2020 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 , Pandis, Spyros N. , Polasky, Stephen , Pope, Clive Arden , Adams, Peter , Donahue, Neil , Marshall, Julian D. , Ezzati, Majid , Muller, Nicholas , Apte, Joshua S. , Azevedo, Inês L , Boies, Adam M. , Brauer, Michael , Burnett, Richard T , Coggins, Jay S. , Hankey, Steve , Hill, Jason , Jaramillo, Paulina , Michalek, Jeremy J. , Millet, Dylan B , Presto, Albert , Matthews, H. Scott
Institution: Carnegie Mellon University , Brigham Young University , Middlebury College , The University of Texas at Austin , University of Washington , Virginia Tech , University of Minnesota , University of British Columbia , Health Canada - Ottawa , Imperial College
EPA Project Officer: Chung, Serena
Project Period: May 1, 2016 through April 30, 2021 (Extended to April 30, 2022)
Project Period Covered by this Report: May 1, 2020 through April 30,2021
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 , Human Health

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 also 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; Baltimore, MD) to quantify factors influencing gradients in pollutant concentrations, to evaluate model predictions, and to 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, 2 and 3), to derive new knowledge on multi-pollutant mortality risk and its variability across the U.S.

 

Research Performed and Results Generated

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. We have completed the 1- km CTM modeling for the Pittsburgh domain, which were evaluated with observations from Project 2. 
  • Speciated and Source-Resolved Exposure Fields for Epidemiological Study: We handed off exposure fields of simulated PM2.5 speciation and source “tagged” PM2.5 to project 5 for epidemiological analysis. A geographically weighted regression technique was applied to the raw CTM output, using speciated measurement data, to reduce some systematic regional biases in the base CTM predictions. 
  • Development of Reduced-Complexity Models (RCMs). The development of a global version of InMAP was completed. We have also initiated the development of a new RCM that is based on Gaussian dispersion modeling principles, similar to APEEP, but not tied to any particular geography (i.e. US counties) in an effort to build a user-configurable tool that can be applied to developing countries, where high-resolution CTM modeling may not be available to train an RCM. 
  • Outreach and Dissemination of RCM tools. A User Guide has been written and added to our CACES web site (www.caces.us) that provides a user-friendly intro to getting and using RCM marginal social costs. We have continued outreach in the form of webinars on RCMs.

Project 2. Air quality observatory

Project 2 is collecting and analyzing air quality observations to characterize spatial (intracity, 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:

  • National exposure models: During the past year we worked closely with Project 3 to combine mobile monitoring and fixed site data to build national-scale LUR models for concentrations of ultrafine particles (UFP) and source-resolved organic aerosol (OA). We recently submitted two papers to Environmental Science & Technology describing these models. We are also collaborating with Project 5 to use the UFP and OA estimates for national-scale epidemiology. 
  • Impacts of nontraditional sources: Our collaborative project with the SEARCH center yielded a paper in Science Advances that examined the potential impacts of emissions from asphalt paving on PM2.5. 
  • Ultrafine particles: We continued our focus on ultrafine particles (UFPs). One paper examined how spatial correlations in UFP and PM2.5 mass may preclude identification of UFP health effects independent of PM2.5; this paper is a collaboration with Project 1. We also published an analysis of national spatial and historical trends in UFP in Atmospheric Environment: X. 
  • Environmental justice: A paper in Environmental Research Letters used our mobile sampling data to examine exposure disparities to source-resolved PM in Pittsburgh and Oakland. A second paper in preparation will use our national OA and UFP estimates to extend this analysis to nationwide. 
  • Quantifying impacts of COVID-related shutdowns on air quality: We used data from our low-cost sensor network in Pittsburgh to quantify changes in traffic-related air pollution and related them to changes in activity associated with COVID shutdowns. A manuscript was published in ES&T Letters covering the impacts on a local scale in Pittsburgh, and a second paper in Science of the Total Environment (led by Project 3) examined national-scale impacts.

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, multi-pollutant 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:

  • Use of microscale variables in national models: During this project period, we finalized the development novel microscale variables for empirical exposure modeling based on Landsat satellite-derived Local Climate Zones (LCZs) data; Google Point of Interest (POI) data that can provide information on sources (e.g., restaurants, gas stations); Yelp data that adds detailed information on restaurant type and location; and (4) object identification from Google Street View (GSV) imagery. We applied these variables using traditional and machine learning models to build empirical exposure models using these datasets. We found that machine learning models outperformed stepwise forward selection models, and were comparable to or improved as compared to the PLS-K (partial least squares – kriging) modeling approach. 
  • Google Street View (GSV) empirical models: We tested image-based approaches for developing national empirical models for NO2 and PM2.5 models using (1) built environment features derived from GSV imagery and (2) satellite estimates of air quality during 2007-2015. 
  • External model evaluation: We continued our assessment of model predictions against the open-source PurpleAir PM2.5 sensor network as another source of independent measurements. We have submitted this work for publication in Environmental Research. 
  • Model intercomparisons: We continued the evaluation of CACES model predictions against predictions from other publicly available or privately shared empirical exposure models, including from the other ACE centers and found generally strong agreement among model methods, with the exception of low concentration areas and the Western US. This is a collaborative project. 
  • National models of ultrafine particles and primary PM (COA, HOA, BC): We have worked with Project 2 to develop national empirical models by combining data from mobile using an AMS (aerosol mass spectrometry – an advanced measurement technique conducted as part of CACES Project 2 mobile-monitoring), fixed-sites across the country, and new land use metrics extracted from the Yelp database. Based on those datasets, we developed the first national estimates of ultrafine particles and primary PM components. 
  • National environmental justice patterns: During this project period, we finalized our national assessment of environmental justice in residential exposure to ambient air pollution (PM2.5, PM10, NO2, O3, CO, SO2) over three decades (1990, 2000 and 2010). Additionally, we continued our national investigation of correlates of within-urban racial/ethnic disparities in NO2 levels. We found that racial/ethnic residential segregation was strongly associated with disparities in NO2 levels. We also compiled a database of criteria used by US federal agencies and states to identify environmental justice communities. Finally, we conducted an analysis of NO2 levels and disparities at public school locations and found that racial and ethnic minority students are more likely to live and attend school in locations with higher NO2 levels than their non-Hispanic white peers.

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:

  • Air quality-related health damages of food. Agriculture is a major source of air pollution, but air quality has been largely absent from discussions about the health and environmental impacts of food. We estimate the air quality–related health impacts of agriculture in the United States. These findings were published in the Proceedings of the National Academy of Science. 
  • Reducing mortality from air pollution in the United States by targeting specific emission sources. Air quality in the U.S. has dramatically improved, yet exposure to air pollution is still associated with 100000–200000 deaths annually. Reducing the number of deaths effectively, efficiently, and equitably relies on attributing them to specific emission sources, but so far, this has been done for only highly aggregated groups of sources, or a select few sources of interest. We estimated mortality in the United States attributable to all domestic, human-caused emissions of primary PM2.5 and secondary PM2.5 precursors, presenting detailed source-specific attributions in four alternate groupings relevant for identifying promising ways to reduce mortality. These findings were published in Environmental Science & Technology Letters. 
  • Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. The Paris Agreement’s goal of limiting the increase in global temperature to 1.5° or 2°C above preindustrial levels requires rapid reductions in greenhouse gas emissions. Although reducing emissions from fossil fuels is essential for meeting this goal, other sources of emissions may also preclude its attainment. We show that even if fossil fuel emissions were immediately halted, current trends in global food systems would prevent the achievement of the 1.5°C target and, by the end of the century, threaten the achievement of the 2°C target. Meeting the 1.5°C target requires rapid and ambitious changes to food systems as well as to all nonfood sectors. The 2°C target could be achieved with less-ambitious changes to food systems, but only if fossil fuel and other nonfood emissions are eliminated soon. These findings were published in Science. 

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:

  • We finished our analysis of the associations of cancer and air pollution using NHIS data and Project 3 PM2.5 exposures. The results were published in Cancer Causes and Controls.
  • We finished another analysis investigating associations of cancer and air pollution but using Surveillance, Epidemiology, and End Results Program (SEER) datasets and Project 3 PM2.5 exposures. The results were published in Environmental Health Perspectives. 
  • We completed an analysis of fine particulate matter air pollution and mortality risk among U.S. cancer patients and survivors using a cohort constructed from the SEER data. The results for this analysis were published in JNCI Cancer Spectrum. 
  • Motivated by interesting BMI-mortality associations that were observed in our air pollution studies using NHIS data, we further investigated the association of BMI and mortality. Specifically, we evaluated the shape of BMI-mortality risk associations and explored the issues of reverse causality and heterogeneity in the unrestricted NHIS data. These results were published in Obesity.
  • We conducted an analysis of cardiopulmonary mortality and fine particulate air pollution by species and source at the MSA level using the unrestricted NHIS data. This research has been submitted for publication and is currently in review. 
  • We completed an analysis of mortality risk associated with both air pollution and greenness in a cohort of cancer patients and survivors using the SEER data. The manuscript reporting these results is in review. 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 fourth CACES SAC meeting was held August 11-12, 2020 over Zoom. The administrative core continued to organize 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

  • Development of the EASIUR model to use the volatility basis set (VBS) for organic PM and to higher resolution. 
  • We expect to submit 1-2 manuscripts related to high-resolution EASIUR. One manuscript would be model development and evaluation with a sample application. A second manuscript would look at the environmental justice implications of transportation changes.
  • We expect to complete development and evaluation of the Gaussian dispersion-based RCM suitable for use in developing countries. 
  • Additional manuscripts related to high-resolution CTM modeling will be submitted.

Project 2. Air quality observatory

  • National environmental justice analysis: We will use our national estimates of UFP and source-resolved OA exposures to quantify environmental injustice for these exposures. We expect to submit a manuscript by the end of 2021.
  • National health study: In collaboration with Project 5, we will use our national estimates of UFP and source-resolved OA exposures in a national health study.
  • Dissemination: Results will be presented conferences and meetings throughout the next year. Multiple manuscripts are in various stages of preparation.

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

  • We will continue to develop new covariates and test these variables in various modeling frameworks. A core focus will be the Google Street View (GSV)-based image analysis - a method that could be scaled to any location where this imagery is available. We will also continue to assess our new modeling frameworks against existing prediction models and independent measurements from Project 2 and the PurpleAir network. We will first focus this work on locations with poor model-performance, with the goal of identifying variables and modeling approaches that improve model-performance, especially withincity prediction performance. 
  • We will publish our PurpleAir model evaluation. In collaboration with other EPA ACE centers, we will write up and publish the empirical model intercomparison analysis. 
  • We will publish our multi-pollutant and multi-decade analysis of national environmental justice patterns in the contiguous US. We also plan to finalize and publish additional national environmental justice (EJ) analyses, including exploring correlates with urban disparities, spatial decomposition of disparities, and disparities at public school locations.

Project 4. Air pollutant control strategies in a changing world

  • Continue evaluation of transportation, electricity generation, agriculture and economywide, with particular focus on agriculture and the extension of work using Global InMAP.
  • Continue to employ updated models from Projects 1 and 3 in forthcoming research efforts, including Global InMAP.

Project 5. Health effects of air pollution and mitigation scenarios

  • Conduct an analysis on mortality risk associated with both greenness and PM2.5 air pollution using the restricted use NHIS cohort data at the census tract level. The revised and resubmitted proposal to conduct this research at an RDC has been approved. 
  • Complete an analysis on mortality risk and various measures of particulate matter air pollution, including PM2.5, PNC/UFP, elemental carbon, POA using the restricted NHIS data at the census tract level. The revised and resubmitted proposal to conduct this research at an RDC has been approved. 
  • Project future age-, sex- cause-specific mortality at the county level. 
  • Estimate, together with projected air pollution concentrations from Project 4, the reduction in deaths of different concentration scenarios and policies. 
  • Convert space-time estimation code from INLA to Nimble in order to allow fitting of more complex models.


Journal Articles: 81 Displayed | Download in RIS Format

Other center views: All 93 publications 81 publications in selected types All 81 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 Bennett JE, Tamura-Wicks H, Parks RM, Burnett RT, Pope III CA, Bechle MJ, Marshall JD, Danaei G, Ezzati M. Particulate matter air pollution and national and county life expectancy loss in the USA: A spatiotemporal analysis. PLoS medicine. 2019 Jul;16(7). R835873 (2018)
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  • Journal Article Chambliss SE, Pinon CPR, Messier KP, LaFranchi B, Upperman CR, Lunden MM, Robinson AL Marchall, JD Apte, JS.Local-and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE OF AMERICA 2021;118(37):e2109249118 R835873 (2020)
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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)
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  • Journal Article Clark M, Hill J, Tilman D. The diet, health,and environment.Annual Review of Environment and Resources 2019; 43:109–134 R835873 (2018)
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  • Journal Article Drosatou AD, Skyllakou K, Theodoritsi GN, Pandis SN. Positive matrix factorization of organic aerosol:Insights from a chemical transport model. Atmospheric Chemistry and Physics 2019;19:973–86. R835873 (2019)
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  • Journal Article Fantke P, McKone TE, Tainio M, Jolliet O, Apte JS, Stylianou KS, et al. Global effect factors for exposure to fine particulate matter. Environmental Science & Technology 2019;53:6855–68 R835873 (2019)
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  • Journal Article Gilmore EA, Heo J, Muller NZ, Tessum CW, Hill J, Marshall J, Adams PJ. An inter-comparison of air quality social cost estimates from reduced-complexity models. Environmental Research Letters. 2019 Apr 18. R835873 (2018)
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  • Journal Article Giordano M, Mailings C, Pandis S, Presto A, McNiell V, Wetervelt D, Beekman M, Subrgamanian R. From low-cost sensors to high-quality data:A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors. JOURNAL OF AEROSOL SCIENCE 2021;158. R835873 (2020)
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  • Journal Article Goodkind AL, Tessum CW, Coggins JS, Hill JD, Marshall JD. Fine-scale damage estimates of particulate matter air pollution reveal opportunities for location-specific mitigation of emissions. Proceedings of the National Academy of Science 2019;116(18):8775-8780 R835873 (2018)
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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 Gu P, Li HZ, Ye Q, Robinson ES, Apte JS, Robinson AL, Presto AA. Intracity variability of particulate matter exposure is driven by carbonaceous sources and correlated with land-use variables. Environmental Science & Technology 2018; 52:11545–11554 R835873 (2018)
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  • Journal Article Robinson ES, Gu P, Ye Q, Li HZ, Shah RU, Apte JS, Robinson AL, Presto AA. Restaurant impacts on outdoor air quality:Elevated organic aerosol mass from restaurant cooking with neighborhood-scale plume extents. Environmental Science & Technology 2018; 52:9285-9294 R835873 (2018)
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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)
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  • Journal Article Hill J, Goodkind A, Tessum C, Thakrar S, Tilman D, Polasky S, Smith T, Hunt N, Mullins K, Clark M, Marshall J. Air-quality-related health damages of maize. Nature Sustainability2019:2;397-403 R835873 (2018)
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  • Journal Article Humes M, Wang M, Kim S, Machesky J, Gentner D, Robinson A, Donahue N, Presto A. Limited Secondary Organic Aerosol Production from Acyclic Oxygenated Volatile Chemical Products. ENVIRONMENTAL SCIENCE TECHNOLOGY 2022;56(8):4806-4815. R835873 (2020)
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  • Journal Article Jain S, Presto A, Zimmerman N. Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network:Comparison of Linear, Machine Learning, and Hybrid Land Use Models. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021;55(13):8631-8641. R835873 (2020)
    R836286 (Final)
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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 Kelp M, Gould T, Austin E, Marshall JD, Yost M, Simpson C, Larson T. Sensitivity analysis of area-wide, mobile source emission factors to high-emitter vehicles in Los Angeles. Atmospheric Environment 2020;223:117212 R835873 (2019)
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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 Li HZ, Gu P, Ye Q, Zimmerman N, Robinson ES, Subramanian R, Apte JS, Robinson AL, Presto AA. Spatially dense air pollutant sampling:Implications of spatial variability on the representativeness of stationary air pollutant monitors. Atmospheric Environment:X. 2019 Apr 1;2:100012. R835873 (2018)
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  • Journal Article Liu L, Hwang T, Lee S, Ouyang Y, Lee B, Smith SJ, Tessum CW, Marshall JD, Yan F, Daenzer K, Bond TC. Health and climate impacts of future United States land freight modelled with global-to-urban models. Nature Sustainability 2019;2:105; doi:10.1038/s41893-019-0224-3. R835873 (2019)
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  • Journal Article Malings C, Westervelt DM, Hauryliuk A, Presto AA, Grieshop A, Bittner A, Beekmann M, R. Subramanian. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa. Atmospheric Measurement Techniques 2020;13:3873–92. doi:10.5194/amt-13-3873-2020. R835873 (2019)
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  • Journal Article Messier KP, Chambliss SE, Alvarez RA, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. Mapping air pollution with Google Street View cars:Efficient approaches with mobile monitoring and land use regression. Environmental Science & Technology 2018;52:12563-12572 R835873 (2018)
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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)
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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)
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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)
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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 Parks RM, Bennett JE, Foreman KJ, Toumi R, Ezzati M. National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016. eLife 2018; 7:e35500. R835873 (2018)
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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 Pope III CA, Lefler JS, Ezzati M, Higbee JD, Marshall JD, Kim SY, Bechle M, Gilliat KS, Vernon SE, Robinson AL, Burnett RT. Mortality Risk and Fine Particulate Air Pollution in a Large, Representative Cohort of US Adults. Environmental health perspectives. 2019 Jul 24;127(7):077007. R835873 (2018)
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  • Journal Article Robinson ES, Shah RU, Messier K, Gu P, Li HZ, Apte JS, Robinson AL, Presto AA. Land-use regression modeling of source-resolved aerosol components from mobile Sampling. Environmental Science & Technology 2019; 53(15):8925-8937 R835873 (2018)
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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 Saha PK, Zimmerman N, Malings C, Hauryliuk A, Li Z, Snell L, Subramanian R, Lipsky E, Apte JS, Robinson AL, Presto AA. Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations. Science of the Total Environment. 2019; 655:473-81 R835873 (2018)
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  • Journal Article Saha PK, Li HZ, Apte JS, Robinson AL, Presto AA. Urban ultrafine particle exposure assessment with land-use regression:Influence of sampling strategy. Environmental Science & Technology 2019; 53:7326-7336 R835873 (2018)
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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 Shah RU, Robinson ES, Gu P, Robinson AL, Apte JS, Presto AA. High spatial resolution mapping of aerosol composition and sources in Oakland, California using mobile aerosol mass spectrometry. Atmospheric Chemistry and Physics 2018; 18(22):16325–16344 R835873 (2018)
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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)
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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 Tessum CW, Apte JS, Goodkind AL, Muller NZ, Mullins KA, Paolella DA, Polasky S, Springer NP, Thakrar SK, Marshall JD, Hill JD. Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. Proceedings of the National Academy of Sciences of the United States of America 2019; 116 (13):6001-6006 R835873 (2018)
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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)
    R835873 (2018)
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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)
    R835873 (2018)
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  • Journal Article Tschofen P, Azevedo IL, Muller NZ. Fine particulate matter damages and value added in the United States economy. Proceedings of the National Academies of Science 2019; 116(40):19857-19862 R835873 (2018)
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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)
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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)
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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 Ye Q, Li HZ, Gu P, Robinson ES, Apte, Sullivan Ryan C., Robinson Allen L., Donahue Neil M., Presto Albert A. Moving beyond fine particle mass:High-spatial resolution exposure to source-resolved atmospheric particle number and chemical mixing state. Environmental Health Perspectives 2020;128:017009. doi:10.1289/EHP5311. R835873 (2019)
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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)
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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)
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  • Full-text: EGU-Full Text PDF
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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)
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  • Journal Article Apte JS, Brauer M, Cohen AJ, Ezzati M, Pope CA. Ambient PM2.5 reduces global and regional life expectancy. Environmental Science & Technology Letters 2018;5:546–51. R835873 (2019)
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  • Journal Article Lu Q, Zhao Y, Robinson AL. Comprehensive organic emission profiles for gasoline, diesel, and gas-turbine engines including intermediate and semi-volatile organic compound emissions. Atmospheric Chemistry and Physics 2018;18:17637–54; doi:10.5194/acp-18-17637-2018. R835873 (2019)
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  • Journal Article Knibbs LD, van Donkelaar A, Martin RV, Bechle MJ, Brauer M, Cohen DD, Cowie CT, Dirgawati M, Guo Y, Hanigan IC, Johnston FH, Marks, GB, Marshal JD, Pereira G, Jalaludin B, Heyworth JS, Morgan GG, Barnett AG. Satellite-based land-use regression for continental-scale long-term Ambient PM2.5M exposure assessment in Australia. Environmental Science & Technology 2018;52:12445–55 R835873 (2019)
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  • Journal Article Lefler JS, Higbee JD, Burnett RT, Ezzati M, Coleman NC, Mann DD, Marshall JD, Bechle M, Wang Y, Robinson AL, Pope, CA. Air pollution and mortality in a large, representative U.S. cohort:multiple-pollutant analyses, and spatial and temporal decompositions. Environmental Health 2019; 18:101 R835873 (2019)
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  • Journal Article Tanzer R, Malings C, Hauryliuk A, Subramanian R, Presto AA. Demonstration of a low-cost multi-pollutant network to quantify intra-urban spatial variations in air pollutant source impacts and to evaluate environmental justice. International Journal of Environmental Research and Public Health 2019;16:2523. doi:10.3390/ijerph16142523. R835873 (2019)
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  • Journal Article Ward JW, Michalek JJ, Azevedo IL, Samaras C, Ferreira P. Effects of on-demand ridesourcing on vehicle ownership, fuel consumption, vehicle miles traveled, and emissions per capital in U.S. States. Transportation Research Part C:Emerging Technologies 2019;108:289–301. doi:10.1016/j.trc.2019.07.026. R835873 (2019)
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  • Journal Article Dimanchev EG, Paltsev S, Yuan M, Rothenberg D, Tessum CW, Marshall JD, Selin NE. Health co-benefits of sub-national renewable energy policy in the US. Environmental Research Letters 2019;14(8):085012 R835873 (2019)
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  • Journal Article Zakoura M, Pandis SN. Improving fine aerosol nitrate predictions using a Plume-in-Grid modeling approach. Atmospheric Environment 2019;215:116887. doi:10.1016/j.atmosenv.2019.116887. R835873 (2019)
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  • Journal Article Lu T, Lansing J, Zhang W, Bechle MJ, Hankey S. Land use regression models for 60 volatile organic compounds:Comparing Google Point of Interest (POI) and city permit data. Science of The Total Environment 2019;677:131–41; doi:10.1016/j.scitotenv.2019.04.285. R835873 (2019)
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  • Journal Article Clark MA, Springmann M, Hill J, Tilman D. Multiple health and environmental impacts of foods. Proceedings of the National Academy of Sciences of the United States of America 2019;116:23357–62 R835873 (2019)
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  • Journal Article Xu H, Bechle MJ, Wang M, Szpiro AA, Vedal S, Bai Y, Marshall JD. National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Science of The Total Environment 2019;655:423–33. doi:10.1016/j.scitotenv.2018.11.125. R835873 (2019)
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  • Journal Article Muller NZ. The derivation of discount rates with an augmented measure of income. Journal of Environmental Economics and Management 2019;95:87–101. doi:10.1016/j.jeem.2019.02.007. R835873 (2019)
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  • Journal Article Alotaibi R, Bechle M, Marshall JD, Ramani T, Zietsman J, Nieuwenhuijsen MJ, Khreis H. Traffic related air pollution and the burden of childhood asthma in the contiguous United States in 2000 and 2010. Environment International 2019;127:858–67. R835873 (2019)
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  • Journal Article Eilenberg SR, Subramanian R, Malings C, Hauryliuk A, Presto AA, Robinson AL. Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment. Journal of Exposure Science & Environmental Epidemiology 2020;30(6):949-61. R835873 (2020)
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  • Journal Article Kelp MM, Jacob DJ, Kutz JN, Marshall JD, Tessum CW. Toward stable, general machine-learned models of the atmospheric chemical system. Journal of Geophysical Research-Atmospheres 2020;125:e2020JD032759. R835873 (2020)
    R840012 (2021)
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  • Journal Article Zimmerman N, Li HZ, Ellis A, Hauryliuk A, Robinson ES, Gu P, Shah RU, Ye Q, Snell L, Subramanian R, Robinson AL, Apte JS, Presto AA. Improving correlations between land use and air pollutant concentrations using wavelet analysis:Insights from a low-cost sensor network. Aerosol Air Quality Resesearch 2020;20:314–28. doi:10.4209/aaqr.2019.03.0124. R835873 (2019)
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  • Journal Article Fabisiak JP, Jackson EM, Brink LL, Presto AA. A risk-based model to assess environmental justice and coronary heart disease burden from traffic-related air pollutants. Environ Health 2020;19:34 R835873 (2019)
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  • Journal Article Parks RM, Bennett JE, Tamura-Wicks H, Kontis V, Toumi R, Danaei G, Ezzati M. Anomalously warm temperatures are associated with increased injury deaths. Nature Medicine 2020;26:65–70. doi:10.1038/s41591-019-0721-y. R835873 (2019)
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  • Journal Article Kim S-Y, Bechle M, Hankey S, Sheppard L, Szpiro AA, Marshall JD. Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015:Role of prediction model parsimony in integrated empirical geographic regression. PLOS ONE 2020;15:e0228535 R835873 (2019)
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  • Journal Article Goodkind AL, Jones BA, Berrens RP. Cryptodamages:Monetary value estimates of the air pollution and human health impacts of cryptocurrency mining. Energy Research & Social Science 2020;59:101281 R835873 (2019)
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  • Journal Article Malings C, Tanzer R, Hauryliuk A, Saha PK, Robinson AL, Presto AA, Subramanian R. Fine particle mass monitoring with low-cost sensors:Corrections and long-term performance evaluation. Aerosol Science and Technology 2020;54:160–74. doi:10.1080/02786826.2019.1623863. R835873 (2019)
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  • Journal Article Pope CA, Coleman N, Pond ZA, Burnett RT. Fine particulate air pollution and human mortality:25+ years of cohort studies. Environmental Research 2020;183:108924. doi:10.1016/j.envres.2019.108924. R835873 (2019)
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  • Journal Article Jorga SD, Kaltsonoudis C, Liangou A, Pandis SN. Measurement of formation rates of secondary aerosol in the ambient urban atmosphere using a dual smog chamber system. Environmental Science & Technology 2020;54:1336–43 R835873 (2019)
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  • Journal Article Lu Q, Murphy BN, Qin M, Adams PJ, Zhao Y, Pye HOT, Efstathiou C, Robinson AL. Simulation of organic aerosol formation during the CalNex study:Updated mobile emissions and secondary organic aerosol parameterization for intermediate-volatility organic compounds. Atmospheric Chemistry and Physics 2020;20:4313–32; doi:10.5194/acp-20-4313-2020. R835873 (2019)
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  • Journal Article Shah RU, Coggon MM, Gkatzelis GI, McDonald BC, Tasoglou A, Huber H, Gilman J, Warneke C, Robinson AL, Presto AA. Urban oxidation flow reactor measurements reveal significant secondary organic aerosol contributions from volatile emissions of emerging Importance. Environmental Science & Technology 2020;54:714–25. doi:10.1021/acs.est.9b06531. . R835873 (2019)
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  • Journal Article Thind MPS, Tessum CW, Azevedo IL, Marshall JD. Fine particulate air pollution from electricity generation in the US:Health impacts by race, income, and geography. Environmental Science & Technology 2019;53:14010–9. doi:10.1021/acs.est.9b02527. R835873 (2019)
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  • Journal Article Thakrar SK, Balasubramanian S, Adams PJ, Azevedo IML, Muller NZ, Pandis SN, Polasky S, Pope CA, Robinson AL, Apte JS, Tessum CW, Marshall JD, Hill JD. Reducing mortality from air pollution in the United States by targeting specific emission sources. Environmetnal Science & Technology Letters 2020. doi:10.1021/acs.estlett.0c00424. R835873 (2019)
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  • Supplemental Keywords:

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

    Relevant Websites:

    The Center for Air, Climate, and Energy Solutions Exit

    Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • 2017 Progress Report
  • 2018 Progress Report
  • 2019 Progress Report
  • Final
  • 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