2017 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
Institution: Harvard University , Massachusetts Institute of Technology
EPA Project Officer: Keating, Terry
Project Period: December 1, 2015 through November 30, 2020
Project Period Covered by this Report: December 1, 2016 through November 30,2017
Project Amount: $10,000,000
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text |  Recipients Lists
Research Category: Air , Health , Climate Change , Integrated Assessment of the Consequences of Climate Change , Air Quality and Air Toxics , Social Science , Airborne Particulate Matter Health Effects , Air Toxics , Health Effects

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

In year 2, our efforts were focused on Objectives 1, 2 and 4.

Objective 1. We conducted a 3-year GEOS-Chem simulation for 2013-2015 to provide continuous information on ozone and PM concentrations for the purpose of epidemiological analyses. 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. The evaluation tested a new model scheme for isoprene secondary organic aerosol (SOA) and was successful in simulating overall organic aerosol levels. Observations of NOx and its oxidation products showed that NOx emissions in the EPA National Emission Inventory (NEI) were biased high by up to 50% (depending on errors in soil NOx emissions), and implementation of this correction provided an improved simulation of ozone. All these developments were incorporated in the GEOS-Chem version used for our multi-year EPA ACE simulation. Long-term relative trends of NEI emissions were applied over the duration of the simulation period.

The simulation was completed in November 2017 and results were 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) ozone, NO2, PM2.5 and its components, and aerosol optical depth (AOD). This was a major deliverable for the project.

Objective 2. Focus during year 2 has been on Particle Emission Inventory using Remote Sensing (PEIRS) to construct spatially- and temporally-resolved emission inventories for PM2.5. Our efforts on this objective during Year 2 have included extending our work to cover more of the USA and the complete targeted time span. Using a mass balance approach, we converted the daily PM2.5 fields to a net addition (reflecting contributions of primary emission, secondary formation, and losses due to deposition) within each 1x1 km grid cell. We have also devoted significant effort toward improving our models. Because the spatial scale of primary PM2.5 emissions is smaller than the scale of secondary formation, discrete wavelet decomposition can be used to separate the net addition fields into two parts: the high frequency fraction and low-frequency fraction. This allows us to better assess primary emissions versus secondary formation and regional emissions.

Objective 4. We wrapped up work begun on statistical models to investigate the meteorological drivers of inter-annual to multidecadal variability of air quality, including ozone and fine particulate matter (PM2.5), in the United States. We examined processes involving climate patterns at different spatial scales, including that of local weather (~100 km), synoptic circulation (~1,000 km) and large-scale climate patterns (~10,000 km).  

In Shen et al. (2017a), we first constructed a statistical model for relating observed PM2.5 to regional meteorology across the United States from 1999 to 2013. We applied the model to an ensemble of global climate models under the RCP4.5 scenario, predicting an annual mean increase of 0.4-1.4 mg/m3 of PM2.5 in the eastern United States by the 2050s. This prediction assumes present-day anthropogenic sources of PM2.5. Mean summertime PM2.5 increases as much as 2-3 mg/m3 in the eastern United States due to faster oxidation rates and greater mass of organic aerosols from biogenic emissions.  

In Shen and Mickley (2017a), we developed a seasonal prediction model for surface ozone in the East. The model predicts June–July–August (JJA) daily maximum 8-h average (MDA8) ozone concentrations using large-scale climate patterns during the previous spring.  In Shen and Mickley (2017b), we examined the influence of El Nino on U.S. surface ozone from 1980 to 2016. We found that each standard deviation increase in the Niño 1+2 index is associated with an increase of 1–2 ppbv ozone in the Atlantic states and a decrease of 0.5–2 ppbv ozone in the south central states. These influences can be predicted 4 months in advance. Finally, in Shen et al. (2017b), we found that U.S. summertime air quality displays strong dependence on North Atlantic sea surface temperatures, resulting from large-scale ocean-atmosphere interactions. We further identified multidecadal variability in surface air quality driven by the Atlantic Multidecadal Oscillation.

Taken together, these studies (1) improve understanding of current trends in U.S. air quality, (2) provide a means to evaluate current chemistry dynamical models such as GEOS-Chem, and (3) allow projection of air quality trends into the future, given meteorological fields from global climate models. 

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

This past year we finalized several manuscripts that ultimately appeared in print.  Highlights include:

Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R2 of the model was good in both cold (10-fold CV R2 = 0.87) and warm seasons (CV R2 = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC in MA, RI and Southern NH. This work was published in Environmental Research (Abu Awad 2017).

Fine particulate matter (PM2.5) measured at a given location is a mix of pollution generated locally and pollution traveling long distances in the atmosphere. Therefore, the identification of spatial scales associated with health effects can inform on pollution sources responsible for these effects, resulting in more targeted regulatory policy. We proposed 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. 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.  We applied our method to a study of birth weights in Massachusetts, U.S.A from 2003-2008 and find that both local and urban sources of pollution are strongly negatively associated with birth weight. Results also suggest that failure to eliminate temporal confounding in previous analyses attenuated the overall effect estimate towards zero, with the effect estimate growing in magnitude once this source of variability is removed. This work was published in the Annals of Applied Statistics this past year (Antonelli et al. 2017).

Recent interest focuses on identifying critical windows of vulnerability associated with prenatal exposure to air pollution during pregnancy. 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. Using a simulation study, we assessed bias in estimates of critical windows obtained using three regression approaches: 1) three separate models to estimate the association with each of the three TAEs; 2) a single model to jointly estimate the association between the outcome and all three TAEs; and 3) a DLM. We use weekly fine particulate matter (PM2.5) exposure data for 238 births in a Boston-area birth cohort and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAE were biased and identified incorrect windows. This bias arose from seasonal trends in PM2.5 that induce correlation between TAEs. Including all TAEs into one model reduced bias. DLM produced estimates that were unbiased and added flexibility to identify critical windows. Analysis of body mass index z-score and fat mass in the same cohort highlights inconsistent estimates from the three methods.  This work was published in American Journal of Epidemiology this past year (Wilson et al. 2017a).

In related work, simultaneous estimation of windows of vulnerability and effect heterogeneity is typically accomplished by fitting a distributed lag model (DLM) stratified by subgroup. However, this does not allow for subgroups to have the same window of vulnerability but different effects within the window or to have different windows but the same within-window effect, which can make full characterization of effect heterogeneity difficult. We proposed a new approach that partitions the DLM into a constrained functional predictor that estimates windows of vulnerability and a scalar effect size that estimates the effect within the window. The proposed method allows for heterogeneity in only the window of vulnerability, only the effect within the window, or in both by allowing each component to be either shared or differ across groups. We used the proposed method to estimate windows of vulnerability in the association between prenatal exposures to fine particulate matter (PM_2.5) and each of birth weight and asthma incidence, and to estimate how these associations vary by sex and maternal obesity status,  in a Boston-area prospective pre-birth cohort study (Wilson et al. 2017b).  These methods have been implemented in several other analyses of prenatal, sex-specific critical windows of air pollution exposure on health outcomes in children (Brunst et al. 2017; Chiu et al. 2017; Lee 2017).

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

The past year was a very successful year for our project. We published two major nationwide studies on the acute and chronic effects of exposure to PM2.5 and ozone.  In Di et al we analyzed the entire Medicare population and looked at the effect of annual average exposure to PM2.5 and ozone on survival in a cohort study. This study included all Medicare beneficiaries for the first time, and importantly, people who live in small cities, towns, and rural areas as well as larger urban areas. We found a highly significant association between PM2.5 and ozone and mortality rates. Importantly, we had 32 million people whose exposure was below the current ambient air quality standard of 12 μg/m3, with 248 million person years of follow-up, and in analyses restricted to just those observations, we found a highly significant association with PM2.5, demonstrating that the mortality effects continue to concentrations well below the current standard.

In the second paper, we performed a case-crossover analysis looking at ozone exposure and PM2.5 exposure and the acute risk of mortality, again, in the entire Medicare population. Once more, both associations were highly significant, and continued well below current air quality standards with no evidence of a threshold.

In Abu Awad et al we extended a BC model we had previously fit in eastern MA to include all of Massachusetts as well as Rhode Island and Southern New Hampshire. We also improved the model by using machine learning, providing estimates with smaller mean squared error on left out monitors, and extended the time period up to 2012. Using this updated exposure we then published a paper (Kingsley 2017) using that exposure (and our PM2.5 model) to look at the role of particles in preterm birth.

In Dai 2017 we used the measurements of particle metal components from our central site to examine the effects of different kinds of particles on DNA methylation in an agnostic analysis. We observed 20 Bonferroni significant (P-value < 9.4£ 10¡9) CpGs for Fe, 8 for Ni, and 1 for V, demonstrating that metal particles influence DNA methylation.

In Wang 2017 we developed a novel method for assessing adaptation to heat waves using temperature and mortality data from 1961 onwards and applied that to the estimated temperatures under 4 different greenhouse gas emissions scenarios from 21 different global climate models. We found adaptation continued up until a maximum temperature. Hence in the Northern US, heat wave related mortality is expected to fall (taking into account continued adaptation) but in the Southeastern US, it is expected to increase as adaptation reaches its limits.

In Nwanaji-Enwerem  et al, 2017, we made the important finding that DNA methylation age, a measure of biological aging, was increased by exposure to PM2.5 (estimated at home addresses with one of our models) and that this effect was modified by genetic variations in genes related to miRNA processing. This effect varied by genotype from between 1 and 3 years of additional aging equivalent as PM2.5 went from 8 to 12 μg/m3, again showing effects below ambient standards.

In another paper by Nwanaji-Enwerem we examined PM2.5 components from a chemical transport model as predictors of DNA methylation age. We found that sulfate and ammonium components were associated with increased methylation age, independent of PM2.5 mass. This held when restricted to PM2.5 concentrations below 12.

In Peng 2017 we showed that short term increases in air temperature resulted in increased lesions in mitochondrial DNA.

In Prada 2017 we reported that PM2.5 from our models was associated with hospital admissions for osteoporosis-related fractures in the Medicare cohort. Simultaneously, in a more detailed cohort in the Boston are we followed a cohort of participants longitudinally and for that both black carbon (from the model above) and PM2.5 were associated with lower serum parathyroid hormone, and black carbon was associated with higher bone mineral density loss over time in multiple sites.

In Schwartz 2017 we addressed the causality of the association between PM2.5 and NO2 with daily deaths, but implementing an instrumental variable analysis for local particles, as well as a negative exposure control. We found significant effects of both pollutants. The instrument was a combination of the height of the planetary boundary layer and wind speed, which influence local pollution concentrations, but are unlikely to otherwise influence daily deaths.

We followed up with another causal modeling approach (Wang 2017), this time for cohort analysis of long term exposure. Using the Medicare population of the Southeast as our cohort, we developed a doubly robust additive hazard model to study survival versus exposure. That is, like a tradition causal model, it give causal estimates if the model for exposure given the covariates is correct, however, it also gives causal estimates if the model for mortality given the covariates is correct. We found a highly significant, causal estimate for the effects of PM2.5 on life expectancy. Separately, we fit a more traditional proportionate hazard model in the same population to examine effect modification. We found higher effects among males, non-whites, persons eligible for Medicaid as well as Medicare, and persons living in neighborhoods with lower SES.

In addition to these publications, we continued to update our pollution models in terms of improving modeling methods and extending the time frame. We expect to produce updated estimates to 2016 for BC, PM2.5, and O3 by early summer.

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 bulk of progress during year 2 for Project 4 has fallen into 5 categories:

  1. Refining the national database on power plants, emissions, ambient pollution, population demographics, and health outcomes among Medicare beneficiaries. Efforts have included the acquisition of a curated database of census-like variables for as all the years available from ESRI Business Analyst (annual data, 2000-present).  We also bought two reserved-usage Dell servers and 30TB of secure storage, within the Harvard RCE high-performance computing cluster (https://rce-docs.hmdc.harvard.edu/).
  2. New reduced-form scalable strategies to produce source-receptor matrices linking individual power plants to US zip codes.  We have continued to refine our strategy based on the Hybrid Single Particle Lagrangian Integrated Trajectory model.  In collaboration with the Carnegie Mellon/UW ACE Center, we have initiated a new strategy based on the recently-proposed Intervention Model for Air Pollution (InMAP) to generate similar source-receptor matrices.  We have also explored purely statistical strategies based on scalable generalized additive models of daily emissions and pollution time series.  Methods are being compared/validated against each other and against observations, and also being used for intervention analyses.
  3. Statistical methods development.  The various methods for obtaining source-receptor models have been used as inputs into novel statistical methods development, including development of new methodology for bipartite causal inference with interference.  We have also continued to pursue methods for general causal inference, multiple mediating variables, spatial confounding adjustment, uncertainty in propensity score “design” stage, causal inference with interference, model averaging for confounder selection, measurement error, causal exposure-response estimation, and statistical network analysis, all in the context of evaluating power plant regulations.
  4. Environmental and Epidemiological Analyses. The above methods development has been in service of analyses of air pollution interventions including analyses of the health benefits of PM nonattainment designations, evaluation of selective (non) catalytic reduction systems for reducing NOx emissions and ambient ozone, mediation analyses of how scrubbers on coal-fired power plants reduce ambient PM, evaluation of the health effects associated with air pollution derived specifically from coal-fired power plants, flexible estimation of causal exposure-response relationships at low levels of ambient pollution.
  5. Software.  Publications are paired with reproducible R scripts and/or packages, hosted on digital repositories.  We deployed a customized version of the SplitR package (the R interface to HYSPLIT) on our new servers.  We also developed and deployed the rinmap R package to generate source-receptor matrices using inMAP.

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

Work on Project 5 during the reporting year has been focused on three main goals. The first has been to continue the sector-, emitter-, and receiver-specific analysis of past and present emissions within the US. To that end, we have completed a study quantifying the fine particulate matter (PM2.5) pollution exchange between the US states. Using the adjoint of the GEOS-Chem chemistry transport model, the state-level population exposure sensitivities to PM2.5 precursor emissions in all other states in the continental US have been calculated, including “domestic” emissions (same-state). The EPA’s National Emissions Inventories from 2004 onwards have been leveraged to provide a high-quality input dataset, while the Sparse Matrix Operator Kernel (SMOKE) Modeling System has been applied to group individual sources into seven sectors: electric power generation, industry, commercial/residential, and four modes of transportation: road, marine, rail and aviation. By applying the estimated emissions distribution to the calculated sensitivity matrices, we have generated impact source-receptor matrices for every pair of states within the contiguous US. This allows each state to estimate not only the degree to which air quality degradation is a result of emissions from a specific sector, but also the degree to which those impacts are within the legislative control of that state. Furthermore, estimates were made for multiple years from 2004 to 2014, showing that while the overall impacts of anthropogenic emissions have been decreasing, the impact per unit emitted has been increasing. This speaks directly to objective 4, by quantifying 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. A manuscript based on this work has been submitted for publication and is currently under peer review.

The second focus area has been on objectives 2 and 3, through extending and applying our economic analysis capabilities. We have made additional enhancements to the U.S. Regional Energy Policy Model (USREP) which will allow us to model energy policies and their impacts with greater precision. These enhancements include the incorporation into USREP of detailed data on the relative costs of energy technologies. On the basis of these changes, we have designed and implemented a new version of USREP (currently in the final stages of development) which models a larger number of sub-national regions and allows for the simulation of state-level energy policies. USREP’s comprehensive coverage of the U.S. economy allows us to account for the broad impacts that energy policies can have on the economy. We are using this model in combination with reduced-form air quality tools (including the source-receptor matrices described above) to simulate energy policies and estimate their effects with regards to both the economy and air quality. This modeling framework provides self-consistent estimates of the costs and benefits of U.S. energy policies. We are also exploring comparisons with other reduced-form tools including the Intervention Model for Air Pollution (InMAP), in collaboration with the Marshall group at the University of Washington.

We have continued an effort to develop a large ensemble of climate simulations relying on the MIT Integrated Global System Model, more specifically a version coupled with the NCAR Community Atmospheric Model, to provide the core basis to examine the impact of future climate change on air quality and health. This specifically serves project objectives 1, 3, and 5. This ensemble builds upon the work by Garcia-Menendez et al. (2015), which investigated the air quality and health benefits from avoided climate change under greenhouse gas mitigation using one of the largest ensemble of climate simulations to drive the global and three-dimensional atmospheric chemistry model CAM-Chem. Because the ensemble includes both multi-decadal and initial condition perturbations, leading to more than 1000 years of simulation air quality, Garcia-Menendez et al. (2017) further examined the impact of natural variability on the robustness of projections of climate change impacts on U.S. ozone pollution. However, this ensemble was designed as time slices, with three 30-year time periods (1981-2010, 2036-2065 and 2086-2115). Thus it does not allow a proper time of emergence analysis, which instead requires transient simulations, to determine how many years it will take for climate change, greenhouse mitigation policies or air pollution policies to be detected, given the large year-to-year variations in the climate and air quality systems. For this reason, we are running a new large ensemble of transient climate simulations to answer these questions. In addition, since the ongoing project will use the GEOS-Chem atmospheric chemistry model instead of the CAM-Chem model, we have modified the source code of the climate model to provide the appropriate input data for the project. The updated climate model is now production ready and the runs will start momentarily. A new, high-performance version of GEOS-Chem (GCHP) has now been adapted to accept these fields, allowing rapid and responsive air quality and composition simulations to be incorporated as a component in the modeling chain.

Future Activities:

During Year 3, the Center will continue with activities discussed above. Specific Project activities for Year 3 are described in the Project Progress Reports included in later sections. Additional planned Center activities during Year 2 include our second annual SAC meeting, which is scheduled for May 30-31, 2018.

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

We have now begun to use the archive of GEOS-Chem output generated for Objective 1 to address major questions that arose from our SOAS and SEAC4RS work. These questions, which have subsequently been supported by other analyses, are: why are NEI NOx emissions so overestimated, and can satellite (OMI) observations of tropospheric NO2 columns provide useful constraints on the patterns of overestimates by sector, region, and season? Previous studies have shown consistency between 2005-present NEI NOx emission trends and OMI NO2 trends; but that then implies the NEI overestimate of NOx emissions has persisted for over a decade. We plan to compare the detailed patterns observed by OMI to those seen in the GEOS-Chem simulation to identify specific source sectors (including soils) and regions that may be responsible for the overestimate, and the extent to which this overestimate persists across seasons. This work will constitute the final thesis project for Harvard PhD student Rachel Silvern.

During Year 3, we also expect to focus intensively on Objective 2, using PEIRS with wavelet decomposition to separate the high frequency/primary emissions from the net addition fields.

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 focus on two areas of research recommended by the Center’s Scientific Advisory Council last May.  These are better characterization of uncertainty associated with exposure-prediction models based on remote-sensing satellite data, and the application of the methods applied in Objective 1 to mortality data, which is objective 4 in the proposal.  More details are as follows:

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.  Over the coming year we will be evaluating the impact of this assumption and developing a way to characterize uncertainty associated with exposure prediction model choice.  Our preliminary results show that even when distinct exposure models predict similarly well, the spatio-temporal pattern of their errors might differ significantly. These complex error structures can lead to misleading evidence of effect heterogeneity across sub-populations, which in turn could be wrongly attributed to other factors that vary in a similar manner either in space (e.g. rural vs. urban) or time (e.g. by season).

Ultimately, we will develop a novel ensemble model framework to integrate information across multiple existing air pollution prediction models across the United States and, for the first time, to comprehensively quantify the uncertainty associated with air pollution exposure assessment. Subsequently, we propose to couple the ensemble predictions with a novel measurement error correction approach, to fully characterize the impact of air pollution on adverse health and the shape of the exposure-response curves. The proposed novel paradigm will greatly improves communication of exposure uncertainty in the health effect estimates both to policy makers and the public and can easily be extended for use at different locations and at a global scale, as well as for other environmental exposures.

During the coming year we will also complete work on Objective 4 of the project, which is to use the spatial scale-specific (regional, sub-regional, and local) temporal variability in PM2.5 mass that we developed in Objective 1 (Antonelli et al. 2017) to identify source types (regional, urban background, or local) and the composition of their emissions driving pollution-induced mortality in Eastern Massachusetts.

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

Future plans include updating our BC, PM2.5, and ozone models, adding a NO2 model, and starting models for PM2.5 components nationwide. Simultaneously we plan to expand our outcomes to include the effects of long term exposure on hospital admissions, and to expand our causal modeling efforts for both acute and chronic exposures.

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

We will continue to refine methodology for reduced-form models to produce spatially and temporally refined source-receptor mappings, including a dispersion implementation of HYSPLIT and investigation of other products produced by other ACE Centers (e.g., the EASIUR model from CMU).  With a newly-hired postdoctoral fellow specializing in environmental engineering, we plan to formalize our comparison of different source-receptor mapping techniques for the specific purposes of evaluating interventions, as well as deploy these methods in a variety of health-outcome studies of interventions. 

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

With the integrated methodology now complete, the first major objective of this reporting period is to complete a comprehensive set of climate simulations (Objectives 1 and 5). Once generated, these will be applied in the newly-configured global chemistry-transport model GCHP, to provide fully transient estimates of ozone and PM2.5 impacts in the US over the next 100 years under different climate scenarios (Objectives 4 and 5). A central focus of year 3 will be to establish an analytical method through which the time of emergence of air quality impacts can be established. This will take advantage of our ensemble approach, with a separate evaluation for each climate scenario based on differences between climate change-driven change and local variability from 1980-2100. The new approach will provide a robust estimate of how and when we expect to be able to observe impacts of climate change on air quality, from state-level to regional and international analysis (Objective 5). On the economic analysis aspects of the project, we will continue work on USREP and expect to complete a study on the economic and air pollution effects of regional energy policies such as the Renewable Portfolio Standards (Objectives 2 and 3). We anticipate no significant additional timeline adjustments for the project.


Journal Articles: 121 Displayed | Download in RIS Format

Other center views: All 133 publications 121 publications in selected types All 121 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 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)
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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)
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  • Journal Article Colicino E, Wilson A, Frisardi MC, Prada D, Power MC, Hoxha M, Dioni L, Spiro III A, Vokonas PS, Weisskopf MG, Schwartz JD, Baccarelli AA. Telomere length, long-term black carbon exposure, and cognitive function in a cohort of older men:the VA Normative Aging Study. Environmental Health Perspectives 2017;125(1):76-81. R835872 (2016)
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  • Journal Article Dai L, Mehta A, Mordukhovich I, Just AC, Shen J, Hou L, Koutrakis P, Sparrow D, Vokonas PS, Baccarelli AA, Schwartz JD. Differential DNA methylation and PM2.5 species in a 450K epigenome-wide association study. Epigenetics 2017;12(2):139-148. R835872 (2016)
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  • Journal Article 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 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 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)
    R834798 (Final)
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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 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 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)
    R834799 (Final)
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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 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 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 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 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 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 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 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 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 Lin C, Christiani D, Lin RT. 0059 A global perspective on coal-fired power plants and lung cancer mortality. Occupational and Environmental Medicine 2017;74:A16. R835872 (2018)
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  • Journal Article Prada D, Zhong J, Colicino E, Zanobetti A, Schwartz J, Dagincourt N, Fang SC, Kloog I, Zmuda JM, Holick M, Herrera LA. Association of air particulate pollution with bone loss over time and bone fracture risk:analysis of data from two independent studies. The Lancet Planetary Health 2017;1(8):e337-347. R835872 (2017)
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  • Journal Article Peng C, Cayir A, Sanchez-Guerra M, Di Q, Wilson A, Zhong J, Kosheleva A, Trevisi L, Colicino E, Brennan K, Dereix AE. Associations of annual ambient fine particulate matter mass and components with mitochondrial DNA abundance. Epidemiology 2017;28(6):763-770. R835872 (2017)
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  • Journal Article Wilson A, Chiu YH, Hsu HH, Wright RO, Wright RJ, Coull BA. Bayesian distributed lag interaction models to identify perinatal windows of vulnerability in children’s health. Biostatistics 2017;18(3):537-552. R835872 (2017)
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  • Journal Article 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 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 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 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 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 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 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)
    R835872 (2018)
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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 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)
    R835872 (2018)
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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)
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    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)
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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)
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  • 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 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 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 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 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)
  • 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)
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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 (2017)
  • Full-text: ScienceDirect-Full text
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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)
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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)
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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)
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  • 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 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 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)
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  • 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)
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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)
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  • 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)
  • Abstract from PubMed
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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)
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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)
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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)
  • Abstract: IOP Science- Abstract
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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)
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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)
  • Abstract: ScienceDirect-Abstract
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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. 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)
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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)
  • Abstract: IOP Science- Abstract
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. The impact of wildfires on particulate carbon in the western USA. Atmospheric Environment 2019;213:1-10.. R835872 (2018)
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  • Journal Article Requia WJ, Coull BA, Koutrakis P. The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts. Science of The Total Environment 2019;682:247-258. R835872 (2018)
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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 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 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 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)
  • Abstract: Abstract
  • Journal Article Di Q, Rowland S, Koutrakis P, Schwartz J (2017) A hybrid model for spatially and temporally resolved ozone exposures in the continental United States. Journal of the Air & Waste Management Association, 67(1):39-52. R835872C001 (2016)
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    Journal Article Tang C, Coull B, Schwartz J, Lyapustin A, Di Q, Koutrakis P (2016) Developing Particle Emission Inventories Using Remote Sensing (PEIRS). Journal of the Air Waste & Management Association. In press. http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1080/10962247.2016.1214630. R835872C001 (2016)
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    Journal Article Requia WJ, Coull BA, Koutrakis P. Multivariate spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts. 2019;252:1942-1952.. R835872 (2018)
  • Abstract: Abstract
  • Supplemental Keywords:

    particles, pollutant mixtures, pollution trends, public policy, data fusion, climate change, regional pollution, multi-resolution spatial analysis, source emissions, local pollution control strategies, wavelet analysis, particles, particulate matter, pollutant mixtures, regional pollution, 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
  • 2018 Progress Report
  • 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