2020 Progress Report: SEARCH: Solutions to Energy, AiR, Climate, and Health

EPA Grant Number: R835871
Center: Solutions for Energy, AiR, Climate and Health Center (SEARCH)
Center Director: Bell, Michelle L.
Title: SEARCH: Solutions to Energy, AiR, Climate, and Health
Investigators: Bell, Michelle L. , Hobbs, Benjamin F.
Institution: Yale University , Northeastern University , Stanford University , University of Chicago , University of Michigan , North Carolina State University , The Johns Hopkins University , Centers for Disease Control and Prevention , Pacific Northwest National Laboratory
EPA Project Officer: Callan, Richard
Project Period: October 1, 2015 through September 30, 2020 (Extended to September 30, 2022)
Project Period Covered by this Report: October 1, 2019 through September 30,2020
Project Amount: $9,999,990
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text |  Recipients Lists
Research Category: Climate Change , Air Quality and Air Toxics , Airborne Particulate Matter Health Effects , Particulate Matter , Air

Objective:

Project 1: Modeling Emissions from Energy Transitions

In Project 1, researchers are collaborating with the SEARCH Center Policy and Decision-Making Support Unit and state air regulatory agencies to develop a suite of energy transition scenarios representing many drivers and shifts in the energy sector that could impact regional emissions and air quality. These transitions are being modeled using the National Energy Modeling System (NEMS). NEMS results will be downscaled and combined with emissions from indirect energy use determined through lifecycle cost assessment (LCA), for input into air quality simulation models, performed by collaborators in Project 3.

Project 2: Assessment of Energy-Related Sources, Factors, and Transitions Using Novel High-Resolution Ambient Air Monitoring Networks and Personal Monitors

The objectives are: Objective 1) develop novel online multipollutant monitors to simultaneously measure air pollutants and GHGs (i.e., CO, CO2, CH4, PM2.5, NO2, O3, SO2, oxidative potential, VOCs); Objective 2) developing a network of sites for stationary monitors during field deployment and protocols for the personal sampling; and Objective 3) measure temporally resolved personal exposures with detailed time-activity information using novel personal multipollutant monitors.

Project 3: Improving Projections of the Spatial and Temporal Changes of Multi-Pollutants to Enhance Assessment of Public Health in a Changing World. PI: Yang Zhang (NCSU)

The main goal of Project 3 is to make critical improvements to online-coupled air quality models (AQMs) and their inputs and outputs, and apply the improved AQMs to estimate the concentrations resulting from energy and emission scenarios (Project 1) to be used in health risk assessments (Project 4). During this reporting period, we have three specific objectives: 1) Perform final regional air quality baseline simulations using FINN fire emissions and test simulations using the emission change factors under various energy transition scenarios; 2) Compare health impacts and associated costs estimated based on complex online models +health model vs. reduced chemistry models through a cross-center collaboration project; 3) Understand the impacts of wildfires on air quality by analyzing global simulations in order to improve the representation of wildfire emissions for air quality simulations.

Project 4: Human Health Impacts of Energy Transitions: Today and Under a Changing World

Decision-makers who protect health from air pollution are faced with complex systems involving multiple emission sources, variation in health response by population and region, and temporal changes such as climate change and economic development. The overall goal of Project 4 is to provide scientific evidence to aid sound policy by investigating: 1) factors that could influence air pollution-health associations, including modifiable factors and factors that could account for regional variability in observed associations (e.g., urbanicity, land-use), for PM2.5 and O3 on risk of cardiovascular and respiratory hospital admissions, including understudied rural populations;

Progress Summary:

Project 1: Development of transition scenarios: The Project 1 and Policy and Decision-Making Support Unit teams held webinar and conference calls to discuss project progress and plans to implement transition scenarios in NEMS. In particular, we have implemented five scenarios in NEMS: the abundant natural gas scenario, the electric vehicle scenario, the port electrification scenario, the building energy efficiency scenario, and the distributed generation/demand response scenario.  All five of the scenarios have been fully simulated in NEMS, and emissions for all energy sectors have been downscaled for four of the five scenarios.   

Modeling transition scenarios in NEMS: In Year 1, we focused on getting a working version of the National Energy Modeling System (NEMS) running.  In Year 2, we set up a working version of NEMS running in our purchased computer, with runs replicating both the Annual Energy Outlook 2016 and the Annual Energy Outlook 2017. The original version of the model acquired from Energy Information Agency required additional work to adapt to our purposes. We sorted out the compilation issues and developed the model in a way so that as later versions of NEMS become available in the future, we can easily adapt those changes to the newer model In Year 3, we have completed three of the five scenarios and are nearly complete with a fourth. In Year 4, we have completed a fourth scenario, and the development of last scenario is in final stages. In Year 5, we completed the final scenario and are bringing the academic papers to fruition as well as engaging in state-level policymaker outreach. The Yale team has provided NEMS modeling results for all scenarios to the JHU team for downscaling. The team has also generated NEMS results for the LCA analysis at Northeastern University.

Downscaling: The output data from NEMS are used for downscaling.  In previous years, we have built software and processes downscale NEMS results, using standard and non-standard results from the Yale-NEMS model produced by our colleagues.  This downscaling method produces a set of emission change rates differentiated by location, sector, and emission species that are being used by our partners in Project 3 for processing and air quality simulation for most economic sectors.  In Year 4, a practical method for downscaling NEMS transportation results was tested and implemented; the basic “grow in place” downscaling of all energy sector emissions for four transition scenarios was completed; and the “site-and-grow” method for EGU emissions was tested and enhanced.  Year 5’s efforts focused on completing the downscaling calculations for most scenarios; analyzing state-level results; and developing a temporal downscaling method that accounts for how meteorological timeseries considered in Project 3’s air quality analyses simultaneously affect renewable energy production, electricity demands, and ultimately EGU emissions.

Life Cycle Assessment: The emphasis in Year 5 has been on publishing the results of the two main LCA tasks: (1) appending material flows to all industrial sectors of the NEMS model (both extraction and manufacturing), allowing for forecasting of the entire physical economy of the United States for seven different energy scenarios (published); and (2) appending emissions of hazardous air pollutants to NEMS, covering all of the air toxics, making use of the USEPA’s Environmentally Extended Input-Output table (USEEIO), and in the process creating a sector mapping between USEEIO and NEMS (in revisions).  Based on this modeling framework, we can project US morbidity and mortality associated with air toxics emissions that conform to nine AEO scenarios, pegged from the latest National Air Toxics Assessment (NATA).  Also, work has been completed on a cross-model statistical comparison of characterization factors (fate+transport+damage) for hazardous air pollutants used in the USEtox life cycle impact assessment method adopted by USEPA, in comparison to those derived from the National Air Toxics Assessment.  Finally, an environmental justice study of community-level exposures to air toxics in Massachusetts was initiated based on raw NATA results.

Project 2: 

The fifth year of Project 2 has focused on the ongoing deployment of the stationary multipollutant monitor and continued in-field/lab testing, development of network data calibrations, analysis of network data, IRB approvals, and preparations for the personal measurements in Obj. 3. The first monitor was deployed in October 2018 and currently we have 44 monitors deployed, with 2 additional repaired two units being deployed in November 2020. Year 5 also included extensive activity on the CACES-SEARCH collaborative project, which is already yielding publications analyzing the data from a successful month-long field and lab campaign in Pittsburgh and Baltimore in Year 4.

Objective 1: We have submitted Buehler et al. “Stationary and Portable Multipollutant Monitors for High Spatiotemporal Resolution Air Quality Studies including Online Calibration” in disseminating the affordable monitoring and calibration techniques used in the SEARCH project. This manuscript includes a detailed description of the design and evaluation of the monitors, as described in previous progress reports for Objective 1 and is thus not described again here. The manuscript is currently under revision.

Objective 2: Multipollutant measurements with the stationary monitoring network continued throughout Year 5 of the project and included measurements during the COVID-19 shutdown in Baltimore, MD. Analysis of the continuously-collected data has been underway in Year 5 with multiple publications in preparation advancing both low-cost measurement methods and spatiotemporally-resolved studies of urban air pollution (Objectives 1-2).

One of our core activities for Objective 2 has focused around the statistical calibration of the monitors to be able to produce maps and other outputs of the monitoring network data. In collaboration with Dr. Abhirup Datta in the Environmental Science Data Support Unit, we developed a statistical calibration approach that allows us to compare our PM2.5 monitor data with the regulatory monitoring data collected by the Maryland Department of the Environment (MDE) to develop calibrations that would be applicable to all monitors. This paper was published this year in Atmospheric Environment developing a linear regression approach to calibrate the laboratory-based temperature and humidity corrections, adding terms for temperature and humidity interaction, and binary variables for daylight or nighttime hours and weekday or weekend. When holding out a 10% validation dataset, we find that our root mean square error is reduced to about 3.5 ug/m3 for hourly averages and about 1.5 ug/m3 for daily averages. Moreover, we found good agreement applying the model developed at the Old Town MDE site to data collected at the Essex site, showing the applicability of the statistical model to all monitors.

Our second approach uses gradient boosted decision trees (NGBoost) to calibrate the raw PM2.5 sensor data (without laboratory calibration) to the MDE data. In Patton et al. (in prep.), we show that this method can provide more accurate than the linear regression described above, even without using laboratory calibrations. In a separate set of analyses, postdoctoral Fellow Misti Zamora has also been evaluating calibrations for all sensors and a manuscript is currently in preparation.  We find that a careful consideration of environmental parameters and sensor cross-sensitivities results in accurate performance of the gas sensors in our monitors.

Objective 3: We received final IRB and EPA approval to begin the human subjects work of Objective 3 right before the COVID-19 shutdown. We had completed all test runs to evaluate performance before beginning the study. We have not been able to resume this work due to the restrictions related to the pandemic.

Additional Center activities:In response to a request from the Scientific Advisory Committee, an inter-project collaboration with SEARCH Project 1 evaluated the interconnections between building energy efficiency and air quality and the resulting manuscript is currently in review. Additionally, Sheu et al. “Human transport of third hand tobacco smoke: A prominent source of hazardous air pollutants into indoor non-smoking environments” was published in Science Advances in March 2020 with considerable coverage in the popular press. Finally, we also published Schilling et al. “An accessible method for screening aerosol filtration identifies poor-performing commercial masks and respirators” used the SEARCH monitors as part of a testing apparatus for mask/respirator testing during the COVID-19 shutdown to address critical PPE shortages in the Yale New Haven hospital system.

SEARCH-CACES Collaborative Project: Activities in the SEARCH-CACES collaborative study in year 5 included a focus on sample analysis and data processing after the collaborative project wrapped up in late summer 2019, and results have begun to be disseminated via conference presentations and manuscripts. This data has included both online (CACES) and offline (SEARCH) measurements of gases and particles collected (a) in Pittsburgh and Baltimore using the CACES mobile laboratory (amongst the high-spatiotemporal measurement networks deployed by SEARCH and CACES) and (b) in experiments conducted in the CACES oxidation chambers. Multiple manuscripts are underway using this data and there are discussions about possible follow-up lab experiments. This data from the mobile lab was a major part of the paper by Khare et al. “Asphalt-related Emissions are a Major Missing Non-Traditional Source of Secondary Organic Aerosol Precursors” published in Science Advances (Sept. 2020). 

Project 3: During this project year, we completed model improvement for the four online regional models (i.e., WRF/Chem, WRF-CAM5, WRF/Chem-ROMS, WRF-CMAQ) and final production simulations for the years of 2008-2012 over the CONUS using all models except for online WRF-CMAQ. To address SAC’s comment, we completed the 5-year simulation using offline WRF-CMAQ and compare it with online WRF-CMAQ. A paper based on this comparison has been submitted for publication. To incorporate SAC’s suggestion to strengthen the linkage with Project 2, we conducted an initial set of WRF/Chem simulations over triple-nested domains at 36-km over CONUS, 6-km over the state of Maryland, and 1-km over Baltimore for July 2019 and evaluated the results using observations from EPA’s AirNow network and the Project 2’s sensor network over Baltimore.  Our results indicate moderate overpredictions of O3 and PM2.5 against both datasets, warranting emission updates used for those simulations.

We worked closely with Project 1 team to finalize the projected emission change factors (ECFs) from the National Energy Modeling System under various energy scenarios. We generated model-ready emissions using the final sets of ECFs and SMOKE under five scenarios including the reference scenario without the clean power plan (refnocpp), the “abundant natural gas” scenario (highNG), the “high electric vehicles penetration” scenario (highEV), the “port electrification” scenario (port), and “high energy efficiency” scenario (highEE).  The simulation results for 2008 under the refnocpp scenario show large decreases for CO, SO2, NOx, VOCs, O3, and PM2.5 over the continental U.S. (CONUS) mainly due to their emission reduction and some increase for NOx, VOCs, and O3 over a few areas in the south and northeastern U.S. Compared to the refnocpp scenario, both highEV and highEE show wide-spread decrease of max 8h O3 with a larger reduction for highEE. The port scenario shows large reduction of O3 over several states near the Gulf of Mexico. The highNG scenario shows heterogeneity with large increases of the max 8h O3 over several states where higher natural gas production is projected and but decreases in southeastern U.S. where large decreases of NOx emissions occur. The reductions of PM2.5 are more apparent for highNG and highEE when compared to the refnocpp, which are also consistent with their precursor emission changes. Among all scenarios, the highEE scenario shows the largest decrease of PM2.5, indicating potentially the largest human health benefits. 

To improve prediction of wildfires, their future changes, and their impacts on human health, we built a machine-learning (ML) model using the Extreme Gradient Boosting (XGBoost) algorithm to predict wildfire burned area in the CONUS at a spatial resolution of 0.25° ´ 0.25° and utilized a novel game-theory-based approach to interpret the eXtreme Gradient Boosting (XGBoost) model. To identify the relative importance of the predictor variables, we used the Shapley additive explanations, a novel approach to resolve and explain variable importance based on game theory. The model incorporates multiple factors reported to contribute to the burned area, including local meteorology, land surface characteristics and socioeconomic variables. Our results show that the model can well reproduce the spatial distribution of the burned area. It successfully captures the large burned area at the border of Arizona–New Mexico and over southern California, Pacific Northwest, and southern Florida. The spatial correlation between the observed and predicted burned area is 0.97, and the corresponding index of agreement is 0.96, showing a good agreement between the long-term observations and predictions. However, the model tends to underestimate the frequency of small fires and overestimate the frequency of large ones, leading to overestimation of the averaged burned area.

SEARCH-CACES Collaborative Project: For SEARCH-CACES collaboration project on the comparison of health impacts estimated based on complex online models +health model vs. reduced complexity models, we estimated the health effects and benefit costs induced by PM2.5 under baseline emission scenario (NEI2011) and future energy transition scenarios (2050) using a reduced complexity air-quality modeling tool (RCM) (i.e., the AirPollution Emission Experiments and Policy Analysis Version 3 (AP3)) and the Environmental Benefits Mapping and Analysis Program Community Edition (BenMAP-CE).  Comparing to the baseline, both models estimate avoided deaths and saved costs with larger values by AP3 than by BenMap-CE.  Comparing to refnocpp, while the estimates are similar under the highEV scenario, those under highNG are different in terms of both magnitude and sign, because WRF/Chem+BenMAP-CE accounts for the nonlinearity of sulfate aerosol formation and interactions with other atmospheric processes.  We have also participated in another SEARCH-CACES collaboration project on the comparison of estimated air pollutants using all models and provided our model results to CACES collaborators. 

Project 4: 

We have accomplished several key tasks related to Project 4. Below we highlight key accomplishments and preliminary results. This represents a subset of our work.

A portion of our work examines vulnerable populations. We have expanded earlier work to examine the association between exposure to PM2.5 and disparities by status as immigrant. We found that this environmental exposure also has different exposure burdens, and therefor likely different health burdens, for immigrants compared to those native-born to the U.S. Interestingly, the exposure disparities were starkest for immigrants from specific regions. This manuscript has been accepted for publication.

A key feature of Project 4 is systematic review and meta-analysis. We have made substantial progress on our work on systematic reviews and meta-analyses to investigate which subpopulations are most vulnerable or susceptible to environmental conditions including air pollution and temperature. In the past year, we have published two systematic reviews, and have several others underway.

We investigated how exposure to greenness may modify the relationship between PM2.5 and risk of mortality for older populations (65 years and older), focusing on long-term exposure. This work has been accepted for publication. Although many studies demonstrated reduced mortality risk with higher greenness, far fewer studies examined modifying effect of greenness in air pollution-health associations. We evaluated residential greenness as an effect modifier for long-term exposure to fine particles (PM2.5) and mortality. Annual PM2.5 averages were estimated using ensemble prediction models. We estimated mortality risk associated with 1 µg/m3 PM2.5 increase using Cox proportional hazards modeling, controlling for demographics, Medicaid eligibility, and area-level covariates. Within each category of urbanicity, Hazard Ratios (HRs) were generally higher in areas with less greenness. For combined disparities, HRs were higher in areas with low greenness or low SES, regardless of the other factor.

We also examined the influence of residential environmental and social factors on health disparities attributable to air pollutant exposure. This paper has also been accepted for publication. We found that intersectionality in the disparities for air pollution and risk of mortality Blacks in poor communities had the highest, most certain PM2.5-mortality estimate.

Our methodological work includes an accepted paper on alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts. An additional methodological paper, accepted for publication, explores fused methods to estimate air pollution exposure, combining data from air quality monitoring networks and air quality modeling data.

Given the pandemic, we have shifted some research to investigate the links between COVID-19 and air pollution. Some of these papers have already been published and several are underway. Specifically, in one study we investigated how air pollution levels were affected by mitigation efforts for the pandemic as an indirect consequence. Our findings have implications for the effects of mitigation efforts and provide insight into the mortality reductions can be achieved from reduced air pollution levels. This work has been published. Other research related to COVID-19 is our investigation of the relationship between local green space and human mobility patterns during the pandemic. This work also has been published.

SEARCH Collaborative Project: In other work, for our study of the temporal trend in in the effect estimates of PM2.5 on hospital admissions, we have incorporated new analysis in response to the SAC to add less urban counties to the study area. This work is part of our Collaborative Project with Harvard University, for which we are working with Joel Schwartz of the Harvard/MIT Center. We have made substantial progress on this project and have a paper in preparation.

 

Future Activities:

Project 1: Modeling transition scenarios in NEMS: Our next steps include completing the analyses and publishing the results. The distributed energy scenario will be passed on to be downscaled.

Downscaling: We will apply the basic growth factor calculation methodology to the fifth scenario (distributed energy resources and continue to coordinate with Project 3 on procedures for sharing and conducting QA/QC and downscaled change factors. We will implement and test the temporal downscaling method for determining hourly EGU emissions that are weather-dependent. 

Life Cycle Assessment: Using the NEMS-linked air toxics model developed under the SEARCH project, the project will examine regional differences in predicted emissions and health damages, disaggregated by region (US census division) and pollutant, which can be used to aid prioritization of pollution prevention and control activities. Also, a statistical comparison will be made of environmental justice communities’ exposure to air toxics in Massachusetts, identifying major drivers of inequalities in terms of proximity and type of industrial facilities. 

 Project 2: Year 6 of the project primarily includes continued maintenance and analysis of the data from the fixed stationary network (Objective 2) and the start of personal exposure monitoring (Objective 3) with a modified deployment strategy. This includes the completion of our calibration approaches and finalizing of datasets to begin the source apportionment work. Objective 2 and 3’s data analysis sub-objectives will then begin with incoming data from the networks. All IRB approvals will be renewed, as required.

Project 3: We will complete final production simulations using four online models and one offline model and their ensemble for the current 5-yr period to obtain the best possible performance for air quality and human exposure studies.  We will conduct a number of simulations using some of those models for a future 5-yr period using projected emissions under future energy transition scenarios to evaluate the impact of projected changes in emissions on future air quality and estimate related health/cost using BenMap-CE and RCMs.  We will also perform sensitivity simulations considering energy/mobile sectors and climate change. For wildfire emissions, we plan to extend the ML model of wildfire burned area to predict emissions from wildfires and apply this model with climate simulations of the present day and future to evaluate the changes of wildfire frequency, burned area, and emissions in the U.S.

Project 4: In the upcoming year, we plan to continue work on our current review and meta-analysis projects that are currently ongoing and submit for publication. Work will continue on the coordination with Project 3 to link estimates of air pollution under a changing climate, generated through Project 3’s air quality and climate change modeling systems as described above, to epidemiological assessment to estimate the health impacts of increased (or decreased) levels of air pollution. We plan to complete our work on the collaborative project, which is a joint effort between Project 4 and the Harvard/MIT Center. We also plan to complete our ongoing work on COVID-19 such as on the links between air pollution and COVID-19/


Journal Articles: 69 Displayed | Download in RIS Format

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Journal Article Armstrong B, Bell ML, de Sousa Zanotti Stagliorio Coelho M, Leon Guo YL, Guo Y, Goodman P, Hashizume M, Honda Y, Kim H, Lavigne E, Michelozzi P. Longer-term impact of high and low temperature on mortality:an international study to clarify length of mortality displacement. Environmental Health Perspectives.2017 Oct 27;125(10):107009
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  • Journal Article Bravo MA, Anthopolos R, Bell ML, Miranda ML. Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: environmental justice applications of downscaled numerical model output. Environment International 2016;92-93:247-255.
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  • Journal Article Bravo MA, Ebisu K, Dominici F, Wang Y, Peng RD, Bell ML. Airborne fine particles and risk of hospital admissions for understudied populations: effects by urbanicity and short-term cumulative exposures in 708 U.S. counties. Environmental Health Perspectives 2017;125(4):594-601.
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  • Journal Article Gentner DR, Xiong F. Tracking pollutant emissions. Nature Geoscience 2017;10(12):883-884.
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  • Journal Article Glotfelty T, He J, Zhang Y. Improving organic aerosol treatments in CESM/CAM5: development, application, and evaluation. Journal of Advances in Modeling Earth Systems 2017;9(2):1506-1539.
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  • Journal Article Goldberg DL, Lamsal LN, Loughner CP, Swartz WH, Lu Z, Streets DG. A high-resolution and observationally constrained OMI NO2 satellite retrieval. Atmospheric Chemistry & Physics 2017;17(18):11403-11421.
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  • Journal Article Goldberg DL, Lu Z, Oda T, Lamsal LN, Liu F, Griffin D, McLinden CA, Krotkov NA, Duncan BN, Streets DG. Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from US megacities. Science of the Total Environment 2019;695:133805.
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  • Journal Article Guo Y, Gasparrini A, Li S, Sera F, Vicedo-Cabrera AM, Coelho MD, Saldiva PH, Lavigne E, Tawatsupa B, Punnasiri K, Overcenco A. Quantifying excess deaths related to heatwaves under climate change scenarios: a multicountry time series modelling study. PLoS Medicine 2018;15(7).
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  • Journal Article He J, He R, Zhang Y. Impacts of Air–sea Interactions on Regional Air Quality Predictions Using a Coupled Atmosphere-Ocean Model in Southeastern US. Aerosol and Air Quality Research. 2018 Apr 1;18:1044-67.
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  • Journal Article Jin L, Berman JD, Warren JL, Levy JI, Thurston G, Zhang Y, Xu X, Wang S, Zhang Y, Bell ML. A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China. Environmental Research 2019;177:108597.
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  • Journal Article Keet CA, Keller JP, Peng RD. Long-term coarse particulate matter exposure is associated with asthma among children in Medicaid. American Journal of Respiratory & Critical Care Medicine 2018;197(6):737-746.
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  • Journal Article Keller JP, Peng RD. Error in estimating area‐level air pollution exposures for epidemiology. Environmetrics 2019;30(8):e2573.
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  • Journal Article Khare P, Gentner DR. Considering the future of anthropogenic gas-phase organic compound emissions and the increasing influence of non-combustion sources on urban air quality. Atmospheric Chemistry and Physics 2018;18(8):5391-5413.
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  • Journal Article Krall JR, Hackstadt AJ, Peng RD. A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources. Statistics in Medicine 2017;36(9):1461-1475.
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  • Journal Article Li H, Dailey J, Kale T, Besar K, Koehler K, Katz HE. Sensitive and selective NO2 sensing based on alkyl- and alkylthio-thiophene polymer conductance and conductance ratio changes from differential chemical doping. ACS Applied Materials & Interfaces 2017;9(24):20501-20507.
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  • Journal Article Lim CC, Hayes RB, Ahn J, Shao Y, Silverman DT, Jones RR, Garcia C, Bell ML, Thurston GD. Long-term exposure to ozone and cause-specific mortality risk in the United States. American Journal of Respiratory and Critical Care Medicine 2019;200(8):1022-1031.
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  • Journal Article Liu JC, Wilson A, Mickley LJ, Dominici F, Ebisu K, Wang Y, Sulprizio MP, Peng RD, Yue X, Son JY, Anderson GB, Bell ML. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 2017;28(1):77-85.
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  • Journal Article Liu JC, Wilson A, Mickley LJ, Ebisu K, Sulprizio MP, Wang Y, Peng RD, Yue X, Dominici F, Bell ML. Who among the elderly is most vulnerable to exposure to and health risks of fine particulate matter from wildfire smoke? American Journal of Epidemiology 2017;186(6):730-735.
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  • Journal Article Liu JC, Peng RD. The impact of wildfire smoke on compositions of fine particulate matter by ecoregion in the Western US. Journal of exposure science & environmental epidemiology. 2018 Sep 5:1.
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  • Journal Article Shi W, Zheng Y, Taylor AD, Yu J, Katz HE. Increased mobility and on/off ratio in organic field-effect transistors using low-cost guanine-pentacene multilayers. Applied Physics Letters 2017;111(4):043301.
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  • Journal Article Shi W, Yu J, Katz HE. Sensitive and selective pentacene-guanine field-effect transistor sensing of nitrogen dioxide and interferent vapor analytes. Sensors and Actuators B: Chemical 2018;254:940-948.
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    R835871 (2017)
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  • Journal Article Silva GS, Warren JL, Deziel NC. Spatial modeling to identify sociodemographic predictors of hydraulic fracturing wastewater injection wells in Ohio census block groups. Environmental Health Perspectives 2018;126(6):067008 (8 pp.).
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    CR839249 (2018)
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  • Journal Article Son JY, Liu JC, Bell ML. Temperature-related mortality:a systematic review and investigation of effect modifiers. Environmental Research Letters 2019;14(7):073004.
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  • Journal Article Son J-Y, Lane KJ, Lee J-T, Bell ML. Urban vegetation and heat-related mortality in Seoul, Korea. Environmental Research 2016;151:728-733.
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  • Journal Article Son J-Y, Lee HJ, Koutrakis P, Bell ML. Pregnancy and lifetime exposure to fine particulate matter and infant mortality in Massachusetts, 2001–2007. American Journal of Epidemiology 2017;186(11):1268-1276.
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  • Journal Article Son J-Y, Lee J-T, Bell ML. Is ambient temperature associated with risk of infant mortality? A multi-city study in Korea. Environmental Research 2017;158:748-752.
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  • Journal Article Warren J, Son JY, Leaderer BP, Bell ML. Investigating the impact of maternal residential mobility on identifying critical windows of susceptibility to ambient air pollution during pregnancy. American Journal of Epidemiology 2017; 187(5):992-1000.
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  • Journal Article Yahya K, Glotfelty T, Wang K, Zhang Y, Nenes A. Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1. Geoscientific Model Development 2017;10(6):2333-2363.
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  • Journal Article Zhang J, Gao Y, Luo K, Leung LR, Zhang Y, Wang K, Fan J. Impacts of compound extreme weather events on ozone in the present and future. Atmospheric Chemistry and Physics (Online). 2018 Jul 13;18(PNNL-SA-135886).
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  • Journal Article Esty DC, ML Bell. Business Leadership in Global Climate Change Responses . American Journal of Public Health2018;108(S2):S80-S84.
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  • Journal Article Levy Zamora M, Xiong F, Gentner D, Kerkez B, Kohrman-Glaser J, Koehler K. Field and laboratory evaluations of the low-cost plantower particulate matter sensor. Environmental Science & Technology 2018;53(2):838-849.
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  • Journal Article Keet CA, Keller JP, Peng RD. Long-term coarse particulate matter exposure is associated with asthma among children in Medicaid. American Journal of Respiratory and Critical Care Medicine. 2018 Mar 15;197(6):737-46.
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  • Journal Article Gong X, Lin Y, Bell ML, Zhan FB. Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA. Environment International 2018;120:181-198.
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  • Journal Article Tang Z, Zhang H, Bai H, Chen Y, Zhao N, Zhou M, Cui H, Lerro C, Lin X, Lv L, Zhang C. Residential mobility during pregnancy in Urban Gansu, China. Health & Place 2018;53:258-263.
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  • Journal Article Heo S, Bell ML, Lee JT. Comparison of health risks by heat wave definition: applicability of wet-bulb globe temperature for heat wave criteria. Environmental Research 2019;168:158-170.
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  • Journal Article Gillingham K, Huang P. Is abundant natural gas a bridge to a low-carbon future or a dead-end?. The Energy Journal 2019;40(2).
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  • Journal Article Nori-Sarma A, Benmarhnia T, Rajiva A, Azhar GS, Gupta P, Pednekar MS, Bell ML. Advancing our understanding of heat wave criteria and associated health impacts to improve heat wave alerts in developing country settings. International Journal of Environmental Research and Public Health 2019;16(12):2089.
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  • Journal Article Berman JD, Jin L, Bell ML, Curriero FC. Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign. Journal of exposure science & environmental epidemiology. 2019 Mar;29(2):248.
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  • Journal Article Berman JD, Jin L, Bell ML, Curriero FC. Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign. Journal of Exposure Science & Environmental Epidemiology 2019;29(2):248-257.
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  • Journal Article Goldberg DL, Lu Z, Streets DG, de Foy B, Griffin D, McLinden CA, Lamsal LN, Krotkov NA, Eskes H. Enhanced capabilities of TROPOMI NO2:estimating NOx from North American cities and power plants. Environmental Science & Technology 2019;53(21):12594-12601.
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  • Journal Article Heo S, Bell ML. Heat waves in South Korea:differences of heat wave characteristics by thermal indices. Journal of Exposure Science & Environmental Epidemiology 2019;29(6):790-805.
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  • Journal Article Sera F, Armstrong B, Tobias A, Vicedo-Cabrera AM, Åström C, Bell ML, Chen BY, de Sousa Zanotti Stagliorio Coelho M, Matus Correa P, Cruz JC, Dang TN. How urban characteristics affect vulnerability to heat and cold:a multi-country analysis. International Journal of Epidemiology 2019;48(4):1101-1112.
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    R835871 (2019)
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  • Journal Article Son JY, Lee JT, Lane KJ, Bell ML. Impacts of high temperature on adverse birth outcomes in Seoul, Korea:disparities by individual-and community-level characteristics. Environmental Research 2019;168:460-466.
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    R835871 (2019)
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  • Journal Article Chen G, Wang A, Li S, Zhao X, Wang Y, Li H, Meng X, Knibbs LD, Bell ML, Abramson MJ, Wang Y. Long-term exposure to air pollution and survival after ischemic stroke:the China national stroke registry cohort. Stroke 2019;50(3):563-570.
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  • Journal Article Heo S, Fong KC, Bell ML. Risk of particulate matter on birth outcomes in relation to maternal socio-economic factors:a systematic review. Environmental Research Letters 2019;14(12):123004.
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    R835871 (2019)
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  • Journal Article Anderson GB, Barnes EA, Bell ML, Dominici F. The future of climate epidemiology:opportunities for advancing health research in the context of climate change. American Journal of Epidemiology 2019;188(5):866-872.
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  • Journal Article Nori-Sarma A, Anderson GB, Rajiva A, ShahAzhar G, Gupta P, Pednekar MS, Son JY, Peng RD, Bell ML. The impact of heat waves on mortality in Northwest India. Environmental Research 2019;176:108546.
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  • Journal Article Heo S, Bell ML. The influence of green space on the short-term effects of particulate matter on hospitalization in the US for 2000–2013. Environmental Research 2019;174:61-68.
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  • Journal Article Yan M, Wilson A, Bell ML, Peng RD, Sun Q, Pu W, Yin X, Li T, Anderson GB. The shape of the concentration-response association between fine particulate matter pollution and human mortality in Beijing, China, and its implications for health impact assessment. Environmental Health Perspectives 2019;127(6):067007.
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  • Journal Article Heo S, Nori-Sarma A, Lee K, Benmarhnia T, Dominici F, Bell ML. The use of a quasi-experimental study on the mortality effect of a heat wave warning system in Korea. International Journal of Environmental Research and Public Health 2019;16(12):2245.
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  • Journal Article Goldberg DL, Gupta P, Wang K, Jena C, Zhang Y, Lu Z, Streets DG. Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States. Atmospheric Environment 2019;199:443-452.
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  • Journal Article Baklanov A, Zhang Y. Advances in air quality modeling and forecasting. Global Transitions 2020;2:261-70.
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  • Journal Article Woo SH, Liu JC, Yue X, Mickley LJ, Bell ML. Air pollution from wildfires and human health vulnerability in Alaskan communities under climate change. Environmental Research Letters 2020;15(9):094019.
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  • Journal Article Khare P, Machesky J, Soto R, He M, Presto AA, Gentner DR. Asphalt-related emissions are a major missing nontraditional source of secondary organic aerosol precursors. Science advances 2020;6(36):eabb9785.
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  • Journal Article Fong KC, Mehta NK, Bell ML. Disparities in exposure to surrounding greenness related to proportion of the population that were immigrants to the United States. International Journal of Hygiene and Environmental Health 2020;224:113434.
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  • Journal Article Rogers HM, Ditto JC, Gentner DR. Evidence for impacts on surface-level air quality in the northeastern US from long-distance transport of smoke from North American fires during the Long Island Sound Tropospheric Ozone Study (LISTOS) 2018. Atmospheric Chemistry and Physics 2020;20(2):671-82.
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  • Journal Article Zhang Y, Yang P, Gao Y, Leung RL, Bell ML. Health and economic impacts of air pollution induced by weather extremes over the continental US. Environment International 2020;143:105921.
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  • Journal Article Sheu R, Stonner C, Ditto JC, Klüpfel T, Williams J, Gentner DR. Human transport of thirdhand tobacco smoke:a prominent source of hazardous air pollutants into indoor nonsmoking environments. Science Advances 2020;6(10):eaay4109.
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  • Journal Article Gillingham KT, Huang P. Long-Run Environmental and Economic Impacts of Electrifying Waterborne Shipping in the United States. Environmental Science & Technology 2020;54(16):9824-33.
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  • Journal Article Nori-Sarma A, Thimmulappa RK, Venkataramana GV, Fauzie AK, Dey SK, Venkareddy LK, Berman JD, Lane KJ, Fong KC, Warren JL, Bell ML. Low-cost NO2 monitoring and predictions of urban exposure using universal kriging and land-use regression modelling in Mysore, India. Atmospheric Environment 2020;226:117395.
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  • Journal Article Shinozuka Y, Saide PE, Ferrada GA, Burton SP, Ferrare R, Doherty SJ, Gordon H, Longo K, Mallet M, Feng Y, Wang Q. Modeling the smoky troposphere of the southeast Atlantic:a comparison to ORACLES airborne observations from September of 2016. Atmospheric Chemistry and Physics 2020;20(19):11491-526.
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  • Journal Article Gao Y, Zhang J, Yan F, Leung LR, Luo K, Zhang Y, Bell ML. Nonlinear effect of compound extreme weather events on ozone formation over the United States. Weather and Climate Extremes 2020;30:100285.
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  • Journal Article Heo S, Lim CC, Bell ML. Relationships between Local Green Space and Human Mobility Patterns during COVID-19 for Maryland and California, USA. Sustainability 2020;12(22):9401.
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  • Journal Article Datta A, Saha A, Zamora ML, Buehler C, Hao L, Xiong F, Gentner DR, Koehler K. Statistical field calibration of a low-cost PM2. 5 monitoring network in Baltimore. Atmospheric Environment 2020;242:117761.
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  • Journal Article Schilling K, Gentner DR, Wilen L, Medina A, Buehler C, Perez-Lorenzo LJ, Pollitt KJ, Bergemann R, Bernardo N, Peccia J, Wilczynski V. An accessible method for screening aerosol filtration identifies poor-performing commercial masks and respirators. Journal of exposure science & environmental epidemiology 2020:1-0.
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  • Journal Article Berman JD, Ebisu K, Peng RD, Dominici F, Bell ML. Drought and the risk of hospital admissions and mortality in older adults in western USA from 2000 to 2013:a retrospective study. The Lancet Planetary Health. 2017 Apr 1;1(1):e17-25.
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  • Journal Article Son JY, Lane KJ, Miranda ML, Bell ML. Health disparities attributable to air pollutant exposure in North Carolina:Influence of residential environmental and social factors. Health & Place 2020;62:102287.
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  • Journal Article Gasparrini A, Guo Y, Sera F, Vicedo-Cabrera AM, Huber V, Tong S, Coelho MD, Saldiva PH, Lavigne E, Correa PM, Ortega NV. Projections of temperature-related excess mortality under climate change scenarios. The Lancet Planetary Health. 2017 Dec 1;1(9):e360-7.
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  • Journal Article Armstrong B, Sera F, Vicedo-Cabrera AM, Abrutzky R, Åström DO, Bell ML, Chen BY, de Sousa Zanotti Stagliorio Coelho M, Correa PM, Dang TN, Diaz MH. The role of humidity in associations of high temperature with mortality:a multicountry, multicity study. Environmental Health Perspectives 2019;127(9):097007.
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    R835871 (2019)
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  • Supplemental Keywords:

    energy systems modeling, downscaling, life cycle assessment,   personal exposure monitoring, personal exposure, air monitoring, Regional modeling, air quality, O3, PM2.5, offline/online air quality models, emission change factors, energy and mobile sectors, wildfire emissions, and ML wildfire model, health analysis, COVID-19, PM2.5, NO2, air pollution, temperature

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    Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • 2017 Progress Report
  • 2018 Progress Report
  • 2019 Progress Report
  • Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R835871C001 Project 1: Modeling Emissions from Energy Transitions
    R835871C002 Project 2: Assessment of Energy-Related Sources, Factors and Transitions Using Novel High-Resolution Ambient Air Monitoring Networks and Personal Monitors
    R835871C003 Project 3: Air Quality and Climate Change Modeling: Improving Projections of the Spatial and Temporal Changes of Multipollutants to Enhance Assessment of Public Health in a Changing World
    R835871C004 Project 4: Human Health Impacts of Energy Transitions: Today and Under a Changing World