2017 Progress Report: Center for Air, Climate, and Energy Solutions (CACES)

EPA Grant Number: R835873
Center: Center for Air, Climate, and Energy Solutions
Center Director: Robinson, Allen
Title: Center for Air, Climate, and Energy Solutions (CACES)
Investigators: Robinson, Allen , Adams, Peter , Apte, Joshua S. , Azevedo, Inês L , Boies, Adam M. , Brauer, Michael , Burnett, Richard T , Coggins, Jay S. , Donahue, Neil , Ezzati, Majid , Hankey, Steve , Hill, Jason , Jaramillo, Paulina , Marshall, Julian D. , Matthews, H. Scott , Michalek, Jeremy J. , Millet, Dylan B , Muller, Nicholas , Pandis, Spyros N. , Polasky, Stephen , Pope, Clive Arden , Presto, Albert
Institution: Carnegie Mellon University , Brigham Young University , Health Canada - Ottawa , Imperial College , Middlebury College , The University of Texas at Austin , University of British Columbia , University of Minnesota , University of Washington , Virginia Polytechnic Institute and State University
EPA Project Officer: Chung, Serena
Project Period: May 1, 2016 through April 30, 2021
Project Period Covered by this Report: May 1, 2017 through April 30,2018
Project Amount: $10,000,000
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text |  Recipients Lists
Research Category: Airborne Particulate Matter Health Effects , Air , Climate Change , Health Effects

Objective:

CACES is a multidisciplinary, multi-institutional research center that is addressing critical questions at the nexus of air, climate, and energy. The center has overarching themes of regional differences, multiple pollutants, and development and dissemination of tools for air quality impact assessment. Novel measurement and modeling approaches are being applied to understand spatial and temporal differences in human exposures and health outcomes. We are investigating a range of technology and policy scenarios for addressing our nation’s air, climate, and energy challenges, and test their potential ability to meet policy goals such as improved health outcomes and cost-effectiveness.

The center is comprised of five thematically and scientifically integrated research projects and one support center. Project 1 is extending existing chemical transport models to high spatial resolution (1 km) with tagged source apportionment and developing a new class of reduced complexity models for air quality and exposure assessment. Project 2 is conducting comprehensive measurements in four cities (Austin, TX; Oakland, CA; Pittsburgh, PA; New York, NY) to quantify factors influencing gradients in pollutant concentrations and develop mechanistic understanding of how pollutant transformations affect population exposures. Project 3 is developing multi-pollutant empirical models at high spatial resolution (~0.1 km), national-scale and over multiple decades. Project 4 is using tools developed in other projects to investigate key air, climate, and energy challenges and their interactions focusing on four main elements: electricity generation; transportation; agriculture; and economy-wide. Project 5 is analyzing nationally representative population-based health data, combined with novel multi-pollutant exposure estimates and source contributions (Projects 1 and 3), to derive new knowledge on multi-pollutant mortality risk and its variability across the United States.

Progress Summary:

Project 1. Mechanistic air quality impact models for assessment of multiple pollutants at high spatial resolution

Project 1 is focused on the development, evaluation and application of mechanistic air quality models, both chemical transport models (CTMs) and reduced-complexity models (RCMs).  Major activities in the past reporting period included:

  • High-resolution (1 km) modeling of present-day air quality.  During this project period we developed inventories and performed preliminary simulations of particle number and other pollutants at 1 km spatial resolution in the Pittsburgh region.  These initial simulations overpredict particle number, but reproduce spatial patterns.  They also identified issues with emissions inventories, specifically with biomass combustion.
  • Development of Reduced-Complexity Models (RCMs).  During this project period, we upgraded the treatment of nitrate aerosols in the AP3 reduced-complexity model (RCM) by fitting CAMx output.  We also developed a web interface for the InMAP and initiated development of a version of EASIUR with high spatial resolution.
  • Accelerating chemical mechanisms. During this project period, A machine learning emulator was developed for the Carbon Bond Mechanism Z (CBM-Z).  The emulator greatly reduces computational times but errors build up resulting in unacceptable predictions at longer time scales.
  • Application of Reduced-Complexity Models (RCMs).  During this project period, RCMs were used to analyze a range of policy relevant air quality questions.  Examples described in this report include an examination of the spatial variability in pollutant social costs using through different RCMs; an analysis of the inter-jurisdictional flows of air pollution and their subsequent health damages for every U.S. county in the lower 48 states; and an examination of the effects of model spatial resolution on results from environmental justice analyses.

Project 2. Air quality observatory

Project 2 is collecting and analyzing air quality observations to characterize spatial (intra-city, urban-to-rural, and inter-city) and temporal distributions of multiple air pollutant species in four cities.  Major activities in the past reporting period included:

  • Pittsburgh measurements: During this project period, we completed field measurements in Pittsburgh. This effort included a combination of stationary and mobile measurements. Stationary measurements were made at 50+ sites using our low-cost sampling platform, the RAMP, to measure PM2.5 and criteria gases. Mobile sampling was conducted in 1 km2 areas centered around 15 of the stationary sites. We also conducted a separate campaign to measure concentrations of ultrafine particles (UFPs) at 30 of the stationary sites that span a range of land use covariates.
  • Oakland measurement campaign: During this project period, we conducted a mobile monitoring of PM composition and sources in Oakland, CA during the summer of 2017. We have combined this dataset with another mobile sampling dataset collected by PI Apte to examine PM sources and to build spatial models for Oakland.
  • Model evaluation: Data from this project are being used to evaluate outputs of chemical transport models (Project 1) and national land use regression models (Project 3). That evaluation is ongoing. Project 1 and Project 3 personnel participate in our bi-weekly project meetings in order to streamline data transfer and model-measurement comparisons.
  • Data analysis: During this project period, ten papers were published or submitted for review by project 2.  Sample results from analyses include: large reductions in outdoor ultrafine particle concentrations in Pittsburgh over the last fifteen years; restaurant emissions drive large spatial patterns in outdoor fine particulate matter exposure; particle mixing varies widely across the Pittsburgh region; and machine learning models can be used to improve the performance of low cost air pollution monitors.

Project 3. Next generation LUR models: Development of nationwide modeling tools for exposure assessment and epidemiology

Project 3 is developing national scale, high spatial resolution (1 km), multi-pollutant (PM2.5, NO2, O3, CO, and subspecies of PM2.5) empirical models of air pollutant concentrations for use in health analysis and investigation of the influence of modifiable factors on human exposure.  Major activities in the past reporting period included:

  • Final version 1 models: During this project period, version 1 covariate data was finalized, and version 1 of the empirical exposure models was completed. Models have been developed for PM2.5 (year 1999-2015), PM10 (1988-2015), NO2 (1979-2015), SO2 (1979-2015), ozone (1979-2015), and CO (1990-2015). Version 1 predictions have been handed off to Project 5 researchers for epidemiological studies.
  • Preliminary model evaluation:  During this project period, initial model evaluation against independent measurements in Pittsburgh, and comparison amongst existing national prediction models.
  • Development of next-gen model covariates: During this project period, we conducted preliminary development of improved land cover dataset at EPA monitor locations using the Local Climate Zones (LCZs) classification system based on Landsat imagery. LCZs offer an alternative to the USGS National Land Cover Database that may provide more detailed urban land classes and the potential for a consistent historical land cover record. Additional development of Google Point of Interest (POI) dataset with more refined land use characteristics and unique sources, such as restaurants, which are not well covered by other covariates.
  • National environmental justice: Preliminary results indicate exposures and disparities have declined from 1990 to 2010 for criteria pollutants. For four pollutants (PM2.5, PM10, NO2, CO), national population-weighted average exposures are higher for racial and ethnic minorities than for non-Hispanic whites; for SO2 and ozone, the reverse holds.

Project 4. Air pollutant control strategies in a changing world

Project 4 is applying chemical transport and reduced-form air quality models to assess the air quality and health impacts of various technology, policy, land-use, and climate scenarios.  Major activities in the past reporting period included:

  • Marginal Emissions Factors for Electricity Generation in the Midcontinent ISO. Environmental consequences of electricity generation are often determined using average emission factors. However, as different interventions are incrementally pursued in electricity systems, the resulting marginal change in emissions may differ from what one would predict based on system-average conditions. We estimated average emission factors and marginal emission factors for CO2, SO2, and NOx from fossil and nonfossil generators in the Midcontinent Independent System Operator (MISO) region during years 2007–2016. Our analysis can usefully be extended to other regions to support effective near-term technical, policy and investment decisions based on marginal rather than only average emission factors.
  • The Effect of Providing Climate and Health Information on Support for Alternative Electricity Portfolios. Support for addressing climate change and air pollution may depend on the type of information provided to the public. We conducted a discrete choice survey assessing preferences for combinations of electricity generation portfolios, electricity bills, and emissions reductions. We found that support for climate mitigation increases when mitigation is accompanied by improvements to air quality and human health. Our findings suggest that the type of emissions information provided to the public will affect their support for different electricity portfolios.
  • Life Cycle Air Quality Impacts on Human Health from Potential Switchgrass Production in the United States. Switchgrass is a promising bioenergy feedstock, but industrial-scale production may lead to negative environmental effects. This study considered one such potential consequence: the life cycle monetized damages to human health from air pollution. This work points to human health damage from air pollution as a potentially large social cost from switchgrass production and suggests means of mitigating that impact via strategic geographical deployment and management.
  • Source-Specific Contributions to Fine Particulate Matter Exposure Disparities in the United States. Studies of inequitable exposure to PM2.5 have generally utilized a) proximity to industrial emissions sources, b) air pollution monitor observations, or c) empirical model predictions to estimate racial-ethnic and socioeconomic disparities. We aimed to advance the literature by investigating the specific emissions sources that contribute most to PM2.5 exposure and disparities in exposure by race-ethnicity and income across the contiguous United States. We found substantial geographic variation in the relative importance of individual source categories for exposure and exposure disparities. This suggests that there is no clear one-size-fits-all policy approach to mitigate inequitable exposure to air pollution across the United States. Instead, these findings and the InMAP model can help states prioritize efforts to further study and address the unique causes of environmental injustice within their borders.

Project 5. Health effects of air pollution and mitigation scenarios

Project 5’s specific aims include (1) estimate multi-pollutant mortality risk surfaces using two large, unique, population-based U.S. datasets and (2) explore regional and temporal variability in those risk surfaces.  Major activities in the past reporting period included:

  • Analysis of National Health Interview Survey (NHIS) data.  We published the results of the preliminary/preparatory study that evaluated associations between long-term PM2.5 exposure and mortality risk using cohorts of the U.S. adult population constructed from public-use NHIS data (Pope et al. 2018).  We completed the proposal/application to Research Data Center (RDC) to conduct analysis using a much larger and complete constructed cohort using the “restricted-use” NHIS data and this proposal was accepted.  We linked the constructed cohort with modeled PM2.5 concentrations (provided by Project 3) at the census tract, developed an a priori analytic plan, pre-wrote the necessary code, and conducted an analysis of estimating hazards ratios associated with long-term PM2.5 exposure at the NHIS RDC in May 2018.  We currently are preparing a manuscript that reports these results.
  • County-Level Mortality Space-Time Study. County level age and gender specific annual mortality rates have been constructed from 1980 to 2015 for all counties in the contiguous United States.  Air pollution county-level exposures had been obtained from Project 3 over the 1999-2015 time period. 

The Administrative Core provides overall oversight, coordination, and integration of the Center. The Administrative Core oversees the quality management structure, which is detailed in the EPA-approved Quality Management Plan. The CACES Science Advisory Committee met for the second time in May 2018 in Minneapolis. The second CACES in-person meeting was held in December 2017 in Pittsburgh.  Finally the administrative core organized monthly conference calls of the project Executive Committee and weekly to monthly calls for groups of investigators for project-specific meetings.

Future Activities:

Project 1. Mechanistic air quality impact models for assessment of multiple pollutants at high spatial resolution

  • Complete source resolved PM2.5 modeling for application to empirical model estimates of Project 3. The results will ultimately be passed to Project 5.
  • Complete the 1 km modeling for Pittsburgh, evaluating the ability of CTMs to predict intraurban pollution variability against observations collected as part of Project 2.
  • Expand EASIUR RCM to include organic PM and predict social costs for VOC with speciation and source-specific mixtures.
  • Increase spatial resolution of EASIUR RCM for environmental justice analysis.
  • Complete development of common website for the three RCMs (APEEP, EASIUR, and InMAP).
  • Write “tutorial paper” about including air quality in cost-benefit analyses.

Project 2. Air quality observatory

  • Deploy network of 13 fixed samplers in Austin to investigate the spatial variability of ultrafine particle number concentrations as a function of proximity to highways, restaurants, and other distributed urban sources. We will also collect 1-2 weeks of AMS data in New York City as part of the Long Island Sound Tropospheric Ozone Study.
  • Perform targeted measurements to quantify PM emission factors from restaurants in Pittsburgh.
  • Leverage sampling design that collects data in nominally similar micro-environments in different cities to identify common pollutant spatial patterns.  To date, we have analyzed data for each city separately.
  • Build land use regression (LUR) or other spatial models for the cities we have sampled. This effort will focus primarily on pollutants for which national models do not exist, such as source-resolved organic aerosol concentrations and ultrafine number concentrations.
  • Evaluate the deterministic (Project 1) and empirical (Project 3) model predictions using data collected in Pittsburgh and Oakland.

Project 3. Next generation LUR models: Development of nationwide modeling tools for exposure assessment and epidemiology

  • Continue to test new covariates (LCZ and Google POI), version 1 models, and alternative modeling framework against existing prediction models and independent measurements from Project 2.
  • For the PM2.5 models, extend existing predictions by using spatial decomposition to estimate proportions attributable to long-range (~50km), near-source (~0.5km), and intermediate (~5km) variability. Then, work with researchers in Project 1 to combine the spatial decomposition estimates with CTM output, to develop source-resolved PM2.5 estimates. Those results would be used by researchers in Project 5, in epidemiological analysis.
  • Continue to analyze national environmental justice patterns, including looking at additional demographic factors beyond race and ethnicity.

Project 4. Air pollutant control strategies in a changing world

  • Continue evaluation of transportation and electricity generation scenarios.
  • Expand efforts in newly-focused research areas of agriculture and economy.
  • Employ updated models from Projects 1 and 3 in forthcoming research efforts.

Project 5. Health effects of air pollution and mitigation scenarios

  • Explore the use of generalize propensity score analysis and related approaches for analysis of the NHIS data.
  • Conduct analysis for multiple pollutants and evaluate sensitivity of results in multiple pollutant models.

Conduct analysis with alternative windows of pollution exposure.


Journal Articles: 27 Displayed | Download in RIS Format

Other center views: All 39 publications 27 publications in selected types All 27 journal articles
Type Citation Sub Project Document Sources
Journal Article Bechle MJ, Millet DB, Marshall JD. Does urban form affect urban NO2 ? Satellite-based evidence for more than 1200 cities. Environmental Science & Technology 2017;51(21):12707-12716. R835873 (2017)
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  • Journal Article Clark LP, Millet DB, Marshall JD. Changes in transportation-related air pollution exposures by race-ethnicity and socioeconomic status:outdoor nitrogen dioxide in the United States in 2000 and 2010. Environmental Health Perspectives 2017;125(9):097012 (10 pp.). R835873 (2016)
    R835873 (2017)
    R835873C001 (2016)
    R835873C003 (2016)
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  • Journal Article Gordon TD, Presto AA, Nguyen NT, Robertson WH, Na K, Sahay KN, Zhang M, Maddox C, Rieger P, Chattopadhyay S, Maldonado H, Maricq MM, Robinson AL. Secondary organic aerosol production from diesel vehicle exhaust: impact of aftertreatment, fuel chemistry and driving cycle. Atmospheric Chemistry and Physics 2014;14(9):4643-4659. R835873 (2017)
    RD834554 (Final)
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  • Journal Article Hankey S, Lindsey G, Marshall JD. Population-level exposure to particulate air pollution during active travel: planning for low-exposure, health-promoting cities. Environmental Health Perspectives 2017;125(4):527-534. R835873 (2017)
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  • Journal Article Hankey S, Marshall JD. Urban form, air pollution, and health. Current Environmental Health Reports 2017;4(4):491-503. R835873 (2017)
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  • Journal Article Heo J, Adams PJ, Gao HO. Public health costs accounting of inorganic PM2.5 pollution in metropolitan areas of the United States using a risk-based source-receptor model. Environment International 2017;106:119-126. R835873 (2016)
    R835873 (2017)
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  • Journal Article Kaltsonoudis C, Kostenidou E, Louvaris E, Psichoudaki M, Tsiligiannis E, Florou K, Liangou A, Pandis SN. Characterization of fresh and aged organic aerosol emissions from meat charbroiling. Atmospheric Chemistry and Physics 2017;17(11):7143-7155. R835873 (2017)
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  • Journal Article Li HZ, Dallmann TR, Li X, Gu P, Presto AA. Urban organic aerosol exposure:spatial variations in composition and source impacts. Environmental Science & Technology 2018;52(2):415-426. R835873 (2017)
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  • Journal Article Muller NZ, Jha A. Does environmental policy affect scaling laws between population and pollution? Evidence from American metropolitan areas. PLoS One 2017;12(8):e0181407 (15 pp.). R835873 (2017)
    R835873C004 (2016)
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  • Journal Article Muller NZ, Matthews PH, Wiltshire-Gordon V. The distribution of income is worse than you think: including pollution impacts into measures of income inequality. PLoS ONE 2018;13(3):e0192461 (15 pp.). R835873 (2017)
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  • Journal Article Muller NZ. Environmental benefit-cost analysis and the national accounts. Journal of Benefit-Cost Analysis 2018;9(1):27-66. R835873 (2017)
    R835873C004 (2016)
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  • Journal Article Nguyen NP, Marshall JD. Impact, efficiency, inequality, and injustice of urban air pollution: variability by emission location. Environmental Research Letters 2018;13(2):024002 (9 pp.). R835873 (2017)
    R833624 (Final)
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  • Journal Article Paolella DA, Tessum CW, Adams PJ, Apte JS, Chambliss S, Hill J, Muller NZ, Marshall JD. Effect of model spatial resolution on estimates of fine particulate matter exposure and exposure disparities in the United States. Environmental Science & Technology Letters 2018;5(7):436-441. R835873 (2017)
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  • Journal Article Pope III CA, Ezzati M, Cannon JB, Allen RT, Jerrett M, Burnett RT. Mortality risk and PM2.5 air pollution in the USA:an analysis of a national prospective cohort. Air Quality, Atmosphere & Health 2018;11(3):245-252. R835873 (2017)
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  • Journal Article Saha PK, Robinson ES, Shah RU, Zimmerman N, Apte JS, Robinson AL, Presto AA. Reduced ultrafine particle concentration in urban air: changes in nucleation and anthropogenic emissions. Environmental Science & Technology 2018;52(12):6798-6806. R835873 (2017)
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  • Journal Article Sergi B, Davis A, Azevedo I. The effect of providing climate and health information on support for alternative electricity portfolios. Environmental Research Letters 2018;13(2):024026 (10 pp.). R835873 (2017)
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  • Journal Article Tessum CW, Hill JD, Marshall JD. InMAP: a model for air pollution interventions. PLoS ONE 2017;12(4):e0176131 (26 pp.). R835873 (2016)
    R835873 (2017)
    R835873C001 (2016)
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  • Journal Article Tessum CW, Hil JD, Marshall JD. InMAP:A model for air pollution interventions. PLoS ONE 12, e0176131, 0.1371/journal.pone.0176131, 2017. R835873C001 (2016)
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  • Journal Article Thakrar SK, Goodkind AL, Tessum CW, Marshall JD, Hill JD. Life cycle air quality impacts on human health from potential switchgrass production in the United States. Biomass and Bioenergy 2018;114:73-82. R835873 (2017)
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  • Journal Article Thind MPS, Wilson EJ, Azevedo IL, Marshall JD. Marginal emissions factors for electricity generation in the Midcontinent ISO. Environmental Science & Technology 2017;51(24):14445–14452. R835873 (2017)
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  • Journal Article Vaishnav P, Horner N, Azevedo IL. Was it worthwhile? Where have the benefits of rooftop solar photovoltaic generation exceeded the cost? Environmental Research Letters 2017;12(9):094015 (14 pp.). R835873 (2017)
    R833864 (Final)
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  • Journal Article Weis A, Jaramillo P, Michalek J. Consequential life cycle air emissions externalities for plug-in electric vehicles in the PJM interconnection. Environmental Research Letters 2016;11(2):024009 (12 pp.). R835873 (2016)
    R835873 (2017)
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  • Full-text: IOP Science-Full Text PDF
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  • Journal Article Ye Q, Gu P, Li HZ, Robinson ES, Lipsky E, Kaltsonoudis C, Lee AKY, Apte JS, Robinson AL, Sullivan RC, Presto AA, Donahue NM. Spatial variability of sources and mixing state of atmospheric particles in a metropolitan area. Environmental Science & Technology 2018;52(12):6807-6815. R835873 (2017)
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  • Journal Article Zakoura M, Pandis SN. Overprediction of aerosol nitrate by chemical transport models: the role of grid resolution. Atmospheric Environment 2018;187:390-400. R835873 (2017)
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  • Journal Article Zhao Y, Saleh R, Saliba G, Presto AA, Gordon TD, Drozd GT, Goldstein AH, Donahue NM, Robinson AL. Reducing secondary organic aerosol formation from gasoline vehicle exhaust. Proceedings of the National Academy of Sciences of the United States of America 2017;114(27):6984-6989. R835873 (2016)
    R835873 (2017)
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  • Journal Article Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, Robinson AL, Subramanian R. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques 2018;11(1):291-313. R835873 (2017)
    R836286 (2017)
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  • Journal Article Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, Robinson AL, Subramanian R. Closing the gap on lower cost air quality monitoring:machine learning calibration models to improve low-cost sensor performance. Atmospheric Measurement Techniques Discussions August 2017 [In review]. R835873 (2016)
    R836286 (2016)
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  • Supplemental Keywords:

    air pollution, climate, energy, health effects, social cost, impact assessment

    Relevant Websites:

    Center for Air, Climate and Energy Solutions Exit

    Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R835873C001 Mechanistic Air Quality Impact Models for Assessment of Multiple Pollutants at High Spatial Resolution
    R835873C002 Air Quality Observatory
    R835873C003 Next Generation LUR Models: Development of Nationwide Modeling Tools for Exposure Assessment and Epidemiology
    R835873C004 Air Pollutant Control Strategies in a Changing World
    R835873C005 Health Effects of Air Pollution and Mitigation Scenarios