2018 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 , Middlebury College , The University of Texas at Austin , University of British Columbia , University of Minnesota , University of Washington , Virginia Polytechnic Institute and State University
EPA Project Officer: Chung, Serena
Project Period: May 1, 2016 through April 30, 2021
Project Period Covered by this Report: May 1, 2018 through April 30,2019
Project Amount: $10,000,000
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text |  Recipients Lists
Research Category: Airborne Particulate Matter Health Effects , Air , Climate Change , Health Effects

Objective:

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

The center is comprised of five thematically and scientifically integrated research projects and one support center. Project 1 is extending existing chemical transport models to high spatial resolution (1 km) with tagged source apportionment and developing a new class of reduced complexity models for air quality and exposure assessment. Project 2 is conducting comprehensive measurements in four cities (Austin, TX; Oakland, CA; Pittsburgh, PA; 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:

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.

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 play an important role in evaluating 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: We performed CTM simulations for 1990, 2001, and 2010 time periods that are being handed off to Project 5 as exposure assessment 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 significant amount of evaluation of these fields has been performed.
  • Development of Reduced-Complexity Models (RCMs). A serious effort is underway to develop a global version of InMAP. 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.
  • Outreach and Dissemination of RCM tools. This past fall, we launched a web site that allows users to obtain marginal social cost results for all three RCMs: APEEP, EASIUR, and InMAP. We had substantial visibility at last year’s CMAS conference, including a tutorial session to introduce users to RCMs. We completed work on an intercomparison paper that shows general consistency in the marginal social costs between the three RCMs. We are in earlier stages of efforts to bring new functionality to our RCM web site by bringing environmental justice metrics online and making source-receptor versions of the three RCMs more easily accessible to users.

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:

  • Model evaluation: Data from this project are being used to evaluate outputs of chemical transport models (Project 1) and national land use regression models (Project 3). That evaluation is ongoing. Project 1 and Project 3 personnel participate in our bi-weekly project meetings in order to streamline data transfer and model-measurement comparisons.
  • Analysis of ultrafine particle concentration patterns: We quantified concentrations of ultrafine particles (UFPs) at 30 locations around Pittsburgh. We observed a factor of 3 variation across the domain, with the highest concentrations in urban street canyons and near major industrial facilities. We used the measurement dataset to build land use regression (LUR) models for UFPs in Pittsburgh, and sub-sampled the dataset to develop guidelines for future measurement campaigns. Lastly, we compared exposures of UFP to PM2.5 and show that these exposures are modestly correlated.
  • Spatial modeling of source-resolved organic aerosol: We built LUR models for source-resolved organic aerosol (OA) measured in Oakland. We demonstrate good model performance for primary OA associated with specific sources such as traffic and cooking, presumably because of a strong physical connection between the emission source and the measurement. Spatial patterns of secondary components like particulate sulfate show poorer performance when estimated using LUR.
  • Multi-city evaluations and environmental justice: We have started to investigate environmental justice using two datasets: (1) the mobile AMS data collected in Pittsburgh and Oakland, and (2) data from our low-cost sensor network in Pittsburgh. The former shows that non-white populations have higher exposures to cooking OA than whites, but similar environmental injustice does not exist for traffic OA. The latter shows that there is not a strong association between PM2.5 or NO2 exposures and socioeconomic variables in Pittsburgh.
  • Data analysis: During this project period, eleven papers were published or submitted for review by project 2. Sample results from analyses include: measurement results from Pittsburgh and Oakland, LUR modeling of UFPs and OA components, and exposure estimation of source-resolved PM mass and particle number concentrations.

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

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

  • New covariates and models: During this project period, we furthered developed improved land cover variables, including the Landsat satellite-derived Local Climate Zones (LCZs) that will allow for spatiotemporally varying landcover variables with historical coverage (~1980s), and Google Point of Interest (POI) dataset that can provide information on sources (e.g., restaurants, gas stations) that are not well covered by existing covariates. We have completed preliminary model building with these new covariates, and more flexible machine learning-based modeling structures (e.g., random forest, gradient boosting) for three pollutants (PM2.5, NO2, ozone) and two years (2010 and 2015). Machine learning models outperformed stepwise forward selection models, and were able to create 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 LCZ and POI for model building.
  • External model evaluation: During this project period, we have continued our assessment of model predictions against independent measurements. In addition to the existing Project 2 measurements collected in Pittsburgh, we have leveraged the open-source PurpleAir PM2.5 sensor network as another source of independent measurements. Preliminary results indicate that predicted PM2.5 concentrations of version 1 model are typically lower than PurpleAir and other measurements and that the models’ ability to capture within-city gradients varies by city (1-61% of explained variation). During this project period we have also evaluated our model predictions against predictions from other publicly available or privately shared models. Preliminary results suggest strong agreement among NO2 models (r=0.93-0.97) and general agreement among PM2.5 (r=0.87-0.96) and PM10 (r=0.84-0.95). For ozone, agreement is poor in California (r=0.55-75) but strong elsewhere (r=0.94-97).
  • 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, 2010). Over this period, exposures have typically declined for all racial/ethnic groups, however, disparities in exposure still remain and the order of least-to-most exposed subgroup remained generally unchanged. Preliminary results indicate urban versus rural patterns in disparity vary by pollutant, for example, disparities in NO2 exposure are largely driven by exposures in urban areas whereas disparities are present in both urban and rural locations for PM2.5. Moreover, within-state disparities vary widely, with the exception of ozone, suggesting large spatial variability in disparities.Spatial decomposition: During this project period, we spatially decomposed the predicted 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. For NO2, concentrations are predominately neighborhood and urban, except in rural locations where long-range transport becomes more important. For PM2.5, concentrations are predominately long-range regardless in both urban and rural locations. Spatial decomposition estimates have been handed off to Project 5 for epidemiological analysis.

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:

  • Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. We linked PM2.5exposure to the human activities responsible for PM2.5pollution. We used these results to explore “pollution inequity”: the difference between the environmental health damage caused by a racial–ethnic group and the damage that group experiences. We showed that, in the United States, PM2.5exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities.
  • Fine-scale damage estimates of particulate matter air pollution reveal opportunities for location-specific mitigation of emissions. We estimated that anthropogenic PM2.5was responsible for 107,000 premature deaths in 2011, at a cost to society of $886 billion. Of these deaths, 57% were associated with pollution caused by energy consumption and another 15% with pollution caused by agricultural activities. A small fraction of emissions, concentrated in or near densely populated areas, plays an outsized role in damaging human health with the most damaging 10% of total emissions accounting for 40% of total damages.
  • Air-quality-related health damages of maize. We showed that reduced air quality resulting from maize production is associated with 4,300 premature deaths annually in the United States, with estimated damages in monetary terms of US$39 billion (range: US$14–64 billion). Increased concentrations of fine particulate matter (PM2.5) are driven by emissions of ammonia—a PM2.5precursor—that result from nitrogen fertilizer use. Our results suggest potential benefits from strategic interventions in maize production, including changing the fertilizer type and application method, improving nitrogen use efficiency, switching to crops requiring less fertilizer, and geographically reallocating production.
  • The Diet, Health, and Environment Trilemma. We discussed how shifts to healthier diets—such as some Mediterranean, pescetarian, vegetarian, and vegan diets—could reduce incidence of diet-related diseases and improve environmental outcomes. In addition, we detail how other interventions to food systems that use known technologies and management techniques would improve environmental outcomes.

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 completed the first round of analyses that evaluated associations between long-term PM2.5 exposure and mortality risk using cohorts of the U.S. adult population constructed from the “restricted-use” NHIS data. We submitted a manuscript that reports these results and expanded some of the analysis in response to SAC, reviewer and editorial comments. The expanded analysis included use of back-casted PM2.5 exposure estimates and alternative windows of exposure and mortality follow up. The final manuscript, entitled, “Mortality Risk and Fine Particulate Air Pollution in a Large, Representative Cohort of U.S. Adults” was published in Environmental Health Perspectives (DOI 10.1289/EHP4438). We have also explored alternative “causal” modeling approaches to analyzing the NHIS cohort data. We tried an instrumental variable (IV) modeling approach and an inverse probability weighting and “doubly robust” modeling approach. A manuscript reporting the inverse probability weighting approach has been completed and is currently in review.
  • 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. This work has been published in PLoS Medicine.

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 third CACES in-person science meeting was held in December 2018 in Pittsburgh. CACES will host the EPA ACE All Centers meeting in Pittsburgh June 18-19, 2019. The third CACES SAC meeting will occur June 19-20, 2019 in Pittsburgh. Finally the administrative core organized monthly conference calls of the project Executive Committee and weekly to monthly calls for groups of investigators for project-specific meetings.

Future Activities:

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

  • High-resolution CTM modeling analyses and manuscripts for Pittsburgh will be completed and submitted.
  • 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.
  • We anticipate that global InMAP will be completed.
  • 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.
  • We plan additional outreach and dissemination around the availability and usage of RCM data sets.

Project 2. Air quality observatory

  • Model Evaluation: We will continue to evaluate the deterministic (Project 1) and empirical (Project 3) model predictions using data collected primarily in Pittsburgh and Oakland, as well as with other available datasets.
  • Collaborative project with SEARCH: In summer 2019, we will collaborate with the SEARCH center to quantify source signatures of OA emissions in greater detail. We will simultaneously sample multiple combustion and non-combustion sources with AMS and sorbent samples in collaboration with Prof. Drew Gentner at Yale.
  • Data synthesis. Our sampling design relies on collecting data in nominally similar micro-environments (e.g., downtown central business districts) in multiple cities. Last year, we began comparing results across cities and looking for common spatial patterns. We will expand this effort in the coming year, including construction of multi-city LUR models and incorporation of additional data collected in Baltimore as part of our collaborative project with SEARCH.
  • 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

  • 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 to identify similar locations with poor performance, as well as identifying variables that improve within-city prediction performance.
  • For the PM2.5 models, extend existing predictions by combining the spatial decomposition estimates with source-resolved CTM output developed by Project 1, to develop source-resolved PM2.5 estimates. Those results would be used by researchers in Project 5, in epidemiological analysis.
  • Continue to analyze national environmental justice patterns, including looking at additional demographic factors beyond race and ethnicity, 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.
  • Continue to employ updated models from Projects 1 and 3 in forthcoming research efforts.
  • Begin development of Project 4 final outputs.

Project 5. Health effects of air pollution and mitigation scenarios

  • Further explore causal modeling approaches.
  • Conduct and report analysis for multiple pollutants (PM2.5, PM2.5-10, SO2, NO2, O3, and CO) and evaluate sensitivity of results in multiple pollutant models.
  • Conduct and report analysis of spatially decomposed PM2.5 exposures.
  • Conduct and report time-dependent analysis with alternative windows of pollution exposure.
  • Conduct and report analyses of air pollution and multiple non-lung cancers.
  • Conduct and report mortality analysis with exposures based on Chemical Transport Model (CTM) results, including source apportionment.
  • Project country mortality and analyze the impacts of alternative PM2.5 scenarios.


Journal Articles: 44 Displayed | Download in RIS Format

Other center views: All 56 publications 44 publications in selected types All 44 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 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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  • 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 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 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)
    R835873 (2018)
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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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    R835873C001 (2016)
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  • Journal Article Tessum CW, Hil JD, Marshall JD. InMAP:A model for air pollution interventions. PLoS ONE 12, e0176131, 0.1371/journal.pone.0176131, 2017. R835873C001 (2016)
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  • Journal Article 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)
    R835873 (2018)
    R833864 (Final)
  • Full-text: IOP-Full Text HTML
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  • Journal Article Weis A, Jaramillo P, Michalek J. Consequential life cycle air emissions externalities for plug-in electric vehicles in the PJM interconnection. Environmental Research Letters 2016;11(2):024009 (12 pp.). R835873 (2016)
    R835873 (2017)
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    R835873C001 (2016)
    R835873C004 (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)
    R835873 (2018)
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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)
    R835873 (2018)
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  • Journal Article Zhao Y, Saleh R, Saliba G, Presto AA, Gordon TD, Drozd GT, Goldstein AH, Donahue NM, Robinson AL. Reducing secondary organic aerosol formation from gasoline vehicle exhaust. Proceedings of the National Academy of Sciences of the United States of America 2017;114(27):6984-6989. R835873 (2016)
    R835873 (2017)
    R835873 (2018)
    R835873C001 (2016)
    R835873C004 (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)
    R835873 (2018)
    R836286 (2017)
  • 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)
    R836286 (2016)
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  • Supplemental Keywords:

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

    Relevant Websites:

    Center for Air, Climate & Energy Solutions Exit

    Progress and Final Reports:

    Original Abstract
  • 2016 Progress Report
  • 2017 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