2018 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 , Schwartz, Joel , Coull, Brent , Dominici, Francesca , Selin, Noelle Eckley
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: Keating, Terry
Project Period: December 1, 2015 through November 30, 2020 (Extended to November 30, 2022)
Project Period Covered by this Report: December 1, 2017 through November 30,2018
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 US. 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.

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

Year 3 was a productive year, across Project 1. Some objectives were advanced more successfully than others; key effort and results for each objective are outlined below.

Objective 1. It was a very productive year for Objective 1. In Year 3, we extended the GEOS-Chem simulation beyond 2013-2015 to include 2016 and 2017. (The previous years were completed in 2017.) The purpose of the GEOS-Chem simulation is to provide continuous information on ozone and PM concentrations for subsequent epidemiological analyses by the EPA-ACE team. The simulation has 0.5°x0.625° resolution over North America and is driven by MERRA-2 assimilated meteorological data from the NASA Global Modeling and Assimilation Office (GMAO). We used the most recent benchmarked version of GEOS-Chem (v11-2-c) including detailed ozone-aerosol chemistry. That version was previously evaluated in detail with observations of ozone, PM, and their precursors over the Southeast US during the SOAS and SEAC4RS campaigns in summer 2013. More details about this simulation, including early validation, can be found in the Year 2 report. The extended simulation was completed in 2018 and results provided to Prof. Joel Schwartz’s group (Project 3) for epidemiological analyses. The GEOS-Chem output archive includes daily surface concentrations of maximum daily 8-hr average (MDA8) O3, NO2, PM2.5 and its components, and aerosol optical depth (AOD). This is a major deliverable for the project.

As part of ongoing validation of the GEOS-Chem simulation, we attempted to reconcile the apparent discrepancy between NO2 trends over the US derived from satellite observations and NOx trends from the National Emission Inventory (NEI) of the US EPA. Tropospheric NO2 columns observed by the OMI satellite instrument over the US reveal a steady decrease from 2005 until 2009 but a flattening afterward, while the NEI reports a steady decrease of US NOx emissions over the 2005-2017 period at a rate of 0.1 Mt yr-1, or 53% over the whole time period. Our analysis showed first that the steady decrease in the NEI NOx emissions is in fact consistent with observations of surface NO2 and ozone concentrations. We further found that the post-2009 flattening of OMI NO2 is likely due to an increasing relative contribution of non-anthropogenic background (mainly from lightning and soils) and not due to flattening of anthropogenic emissions. This result was confirmed by contrasting OMI NO2 trends in winter over urban areas, where background NO2 levels are low, against such trends in summer over rural areas, where background levels are high. The winter urban NO2 columns show a significant 2005-2017 decrease, while the summer rural NO2 columns reveal no significant 2005-2017 trend. Using GEOS-Chem, we tested our hypothesis on the effects of background levels on trends in column NO2. Our work confirms the success of sustained efforts to improve US air quality over the 2005-2017 time period.

In addition to reconciling the trends of satellite data and the NEI inventory, we also identified a large discrepancy between observed and modeled ratios of NO/NO2 over the southeast United States during August–September 2013. We suggested that either unrecognized chemistry or errors in modeled cycling between NO, NO2, and ozone could explain this discrepancy. Either explanation would have important implications for global tropospheric chemistry and for the interpretation of satellite observations of NO2.

Objective 2. During Year 3, we devoted significant time and effort to developing the Particle Emissions Inventory using Remote Sensing (PEIRS). We employed the wavelet decomposition method described in Project 2 to decompose the daily MAIAC AOD (1km resolution) into three parts based on the spatial scale of the signal. To isolate the local emissions from the transported particles, we removed long-distance transportation by setting aside the low and intermediate frequency parts and keeping the high-frequency part. We designed a kernel based on Gaussian dispersion and performed a two-dimensional matrix convolution to calculate the mass difference between the upwind and downwind cells. Based on the mass-balance assumption, this difference was used as the estimation of daily primary emission.

We encountered several issues. Firstly, the wind direction is not evenly distributed. There is a region- and season-dependent prevailing wind direction impacted by terrain or climate factors. Even with inverse-likelihood weighting, we can only detect the upwind-downwind difference from a limited range of wind directions. This may introduce bias caused by the different background emission level of upwind and downwind cells. Second, the wind field reanalysis data we used in the study is at the height of 10 m. No small scale terrain effects on wind direction and velocity can be captured by this wind field. Third, we found that even though MAIAC is of better quality than the Deep Blue algorithm, it is also sensitive to the surface reflection. The difference of AOD between adjacent cells can be contributed by the distinct reflection. This is common in urban environments where small-scale variation of ground reflection due to construction is widespread. In these cases, the signal caused by the reflection gradient is stronger than the AOD and difficult to filter out. The estimation using the aforementioned methods has extremely high rate of negative emission intensity where the land surface reflection varied in a short distance. Fourth, the spatial uncertainty of the grid location persists. The nominal resolution of MAIAC is 1 km, but the actual size of area covered by the pixel varies by the viewing angle. At the edge of every swath, the actual spatial resolution is always over 5 km. Within the MAIAC algorithm, this was averaged. However, this smoothing adds additional uncertainty when we tried to calculate the particle emission rates at 1 km resolution.

In addition to the difficulties encountered with the emission rate estimation, we have not succeeded in validating the PEIRS result with NEI point data. We obtained point-based NEI data following EPA’s international emissions inventory conference. We tried skipping the mass balance steps and using machine learning algorithms to detect the potential sources directly. The known point sources reported in NEI were used as a training dataset to train random forest, deep neural network and gradient boosting models. All three methods captured the ground-surface reflection variation which highlights the road network, residential buildings and other industrial buildings without detecting point sources within the bright patches.

Given the limitations with MAIAC and the other issues described above, further effort with this particular objective is unlikely to result in successful development of a 1x1 km particle emissions inventory.

Objective 3. For objective 3, initially we have applied the cluster approach at regional scale. We have worked on three papers (to be submitted soon) focusing on Eastern Massachusetts as study area.

In the first paper, we assessed the spatial patterns of ambient PM2.5 elemental concentrations considering air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 components of ambient PM2.5, which included S, K, Ca, Fe, Zn, Cu, Ti, Al, Pb, V, and Ni. The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in 2 clusters; for Fe, Zn, V, and Ni in 3 clusters; and for K resulted in 12 clusters. Land use, population, and daily traffic were the variables that divided the study area into cluster of sites more effectively. We used an R2 value to estimate the potential from each variable in discriminating clusters. The larger R2 value, the better the discrimination among the sites. For example, population had the highest R2 value when the analysis was performed for S, Ca, Zn, Ti, Al, Pb, and V; land use presented the highest R2 value for Cu, V, and Ni; and, traffic showed the highest R2 value for individual PM2.5 concentration.

In the second paper, we evaluated the influence of clusters (those cluster estimated in the first paper) on modeling PM2.5 constituents. We hypothesized 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. ). 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. The model with cluster presented a better performance for all PM2.5 components, except for Pb, which the R2 value decreased 8.51% when we included the clusters in the analysis; and for V, which the R2 value did not change with the clusters. Models for Cu and Fe explained the highest concentration variance. The R2 value for the model without cluster was 0.55 for both pollutants. When we accounted for clusters, R2 value increased 13 and 7% for Cu (R2 = 62) and Fe (R2 = 59), respectively. The models for K and S presented the lowest performance for both models with and without cluster (although the model with cluster improved substantially the R2 values).

In the third paper, we compered 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. The OK model performed poorest for all PM2.5 components, with R2 under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, which the R2 value increased to 0.62 and 0.59, respectively. These elements presented the highest R2 value from the hybrid model. The forest model presented the best performance, with R2 above 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R2 value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with R2 value equal to 0.54, 0.37, and 0.44, respectively. The results found in this paper can be useful for the cluster framework proposed in the objective 3.

Using the background obtained from these three initial papers, we worked on a fourth paper (to be submitted soon) changing the spatial scale -form local scale to national scale (U.S.A). Our objective was to investigate spatial differences of air pollution mixtures across the US. We employed spatially constrained clustering approach (based on k-means algorithm) to group air pollution monitoring sites that exhibit distinct pollutant profiles or mixtures in the US over 9 years (2008 – 2016). We accounted for 20 chemical components of PM2.5. The resulting clusters of pollution mixtures are characterized and validated based on source emissions represented by land-use information. Our analysis resulted in 27 clusters. We estimated that Cu, Se, NO3-, Cr, and Ba were the top five species that divided the study area into cluster of sites more effectively. Our analysis resulted in 11 clusters with single site. Five clusters (cluster 1, 3, 7, 13, and 26) had more than 4 sites. Among the clusters with more than 4 sites, the cluster 13 was the one with the highest number of sites, 33 air pollution monitoring stations. The cluster 13 is located in northwest and part of the Midwest (Ohio, Indiana, Illinois, and Wisconsin). The cluster 1 has 14 sites and it covers part of the southeast, including the states of North Carolina, South Carolina, Georgia, and Florida. The southwest has a very prominent cluster with 8 sites (cluster 26), covering part of the Louisiana, Mississippi, Texas, and Arkansas. In the west coast, two clusters were highlighted in our analysis, cluster 3 in California and cluster 7 in Washington and part of Oregon. Both clusters with 5 sites. Observing the concentration ratios (concentrations of the species i / concentration of PM2.5) for some of these clusters, our results show that clusters 3 and 7 in the west coast represent sites with high Na ratios. Cluster 13 in the northwest and part of the Midwest represents sites with high SO42- ratio. The cluster 16 with a single site in northeast has the highest SO42- ratio, representing almost the third quartile of the SO42- ratio.

We also worked on some trends analyses, which will be incorporated into the objective 3 in order to investigate the impact of regulations, climate change, and modifiable factors on regional mixtures. The initial results of this part are in two other papers (paper 5 and paper 6 – both will be submitted soon).

In the paper 5, we employed generalized additive models (GAMs) to estimate weather-associated changes in PM2.5 composition in the US during 1988-2017. We considered seven components of ambient PM2.5, which included elemental carbon (EC), organic carbon (OC), nitrate, sulfate, sodium, ammonium, and silicon. The impact of long-term weather changes on each PM2.5 component was defined in our study as “weather penalty”. Nationally, temperature decreased in the warm season and increased in the cold period. Wind speed decreased in the both seasons. Relative humidity increased in the warm season and decreased in the cold season. The weather changes between 1988 and 2017 were associated with most of PM2.5 components during both warm and cold seasons. The direction and the magnitude of the weather penalty varied considerably over the space and seasons. In the warm season, our findings suggest a nationwide weather penalty for EC, OC, nitrate, sulfate, sodium, ammonium, and silicon of 0.04, 0.21, 0.04, 0.35, -0.01, 0.05, and 0.01 µg/m3, respectively. In the cold season, the estimated total penalty was 0.04, 0.21, 0.06, 0.04, -0.01, -0.02, and 0.02 µg/m3, respectively.

In the paper 6, we applied the same method as paper 5 to quantify the long-term impacts of wildfires on ambient particulate carbon (OC and EC) levels in the western U.S over the last 30 years. Our results show that in the warm season, the total wildfire penalty (for the period 1988 – 2016) on EC and OC of 0.0011 µg/m3 (95%CI: 0.0009 and 0.0014) and 0.015 µg/m3 (95%CI: 0.006 and 0.023), respectively. In the cold season, the estimated total penalty for EC was 0.034 µg/m3 (95%CI: 0.004 and 0.065) and for OC was 0.033 µg/m3 (95%CI: 0.003 and 0.063).

pProject 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 several manuscripts that ultimately appeared in print. Highlights include:

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. Use of these exposure models has led to strong evidence of air pollution-related adverse health effects, and in some instances, evidence of heterogeneity of these health effects across sub-populations. 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 have 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. By reporting the spatio-temporal weights and uncertainty estimates back to the groups that developed the prediction models they can identify “trouble” points in space and time and improve their models. With our findings we can also identify high uncertainty areas to inform placement of future monitors. The proposed exposure assessment and development activities are poised to inform numerous regional and national epidemiological studies—fully propagating intra- and inter-model uncertainty for the first time—as well as improve the exposure models used for prominent international assessments such as the Global Burden of Disease. To address computational scalability, we proposed an innovative approach that instead of estimating the spatial structure of an enormous prediction error variance-covariance matrix—as in existing approaches to quantify errors related to a single prediction model—we incorporated the spatio-temporal structure in the individual posterior distributions of these predictions.

This work on full quantification of uncertainty associated with exposure estimates derived from model ensembles has been submitted for peer-review and has won an award. The paper “Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process” was accepted by peer review to the Bayesian nonparametric workshop at the prestigious NIPS conference in December 2018. Further, it won honorable mention for a student paper award of the American Statistical Association’s (ASA’s) Section on Statistics and the Environment of the Joint Statistics Meeting to be held in Denver, CO, in August 2019.

 

In the first two years of Project 2, in Objective 1 we developed a two-dimensional wavelet decomposition that alleviates restrictive assumptions required for standard wavelet decompositions. Using this method we decomposed daily surfaces of PM2.5 to identify which scales of pollution are most associated with adverse health outcomes (Antonelli et al. 2017). A key feature of the approach is that it can remove the purely temporal component of variability in PM2.5 levels and calculate effect estimates derived solely from spatial contrasts. This eliminates the potential for unmeasured confounding of the exposure - outcome associations by temporal factors, such as season. During the past year we have conducted work on Objective 4 of the project, which is to use 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 Spring 2019.

Recent interest focuses on identifying critical windows of vulnerability associated with prenatal exposure to air pollution during pregnancy. In Year 2 of this Project, we showed that an analysis based on a distributed lag model (DLM) can yield estimates of a critical window different from those from an analysis that regresses the outcome on each of the three trimester average exposures (TAEs), which is the standard approach typically used in the environmental health literature. Moreover, interest remains high on estimating health risks associated with air pollution mixtures. However, there currently do not existing any methods to estimate the distributed lag function of a mixture. In the past year, Project 2 investigators developed one of the first multi-pollutant distributed lag models. 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 have completed a manuscript describing the methods and results that is currently being circulated among co-authors, and we plan to submit it for publication in March 2019.

Epidemiologic studies of the short-term effects of ambient particulate matter (PM) on the risk of acute cardiovascular or cerebrovascular events often use data from administrative databases in which only the date of hospitalization is known. A common study design for analyzing such data 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. In the past year we wrote and submitted a paper that developed a marginal likelihood estimator, a regression calibration estimator, and a conditional score estimator, as well as parametric bootstrap versions of each, to 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. Based on both simulation and the case study, 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. We submitted this paper to the journal Biometrics, which has invited a revision.

Project 2 investigators have applied several cutting-edge machine learning methods to model the spatio-temporal variation in ambient metal concentrations, as measured via XRF, in the greater Boston area.

Predictions based on gradient boosting machine (GBM) predict ambient concentrations well. In a GBM, weak learners are modified at each stage to minimize a pre-specified loss function. They are chained together so the second learner is designed to improve on the fit from the first. After several iterations, the learner will be able to make predictions with greater accuracy. We have found that the performance of the model predictions vary greatly from metal to metal. We have had the most success modeling ambient Iron (Fe) and lead (Pb) concentrations, with cross-validated R2 of approximately 0.70 for these two metals in the greater-Boston area. We are currently preparing a manuscript describing these results.

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

 

Year 3 was productive for our Project. During Year 3, we have continued to advance our efforts on all project objectives. We have investigated the effects of long term exposure on hospital admissions; effects of climate change on human health; effects of local and regional pollutants, and; causal modeling efforts for both acute and chronic exposures. Some highlights of our progress are detailed below.

In 135 U.S. cities, we demonstrated causal effects of locally generated pollutants on daily deaths between 1999 and 2010, at concentrations below the current EPA daily PM2.5 standard. In Schwartz et al. 2018, we used three methods which, under different assumptions, provide causal marginal estimates of effect: a marginal structural model, an instrumental variable analysis, and a negative exposure control. The instrumental approach used planetary boundary layer, wind speed, and air pressure as instruments for concentrations of local pollutants; the marginal structural model separated the effects of NO2 from the effects of PM2.5, and the negative exposure control provided protection against unmeasured confounders.

We found that in 7.3 million deaths, the instrumental approach estimated a mortality increase of 1.5% [95% confidence interval (CI): 1.1%, 2.0%] per 10 µg/m3 increase in local pollution indexed as PM2.5. The negative control exposure was not associated with mortality. Restricting our analysis to days with PM2.5 below 25 µg/m3, we found a 1.70% (95% CI 1.11%, 2.29%) increase. With marginal structural models, we found positive significant increases in deaths with both PM2.5 and NO2. On days with PM2.5 below 25 µg/m3, we found a 0.83% (95% CI 0.39%, 1.27%) increase. Including negative exposure controls changed estimates minimally.

We jointly investigated the association of short and long-term exposures to PM2.5 and temperature with hospital admissions, and explored the modification of the associations with the short-term exposures by one another and by temperature variability. In Yitshak-Sade et al., we constructed daily ZIP code counts of respiratory, cardiac and stroke hospital admissions of adults ≥65 (N=2,015,660) across New-England (2001−2011). Daily PM2.5 and temperature exposure estimates were obtained from satellite-based spatio-temporally resolved models. For each admission cause, a Poisson regression was fit on short- and long-term exposures, with a random intercept for ZIP code. Modifications of the short-term effects were tested by adding interaction terms with temperature, PM2.5 and temperature variability. We observed associations between short and long-term exposures for all of the outcomes, with stronger effects of long-term exposures to PM2.5. For respiratory admissions, the short-term PM2.5 effect (percent increase per IQR) was larger on warmer days (1.12% versus −0.53%) and in months of higher temperature variability (1.63% versus −0.45%). The short-term temperature effect was higher in months of higher temperature variability as well. For cardiac admissions, the PM2.5 effect was larger on colder days (0.56% versus −0.30%) and in months of higher temperature variability (0.99% versus −0.56%). We observed synergistic effects of short-term exposures to PM2.5, temperature and temperature variability. Long-term exposures to PM2.5 were associated with larger effects compared to short-term exposures.

In Zanobetti and O’Neill, we assessed and reviewed current literature on associations between characteristics (mean, variability, extremes) of ambient temperatures and human health. We were motivated by concerns that climate change, which operates on a time frame of decades or longer, may influence not only shorter-term associations between weather and health (daily/weekly) but also have enduring implications for population health. We reviewed 26 papers on the health effects of longer-term (3 weeks to years) exposures to ambient temperature published between 2010 and 2017 to investigate whether health outcomes have been associated with longer-term exposures. We included studies on a diverse range of health outcomes (mortality, morbidity, respiratory disease, obesity, suicide, infectious diseases, and allergies among various age groups), with the exception of vector-borne diseases such as malaria. Longer-term exposures were considered to be exposures to annual and seasonal temperatures and temperature variability. We found that regional and local temperatures, and changing conditions in weather due to climate change, were associated with a diversity of health outcomes through multiple mechanisms.

In Blomberg et al., we assessed potential modification of radon on PM2.5 -associated daily mortality in 108 U.S. cities using a two-stage statistical approach. First, city- and season-specific PM2.5 mortality risks were estimated using over-dispersed Poisson regression models. These PM2.5 effect estimates were then regressed against mean city-level residential radon concentrations to estimate overall PM2.5 effects and potential modification by radon. Radon exposure estimates based on measured short-term basement concentrations and modeled long-term living-area concentrations were both assessed. We found that exposure to PM2.5 was associated with total, cardiovascular, and respiratory mortality in both the spring and the fall. In addition, higher mean city-level radon concentrations increased PM2.5-associated mortality in the spring and fall. For example, a 10 μg/m3 increase in PM2.5 in the spring at the 10th percentile of city averaged short-term radon concentrations (21.1 Bq/m3) was associated with a 1.92% increase in total mortality (95% CI: 1.29, 2.55), whereas the same PM2.5 exposure at the 90th radon percentile (234.2 Bq/m3) was associated with a 3.73% increase in total mortality (95% CI: 2.87, 4.59). Results were robust to adjustment for spatial confounders, including average planetary boundary height, population age, percent poverty and tobacco use. While additional research is necessary, this study suggests that radon enhances PM2.5 mortality. This is of significant regulatory importance, as effective regulation should consider the increased risk for particle mortality in cities with higher radon levels. In this large national study, we found that city-averaged indoor radon concentration was a significant effect modifier of PM2.5-associated total, cardiovascular, and respiratory mortality risk in the spring and fall. These results suggest that radon may enhance PM2.5-associated mortality. In addition, local radon concentrations partially explain the significant variability in PM2.5 effect estimates across U.S. cities, noted in this and previous studies.

Other Project 3 efforts during Year 3 have included updating our BC, PM2.5, O3 models and working on our NO2 model.

pProject 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 3 was very successful in three primary domains: 1) refinement of our reduced-complexity atmospheric model for pollution transport from power plants and 2) further development of statistical methodology tailored to the analysis of air quality regulations and 3) the deployment of work in domains (1) and (2) into epidemiological analyses. In particular, the addition of Lucas Henneman (postdoctoral associate, atmospheric engineer and data scientist) solidified our ability to repurpose the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model for the purpose of characterizing pollution transport and population exposures from power plants. The current product, which we call HYSPLIT Average Dispersion model (HyADS), has shown the ability to approximate the transport of SO2 emissions from coal-fired power plants effectively enough to: 1) generate source-receptor matrices of how an arbitrary set of power plants impacts an arbitrary set of population locations and 2) characterize population location (e.g., zip code) level exposures to pollution originating specifically from power plants. Importantly, the method can do so in a much more computationally nimble and scalable way that supports its use when full-scale chemical transport models would not be practicable due to computation. A manuscript evaluating the HyADS model has been accepted for publication in Atmospheric Environment, and a separate manuscript using the model to evaluate health impacts of changes in coal power plant emissions has been accepted for publication in Epidemiology.

We have made progress towards publication on several statistical methods projects. For ongoing methods development regarding causal inference with interference, a paper on cluster and population treatment allocation programs is at the second round of revisions for Biometrics, and we have also submitted a paper defining bipartite causal inference with interference. Several other papers relating to statistical topics of mediation analysis, time-varying mediation analysis, uncertainty in the design stage of propensity score analyses, mitigating the consequences of strong confounding/lack of overlap in causal analyses, time-varying treatments, continuous treatments, measurement error, and machine learning methodology for causal inference have been recently submitted, invited for revision, or published. All of this work is conducted in the context of evaluating impacts of power plant interventions, emissions, or other air pollution related exposures.

Finally, we have completed several key epidemiological publications. One paper evaluating the health impacts of nonattainment designations for PM standards has been published this year in Epidemiology. Another outlining the potential health impacts of the current presidential administration’s environmental agenda appeared in JAMA.

pProject 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 been distributed among three main goals. The first has been a significant expansion of the cross-state air pollution project under project objectives 2 and 4. This work quantifies health impacts resulting from pollution which crosses state boundaries, comparing these imported and exported impacts to the in-state impacts from local emissions. Based on feedback received during the first round of peer review, the project was expanded to include ozone pollution in addition to particulate matter. This necessitated the development of new adjoint code and a set of 96 GEOS-Chem adjoint simulations, quantifying the impact of each state, emissions sector, and emitted species on the ozone concentrations in every state within the contiguous United States. An additional set of 10 conventional simulations was also completed to quantify non-linearity in the response of US air quality to anthropogenic emissions. We found that ~30% of the total health burden of US air quality is due to ozone exposure as opposed to PM2.5. However, due to the high non-linearity of the response of ozone concentrations to changes in emissions, only ~10% of the benefits of a small reduction in emissions across the US would be the result of changes in ozone concentrations. This discrepancy between the “average” and “marginal” sensitivity of US air to changes in emissions will have direct relevance to decision-making regarding US emissions. We also found that, due to its longer lifetime, 70% of ozone-related impacts from US emissions are incurred outside of the originating state, compared to only 40% for PM2.5.

More broadly, this work quantifies the role that changes in the mix and magnitude of anthropogenic emissions has affected and will continue to affect local air quality. It also provides crucial information for the evaluation of the impact of related energy policy, by giving a numerical estimate of the co-benefits or tradeoffs which might be incurred if other policies penalize or incentivize specific states or industrial sectors. Following the first round of reviews and the additional work described above, the manuscript has been resubmitted for publication and is again under peer review.

This work has also resulted in two new studies. Using the data produced from our work on cross-state air pollution, 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 shows that the US-mean CPCC exceeds the social costs of carbon used by the current and previous administrations. We also find that the CPCC varies significantly across the US, with the highest state-average CPCC being 15 times greater than the lowest. A manuscript based on this work is currently in preparation. 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. Changes in demand are calculated for 21 different carbon price scenarios, using a dispatch model which can estimate pollutant emissions on a plant-by-plant, hour-by-hour basis for a full year. During this project year, emissions estimates were generated for all scenarios, and are now undergoing validation prior to calculation of air quality damages using the same approach as was used for the cross-state air pollution study.

The second goal of year 3 was to advance work on quantifying the time-of-emergence of air quality impacts over the 21st century (Objectives 1 and 5). We had aimed to complete an ensemble of 120-year simulations using the Community Atmospheric Model (CAM) v3.1 during the project year. This work was delayed by the discovery of non-physical output in the CAM meteorological forecasts, requiring an intensive debugging effort before the simulations could be completed. This debugging has now been completed, and test output from CAM has been verified as physical by comparison to existing output from an unrelated global climate model, the NASA GEOS data assimilation system. Simulations are now underway to complete the climate ensemble needed to drive the GCHP air quality model and thereby produce an “air quality ensemble” for the 1980-2100 period. In addition to the climate dataset, a methodology was also identified during the project year which will allow the quantification of the “time of emergence” of air quality impacts due to meteorological factors (Objective 5). This is based on statistical analysis of variability within and between air quality ensemble members.

The third goal of year 3 concerned the effectiveness of state-level climate and energy policies in United States, with a focus on the effects on power sector output, emissions, and air quality (Objectives 2 and 3). We compiled a unit-level emissions inventory for fossil fuel power plants in the U.S. (collaborating with project 4 at HSPH/MIT ACE center), associating emissions from each unit with individual policy programs. We examined the effects of six energy and climate policies on power sector output and emissions: renewable portfolio standards (RPS); energy efficiency standards; regional greenhouse gas initiative (RGGI); decoupling revenue from sales; mandatory green power; and CO2 emissions standards. on power sector generations and emissions. The most significant finding was that RPS reduced unit-level emissions by 7-9% on average relative to electricity generating units in non-RPS states.

This analysis was complemented by a study of how wind power influences power sector emissions, using an hourly emissions and wind power dataset. We statistically estimated the effects of wind power on generation and emission for each power generating unit, which allowed us to capture both heterogeneity (between power plants) and locality of the air pollution outcomes. We find that cleaner gas-fired units are more sensitive than other units to changes in wind power output, which leads to reduced emission offsets (due to wind power) compared with previous literature estimates.

Within the same set of objectives, we also finalized results and a manuscript for a paper titled “Health Co-Benefits of Sub-National Renewable Energy Policy in the U.S.” The research leverages the MIT United States Regional Energy Model and the InMAP model to comprehensively explore the costs and benefits of Renewable Portfolio Standards in the U.S. with a focus on the air quality health co-benefits. We found that the air quality co-benefits of RPSs in the Rust Belt region alone exceed the total economic costs of these policies, subject to uncertainty in the concentration-response and Value of Statistical Life assumptions. This paper will shortly be submitted for peer review.

Future Activities:

During Year 4, the Center will continue with activities discussed above. Specific Project activities for Year 4 are described in the Project Progress Reports included in later sections. Additional planned Center activities during Year 4 include our third annual SAC meeting, which is scheduled for June 10-11, 2019.

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

During Year 4, we will continue our work on Objectives 1, 3, and 4. In previous research, we found that model estimates of surface ozone concentrations tended to be biased high in the Southeast US and this was of concern for designing effective emission control strategies to meet air quality standards (Travis et al., 2016). Using GEOS-Chem, we determined that at least some of the bias can be traced to NEI emissions for NOx from mobile sources, at least for summer 2013 during the SEAC4RS campaign. In ongoing work, we are revisiting the Travis et al. (2016) result, to better understand the sources of the emissions bias in the NEI inventory. To date, we find that the bias appears relatively constant over the 2005-2017 timeframe.

We will continue our analysis of the impact of regulations, climate change, and modifiable factors on regional mixtures. Initially, our focus will be on completion and submission of the manuscripts in preparation. Understanding the impact of these factors during the period 1988-2016 will be essential to undertaking Objective 4.

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 better 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, the paper describing the gradient boosting models for the XRF data in the greater Boston area, and the paper describing analytic methods for adjusting for exposure measurement error in the case-crossover design.

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

During Year 4, we will continue our work on local- and region-specific estimates of past and future health effects and the benefits of changes in modifiable factors and adaptation. We will also continue with our causal analyses on the effects of pollution mixtures, sources, and emissions on health and the regional differences in concentration-response and NO2 modeling.

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 4 we will continue development along all lines described above. Early stage development on using the outputs of the HyADS reduced complexity model to inform development of new statistical methods for causal inference with interference and network analysis will continue and is expected to result in several newly submitted manuscripts in statistics and epidemiology journals. With the recent validation of HyADS, we are at early stages of investigations tailored to the health benefits of specific energy transitions, such as fuel retrofits, scrubber installs, and plant retirements, which we expect to be central to development during the remainder of the funding period. We are also 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. Work in collaboration with members of the Project 5 team is also ongoing to compare HyADS results against GEOS-Chem Adjoint for comparing individual coal power plant impacts on select areas across different regions of the US. We are also working on publications to highlight the open access and usability of our databases and the HyADS reduced-complexity model towards the dissemination of these tools for others to use in their own research.

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

Our first goal for project year 4 is to complete the CAM-based climate simulation ensemble. Although delayed as explained above, we now expect this to be complete in early 2019. This will enable us to complete our 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 (Objectives 1 and 5). 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).

During the next year, we also intend to leverage the previous year’s work on offsets of energy emissions by wind power. We will use this data to estimate the effects of renewable policies on air quality and human health in U.S, applying a state-of-the-art chemical transport model (GEOS-Chem). This will allow us to quantify the relative effectiveness of renewable policy as an approach to reducing air pollution. 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).

We are also beginning a new project, quantifying air quality tradeoffs and benefits associated with recent state and federal environmental legislation. For each identified policy, one or several counterfactual emissions inventories will be developed. The GEOS-Chem nested model will be used to estimate the effect that each policy had, or would have had, on ozone and particulate matter concentrations over the affected period at a resolution of ~30 km across the contiguous United States. Air quality impacts will be monetized and compared to the expected economic costs and benefits of the legislation (Objectives 1 and 3). Work during year 4 will focus on finding candidate policies and developing a methodology for estimating their effects on emissions.

In addition to completing in-project objectives, we intend to intensify our collaboration with project 1. 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 both projects investigating air quality trade-offs associated with carbon policy. We anticipate no significant additional timeline adjustments.

References:

Travis, K. R., et al., Why do models overestimate surface ozone in the Southeast United States?, Atmos. Chem. Phys., 16, 13561-13577, 2016.


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)
    R835872 (2017)
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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)
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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)
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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)
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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)
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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)
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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)
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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 JD, Di Q, Requia WJ, Dominici F, Zanobetti A. A Direct Estimate of the Impact of PM2.5, NO2, and O3 Exposure on Life Expectancy Using Propensity Scores. Epidemiology 2021; 32(4):469-476. R835872 (2020)
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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 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 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 (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 Bose S, Chiu YH, Hsu HH, Di Q, Rosa MJ, Lee A, Kloog I, Wilson A, Schwartz J, Wright RO, Cohen S. Prenatal nitrate exposure and childhood asthma:influence of maternal prenatal stress and fetal sex. American Journal of Respiratory and Critical Care Medicine 2017;196(11):1396-1403. R835872 (2017)
    R835872 (2018)
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  • Journal Article Chiu YH, Hsu HH, Wilson A, Coull BA, Pendo MP, Baccarelli A, Kloog I, Schwartz J, Wright RO, Taveras EM, Wright RJ. Prenatal particulate air pollution exposure and body composition in urban preschool children:examining sensitive windows and sex-specific associations. Environmental Research 2017;158:798-805. 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)
  • Abstract:
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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)
    R836156 (Final)
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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)
  • Abstract: TFO- Abstract
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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)
  • Abstract: IOP Science- Abstract
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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)
    R835871 (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)
  • Abstract: ScienceDirect- Abstract
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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)
  • Full-text: CHEST - Full Text HTML
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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
  • Full-text: PCM - Full Text PDF
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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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  • Full-text: ACS - Full Text HTML
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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)
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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
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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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  • Abstract: European Geosciences Union Abstract HTML
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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)
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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)
    not available
    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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  • 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, multi-resolution spatial analysis, source emissions, local pollution control strategies, wavelet analysis, particulate matter, pollutant mixtures, risk analysis, causal modeling, accountability assessment, power-generating sector, intervention evaluation, source-receptor mapping, PM2.5, ozone, adjoint method, air quality, greenhouse gas, computable general equilibrium, mercury, polycyclic aromatic hydrocarbons, PAHs

    Relevant Websites:

    Harvard/MIT Air, Climate & Energy Center Exit

    MIT Joint Program on the Science and Policy of Global Change Exit

    Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • 2017 Progress Report
  • 2019 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