2016 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 , Pope, Clive Arden , Millet, Dylan B , Marshall, Julian D. , Michalek, Jeremy J. , Azevedo, Inês L , Boies, Adam M. , Pandis, Spyros N. , Coggins, Jay S. , Apte, Joshua S. , Matthews, H. Scott , Burnett, Richard T , Presto, Albert , Hill, Jason , Ezzati, Majid , Brauer, Michael , Donahue, Neil , Muller, Nicholas , Jaramillo, Paulina , Adams, Peter , Polasky, Stephen , Hankey, Steve
Current 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 , Virginia Polytechnic Institute and State University
Current 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, 2016 through April 30,2017
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:

The Center for Air, Climate, and Energy Solutions (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 testing 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 three cities (Austin, TX; Oakland, CA; and Pittsburgh, PA) to quantify factors influencing gradients in pollutant concentrations and develop mechanistic understanding of how pollutant transformations affect population exposures. Project 3 is developing multi-pollutant land-use regression (LUR) 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; land use; and climate-dependent emissions, transport and chemistry. Project 5 is analyzing nationally representative population-based health data, combined with novel multi-pollutant exposure estimates and source contributions (Projects 1 and 3), to derive new knowledge on multi-pollutant mortality risk and its variability across the United States.

Progress Summary:

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

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

  • Historical modeling of exposure to PM2.5 and related pollutants (1980-2015) for the continental United States.  During this project period research focused on the development of emissions inventories and meteorological inputs for the historical simulations.  Records from 1980 to present of dozens of spatially-resolved activity indicators (e.g., vehicle-miles traveled, acres of agricultural tillage, BTUs of power plant output) have been compiled. Published and measured emission factors have been gathered and compared. 
  • High-resolution (1 km) modeling of present-day air quality. During this project period research focused on developing high spatial resolution inventories for mobile and cooking sources for Pittsburgh. Using restaurant location data from the Google Places API, we have obtained restaurant data for the Pittsburgh Combined Statistical Area (CSA). Using these data, we demonstrated that the population density (the standard activity surrogate for cooking) is not well correlated with restaurant density at high spatial scales.
  • Development of Reduced-Complexity Models (RCMs).  During this project period significant progress was made on the development of three RCMs: AP2, EASIUR, and InMap. This included improved treatments of nitrate aerosol in AP2, creation of a source-receptor version of EASIUR, and development of a neural-network based chemical mechanism emulator for InMAP. 
  • Evaluation of Reduced-Complexity Models (RCMs).  During this project period, we compared among the three models the marginal social costs of SO2, NOx, NH3, and inert primary PM2.5 ground-level emissions in each county in the continental United States.  We also evaluated all three models using ambient air quality data. Despite fundamental structural differences among the three models, predicted marginal social costs are generally within the same order of magnitude and usually within a factor of 2 or 3. The agreement varies with the complexity of the chemistry that links the emissions to their equivalent ambient PM2.5 concentrations; predictions are most similar for primary PM2.5 and most different for NOx and NH3. Even with these differences, the three models generate robust rankings of national-level air quality policies based on social costs and benefits summed across pollutants and geographical locations.

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 three cities.  Major activities in the past reporting period included:

  • Evaluate performance of low-cost RAMP monitors.  Real-time Affordable Multi-Pollutant (RAMP) monitors measure CO, NO2, SO2, O3, and CO2 using low cost-sensors. Nineteen RAMPs were tested via co-location with the supersite on the Carnegie Mellon University campus and through laboratory calibration. We developed and evaluated three calibration strategies: a standard laboratory calibration; an empirical ambient calibration using a multi-linear regression that included sensor voltage, temperature, and RH as independent variables; and a machine-learning based calibration.  The machine learning algorithm performs the best; using it, every RAMP monitor evaluated meets the U.S. EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrated that a 4-week co-location period prior to deployment provides sufficient data for calibration model building.
  • Perform case studies of modifiable factors. We deployed RAMPs and additional instrumentation to create a monitoring network to investigate spatial and temporal patterns of air pollution in Pittsburgh. This network was used to conduct two multi-month case studies: (i) urban-rural transect and (ii) downtown business district (high traffic area with street canyons). 
  • High spatial mobile monitoring around air pollution sources. To complement the network of fixed sites, in-motion sampling with a mobile laboratory equipped with an aerosol mass spectrometer (AMS) and other instrumentation was used to characterize concentrations of a suite of air pollutants at high spatial resource around roads, restaurants, and other sources in Pittsburgh. This monitoring shows greatly elevated (up to a factor of 10) organic aerosol concentrations up to several hundred meters downwind of many restaurants. In contrast, organic aerosol concentrations on heavily-congested highways and other traffic dominated areas are only modestly higher than the urban background. This highlights the importance of cooking as a source for local exposures, especially given that restaurants often are located in residential neighborhoods.

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:

  • Data compilation: We completed the assembly and processing of the necessary air pollution and geographic data for the development of empirical air pollution models.
  • Development of modeling framework:  Our modeling approach employs 2-stage partial least squares (PLS) + Universal Kriging to estimate annual average concentrations. PLS leverages predictive information from a large number of geographic covariates with less concern for model overfitting, while also limiting the impact of geographic covariate outliers. Making predictions at ~8-10 million Census block centroids for 6+ pollutants and 36 years (1980-2015) is a computationally intensive task.  Employing PostGIS and parallel processing, we are able to calculate our geographic covariates at all Census block centroids in ~20 days on a 10-node server. The improvement in processing of covariates with PostGIS (~100× faster than our previously published models using Python and ArcGIS) has dramatically improved our (and other researchers) ability to make fine-scale spatial predictions over very large geographic scales. 
  • Preliminary model building:  Preliminary national-scale land use regression (LUR) models have been developed for PM2.5 (1999-2015), NO2 (1979-2015), SO2 (1979-2015), O3 (1979-2015), and CO (1990-2015).  The preliminary PM2.5 models exhibit good performance across all years (CV-R2: 0.72-0.90). Preliminary NO2 models exhibit good performance for years 1981-2014 (CV-R2: 0.73-0.89, CCV-R2: 0.45-0.76). Preliminary O3 models exhibit good performance for years 1990-2014 (CV-R2: 0.69-0.79, CCV-R2: 0.52 -0.71), but much poorer performance prior to 1989 (CV-R2: 0.51-0.66, CCV-R2: 0.09-0.39). Preliminary SO2 and CO models exhibit poor-to-moderate model performance. Initial model results for PM2.5 and NO2 have been provided to Project 5 to begin working through linking exposure estimates with health data. 

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:

  • United States economy-wide PM2.5 damages: We estimated air quality-related health effects for each of 428 sectors of the U.S. economy, the largest fractions of which were physically produced by electricity generation but induced by demand for manufactured goods. This alternative framing of air-quality related health impacts, which reveals the embodied health impacts of economic consumption, offers novel opportunities for strategies of air quality improvement. We used this new framework to explore health equity effects by economic sector, finding that Hispanic and Black populations are disproportionately impacted by electricity generation for manufacturing. 
  • Greenhouse gas and criteria air pollutant emissions under future policy scenarios:  We are comparing the economic efficiency of homogeneous versus heterogeneous air pollution regulations in the presence and absence of greenhouse gas regulations in the United States. Homogeneous regulations treat all emissions the same (what is currently in the United States) versus heterogeneous regulations that vary according to the magnitude of damage caused by the emission of a specific species in a specific location.
  • Air pollution impacts of corn production:  Using a life-cycle impact analysis and reduced complexity models, we performed spatially explicit analysis to estimate the damages of corn production. We estimate mean damages of $3.27/bu of corn produced in the United States, with 65% from ammonia emissions. Ammonia damages are more than six times larger than damages from GHG emissions. Spatial variation of damages is large, with the least damaging 5% of corn produced with damages less than $1.56/bu, and the most damaging 5% of corn produced with damages more than $6.17/bu.
  • VSL and mortality risk age adjustments:  We estimated the impact of including age differences in both the value of statistical life (VSL) and the risk of mortality. When we adjust both the VSL and mortality risk by age the total damages are lower than without an age adjustment, but not dramatically lower as conventional wisdom and past estimates indicate.
  • Controlling secondary organic aerosol (SOA) production from gasoline vehicle emissions: We found a strongly nonlinear relationship between SOA formation from gasoline vehicle exhaust and the atmospheric ratio of non-methane-organic-gas-to-NOx (NMOG:NOx). We investigated the implications of this relationship for the Los Angeles area. Although organic gas emissions from gasoline vehicles in Los Angeles are expected to fall by almost 80% over the next two decades, we predict no reduction in SOA production due to the effects of rising NMOG:NOx on SOA yields. This highlights the importance of integrated emission control policies for NOx and organic gases.

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 have conducted a preliminary/preparatory study that evaluated associations between long-term PM2.5 exposure and mortality risk using cohorts of the U.S. adult population constructed from public-use NHIS data. Mortality hazard ratios (HRs) were estimated using Cox proportional hazards regression models, controlling for age, race, sex, income, marital status, education, body mass index, and smoking status. Estimated HRs for all-cause and cardiovascular mortality, associated with a 10 µg/m3 exposure increment of PM2.5, were 1.06 (1.01-1.11) and 1.34 (1.21-1.48), respectively, in models that controlled for various individual risk factors including smoking. This preliminary study demonstrates that the NHIS survey data with mortality linkage can be effectively used to evaluate mortality associations with air pollution.   
  • County-level mortality space-time study: We have established a time consistent set of data for 3,082 counties (essentially the entire continental U.S.) and carried out test analyses for a suite of models using age- and county-specific death rates based on national mortality and population data.

The Administrative Core provides overall oversight, coordination, and integration of the Center. Since initial funding of the Center, the Administrative Core has developed a quality management structure, which is detailed in the EPA-approved Quality Management Plan. An 11 member Science Advisory Committee was selected and the first annual meeting was held in January 2017 in Pittsburgh. An in-person center meeting was held in September 2016 in Pittsburgh. Finally, the Administrative Core has organized monthly conference calls of the project Executive Committee and weekly to monthly calls for groups of investigators for project-specific meetings.

Future Activities:

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

  • Complete and evaluate historical modeling of criteria pollutants for the 1980 to 2015 time period. The results will be passed to Projects 3 and 5. 
  • Complete the 1 km emissions inventories and modeling for Pittsburgh, evaluating the ability of CTMs to predict intraurban pollution variability against observations collected as part of Project 2. 
  • Continue enhancements of RCMs including extension of EASIUR to include social cost estimates for VOCs and enhancements to the treatment of difficult species like nitrate PM2.5 will be completed for AP2 and InMAP.

Project 2: Air quality observatory

  • Deploy a 50-location, low-cost monitor network in Pittsburgh.
  • Conduct mobile sampling in a high exposure, environmental justice area near the port of Oakland in Oakland, CA to quantify magnitude and sources of hotspots of fine particulate matter.
  • Measure emission factors from restaurants in Pittsburgh area using mobile measurements and tracer flux techniques. 
  • Provide quality assured high resolution air quality data collected in Pittsburgh to Projects 1 and 3 for model evaluation.

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

  • Continue model development to assess the geographic covariates selected into each model, particularly for poorer performing pollutants and years.
  • Provide Project 5 census block centroid estimates for all years (1980-2015), and county-level population-weighted estimates for years 1982-2013 to correspond with National Center for Health Statistics (NCHS) county mortality data.

Project 4: Air pollutant control strategies in a changing world 

  • Expand on the EPA US-TIMES model by incorporating region and sector-specific emissions damage values derived from the Estimating Air pollution Social Impact Using Regression (EASIUR) and the Air Pollution Emission Experiments and Policy analysis (AP2) models. 
  • Evaluate air quality scenarios associated with climate-dependent biogenic and wildfire emissions.
  • Perform simulations with PMCAMx for 2050 to evaluate applicability of future-day EASIUR to future scenarios.

Project 5: Health effects of air pollution and mitigation scenarios 

  • Complete proposal/application to Research Data Center (RDC) to access NHIS data.
  • Link CACES generated estimates for census block of residence to health data allowing for much greater spatial resolution.  
  • Perform preliminary health analyses using linked health and CACES exposure estimates.


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)
    R836286 (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)
    R835873C004 (2016)
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  • Journal Article Muller NZ, Matthews PH, Wiltshire-Gordon V. The distribution of income is worse than you think: including pollution impacts into measures of income inequality. PLoS ONE 2018;13(3):e0192461 (15 pp.). R835873 (2017)
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  • Journal Article Muller NZ. Environmental benefit-cost analysis and the national accounts. Journal of Benefit-Cost Analysis 2018;9(1):27-66. R835873 (2017)
    R835873C004 (2016)
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  • Journal Article Nguyen NP, Marshall JD. Impact, efficiency, inequality, and injustice of urban air pollution: variability by emission location. Environmental Research Letters 2018;13(2):024002 (9 pp.). R835873 (2017)
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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)
    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)
    R835873 (2018)
    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 assessment

    Relevant Websites:

    The Center for Air, Climate, and Energy Solutions Exit

    Progress and Final Reports:

    Original Abstract
  • 2017 Progress Report
  • 2018 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