Koutrakis P, Hart JE, Coull BA, Schwartz J, Laden F, Zhang JJ, Gong J, Moy ML, Garshick E. Indoor black carbon of outdoor origin and oxidative stress biomarkers in patients with chronic obstructive pulmonary disease. Environment International 2018;115:188-195. R835872 (2018)
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  • Journal Article Hart JE, Grady ST, Laden F, Coull BA, Koutrakis P, Schwartz JD, Moy ML, Garshick E. Effects of indoor and ambient black carbon and PM 2.5 on pulmonary function among individuals with COPD. Environmental Health Perspectives 2018;126(12):127008. R835872 (2018)
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  • Journal Article Kingsley SL, Eliot MN, Glazer K, Awad YA, Schwartz JD, Savitz DA, Kelsey KT, Marsit CJ, Wellenius GA. Maternal ambient air pollution, preterm birth and markers of fetal growth in Rhode Island:results of a hospital-based linkage study. Journal of Epidemiology and Community Health 2017;71(12):1131-1136. R835872 (2017)
    R834798 (Final)
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  • Journal Article Lee KH, Tadesse MG, Baccarelli AA, Schwartz J, Coull BA. Multivariate Bayesian variable selection exploiting dependence structure among outcomes:application to air pollution effects on DNA methylation. Biometrics 2017;73(1):232-241. R835872 (2017)
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  • Journal Article Lee M, Schwartz J, Wang Y, Dominici F, Zanobetti A. Long-term effect of fine particulate matter on hospitalization with dementia. Environmental Pollution 2019;254:112. R835872 (2019)
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  • Journal Article Li J, Garshick E, Huang S, Koutrakis P. Impacts of El Nino-Southern Oscillation on surface dust levels across the world during 1982-2019. Sci Total Environ 2021; 769:144566 R835872 (2020)
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  • Journal Article Li W, Dorans KS, Wilker EH, Rice MB, Long MT, Schwartz J, Coull BA, Koutrakis P, Gold DR, Fox CS, Mittleman MA. Residential proximity to major roadways, fine particulate matter, and hepatic steatosis: the Framingham Heart Study. American Journal of Epidemiology 2017;186(7):857-865. R835872 (2016)
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  • Journal Article Li W, Dorans KS, Wilker EH, Rice MB, Ljungman PL, Schwartz JD, Coull BA, Koutrakis P, Gold DR, Keaney Jr JF, Vasan RS, Benjamin EJ, Mittleman MA. Short-term exposure to ambient air pollution and biomarkers of systemic inflammation: the Framingham Heart Study. Arteriosclerosis, Thrombosis, and Vascular Biology 2017;37(9):1793-1800. R835872 (2016)
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  • Journal Article Li W, Dorans KS, Wilker EH, Rice MB, Kloog I, Schwartz JD, Koutrakis P, Coull BA, Gold DR, Meigs JB, Fox CS, Mittleman MA. Ambient air pollution, adipokines, and glucose homeostasis: the Framingham Heart Study. Environment International 2018;111:14-22. R835872 (2016)
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  • Journal Article Li W, Nyhan MM, Wilker EH, Vieira CL, Lin H, Schwartz JD, Gold DR, Coull BA, Aba AM, Benjamin EJ, Vasan RS. Recent exposure to particle radioactivity and biomarkers of oxidative stress and inflammation:the Framingham Heart Study. Environment International 2018;121:1210-1216. R835872 (2019)
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  • Journal Article MacNaughton P, Eitland E, Kloog I, Schwartz J, Allen J. Impact of particular matter exposure and surrounding “greenness” on chronic absenteeism in Massachusetts public schools. International Journal of Environmental Research and Public Health 2017;14(2):E207. R835872 (2016)
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  • Journal Article Makar M, Antonelli J, Di Q, Cutler D, Schwartz J, Dominici F. Estimating the causal effect of low levels of fine particulate matter on hospitalization. Epidemiology 2017;28(5):627-634. R835872 (2016)
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  • Journal Article Martins M, Lawrence J, Ferguson S, Wolfson JM, Koutrakis P. Development and evaluation of a mobile laboratory for collecting short-duration near-road fine and coarse ambient particle and road dust samples. J Air Waste Manag Assoc 2021; 71(2):118-128. R835872 (2020)
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  • Journal Article Masri S, Garshick E, Hart J, Bouhamra W, Koutrakis P. Use of visual range measurements to predict fine particulate matter exposures in Southwest Asia and Afghanistan. Journal of the Air & Waste Management Association 2017;67(1):75-85. R835872 (2016)
    R834798 (Final)
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  • Journal Article Masri S, Garshick E, Coull B, Koutrakis P. A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan. Journal of the Air & Waste Management Association 2017;67(1):86-95. R835872 (2016)
    R834798 (Final)
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  • Journal Article 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. R835872 (2016)
    R834799 (Final)
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  • Journal Article McGuinn LA, Windham GC, Kalkbrenner AE, Bradley C, Di Q, Croen LA, Fallin MD, Hoffman K, Ladd-Acosta C, Schwartz J, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL. Early Life Exposure to Air Pollution and Autism Spectrum Disorder:Findings from a Multisite Case-Control Study. Epidemiology 2020; 31(1):103-114. R835872 (2020)
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  • Journal Article Moch JM, Dovrou E, Mickley LJ, Keutsch FN, Liu Z, Wang Y, Dombek TL, Kuwata M, Budisulistiorini SH, Yang L, Decesari S, Paglione M, Alexander B, Shao J, Munger JW, Jacob DJ. Global Importance of Hydroxymethanesulfonate in Ambient Particulate Matter:Implications for Air Quality. J Geophys Res Atmos 2020; 125(18):e2020JD032706. R835872 (2020)
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  • Journal Article Nassan FL, Wang C, Kelly RS, Lasky-Su JA, Vokonas PS, Koutrakis P, Schwartz JD. Ambient PM2.5 species and ultrafine particle exposure and their differential metabolomic signatures. Environ Int 2021; 151:106447. R835872 (2020)
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  • Journal Article Nassan FL, Kelly RS, Kosheleva A, Koutrakis P, Vokonas PS, Lasky-Su JA, Schwartz JD. Metabolomic signatures of the long-term exposure to air pollution and temperature. Environ Health 2021; 20(1):3. R835872 (2020)
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  • Journal Article Nwanaji-Enwerem JC, Colicino E, Dai L, Di Q, Just AC, Hou L, Vokonas P, De Vivo I, Lemos B, Lu Q, Weisskopf MG, Baccarelli AA, Schwartz JD. miRNA processing gene polymorphisms, blood DNA methylation age and long-term ambient PM2.5 exposure in elderly men. Epigenomics 2017;9(12):1529-1542. R835872 (2016)
    R832416 (Final)
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  • Journal Article Nwanaji-Enwerem JC, Bind M-A, Dai L, Oulhote Y, Colicino E, Di Q, Just AC, Hou L, Vokonas P, Coull BA, Weisskopf MG, Baccarelli AA, Schwartz JD. Editor’s highlight: Modifying role of endothelial function gene variants on the association of long-term PM2.5 exposure with blood DNA methylation age: the VA Normative Aging Study. Toxicological Sciences 2017;158(1):116-126. R835872 (2016)
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  • Journal Article Nwanaji-Enwerem JC, Colicino E, Dai L, Cayir A, Sanchez-Guerra M, Laue HE, Nguyen VT, Di Q, Just AC, Hou L, Vokonas P, Coull BA, Weisskopf MG, Baccarelli AA, Schwartz JD. Impacts of the mitochondrial genome on the relationship of long-term ambient fine particle exposure with blood DNA methylation age. Environmental Science & Technology 2017;51(14):8185-8195. R835872 (2016)
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  • Journal Article Nyhan MM, Rice M, Blomberg A, Coull BA, Garshick E, Vokonas P, Schwartz J, Gold DR, Koutrakis P. Associations between ambient particle radioactivity and lung function. Environment International 2019;130:104795. R835872 (2019)
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  • Journal Article Papadogeorgou G, Dominici F. A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter. Annals of Applied Statistics 2020; 14(2):850-871. R835872 (2020)
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  • Journal Article Phipatanakul W, Koutrakis P, Coull BA, Kang CM, Wolfson JM, Ferguson ST, Petty CR, Samnaliev M, Cunningham A, Sheehan WJ, Gaffin JM, Baxi SN, Lai PS, Permaul P, Liang L, Thorne PS, Adamkiewicz G, Brennan KJ, Baccarelli AA, Gold DR. The school inner-city asthma intervention study: design, rationale, methods, and lessons learned. Contemporary Clinical Trials 2017;60:14-23. R835872 (2016)
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  • Journal Article Pozzer A, Dominici F, Haines A, Witt C, Munzel T, Lelieveld J. Regional and global contributions of air pollution to risk of death from COVID-19. Cardiovasc Res 2020; 116(14):2247-2253. R835872 (2020)
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  • Journal Article Reilly JM, Chen YHH, Jacoby HD. The COVID-19 effect on the Paris agreement. Humanities and Social Sciences Communications 2021; 8(1) R835872 (2020)
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  • Journal Article Requia WJ, Roig HL, Koutrakis P, Adams MD. Modeling spatial patterns of traffic emissions across 5570 municipal districts in Brazil. Journal of Cleaner Production 2017;148:845-853. R835872 (2016)
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  • Journal Article Requia WJ, Adams MD, Arain A, Koutrakis P, Lee W-C, Ferguson M. Spatio-temporal analysis of particulate matter intake fractions for vehicular emissions: hourly variation by micro-environments in the Greater Toronto and Hamilton Area, Canada. Science of the Total Environment 2017;599-600:1813-1822. R835872 (2016)
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  • Journal Article Requia WJ, Dalumpines R, Adams MD, Arain A, Ferguson M, Koutrakis P. Modeling spatial patterns of link-based PM2.5 emissions and subsequent human exposure in a large Canadian metropolitan area. Atmospheric Environment 2017;158:172-180. R835872 (2016)
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  • Journal Article Requia WJ, Adams MD, Arain A, Koutrakis P, Ferguson M. Carbon dioxide emissions of plug-in hybrid electric vehicles: a life-cycle analysis in eight Canadian cities. Renewable and Sustainable Energy Reviews 2017;78:1390-1396. R835872 (2016)
    R835872C005 (2016)
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  • Journal Article Requia WJ, Higgins CD, Adams MD, Mohamed M, Koutrakis P. The health impacts of weekday traffic: a health risk assessment of PM2.5 emissions during congested periods. Environment International 2018;111:164-176. R835872 (2016)
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. Regional air pollution mixtures across the continental US. Atmospheric Environment 2019;213(5):258-272. R835872 (2018)
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  • Journal Article Rosa MJ, Hsu HH, Just AC, Brennan KJ, Bloomquist T, Kloog I, Pantic I, Garcia AM, Wilson A, Coull BA, Wright RO. Association between prenatal particulate air pollution exposure and telomere length in cord blood:Effect modification by fetal sex. Environmental Research 2019;172:495-501. R835872 (2019)
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  • Journal Article Schwartz J, Wei Y, Yitshak-Sade M, Di Q, Dominici F, Zanobetti A. A national difference in differences analysis of the effect of PM2.5 on annual death rates. Environ Res 2021; 194:110649. R835872 (2020)
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  • Journal Article Schwartz J, Bind MA, Koutrakis P. (2017) Estimating causal effects of local air pollution on daily deaths:effect of low levels. Environ Health Perspect 125:23–29; http://dx.doi.org/10.1289/EHP232. R835872C003 (2016)
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    Journal Article Shen L, Mickley LJ. Effects of El Niño on summertime ozone air quality in the eastern United States. Geophysical Research Letters 2017;44(24):12543-12550. R835872 (2016)
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  • Journal Article Shen L, Mickley LJ, Leibensperger EM, Li M. Strong dependence of U.S. summertime air quality on the decadal variability of Atlantic sea surface temperatures. Geophysical Research Letters 2017;44(24):12527-12535. R835872 (2016)
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  • Journal Article Shen L, Mickley LJ, Murray LT. Influence of 2000–2050 climate change on particulate matter in the United States:results from a new statistical model. Atmospheric Chemistry and Physics 2017;17(6):4355-4367. R835872 (2016)
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  • Journal Article Shen L, Mickley LJ. Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns. Proceedings of the National Academy of Sciences of the United States of America 2017;114(10):2491-2497. R835872 (2016)
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  • Journal Article Shi L, Wu X, Danesh Yazdi M, Braun D, Abu Awad Y, Wei Y, Liu P, Di Q, Wang Y, Schwartz J, Dominici F, Kioumourtzoglou M-A, Zanobetti A. Long-term effects of PM2·5 on neurological disorders in the American Medicare population:a longitudinal cohort study. The Lancet Planetary Health 2020; 4(12):e557-e565. R835872 (2020)
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  • Journal Article Silva E, Huang S, Lawrence J, Martins MAG, Li J, Koutrakis P. Trace element concentrations in ambient air as a function of distance from road. J Air Waste Manag Assoc 2021; 71(2):129-136. R835872 (2020)
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  • Journal Article Stern RA, Koutrakis P, Martins MAG, Lemos B, Dowd SE, Sunderland EM, Garshick E. Characterization of hospital airborne SARS-CoV-2. Respir Res 2021; 22(1):73. R835872 (2020)
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  • Journal Article Tang CH, Coull BA, Schwartz J, Lyapustin A, Di Q, Koutrakis P. Developing particle emission inventories using remote sensing (PEIRS). Journal of the Air & Waste Management Association 2017;67(1):53-63. R835872 (2016)
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  • Journal Article Tang CH, Kourakis P, Schwartz J, Coull BA, Di Q. Trends and spatial patterns of fine resolution aerosol optical depth-derived PM2.5 emissions in Northeast United States from 2002 to 2013. Journal of the Air & Waste Management Association 2017;67(1):64-74. R835872 (2016)
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  • Journal Article Tang CH, Garshick E, Grady S, Coull B, Schwartz J, Koutrakis P. Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households. Journal of Exposure Science & Environmental Epidemiology 2018;28(2):125-130. R835872 (2016)
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  • Journal Article Tang CH, Garshick E, Grady S, Coull B, Schwartz J, Koutrakis P. Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households. Journal of Exposure Science and Environmental Epidemiology 2018;28(2):125. R835872 (2017)
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  • Journal Article Vieira CL, Koutrakis P, Huang S, Grady S, Hart JE, Coull BA, Laden F, Requia W, Schwartz J, Garshick E. Short-term effects of particle gamma radiation activities on pulmonary function in COPD patients. Environ Res 2019;221-227. R835872 (2016)
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  • Journal Article Vohra K, Vodonos A, Schwartz J, Marais EA, Sulprizio MP, Mickley LJ. Global mortality from outdoor fine particle pollution generated by fossil fuel combustion:Results from GEOS-Chem. Environ Res 2021; 195:110754. R835872 (2020)
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  • Journal Article Wang Y, Shi L, Lee M, Liu P, Di Q, Zanobetti A, Schwartz JD. Long-term exposure to PM2.5 and mortality among older adults in the Southeastern US. Epidemiology 2017;28(2):207-214. R835872 (2016)
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  • Journal Article Wang Y, Lee M, Liu P, Shi L, Yu Z, Awad YA, Zanobetti A, Schwartz JD. Doubly robust additive hazards models to estimate effects of a continuous exposure on survival. Epidemiology 2017;28(6):771-779. R835872 (2016)
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  • Journal Article Wang Y, Nordio F, Nairn J, Zanobetti A, Schwartz JD. Accounting for adaptation and intensity in projecting heat wave-related mortality. Environmental Research 2018;161:464-471. R835872 (2016)
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  • Journal Article Ward-Caviness CK, Weaver AM, Buranosky M, Pfaff ER, Neas LM, Devlin RB, Schwartz J, Di Q, Cascio WE, Diaz-Sanchez D. Associations Between Long-Term Fine Particulate Matter Exposure and Mortality in Heart Failure Patients. J Am Heart Assoc 2020; 9(6):e012517. R835872 (2020)
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  • Journal Article Ward-Caviness CK, Danesh Yazdi M, Moyer J, Weaver AM, Cascio WE, Di Q, Schwartz JD, Diaz-Sanchez D. Long-Term Exposure to Particulate Air Pollution Is Associated With 30-Day Readmissions and Hospital Visits Among Patients With Heart Failure. J Am Heart Assoc 2021; 10(10):e019430. R835872 (2020)
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  • Journal Article Wu X, Nethery RC, Sabath MB, Braun D, Dominici F. Air pollution and COVID-19 mortality in the United States:Strengths and limitations of an ecological regression analysis. Science Advances 2020; 6(45) R835872 (2020)
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  • Journal Article Zanobetti A, Coull BA, Kloog I, Sparrow D, Vokonas PS, Gold DR, Schwartz J. Fine-scale spatial and temporal variation in temperature and arrhythmia episodes in the VA Normative Aging Study. Journal of the Air & Waste Management Association 2017;67(1):96-104. R835872 (2016)
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  • Journal Article Zanobetti A, Coull BA, Luttmann-Gibson H, van Rossem L, Rifas-Shiman SL, Kloog I, Schwartz JD, Oken E, Bobb JF, Koutrakis P, Gold DR. Ambient Particle Components and Newborn Blood Pressure in Project Viva. J Am Heart Assoc 2021; 10(1):e016935. R835872 (2020)
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  • Journal Article Zemplenyi M, Meyer MJ, Cardenas A, Hivert M-F, Rifas-Shiman SL, Gibson H, Kloog I, Schwartz J, Oken E, DeMeo DL, Gold DR, Coull BA. Function-on-function regression for the identification of epigenetic regions exhibiting windows of susceptibility to environmental exposures. Annals of Applied Statistics 2021; 15(3):1366-1385. R835872 (2020)
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  • Journal Article Zhang S, Breitner S, Cascio WE, Devlin RB, Neas LM, Ward-Caviness C, Diaz-Sanchez D, Kraus WE, Hauser ER, Schwartz J, Peters A, Schneider A. Association between short-term exposure to ambient fine particulate matter and myocardial injury in the CATHGEN cohort. Environ Pollut 2021; 275:116663. R835872 (2020)
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  • Journal Article Zigler CM, Choirat C, Dominici F. Impact of National Ambient Air Quality Standards nonattainment designations on particulate pollution and health. Epidemiology 2018;29(2):165-174. R835872 (2016)
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  • Journal Article Zigler CM, Papadogeorgou G. Bipartite Causal Inference with Interference. Statistical Science 2021; 36(1):109-123. R835872 (2020)
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  • Journal Article Wilson A, Chiu YH, Hsu HH, Wright RO, Wright RJ, Coull BA. Potential for bias when estimating critical windows for air pollution in children’s health. American Journal of Epidemiology 2017;186(11):1281-1289. R835872 (2018)
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  • Journal Article Dominici F, Zigler C. Best practices for gauging evidence of causality in air pollution epidemiology. American Journal of Epidemiology 2017;186(12):1303-1309. R835872 (2017)
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  • Journal Article Awad YA, Koutrakis P, Coull BA, Schwartz J. A spatio-temporal prediction model based on support vector machine regression: ambient black carbon in three New England States. Environmental Research 2017;159: 427-434. R835872 (2017)
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  • Journal Article Lin C, Christiani D, Lin RT. 0059 A global perspective on coal-fired power plants and lung cancer mortality. Occupational and Environmental Medicine 2017;74:A16. R835872 (2018)
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  • Journal Article Prada D, Zhong J, Colicino E, Zanobetti A, Schwartz J, Dagincourt N, Fang SC, Kloog I, Zmuda JM, Holick M, Herrera LA. Association of air particulate pollution with bone loss over time and bone fracture risk:analysis of data from two independent studies. The Lancet Planetary Health 2017;1(8):e337-347. R835872 (2017)
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  • Journal Article Peng C, Cayir A, Sanchez-Guerra M, Di Q, Wilson A, Zhong J, Kosheleva A, Trevisi L, Colicino E, Brennan K, Dereix AE. Associations of annual ambient fine particulate matter mass and components with mitochondrial DNA abundance. Epidemiology 2017;28(6):763-770. R835872 (2017)
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  • Journal Article Wilson A, Chiu YH, Hsu HH, Wright RO, Wright RJ, Coull BA. Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children’s health. Biostatistics 2017;18(3):537-552. R835872 (2017)
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  • Journal Article Rosa MJ, Just AC, Guerra MS, Kloog I, Hsu HH, Brennan KJ, García AM, Coull B, Wright RJ, Rojo MM, Baccarelli AA. Identifying sensitive windows for prenatal particulate air pollution exposure and mitochondrial DNA content in cord blood. Environment International 2017;98:198-203. R835872 (2017)
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  • Journal Article Rosa MJ, Pajak A, Just AC, Sheffield PE, Kloog I, Schwartz J, Coull B, Enlow MB, Baccarelli AA, Huddleston K, Niederhuber JE. Prenatal exposure to PM2.5 and birth weight:a pooled analysis from three North American longitudinal pregnancy cohort studies. Environment International 2017;107:173-180. R835872 (2017)
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  • Journal Article Rosa MJ, Just AC, Kloog I, Pantic I, Schnaas L, Lee A, Bose S, Chiu YH, Hsu HH, Coull B, Schwartz J. Prenatal particulate matter exposure and wheeze in Mexican children:effect modification by prenatal psychosocial stress. Annals of Allergy, Asthma & Immunology 2017;119(3):232-237. R835872 (2017)
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  • Journal Article Peng C, Sanchez-Guerra M, Wilson A, Mehta AJ, Zhong J, Zanobetti A, Brennan K, Dereix AE, Coull BA, Vokonas P, Schwartz J. Short-term effects of air temperature and mitochondrial DNA lesions within an older population. Environment International 2017;103:23-29. R835872 (2017)
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  • Journal Article Kioumourtzoglou MA, Power MC, Hart JE, Okereke OI, Coull BA, Laden F, Weisskopf MG. The association between air pollution and onset of depression among middle-aged and older women. American Journal of Epidemiology 2017;185(9):801-809. R835872 (2017)
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  • Journal Article Li W, Dorans KS, Wilker EH, Rice MB, Kloog I, Schwartz JD, Koutrakis P, Coull BA, Gold DR, Meigs JB, Fox CS. Ambient air pollution, adipokines, and glucose homeostasis:The Framingham heart study. Environment International 2018;111:14-22. R835872 (2018)
    R834798 (Final)
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  • Journal Article Cutler D, Dominici F. A breath of bad air:cost of the Trump environmental agenda may lead to 80 000 extra deaths per decade. JAMA 2018;319(22):2261-2262. R835872 (2018)
    not available
    Journal Article Lin CK, Lin RT, Chen PC, Wang P, De Marcellis-Warin N, Zigler C, Christiani DC. A global perspective on sulfur oxide controls in coal-fired power plants and cardiovascular disease. Scientific Reports 2018;8(1):2611. R835872 (2018)
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  • Journal Article Schwartz J, Fong K, Zanobetti A. A national multicity analysis of the causal effect of local pollution, NO2, and PM2.5 on mortality. Environmental Health Perspectives 2018;126(8):087004. R835872 (2018)
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  • Journal Article Papadogeorgou G, Choirat C, Zigler CM. Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching. Biostatistics 2018;20(2):256-272. R835872 (2017)
    R835872 (2018)
  • Abstract: Abstract
  • Journal Article Sheffield PE, Speranza R, Chiu YH, Hsu HH, Curtin PC, Renzetti S, Pajak A, Coull B, Schwartz J, Kloog I, Wright RJ. Association between particulate air pollution exposure during pregnancy and postpartum maternal psychological functioning. PloS One 2018;13(4):e0195267. R835872 (2018)
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  • Journal Article Nyhan MM, Coull BA, Blomberg AJ, Vieira CL, Garshick E, Aba A, Vokonas P, Gold DR, Schwartz J, Koutrakis P. Associations between ambient particle radioactivity and blood pressure:the NAS (Normative Aging Study). Journal of the American Heart Association 2018;7(6):e008245. R835872 (2018)
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  • Journal Article Rokoff LB, Rifas-Shiman SL, Coull BA, Cardenas A, Calafat AM, Ye X, Gryparis A, Schwartz J, Sagiv SK, Gold DR, Oken E. Cumulative exposure to environmental pollutants during early pregnancy and reduced fetal growth:the project viva cohort. Environmental Health 2018;17(1):19. R835872 (2018)
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  • Journal Article Schwartz JD, Wang Y, Kloog I, Yitshak-Sade MA, Dominici F, Zanobetti A. Estimating the effects of PM on life expectancy using causal modeling methods. Environmental Health Perspectives 2018;126(12):127002.. R835872 (2019)
  • Abstract: Abstract and Full Text
  • Journal Article Ananth CV, Kioumourtzoglou MA, Huang Y, Ross Z, Friedman AM, Williams MA, Wang S, Mittleman MA, Schwartz J. Exposures to air pollution and risk of acute-onset placental abruption:a case-crossover study. Epidemiology 2018;29(5):631-638. R835872 (2018)
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  • Journal Article Zigler CM, Choirat C, Dominici F. Impact of national ambient air quality standards nonattainment designations on particulate pollution and health. Epidemiology 2018;29(2):165-164. R835872 (2017)
    R835872 (2018)
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  • Journal Article Garshick E, Grady ST, Hart JE, Coull BA, Schwartz JD, Laden F, Moy ML, Koutrakis P. Indoor black carbon and biomarkers of systemic inflammation and endothelial activation in COPD patients. Environmental Research 2018;165:358-364. R835872 (2018)
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  • Journal Article Rice MB, Rifas-Shiman SL, Litonjua AA, Gillman MW, Liebman N, Kloog I, Luttmann-Gibson H, Coull BA, Schwartz J, Koutrakis P, Oken E. Lifetime air pollution exposure and asthma in a pediatric birth cohort. Journal of Allergy and Clinical Immunology 2018;141(5):1932-1934. R835872 (2018)
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  • Journal Article Ljungman PL, Li W, Rice MB, Wilker EH, Schwartz J, Gold DR, Koutrakis P, Benjamin EJ, Vasan RS, Mitchell GF, Hamburg NM. Long-and short-term air pollution exposure and measures of arterial stiffness in the Framingham heart study. Environment International 2018;121:139-147. R835872 (2018)
  • Abstract: ScienceDirect- Abstract
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  • Journal Article Zanobetti A, O’Neill MS. Longer-term outdoor temperatures and health effects:a review. Current Epidemiology Reports 2018;5(2):125-139. R835872 (2018)
    R836156 (2019)
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    R836156 (Final)
  • Abstract: Springer- Abstract
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  • Journal Article Wilson A, Zigler CM, Patel CJ, Dominici F. Model‐averaged confounder adjustment for estimating multivariate exposure effects with linear regression. Biometrics 2018;74(3):1034-1044. R835872 (2017)
    R836156 (2019)
    R836156 (2020)
    R836156 (Final)
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  • Journal Article Gaffin JM, Hauptman M, Petty CR, Sheehan WJ, Lai PS, Wolfson JM, Gold DR, Coull BA, Koutrakis P, Phipatanakul W. Nitrogen dioxide exposure in school classrooms of inner-city children with asthma. Journal of Allergy and Clinical Immunology 2018;141(6):2249-2255. R835872 (2018)
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  • Journal Article Silvern RF, Jacob DJ, Travis KR, Sherwen T, Evans MJ, Cohen RC, Laughner JL, Hall SR, Ullmann K, Crounse JD, Wennberg PO. Observed NO/NO2 ratios in the upper troposphere imply errors in NO‐NO2‐O3 cycling kinetics or an unaccounted NOx reservoir. Geophysical Research Letters 2018;45(9):4466-4474. R835872 (2018)
  • Full-text: Wiley-Full text
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  • Journal Article Lee A, Hsu HH, Chiu YH, Bose S, Rosa MJ, Kloog I, Wilson A, Schwartz J, Cohen S, Coull BA, Wright RO. Prenatal fine particulate exposure and early childhood asthma:effect of maternal stress and fetal sex. Journal of Allergy and Clinical Immunology 2018;141(5):1880-1886. R835872 (2017)
    R835872 (2018)
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  • Journal Article Lee AG, Le Grand B, Hsu HH, Chiu YH, Brennan KJ, Bose S, Rosa MJ, Brunst KJ, Kloog I, Wilson A, Schwartz J. Prenatal fine particulate exposure associated with reduced childhood lung function and nasal epithelia GSTP1 hypermethylation:sex-specific effects. Respiratory Research 2018;19(1):76. R835872 (2018)
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  • Journal Article Bose S, Rosa MJ, Chiu YH, Hsu HH, Di Q, Lee A, Kloog I, Wilson A, Schwartz J, Wright RO, Morgan WJ. Prenatal nitrate air pollution exposure and reduced child lung function:timing and fetal sex effects. Environmental research 2018;167:591-597. R835872 (2018)
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  • Journal Article Brunst KJ, Sanchez-Guerra M, Chiu YH, Wilson A, Coull BA, Kloog I, Schwartz J, Brennan KJ, Enlow MB, Wright RO, Baccarelli AA. Prenatal particulate matter exposure and mitochondrial dysfunction at the maternal-fetal interface:effect modification by maternal lifetime trauma and child sex. Environment International 2018;112:49-58. R835872 (2018)
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  • Journal Article Li W, Nyhan MM, Wilker EH, Vieira CL, Lin H, Schwartz JD, Gold DR, Coull BA, Aba AM, Benjamin EJ, Vasan RS. Recent exposure to particle radioactivity and biomarkers of oxidative stress and inflammation:the Framingham heart study. Environment International 2018;121:1210-1216. R835872 (2018)
    R835872 (2019)
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  • Journal Article Sordillo JE, Switkowski KM, Coull BA, Schwartz J, Kloog I, Gibson H, Litonjua AA, Bobb J, Koutrakis P, Rifas-Shiman SL, Oken E. Relation of prenatal air pollutant and nutritional exposures with biomarkers of allergic disease in adolescence. Scientific Reports 2018;8(1):10578. R835872 (2018)
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  • Journal Article Fong K, Kloog I, Coull B, Koutrakis P, Laden F, Schwartz J, James P. Residential greenness and birthweight in the state of Massachusetts, USA. International Journal of Environmental Research and Public Health 2018;15(6):1248. R835872 (2018)
    R836156 (2019)
    R836156 (2020)
    R836156 (Final)
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  • Journal Article Gaskins AJ, Hart JE, Mínguez-Alarcón L, Chavarro JE, Laden F, Coull BA, Ford JB, Souter I, Hauser R. Residential proximity to major roadways and traffic in relation to outcomes of in vitro fertilization. Environment International 2018;115:239-246.. R835872 (2018)
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  • Journal Article Peng C, den Dekker M, Cardenas A, Rifas-Shiman SL, Gibson H, Agha G, Harris MH, Coull BA, Schwartz J, Litonjua AA, DeMeo DL. Residential proximity to major roadways at birth, DNA methylation at birth and midchildhood, and childhood cognitive test scores:project viva (Massachusetts, USA). Environmental Health Perspectives 2018;126(9):097006. R835872 (2018)
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  • Journal Article Huang S, Lawrence J, Kang CM, Li J, Martins M, Vokonas P, Gold DR, Schwartz J, Coull BA, Koutrakis P. Road proximity influences indoor exposures to ambient fine particle mass and components. Environmental Pollution 2018;243:978-987. R835872 (2018)
  • Abstract: Abstract
  • Journal Article Bobb JF, Henn BC, Valeri L, Coull BA. Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. Environmental Health 2018;17(1):67. R835872 (2018)
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  • Journal Article Yitshak-Sade M, Bobb JF, Schwartz JD, Kloog I, Zanobetti A. The association between short and long-term exposure to PM2.5 and temperature and hospital admissions in New England and the synergistic effect of the short-term exposures. Science of The Total Environment 2018;639:868-875. R835872 (2018)
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  • Journal Article Henneman LR, Mickley LJ, Zigler CM. Air pollution accountability of energy transitions:the relative importance of point source emissions and wind fields in exposure changes. Environmental Research Letters 2019;14(11):115003. R835872 (2019)
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  • Journal Article Rice MB, Li W, Schwartz J, Di Q, Kloog I, Koutrakis P, Gold DR, Hallowell RW, Zhang C, O'Connor G, Washko GR. Ambient air pollution exposure and risk and progression of interstitial lung abnormalities:the Framingham Heart Study. Thorax 2019;74(11):1063-1069. R835872 (2019)
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  • Journal Article Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y. An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International 2019;130:104909. R835872 (2019)
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  • Journal Article Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y. Assessing NO2 concentration and model uncertainty with high spatiotemporal resolution across the contiguous United States using ensemble model averaging. Environmental Science & Technology 2019;54(3):1372-1384. R835872 (2019)
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  • Journal Article Rice MB, Li W, Wilker EH, Gold DR, Schwartz J, Zanobetti A, Koutrakis P, Kloog I, Washko GR, O'Connor GT, Mittleman MA. Association of outdoor temperature with lung function in a temperate climate. European Respiratory Journal 2019;53(1):1800612. R835872 (2018)
  • Abstract: ERJ- Abstract
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  • Journal Article Wei Y, Wang Y, Lin C-K, Yin K, Yang J, Shi L, Li L, Zanobetti A, Schwartz JD. Associations between seasonal temperature and dementia-associated hospitalizations in New England. Environment International 2019;126:228-233. R835872 (2019)
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  • Journal Article Wu X, Braun D, Kioumourtzoglou MA, Choirat C, Di Q, Dominici F. Causal inference in the context of an error prone exposure:air pollution and mortality. The Annals of Applied Statistics 2019;13(1):520-547. R835872 (2018)
  • Abstract: Project Euclid- Abstract
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  • Journal Article Papadogeorgou G, Mealli F, Zigler CM. Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics 2019; 75(3):778-787. R835872 (2018)
    R835872 (2019)
  • Abstract: Abstract
  • Journal Article Henneman LR, Choirat C, Ivey C, Cummiskey K, Zigler CM. Characterizing population exposure to coal emissions sources in the United States using the HyADS model. Atmospheric Environment 2019;203:271-280. R835872 (2018)
    R835872 (2019)
  • Abstract: ScienceDirect- Abstract
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  • Journal Article Requia WJ, Jhun I, Coull BA, Koutrakis P. Climate impact on ambient PM2.5 elemental concentration in the United States:a trend analysis over the last 30 years. Environment International 2019;131:104888. R835872 (2018)
    R835872 (2019)
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  • Journal Article Yitshak-Sade M, Blomberg AJ, Zanobetti A, Schwartz JD, Coull BA, Kloog I, Dominici F and Koutrakis P. County-level radon exposure and all-cause mortality risk among Medicare beneficiaries. Environ Int 2019; 130:104865. R835872 (2019)
    not available
    Journal Article Blomberg AJ, Coull BA, Jhun I, Vieira CL, Zanobetti A, Garshick E, Schwartz J, Koutrakis P. Effect modification of ambient particle mortality by radon:a time series analysis in 108 US cities. Journal of the Air & Waste Management Association 2019;69(3):266-276. R835872 (2018)
    R835872 (2019)
  • Abstract from PubMed
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  • Journal Article Rhee J, Fabian MP, Ettinger de Cuba S, Coleman S, Sandel M, Lane KJ, Yitshak Sade M, Hart JE, Schwartz J, Kloog I, Laden F. Effects of maternal homelessness, supplemental nutrition programs, and prenatal PM2.5 on birthweight. International Journal of Environmental Research and Public Health 2019;16(21):4154. R835872 (2019)
    R836156 (2020)
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  • Journal Article Gilstrap LG, Dominici F, Wang Y, El-Sady MS, Singh A, Di Carli MF, Falk RH, Dorbala S. Epidemiology of cardiac amyloidosis-associated heart failure hospitalizations among fee-for-service medicare beneficiaries in the United States. Circulation Heart Failure 2019;12(6):e005407. R835872 (2018)
  • Abstract: Abstract
  • Journal Article Shtein A, Kloog I, Schwartz J, Silibello C, Michelozzi P, Gariazzo C, Viegi G, Forastiere F, Karnieli A, Just AC, Stafoggia M. Estimating daily PM2.5 and PM10 over Italy using an ensemble model. Environmental Science & Technology 2019;54(1):120-128. R835872 (2019)
  • Abstract: Abstract
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  • Journal Article Nethery RC, Mealli F, Dominici F. Estimating population average causal effects in the presence of non-overlap:the effect of natural gas compressor station exposure on cancer mortality. The Annals of Applied Statistics 2019;13(2):1242-1267. R835872 (2019)
    R836156 (2019)
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    R836156 (Final)
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  • Journal Article Yitshak-Sade M, Kloog I, Zanobetti A, Schwartz JD. Estimating the causal effect of annual PM2.5 exposure on mortality rates in the Northeastern and mid-Atlantic states. Environmental Epidemiology 2019;3(4):e052. R835872 (2019)
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space. Environmental Research 2019;175:421-433. R835872 (2018)
    R835872 (2019)
  • Abstract: Abstract
  • Journal Article Gaskins AJ, Minguez-Alarcon L, Fong KC, Abdelmessih S, Coull BA, Chavarro JE, Schwartz J, Kloog I, Souter I, Hauser R, Laden F. Exposure to fine particulate matter and ovarian reserve among women from a fertility clinic. Epidemiology 2019;30(4):486-491. R835872 (2019)
  • Abstract: CDN- Abstract
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  • Journal Article Vieira CL, Alvares D, Blomberg A, Schwartz J, Coull B, Huang S, Koutrakis P. Geomagnetic disturbances driven by solar activity enhance total and cardiovascular mortality risk in 263 US cities. Environmental Health 2019;18(1):83. R835872 (2019)
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  • Journal Article Dimanchev EG, Paltsev S, Yuan M, Rothenberg D, Tessum CW, Marshall JD, Selin NE. Health co-benefits of sub-national renewable energy policy in the US. Environmental Research Letters 2019;14(8):085012 R835872 (2018)
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  • Full-text: IOPscience
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  • Journal Article Antonelli J, Parmigiani G, Dominici F. High-dimensional confounding adjustment using continuous spike and slab priors. Bayesian Analysis 2019;14(3):805-828. R835872 (2019)
    R836156 (2020)
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  • Journal Article Rhee J, Dominici F, Zanobetti A, Schwartz J, Wang Y, Di Q, Balmes J, Christiani DC. Impact of long-term exposures to ambient PM2.5 and ozone on ARDS risk for older adults in the United States. Chest 2019; 156(1):71-79. R835872 (2018)
    R835872 (2019)
  • Abstract: Abstract
  • Journal Article Gao X, Colicino E, Shen J, Kioumourtzoglou MA, Just AC, Nwanaji-Enwerem JC, Coull B, Lin X, Vokonas P, Zheng Y, Hou L. Impacts of air pollution, temperature, and relative humidity on leukocyte distribution:an epigenetic perspective. Environment International 2019;126:395-405. R835872 (2019)
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  • Journal Article Yazdi MD, Wang Y, Di Q, Zanobetti A, Schwartz J. Long-term exposure to PM2.5 and ozone and hospital admissions of Medicare participants in the Southeast USA. Environment International 2019;130:104879. R835872 (2019)
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  • Journal Article Fleisch AF, Aris IM, Rifas-Shiman SL, Coull BA, Luttmann-Gibson H, Koutrakis P, Schwartz JD, Kloog I, Gold DR, Oken E. Prenatal exposure to traffic pollution and childhood body mass index trajectory. Frontiers in Endocrinology 2019;9:771. R835872 (2018)
    R835872 (2019)
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  • Journal Article Fong KC, Di Q, Kloog I, Laden F, Coull BA, Koutrakis P, Schwartz JD. Relative toxicities of major particulate matter constituents on birthweight in Massachusetts. Environmental Epidemiology 2019;3(3). R835872 (2019)
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  • Journal Article Wei Y, Wang Y, Di Q, Choirat C, Wang Y, Koutrakis P, Zanobetti A, Dominici F and Schwartz JD. Short term exposure to fine particulate matter and hospital admission risks and costs in the Medicare population:time stratified, case crossover study. BMJ 2019; 367:l6258. R835872 (2019)
    not available
    Journal Article Li W, Dorans KS, Wilker EH, Rice MB, Ljungman PL, Schwartz JD, Coull BA, Koutrakis P, Gold DR, Keaney Jr JF, Vasan RS. Short-term exposure to ambient air pollution and circulating biomarkers of endothelial cell activation:The Framingham heart study. Environmental Research 2019;171:36-43. R835872 (2018)
  • Abstract: Abstract
  • Journal Article Wright RJ, Coull BA. Small but mighty:prenatal ultrafine particle exposure linked to childhood asthma incidence. American Journal of Respiratory and Critical Care Medicine 2019;199(12):1448-1450. R835872 (2019)
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  • Journal Article Gaskins AJ, Minguez-Alarcon L, Fong KC, Abu Awad Y, Di Q, Chavarro JE, Ford JB, Coull BA, Schwartz J, Kloog I, Attaman J. Supplemental folate and the relationship between traffic-related air pollution and livebirth among women undergoing assisted reproduction. American Journal of Epidemiology 2019;188(9):1595-1604. R835872 (2019)
  • Abstract: OUP- Abstract
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  • Journal Article Jhun I, Kim J, Cho B, Gold DR, Schwartz J, Coull BA, Zanobetti A, Rice MB, Mittleman MA, Garshick E, Vokonas P. Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area. Journal of the Air & Waste Management Association 2019;69(8):900-917. R835872 (2019)
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  • Journal Article Dedoussi IC, Allroggen F, Flanagan R, Hansen T, Taylor B, Barrett SR, Boyce JK. The co-pollutant cost of carbon emissions:an analysis of the US electric power generation sector. Environmental Research Letters 2019;14(9):094003. R835872 (2018)
    R835872 (2019)
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  • Journal Article Anderson GB, Barnes EA, Bell ML, Dominici F. The future of climate epidemiology:opportunities for advancing health research in the context of climate change. American Journal of Epidemiology 2019;188(5):866-872. R835872 (2019)
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. The impact of wildfires on particulate carbon in the western USA. Atmospheric Environment 2019;213:1-10.. R835872 (2018)
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts. Science of The Total Environment 2019;682:247-258. R835872 (2018)
    R835872 (2019)
  • Abstract: Abstract
  • Journal Article Gaskins AJ, Fong KC, Abu Awad Y, Di Q, Minguez-Alarcon L, Chavarro JE, Ford JB, Coull BA, Schwartz J, Kloog I, Souter I. Time-varying exposure to air pollution and outcomes of in vitro fertilization among couples from a fertility clinic. Environmental Health Perspectives 2019;127(7):077002. R835872 (2019)
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  • Journal Article Silvern RF, Jacob DJ, Mickley LJ, Sulprizio MP, Travis KR, Marais EA, Cohen RC, Laughner JL, Choi S, Joiner J, Lamsal LN. Using satellite observations of tropospheric NO_2 columns to infer long-term trends in US NOx emissions:the importance of accounting for the free tropospheric NO2 background. Atmospheric Chemistry and Physics 2019;19(13):8863-8878. R835872 (2019)
  • Abstract: Caltech- Abstract
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  • Journal Article Gaskins AJ, Minguez-Alarcon L, Williams PL, Chavarro JE, Schwartz JD, Kloog I, Souter I, Hauser R, Laden F and Team ES. Ambient air pollution and risk of pregnancy loss among women undergoing assisted reproduction. Environ Res 2020; 191:110201. R835872 (2020)
  • Abstract from PubMed
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  • Journal Article Liu T, Mickley LJ, Marlier ME, DeFries RS, Khan MF, Latif MT, Karambelas A. Diagnosing spatial biases and uncertainties in global fire emissions inventories:Indonesia as regional case study. Remote Sensing of Environment 2020;237:111557. R835872 (2019)
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  • Journal Article Gaffin JM, Hauptman M, Petty CR, Haktanir-Abul M, Gunnlaugsson S, Lai PS, Baxi SN, Permaul P, Sheehan WJ, Wolfson JM, Coull BA, Gold DR, Koutrakis P and Phipatanakul W. Differential Effect of School-Based Pollution Exposure in Children With Asthma Born Prematurely. Chest 2020; 158(4):1361-1363. R835872 (2020)
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  • Journal Article Thomas EG, Trippa L, Parmigiani G, Dominici F. Estimating the Effects of Fine Particulate Matter on 432 Cardiovascular Diseases Using Multi-Outcome Regression With Tree-Structured Shrinkage. Journal of the American Statistical Association 2020; 115(532):1689-1699. R835872 (2020)
  • Full-text: Taylor & Francis - Full Text HTML
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  • Journal Article Cserbik D, Chen JC, McConnell R, Berhane K, Sowell ER, Schwartz J, Hackman DA, Kan E, Fan CC and Herting MM. Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure. Environ Int 2020; 143:105933. R835872 (2020)
    R835873 (2020)
  • Full-text: NIH Full Text PDF
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  • Journal Article Kang CM, Liu M, Garshick E and Koutrakis P. Indoor Particle Alpha Radioactivity Origins in Occupied Homes. Aerosol Air Qual Res 2020; 20(6). R835872 (2020)
  • Abstract from PubMed
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  • Journal Article Qiu X, Wei Y, Wang Y, Di Q, Sofer T, Awad YA, Schwartz J. Inverse probability weighted distributed lag effects of short-term exposure to PM2.5 and ozone on CVD hospitalizations in New England Medicare participants-Exploring the causal effects. Environmental Research 2020;182:109095. R835872 (2019)
  • Abstract: Abstract
  • Journal Article Liu M, Kang CM, Wolfson JM, Li L, Coull B, Schwartz J and Koutrakis P. Measurements of Gross alpha-and beta-Activities of Archived PM2.5 and PM10 Teflon Filter Samples. Environ Sci Technol 2020; 54(19):11780-11788. R835872 (2020)
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  • Journal Article Dedoussi IC, Eastham SD, Monier E, Barrett SR. Premature mortality related to United States cross-state air pollution. Nature 2020;578(7794):261-265. R835872 (2019)
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  • Abstract: Abstract
  • Journal Article Huang S, Xiong J, Vieira CLZ, Blomberg AJ, Gold DR, Coull BA, Sarosiek K, Schwartz JD, Wolfson JM, Li J and Koutrakis P. Short-term exposure to ambient particle gamma radioactivity is associated with increased risk for all-cause non-accidental and cardiovascular mortality. Sci Total Environ 2020; 721:137793. R835872 (2020)
  • Abstract from PubMed
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  • Journal Article Huang S, Garshick E, Vieira CL, Grady ST, Schwartz JD, Coull BA, Hart JE, Laden F, Koutrakis P. Short-term exposures to particulate matter gamma radiation activities and biomarkers of systemic inflammation and endothelial activation in COPD patients. Environmental Research 2020;180:108841. R835872 (2019)
  • Abstract: Abstract
  • Journal Article Li Y, Mickley LJ, Liu P, Kaplan JO. Trends and spatial shifts in lightning fires and smoke concentrations in response to 21st century climate over the national forests and parks of the western United States. ,em> Atmospheric Chemistry and Physics 2020;20(14):8827-38. R835872 (2020)
    R835875 (Final)
  • Full-text: European Geosciences Union Full Text PDF
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  • Abstract: Gale Academic Onefile Abstract HTML
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. Where air quality has been impacted by weather changes in the United States over the last 30 years?. Atmospheric Environment 2020;224:117360. R835872 (2019)
  • Abstract: ScienceDirect- Abstract
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  • Journal Article Li J, Garshick E, Hart JE, Li L, Shi L, Al-Hemoud A, Huang S and Koutrakis P. Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing. Environ Int 2021; 151:106445. R835872 (2020)
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  • Journal Article Henneman LRF, Shen H, Hogrefe C, Russell AG, Zigler CM. Four decades of United States mobile source pollutants:Spatial–temporal trends assessed by ground-based monitors, air quality models, and satellites. Environmental Science & Technology 2021;55:882–92 R835872 (2020)
    R835880 (Final)
  • Abstract from PubMed
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  • Journal Article Danesh Yazdi M, Wang Y, Di Q, Wei Y, Requia WJ, Shi L, Sabath MB, Dominici F, Coull BA, Evans JS, Koutrakis P and Schwartz JD. Long-Term Association of Air Pollution and Hospital Admissions Among Medicare Participants Using a Doubly Robust Additive Model. Circulation 2021; 143(16):1584-1596. R835872 (2020)
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  • Full-text: Circulation - Full Text HTML
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  • Journal Article Lawrence J, Martins M, Liu M and Koutrakis P. Measurement of the gross alpha activity of the fine fractions of road dust and near-roadway ambient particle matter. J Air Waste Manag Assoc 2021; 71(2):147-155. R835872 (2020)
  • Abstract from PubMed
  • Full-text: Taylor & Francis - Full Text HTML
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  • Journal Article Li Y, Mickley LJ, Kaplan JO. Response of dust emissions in southwestern North America to 21st century trends in climate, CO 2 fertilization, and land use:implications for air quality. Atmospheric Chemistry and Physics 2021 ;21(1):57-68. R835872 (2020)
    R835875 (Final)
  • Full-text: ProQuest Full Text PDF
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  • Journal Article Di Q, Rowland S, Koutrakis P, Schwartz J. (2017) A hybrid model for spatially and temporally resolved ozone exposures in the continental United States. J Air and Waste Management Association, 67.1:39-52. R835872C003 (2016)
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    Journal Article Wang, Y., Shi, L.H., Lee, M., Liu, P.F., Di, Q., Zanobetti, A., and Schwartz, J. (2017). Long-term Exposure to PM2.5 and Mortality Among Older Adults in the Southeastern US. Epidemiology 28, 207-214. R835872C003 (2016)
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    Journal Article Antonelli, J., Zigler, C., and Dominici, F. (2017). Guided Bayesian Imputation to Adjust for Confounding when Combining Heterogeneous Data Sources in Comparative Effectiveness Research. Biostatistics 1–16 doi:10.1093/biostatistics/kxx003, 1-16. R835872C005 (2016)
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    Journal Article Tang C, Coull B, Schwartz J, Lyapustin A, Di Q, Koutrakis P (2016) Trends and Spatial Patterns of Fine Resolution AOD-Derived PM2.5 Emissions in the Northeast United States from 2002 to 2013. Journal of the Air Waste & Management Association. In press. http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1080/10962247.2016.1218393. R835872C001 (2016)
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    Journal Article Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J (2016) Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environmental Science & Technology, 50(9):4712-4721. R835872C001 (2016)
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    Journal Article Henneman LR, Choirat C, Zigler CM. Accountability assessment of health improvements in the United States associated with reduced coal emissions between 2005 and 2012. Epidemiology,/em> 2019;30(4):477-485.. R835872 (2019)
  • Abstract: Wolters Kumar- Abstract
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  • Journal Article Peralta AA, Schwartz J, Gold DR, Coull B, Koutrakis P. Associations between acute and long-term exposure to PM2.5 components and temperature with QT interval length in the VA Normative Aging Study. European Journal of Preventive Cardiology 2021. R835872 (2020)
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  • Journal Article Henneman LR, Dedoussi IC, Casey JA, Choirat C, Barrett SR, Zigler CM. Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions. Journal of Exposure Science & Environmental Epidemiology 2020:1-10. R835872 (2019)
  • Abstract: Nature- Abstract and Full Text PDF
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  • Journal Article Coull BA, Lee S, McGee G, Manjourides J, Mittleman MA and Wellenius GA. Corrections for measurement error due to delayed onset of illness for case-crossover designs. Biometrics 2019. R835872 (2019)
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    Journal Article Lee K, Small DS, Dominici F. Discovering effect modification and randomization inference in air pollution studies. arXiv preprint arXiv:1802.06710. 2018 Feb 19. R835872 (2018)
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    Journal Article Nethery RC, Mealli F, Sacks JD, Dominici F. Evaluation of the Health Impacts of the 1990 Clean Air Act Amendments Using Causal Inference and Machine Learning. Journal of the American Statistical Association 2020:1-12 R835872 (2020)
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  • Journal Article Kim C, Henneman LR, Choirat C, Zigler CM. Health effects of power plant emissions through ambient air quality. Journal of the Royal Statistical Society:Series A. 2020. [Epub ahead of print]. R835872 (2019)
  • Abstract: Abstract
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  • Journal Article Di Q, Rowland S, Koutrakis P, Schwartz J (2017) A hybrid model for spatially and temporally resolved ozone exposures in the continental United States. Journal of the Air & Waste Management Association, 67(1):39-52. R835872C001 (2016)
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    Journal Article Tang C, Coull B, Schwartz J, Lyapustin A, Di Q, Koutrakis P (2016) Developing Particle Emission Inventories Using Remote Sensing (PEIRS). Journal of the Air Waste & Management Association. In press. http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1080/10962247.2016.1214630. R835872C001 (2016)
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    Journal Article Requia WJ, Coull BA, Koutrakis P. Multivariate spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts. 2019;252:1942-1952.. R835872 (2018)
    R835872 (2019)
  • Abstract: Abstract
  • Journal Article Gao X, Koutrakis P, Blomberg AJ, Coull B, Vokonas P, Schwartz J, Baccarelli AA. Short-term ambient particle radioactivity level and renal function in older men:Insight from the Normative Aging Study. Environment International 2019;131:105018. R835872 (2019)
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  • Abstract: Abstract
  • Journal Article Liao SX, Zigler CM. Uncertainty in the design stage of two‐stage Bayesian propensity score analysis. Statistics in Medicine 2020. [Epub ahead of print]. doi:10.1002/sim.8486. R835872 (2019)
  • Abstract: Wiley- Abstract
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  • Journal Article Liu JZ. Variable Selection with Rigorous Uncertainty Quantication using Deep Bayesian Neural Networks:Posterior Concentration and Bernstein-von Mises Phenomenon. arXiv 2019:1912.01189v01181. R835872 (2020)
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  • Supplemental Keywords:

    particles, pollutant mixtures, pollution trends, regional pollution, public policy, data fusion, climate change, regional pollution, multi-resolution spatial analysis, source emissions, local pollution control strategies, wavelet analysis, particulate matter, risk analysis, causal modeling, accountability assessment, power-generating sector, intervention evaluation, source-receptor mapping, causal inference, interference, PM2.5, ozone, adjoint method, air quality, greenhouse gas, computable general equilibrium, mercury, polycyclic aromatic hydrocarbons, PAHs

    Relevant Websites:

    Harvard ACE Center Exit

    Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • 2017 Progress Report
  • 2018 Progress Report
  • 2020 Progress Report
  • 2021
  • Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R835872C001 Project 1: Regional Air Pollution Mixtures: The Past and Future Impacts of Emission Controls and Climate Change on Air Quality and Health
    R835872C002 Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate and Particle-induced Mortality
    R835872C003 Project 3: Causal Estimates of Effects of Regional and National Pollution Mixtures on Health: Providing Tools for Policy Makers
    R835872C004 A Causal Inference Framework to Support Policy Decisions by Evaluating the Effectiveness of Past Air Pollution Control Strategies for the Entire United States
    R835872C005 Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts