2019 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 , Health Canada - Ottawa , Imperial College , University of British Columbia , University of Minnesota , The University of Texas at Austin , Virginia Tech , University of Washington
Current 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, 2019 through April 30,2020
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 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 and 3), to derive new knowledge on multi-pollutant mortality risk and its variability across the U.S.

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 have completed the bulk of the 1-km modeling for the Pittsburgh domain. Observations collected in Project 2 with a high density of sites within the city are being used to evaluate these simulations. Two manuscripts on PM2.5 simulations and one on ultrafines are in draft stages.
  • Speciated and Source-Resolved Exposure Fields for Epidemiological Study: Our previous annual report described CTM simulations for 1990, 2001, and 2010 time periods that we are working to hand off to Project 5 for epidemiological analysis. These exposure fields include PM2.5 speciation and are “tagged” to identify the source category that emitted the PM2.5 or its precursor. A geographically weighted regression technique has been applied to the raw CTM output, using speciated measurement data, to reduce some systematic regional biases in the CTM that may have interfered with subsequent epidemiological analyses.
  • Development of Reduced-Complexity Models (RCMs). A global version of InMAP is near completion. We have started development on 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. Additionally, we completed our initial attempt to develop a machine learning emulator for a time-consuming chemical mechanism, the Carbon Bond Mechanism Z. Although the approach shows some promise, additional work is needed to ensure consistently high performance. Treatment of nitrate PM in APEEP was updated as a result of our earlier intercomparison efforts and has been applied in subsequent studies.
  • Evaluation of RCMs. We have performed a set of CTM simulations to assess how marginal social costs might change under future emissions regimes due to nonlinearities in PM2.5 formation. The results show remarkable robustness in the results due to atmospheric chemistry alone, i.e. assuming one can account for future changes in population and VSL. An exception is the marginal social costs for ammonia, which decline considerably under future scenarios where SO2 and NOx emissions decrease.
  • 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 (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:

  • Collaboration across projects within CACES: During the past year we worked closely with Projects 1 and 3. Data from this project are being used to evaluate outputs of chemical transport models (Project 1) and national land use regression models (Project 3). We also worked with Project 3 to evaluate the applicability of low-cost sensors for building national-scale PM2.5 exposure models and to incorporate environmental justice into analyses coming from this project.
  • Cross-center collaboration with SEARCH: Our collaborative project with the SEARCH center involved additional data collection in Pittsburgh and Baltimore in July-August 2019. We are currently working on data analysis and will present results at upcoming conferences in fall 2020.
  • Ultrafine particles: We continued our focus on ultrafine particles (UFPs). One paper under review examines 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 are also preparing an analysis of national spatial and temporal trends in UFP; a version of this analysis was presented at the HEI annual meeting in May 2020.
  • Spatial modeling of source-resolved organic aerosol: Last year we built LUR models for source-resolved organic aerosol (OA) measured in Oakland, CA. This year we expanded that modeling effort to multiple cities (including Pittsburgh and Baltimore) with a goal of making estimates of nationwide exposures to source-resolved OA.
  • Comparing mobile and fixed observations of black carbon: As a means of validating mobile monitoring as an exposure assessment technique, we compared mobile- and fixed- site observations of BC in Oakland, CA based on Google Street View car measurements and a dense network of fixed site sensors. The comparison demonstrated that both measurement techniques reproduce similar spatial patterns with high fidelity.
  • High-resolution exposure data and environmental disparities: For multiple high- resolution exposure measurement and modeling datasets for Pittsburgh, Oakland, and other cities, we have evaluated exposure disparities by pollutant/source marker, race- ethnicity, and income. One key finding of our work is that measurement datasets often demonstrate larger exposure disparities than land-use regression models.
  • Quantifying impacts of COVID-related shutdowns on air quality: Our low-cost sensor network in Pittsburgh has been collecting data continuously through the implementation of social distancing measures. These measures have reduced activity levels, particularly traffic volumes. We quantified changes in traffic-related air pollution and related them to changes in activity; a manuscript is currently under review at ES&T Letters.

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.

The main accomplishment during the past reporting period is that the journal article describing our national estimates (S-Y Kim et al., 2020) was published, and the estimates are now available online. This publication (the journal article; and the model-estimates being publicly available) were a major goal, and now a major accomplishment, of CACES. These estimates were used in multiple epidemiological investigations within CACES (see updates for Project 5).

As described next, major additional activities in the past reporting period included further development of empirical-model methods, specific model-measurement comparisons in multiple cities (including based on measurements conducted in CACES Project 2; these comparisons are in addition to model-measurement comparisons in the S-Y Kim et al., 2020, article), investigation of spatial patterns and a spatial decomposition analysis, and use of model estimates to investigate exposure disparities (“environmental justice”):

  • New covariates and models: During this project period, we further developed improved land cover variables for the contiguous US: (1) Landsat satellite-derived Local Climate Zones (LCZs) that will allow for spatiotemporally varying landcover variables with historical coverage (~1980s), (2) Google Point of Interest (POI) data that can provide information on sources (e.g., restaurants, gas stations) that are not well covered by existing covariates, (3) Yelp data that adds detailed information on restaurant type and location, and (4) object identification from Google Street View (GSV) imagery. We have completed model building comparisons with these new covariates using more flexible machine learning-based modeling structures (e.g., random forest, gradient boosting) for all criteria pollutants (PM2.5, PM10, NO2, ozone, CO, SO2) and two years (2010 and 2015). Machine learning models outperformed stepwise forward selection models, and were comparable or improved as compared to the PLS-UK (partial least squares – universal kriging) modeling approach. These findings suggest that the ML approach allows for creation of similar performing models with only the new covariates and satellite-based air pollution measurements, highlighting the utility of a flexible ML approach and supporting our use of new covariates for model building. Additionally, we have worked with Project 2 to develop the Yelp database and data on restaurants, to develop empirical models across multiple cities with data from mobile AMS (accelerator mass spectrometry – an advanced measurement technique conducted as part of CACES Project 2 mobile-monitoring).
  • External model evaluation: We have continued our assessment of model predictions against independent measurements. In addition to the existing Project 2 measurements collected in Pittsburgh (and, in addition to the model-measurement comparisons conducted as part of model-building and model-testing in the S-Y Sun et al., 2020 article), we have leveraged the open-source PurpleAir PM2.5 sensor network as another source of independent measurements. Preliminary results indicate that EPA PM2.5 measurements and predicted PM2.5 concentrations from our version 1 model are typically lower than PurpleAir measurements. LUR models built using PurpleAir measurements exhibit differing within- city spatial patterns than models built with only regulatory data. Several explanations are possible, one of which is that monitoring siting (which differs for PurpleAir versus regulatory monitors) may impact model predictions. Finally, during this project period we have also evaluated our model predictions against predictions from other publicly available or privately shared models, including from the other ACE centers. Preliminary results suggest strong agreement among empirical models built from regulatory monitor data.
  • National environmental justice patterns: During this project period, we continued 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). Major updates from this project period include analysis of disparities by race and income. Average exposures are generally higher for low-income than for high-income households, but exposure-disparities are smaller by income than by race-ethnicity. Racial-ethnic exposure- disparities are similar controlling, versus not controlling, for income. The article describing this investigation is currently in review.
  • Spatial decomposition: During this project period, we finalized and published our spatially decomposed predictions of PM2.5 and NO2 concentrations for years 2000-2015. For each prediction location, a local minimum was calculated within several buffer lengths (1km, 10km, 100km) and used to divide predicted concentrations into near-source (i.e., prediction – 1km minimum), neighborhood, urban background, and long range. These results have been used in epidemiological investigations (CACES Project 5).

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:

  • Fine particulate matter damages and value added in the US economy. In 1999, the National Research Council published a report calling for the integration of externality costs from air pollution into the national accounts. So far, this call for action has not materialized. We have updated estimates of externality costs for the United States for the most recently available data, within the appropriate economic framework, and did so comprehensively through the use of multiple integrated assessment models and for several years.
  • Near term carbon tax policy in the US Economy: limits to deep decarbonization. We explored carbon dioxide (CO2) tax policies from 2015 to 2030 in the United States economy using an energy system least-cost optimization model. We reported limited near-term decarbonization opportunities outside of the electricity sector, which results in substantial CO2 tax revenue through 2030. We found asymmetric deadweight loss from implementing mistakenly high or low CO2 taxes, providing efficiency-based support for the precautionary principle. Despite CO2 reductions occurring mainly in the electric sector, the estimated abatement herein is consistent with the US nationally determined contributions established under the Paris Agreement.
  • Multiple health and environmental impacts of foods. Dietary choices are a leading global cause of mortality and environmental degradation and threaten the attainability of the UN’s Sustainable Development Goals and the Paris Climate Agreement. To inform decision making and to better identify the multifaceted health and environmental impacts of dietary choices, we described how consuming 15 different food groups is associated with 5 health outcomes and 5 aspects of environmental degradation. We found that foods associated with improved adult health also often have low environmental impacts, indicating that the same dietary transitions that would lower incidences of noncommunicable diseases would also help meet environmental sustainability targets.

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 extended our analysis of the NHIS cohort data and CACES exposure estimates generated by Project 3 using alternative “causal” modeling approaches and an inverse probability weighting and “doubly robust” modeling approach. We also completed a multi-pollutant analysis using the NHIS cohort, including spatial decomposed PM2.5 estimates. Finally, we used both the NHIS and Surveillance, Epidemiology, and End Results Program (SEER) datasets to investigate the association of cancer with the Project 3 PM2.5 exposures.
  • County-Level Mortality Space-Time Study. We completed an analysis of the association between PM2.5 and country level mortality using the complete vital registration data from 1999 to 2015. We investigated if deaths from various unintentional (transport, falls and drownings) and intentional (assault and suicide) injuries might be affected by anomalously warm temperatures that occur today and are expected to become increasingly common as a result of global climate change.
  • Meta-analyses: We completed and published a review and meta-analysis of cohort studies of long-term exposure to PM2.5 air pollution and mortality (all cause, cardiovascular, and lung cancer). Finally, we initiated research on the air pollution and mortality and non-attainment of PM2.5 National Ambient Air Quality Standards.

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 in-person science meeting was held in December 2019 in Pittsburgh. CACES hosted the EPA ACE All Centers meeting in Pittsburgh June 18-19, 2019.  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

  • High-resolution CTM modeling analyses and manuscripts for Pittsburgh will be completed and submitted.
  • CTM-based exposure estimates, with source resolution and speciation, will be finalized and submitted to Project 5. These estimates will use available observations to correct some systematic regional biases in the CTM output.
  • We will develop marginal social cost estimates for EASIUR based on the volatility basis set (VBS) framework. These will be the first marginal social cost estimates for primary organic aerosol (POA) and VOC emissions that account for semi-volatility of POA and multi-generation oxidative “aging” of VOCs, including IVOCs, that have appreciable effects on secondary organic aerosol formation.
  • EASIUR, which is currently derived on a 36 km CTM grid, will be extended to higher resolution.
  • A Gaussian dispersion-based RCM, similar to APEEP, but applicable to other countries will see continued development.
  • We plan to substantially increase the functionality of our web site for RCM data sets. This includes completing and disseminating the ability to use source-receptor data sets in analyses and will also include a set of environmental justice (EJ) metrics so that EJ analyses will be a straightforward and standard component of any future emissions analyses.

Project 2. Air quality observatory

  • Data synthesis and integration with modeling tools: We have completed data collection in multiple cities. Analyses going forward will focus on synthesizing this data across the sampled cities and continued collaboration with Projects 1 and 3 to use our data in model evaluation. We are also looking at ways to incorporate the knowledge gained in this project to improve the next generation of chemical transport models. One example is an expected output from our collaborative project with SEARCH, which will be new emissions estimates of PM mass, size, and composition from urban cooking sources.
  • COVID-related impacts: We will continue to monitor changes in air quality driven by COVID-related social distancing measures. We will be able to use our low-cost sensor network in Pittsburgh to capture spatial variations in air pollution changes as Pennsylvania gradually moves back to business as usual, and relate these changes to modifiable factors.
  • 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 epidemiolog

  • Continue to develop new covariates, including a Google Street View (GSV) -based image analysis, and continue to test the new covariates (LCZ, POI, and GSV), version 1 models, and alternative modeling frameworks against existing prediction models and independent measurements from Project 2 and PurpleAir. A primary goal of this work is methodological: to identify similar locations with poor performance, as well as identifying variables that improve within-city prediction performance.
  • If time allows, extend existing PM2.5 predictions by combining the spatial decomposition estimates with source-resolved CTM output developed by Project 1, to develop source- resolved PM2.5 estimates. If those results happen and are sufficiently reliable, they may be used by researchers in CACES Project 5 (epidemiological analysis).
  • Continue to analyze national environmental justice (EJ) patterns, including looking at additional demographic factors beyond race and ethnicity, interstate versus intrastate disparities, and a sensitivity analysis restricted to locations with monitoring data (versus model predictions).

Project 4. Air pollutant control strategies in a changing world

  • Continue evaluation of transportation, electricity generation, agriculture and economy- wide, 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 MSA-level analyses of the NHIS cohort data using CTM modeled (Project 1) pollution estimates. This will include composition and source-resolved PM2.5.
  • Investigate the association of mortality with greenness and PM2.5 in cancer survivor (SEER) cohort.
  • Investigate the association of BMI and mortality using the unrestricted NHIS data (interesting training analysis).
  • Project future age-, sex- cause-specific mortality at the county level.
  • Estimate, together with projected air pollution concentrations, the reduction in deaths of different concentration scenarios and policies.


Journal Articles: 107 Displayed | Download in RIS Format

Other center views: All 119 publications 107 publications in selected types All 107 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 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 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)
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    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 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 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 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)
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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 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)
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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, 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 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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  • 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 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 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 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 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 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)
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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 Cserbik D, Chen JC, McConnell R, Berhane K, Sowell ER, Schwartz J, Hackman DA, Kan E, Fan CC and Herting MM. Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure. Environ Int 2020; 143:105933. R835873 (2020)
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  • Journal Article Bruchon MB, Michalek JJ, Azevedo IL. Effects of Air Emission Externalities on Optimal Ridesourcing Fleet Electrification and Operations. Environmental Science & Technology 2021 ;55(5):3188-200. R835873 (2020)
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  • Journal Article Tang R, Lu Q, Guo S, Wang H, Song K, Yu Y, Tan R, Liu K, Shen R, Chen S, Zeng L. Measurement report:Distinct emissions and volatility distribution of intermediate-volatility organic compounds from on-road Chinese gasoline vehicles:implication of high secondary organic aerosol formation potential. Atmospheric Chemistry and Physics 2021 ;21(4):2569-83. R835873 (2020)
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  • Journal Article Roth MB, Adams PJ, Jaramillo P, Muller NZ. Near term carbon tax policy in the US Economy:limits to deep decarbonization. Environmental Research Communications 2020 ;2(5):051004. R835873 (2020)
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  • Journal Article Sergi BJ, Adams PJ, Muller NZ, Robinson AL, Davis SJ, Marshall JD, Azevedo IL. Optimizing emissions reductions from the us power sector for climate and health benefits. Environmental science & technology 2020 ;54(12):7513-23. R835873 (2020)
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  • Abstract: PubMed Abstract HTML
  • Journal Article Tessum CW, Paolella DA, Chambliss SE, Apte JS, Hill JD, Marshall JD. PM2. 5 polluters disproportionately and systemically affect people of color in the United States. Science Advances 2021 ;7(18):eabf4491. R835873 (2020)
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  • Journal Article Presto AA, Saha PK, Robinson AL. Past, present, and future of ultrafine particle exposures in North America. Atmospheric Environment:X 2021;10:100109. R835873 (2020)
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  • Journal Article Gasparik JT, Ye Q, Curtis JH, Presto AA, Donahue NM, Sullivan RC, West M, Riemer N. Quantifying errors in the aerosol mixing-state index based on limited particle sample size. Aerosol Science and Technology 2020 ;54(12):1527-41. R835873 (2020)
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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)
    R835873 (2020)
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  • Journal Article Sergi B, Azevedo I, Davis SJ, Muller NZ. Regional and county flows of particulate matter damage in the US. Environmental Research Letters 2020 ;15(10):104073. R835873 (2020)
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  • Journal Article Konstantinoudis G, Padellini T, Bennett J, Davies B, Ezzati M, Blangiardo M. Response to “Re:Long-term exposure to air-pollution and COVID-19 mortality in England:A hierarchical spatial analysis”. Environment International 2021 ;150:106427. R835873 (2020)
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  • Journal Article Garcia III GR, Coleman NC, Pond ZA, Pope III CA. Shape of BMI–Mortality Risk Associations:Reverse Causality and Heterogeneity in a Representative Cohort of US Adults. Obesity 2021 ;29(4):755-66. R835873 (2020)
  • Abstract: PubMed Abstract HTML
  • Journal Article Shah RU, Robinson ES, Gu P, Apte JS, Marshall JD, Robinson AL, Presto AA. Socio-economic disparities in exposure to urban restaurant emissions are larger than for traffic. Environmental Research Letters 2020 ;15(11):114039. R835873 (2020)
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  • Journal Article Saha PK, Sengupta S, Adams P, Robinson AL, Presto AA. Spatial Correlation of Ultrafine Particle Number and Fine Particle Mass at Urban Scales:Implications for Health Assessment. Environmental Science & Technology 2020;54(15):9295-304. R835873 (2020)
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  • Journal Article Ward JW, Michalek JJ, Samaras C, Azevedo IL, Henao A, Rames C, Wenzel T. The impact of Uber and Lyft on vehicle ownership, fuel economy, and transit across US cities. Iscience 2021;24(1):101933. . R835873 (2020)
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  • Journal Article Tong F, Azevedo IM. What are the best combinations of fuel-vehicle technologies to mitigate climate change and air pollution effects across the United States?. Environmental Research Letters 2020 ;15(7):074046. Wang Y, Bechle MJ, Kim SY, Adams PJ, Pandis SN, Pope III CA, Robinson AL, Sheppard L, Szpiro AA, Marshall JD. Spatial decomposition analysis of NO2 and PM2. 5 air pollution in the United States. Atmospheric environment 2020 ;241:117470. R835873 (2020)
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  • Journal Article Cain KP, Liangou A, Davidson ML, Pandis SN. α-Pinene, Limonene, and Cyclohexene Secondary Organic Aerosol Hygroscopicity and Oxidation Level as a Function of Volatility. Aerosol and Air Quality Research 2021 ;21. R835873 (2020)
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  • Supplemental Keywords:

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

    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
  • 2020 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