Grantee Research Project Results
Final Report: Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate and Particle-induced Mortality
EPA Grant Number: R835872C002Subproject: this is subproject number 002 , established and managed by the Center Director under grant R835872
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Regional Air Pollution Mixtures
Center Director: Koutrakis, Petros
Title: Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate and Particle-induced Mortality
Investigators: Coull, Brent , Koutrakis, Petros , Schwartz, Joel
Institution: Harvard University
EPA Project Officer: Keating, Terry
Project Period: December 1, 2015 through November 30, 2020
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 , Airborne Particulate Matter Health Effects
Objective:
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 health effects of pollution mixtures in the region. 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 adverse birth outcomes 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.
Conclusions:
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 (Antonelli et al. 2017). 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 found 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.
Understanding the factors that affect spatial differences in PM2.5 composition is crucial for implementing emissions control and health policies. Although previous studies have explored modeling of spatial patterns as a tool to improve human exposure assessment, little work has employed a multivariate clustering approach to identify spatial patterns in particle composition. We used this approach to assess the spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts in the United States (Requia et al. 2019a). We considered air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 elemental components of ambient PM2.5. The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in: 2 clusters for Fe, Zn, V, and Ni; 3 clusters; and for 12 clusters for K. Overall, our findings suggest substantial variation of clusters among PM2.5 components. In addition, land use, population density, and daily traffic were used as variables to more effectively characterize clusters of sites. This study improved the ability to model both the between- and within-area variability of source emissions and pollution regime, using concentrations of PM2.5 components.
In a second paper, we attempted to systematically compare exposure estimates from different PM2.5 constituent models (Requia et al. 2019b). We compared the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The forest model presented the best performance, with R2 values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. The results presented here can be useful for the environmental health community to more accurately estimate PM2.5 constituents over space.
In a third paper in this series, we proposed that areas representing clusters of PM2.5 elements are potential predictor variables to be included in spatial models for particle composition (Requia et al. 2019c). The inclusion of these clusters may minimize the exposure misclassification. We evaluated the influence of spatial patterning on modeling of 10 components of ambient PM2.5, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. This study was performed in three stages. First, we applied a hybrid approach (combination of Empirical Bayesian Kriging and land use regression) to estimate spatial variability for each one of the 10 components of ambient PM2.5. In the second stage, we applied the same hybrid approach adding clusters of each PM2.5 component to the set of predictor variables. Finally, in the last stage, we compared the estimates obtained from the model without clusters (first stage) and the model with clusters (second stage). Overall, our findings suggest significant influence of spatial clusters on modeling some PM2.5 components. We observed that the clusters may affect the error of the prediction values and especially the proportion of explained variance for most of the PM2.5 constituents evaluated in this study. This work appeared in Science in the Total Environment.
In separate work, an emerging technique in air pollution exposure assessment is to ensemble several competing exposure prediction models. We introduced a new Bayesian nonparametric ensemble (BNE) framework that adaptively combines models based on their predictive accuracy in the over space and time and also nonparametrically models the ensemble's predictive cumulative density function (CDF) so that the model's quantification of the predictive uncertainty is consistent with observed data (Liu et al. 2019). We showed the model can improve upon the predictive performance of an ensemble model with naive distribution assumptions when the data distribution is complex. We applied the method to data simulated from one- and two-dimensional complex nonlinear regression models, and generated a spatial prediction model and estimated the associated prediction uncertainties for fine particle levels in eastern Massachusetts, USA. This work appeared in Advances in Neural Information Processing Systems (NeurIPS), a highly regarded, competitive peer-reviewed computer science proceedings.
We have also applied the approach to ensemble (i.e. integrate) results from four different models that yield predictions over the continental U.S., including models generated recently by Harvard, other ACE Centers (CACES), among several others. We have generated uncertainty factors across the U.S., including measures of disagreement across the models, and overall uncertainties of the ensembled predictions, and are currently identifying factors most associated with these different sources of error, including pollution levels, population density, distance from monitoring locations, elevation, and temporal factors such as season and year. This follow-up paper is complete and about to be submitted for publication.
We published several papers investigating the effects of PM2.5 composition on birth outcomes in Eastern Massachusetts. First, we examined which elemental components of PM2.5 are responsible for previously reported associations between PM2.5 and neonatal BP in 1131 mother-infant pairs in Project Viva, a Boston-area prebirth cohort (Zanobetti et al. 2021). We measured systolic BP (SBP) and diastolic BP (DBP) at a mean age of 30 hours. We calculated average exposures during the 2 to 7 days before birth for the PM2.5 components-aluminum, arsenic, bromine, sulfur, copper, iron, zinc, nickel, vanadium, titanium, magnesium, potassium, silicon, sodium, chlorine, calcium, and lead-measured at the Harvard supersite. Our findings suggested that prenatal exposures to particulate matter components, and particularly nickel, may increase newborn BP.
Second, there exists keen interest on the biologic mechanisms of the health effects of prenatal exposure to fine particulate matter (PM2.5) on birth and early life outcomes. One particular mechanism receiving keen interest is the alteration of epigenetic traits such as DNA methylation. We proposed a cluster based Sparse Canonical Correlation Analysis (sCCA) to test associations between a multi-pollutant mixture and high-dimensional DNA methylation. Through simulation studies, we show that the proposed method yields greater efficiency relative to existing methods. We applied the proposed methods to data from the pre-birth Project Viva cohort, and showed that Ni and Vi, both generated by oil combustion courses, are PM2.5 components most consistently associated with differentially methylated regions. We have developed an R package that implements the methods (https://jennyjyounglee.github.io/AclustsCCA/).
Third, we presented the first study that aims to identify prenatal windows of susceptibility to air pollution exposures in cord blood DNA methylation (Zemplenyi et al. 2021). In particular, we propose a function-on-function regression model that leverages data from nearby DNA methylation probes to identify epigenetic regions that exhibit windows of susceptibility to PM2:5. By incorporating the covariance structure among both the multivariate DNA methylation outcome and the time-varying exposure under study, this framework yields greater power to detect windows of susceptibility and greater control of false discoveries than methods that model probes independently. We applied the method to analyze the DNA methylation data from the Project Viva birth cohort, and identified a window of susceptibility to PM2.5 exposure in the middle of the third trimester of pregnancy in an epigenetic region selected based on prior studies of air pollution effects on epigenome-wide methylation.
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 (Wilson et al. 2017a). We used 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.
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 (Wilson et al. 2017b). 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. 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; A Lee et al. 2017; Rosa et al. 2019).
Interest remains high on estimating health risks associated with air pollution mixtures. However, there were very few methods to estimate critical windows of prenatal exposure for a mixture of air pollutants in children’s health research. We developed a multi-pollutant distributed lag model (Wilson et al. 2022). 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.
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. We 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 (Coull et al. 2020). 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 developed scalable Gaussian Process Regression for estimating health effects of air pollution mixtures. This work was motivated because the popular Bayesian kernel machine regression (BKMR) approach for estimating the health effects of an environmental mixture was originally motivated by the need to estimate effects in small to moderately sized cohort studies. Accordingly, the algorithms for model fitting do not scale up well in big data settings, such as those encountered when interest focuses on electronic health records or other administrative data. We scaled up BKMR and Gaussian process regression more generally to settings involving hundreds of thousands to millions of records. We applied the new approach to estimate associations between a mixture of ambient air pollutants and approximately 650,000 birthweights recorded in Massachusetts, USA during 2001-2012. This work has been presented at a national meeting (Sonabend et al. 2020).
Journal Articles on this Report : 77 Displayed | Download in RIS Format
| Other subproject views: | All 79 publications | 78 publications in selected types | All 77 journal articles |
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| Other center views: | All 453 publications | 420 publications in selected types | All 419 journal articles |
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Antonelli J, Schwartz J, Kloog I, Coull BA. 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) R835872C002 (Final) R834798 (Final) |
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Blomberg A, Nyhan M, Bind M, Vokonas P, Coull B, Schwartz J, Koutrakis P. The role of ambient particle radioactivity in inflammation and endothelial function in an elderly cohort. Epidemiology 2020;31(4):499-508. |
R835872 (2020) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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Blomberg A, Li L, Schwartz J, Coull B, Koutrakis P. Exposure to particle beta radiation in greater Massachusetts and factors influencing its spatial and temporal variability. Environmental Science & Technology 2020;54(11):6575-6583. |
R835872 (2020) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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Busenkell E, Collins C, Moy M, Hart J, Grady S, Coull B, Schwartz J, Koutrakis P, Harshick E. Modification of associations between indoor particulate matter and systemic inflammation in individuals with COPD. ENVIRONMENT RESEARCH 2022;209(112802). |
R835872 (2020) R835872C002 (Final) R835872C003 (Final) R834798 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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Hood R, James P, Fong K, Minguez-Alcaron L, Coull B, Schwartz J, Kloog I, Laden F, Gasking A. The influence of fine particulate matter on the association between residential greenness and ovarian reserve. ENVIRONMENTAL RESEARCH 2021;197:111162. |
R835872 (2020) R835872C002 (Final) R835872C003 (Final) R834798 (Final) |
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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. |
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Li L, Blomberg A, Spengler J, Coull B, Schwartz J, Koutrakis P. Unconventional oil and gas development and ambient particle radioactivity. NATURE COMMUNICATIONS 2020;11(1):5002. |
R835872 (2020) R835872C002 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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Liu M, Kang C, Wolfson J, Mikhail L, Coull B, Schwartz J, Koutrakis P. Measurements of gross alpha-and beta-activities of archived PM2.5 and PM10 teflon filter samples. Environmental Science & Technology 2020;54(19):11780-11788. |
R835872 (2020) R835872C001 (Final) R835872C002 (Final) |
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Peralta A, Link M, Schwartz J, Luttmann-Gibson H, Dockery D, Blombarg A, Wei Y, Mittleman M, Gold D, Laden F, Coull B, Koutrakis P. Exposure to air pollution and particle radioactivity with the risk of ventricular arrhythmias. Circulation 2020;142(9):858-867. |
R835872 (2020) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C001 (Final) R835872C002 (Final) |
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Wang C, Cardenas A, Hutchinson J, Just A, Heiss J, Hou L, Zheng Y, Coull B, Kosheleva A, Koutrakis P, Baccarelli A, Schwartz J. Short-and intermediate-term exposure to ambient fine particulate elements and leukocyte epigenome-wide DNA methylation in older men:the Normative Aging Study. ENVIRONMENTAL INTERNATIONAL 2022;158. |
R835872 (2020) R835872C002 (Final) R835872C003 (Final) |
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Wei Y, Coull B, Koutrakis P, Yang J, Li L, Zanobetti A, Schowatz J. Assessing additive effects of air pollutants on mortality rate in Massachusetts. ENVIRONMENTAL HEALTH 2021;20(1):19. |
R835872 (2020) R835872C002 (Final) R835872C003 (Final) R836156 (Final) |
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Wright R, Shu HL, Coull B, Simon M, Hudda N, Schwartz J, Kloog I, Durant J. Prenatal ambient ultrafine particle exposure and childhood asthma in the Northeastern United States. American Journal of Respiratory and Critical Care Medicine 2021;204(7):788-796. |
R835872 (2020) R835872C002 (Final) R835872C003 (Final) |
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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. Journal of the American Heart Association 2021; 10(1):e016935. |
R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) |
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Ierodiakonou D, Zanobetti A, Coull BA, Melly S, Postma DS, Boezen HM, Vonk JM, Williams PV, Shapiro GG, McKone EF, Hallstrand TS, Koenig JQ, Schildcrout JS, Lumley T, Fuhlbrigge AN, Koutrakis P, Schwartz J, Weiss ST and Gold DR. Ambient air pollution, lung function, and airway responsiveness in asthmatic children. Journal of Allergy and Clinical Immunology 2016; 137(2):390-399. |
R835872C002 (Final) R834798 (Final) |
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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) R835872C002 (Final) R834798 (Final) |
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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) R835872C002 (Final) R834798 (Final) |
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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. |
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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. |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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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. |
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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) R835872C002 (Final) R834798 (Final) |
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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. |
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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) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) R834798 (Final) |
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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) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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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. |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) |
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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) R835872C001 (Final) R835872C002 (Final) |
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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) R835872C002 (Final) |
Exit |
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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) R835872 (2020) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) R834798 (Final) |
Exit Exit |
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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) R835872C001 (Final) R835872C002 (Final) R834798 (Final) |
Exit |
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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) R835872C001 (Final) R835872C002 (Final) R834798 (Final) |
Exit |
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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) R835872C001 (Final) R835872C002 (Final) R834798 (Final) |
Exit |
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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) R835872C002 (Final) R835872C003 (Final) R834798 (Final) |
Exit Exit |
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Mork D, Kioumourtzoglou MA, Weisskopf M, Coull BA, Wilson A. Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution. ARXIV PREPRINT ARXIV 2019;13763. |
R835872 (Final) R835872C002 (Final) R839278 (2019) R839278 (Final) |
Exit |
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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) R835872C002 (Final) R835872C003 (Final) |
Exit |
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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) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) |
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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) R835872C002 (Final) R835872C004 (Final) |
Exit |
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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) R835872C002 (Final) R835872C003 (Final) |
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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) R835872C002 (Final) R835872C003 (Final) R834798 (Final) |
Exit Exit |
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Dolcini J, Kioumourtzoglou MA, Cayir A, Sanchez-Guerra M, Brennan KJ, Dereix AE, Coull BA, Spiro III A, Vokonas P, Schwartz J, Baccarelli AA. Age and mitochondrial DNA copy number influence the association between outdoor temperature and cognitive function:insights from the VA Normative Aging Study. Environmental Epidemiology 2020;4(4):e0108. |
R835872C002 (Final) |
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Rosofsky AS, Fabian MP, Ettinger de Cuba S, Sandel M, Coleman S, Levy JI, Coull BA, Hart JE and Zanobetti A. Prenatal Ambient Particulate Matter Exposure and Longitudinal Weight Growth Trajectories in Early Childhood. Int J Environ Res Public Health 2020; 17(4). |
R835872C002 (Final) R835872C003 (Final) |
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Hauptman M, Gaffin JM, Petty CR, Sheehan WJ, Lai PS, Coull B, Gold DR, Phipatanakul W. Proximity to major roadways and asthma symptoms in the School Inner-City Asthma Study. The Journal of Allergy and Clinical Immunology 2020;145(1):119-126. |
R835872C002 (Final) |
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Fong KC, Yitshak-Sade M, Lane KJ, Fabian MP, Kloog I, Schwartz JD, Coull BA, Koutrakis P, Hart JE, Laden F and Zanobetti A. Racial disparities in associations between neighborhood demographic polarization and birth weight. International Journal of Environmental Research and Public Health 2020; 17(9). |
R835872C002 (Final) R835872C003 (Final) |
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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. |
R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
Exit Exit |
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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) R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
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Peralta AA, Schwartz J, Gold DR, Coull B and Koutrakis P. Associations between PM2.5 metal components and QT interval length in the Normative Aging Study. Environ Res 2021; 195:110827. |
R835872C002 (Final) R835872C003 (Final) |
Exit |
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Sordillo JE, Cardenas A, Qi C, Rifas-Shiman SL, Coull B, Luttmann-Gibson H, Schwartz J, Kloog I, Hivert MF, DeMeo DL, Baccarelli AA, Xu CJ, Gehring U, Vonk JM, Koppelman G, Oken E and Gold DR. Residential PM2.5 exposure and the nasal methylome in children. Environ Int 2021; 153:106505. |
R835872C002 (Final) |
not available |
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Gao X, Coull B, Lin X, Vokonas P, Spiro A, Hou L, Schwartz J and Baccarelli AA. Short-term air pollution, cognitive performance and nonsteroidal anti-inflammatory drug use in the Veterans Affairs Normative Aging Study. Nature Aging 2021; 1(5):430-437. |
R835872C002 (Final) R835872C003 (Final) |
Exit |
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Gao X, Koutrakis P, Coull B, Lin X, Vokonas P, Schwartz J and Baccarelli AA. Short-term exposure to PM2.5 components and renal health:Findings from the Veterans Affairs Normative Aging Study. J Hazard Mater 2021; 420:126557. |
R835872C002 (Final) R835872C003 (Final) |
Exit Exit |
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Leung M, Weisskopf MG, Laden F, Coull BA, Modest AM, Hacker MR, Wylie BJ, Wei Y, Schwartz J and Papatheodorou S. Exposure to PM2.5 during Pregnancy and Fetal Growth in Eastern Massachusetts, USA. Environ Health Perspect 2022; 130(1):17004. |
R835872C002 (Final) R835872C003 (Final) |
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Vanoli J, Coull BA, Ettinger de Cuba S, Fabian PM, Carnes F, Massaro MA, Poblacion A, Bellocco R, Kloog I, Schwartz J, Laden F and Zanobetti A. Postnatal exposure to PM2.5 and weight trajectories in early childhood. Environ Epidemiol 2022; 6(1):e181. |
R835872C002 (Final) R835872C003 (Final) |
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Moody EC, Cantoral A, Tamayo-Ortiz M, Pizano-Zárate ML, Schnaas L, Kloog I, Oken E, Coull B, Baccarelli A, Téllez-Rojo MM, Wright RO. Association of prenatal and perinatal exposures to particulate matter with changes in hemoglobin A1c levels in children aged 4 to 6 years. JAMA Network Open 2019;2(12):e1917643. |
R835872C002 (Final) |
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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. |
R835872C002 (Final) R835872C003 (Final) |
Exit |
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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) R835872C002 (Final) |
not available |
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Huang S, Koutrakis P, Grady ST, Vieira CLZ, Schwartz JD, Coull BA, Hart JE, Laden F, Zhang JJ and Garshick E. Effects of particulate matter gamma radiation on oxidative stress biomarkers in COPD patients. J Expo Sci Environ Epidemiol 2020. |
R835872C001 (Final) R835872C002 (Final) R835872C003 (Final) |
Exit |
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Wilson A, Hsu HH, Chiu YH, Wright RO, Wright RJ, Coull BA. Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children’s health studies. The Annals of Applied Statistics 2022;16(2):1090. |
R835872C002 (Final) |
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Cowell WJ, Brunst KJ, Malin AJ, Coull BA, Gennings C, Kloog I, Lipton L, Wright RO, Enlow MB, Wright RJ. Prenatal exposure to PM2.5 and cardiac vagal tone during infancy:findings from a multiethnic birth cohort. Environmental Health Perspectives. 2019;127(10):107007. |
R835872C002 (Final) R835872C003 (Final) |
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Liu JZ. Variable Selection with Rigorous Uncertainty Quantication using Deep Bayesian Neural Networks:Posterior Concentration and Bernstein-von Mises Phenomenon. arXiv 2019:1912.01189v01181. |
R835872C002 (Final) |
Exit Exit |
Supplemental Keywords:
Critical windows of exposure, Multi-resolution spatio-temporal analysis, Spatio-temporal model ensembles, Uncertainty quantification, Health effects of PM2.5 elemental composition.Relevant Websites:
Irregular 2D Wavelets - Github Exit
REGIMES (REGression In Multivariate Exposure Settings) - Github Exit
Bayesian kernel machine regression - Github
Progress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R835872 Regional Air Pollution Mixtures 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
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
77 journal articles for this subproject
Main Center: R835872
453 publications for this center
419 journal articles for this center