AnchorMenuSkin Not Found
Center for Air, Climate, & Energy Solutions

A goal of the Center is to democratize state-of-the-art modeling and policy analysis tools. Currently, estimates from CACES-developed models for (1) outdoor air pollution concentrations and (2) economic damages caused by human exposure to air pollution are publicly available to download.

Economic damages are derived from three separate reduced complexity models (RCMs) that estimate human health impacts from emissions. These models are also publicly available. 


Measure air pollutant concentrations across the country to improve the health of vulnerable populations.

Develop and disseminate tools for scientists, policymakers, and citizens to predict the health impacts and social costs of air pollution.

Evaluate the impact of future scenarios for electricity, transportation, and urban development on air quality and human health.

Quantify regional variation in mortality, heart disease, and other health conditions due to air pollution using novel national exposure and health data.

The Center for Air, Climate, and Energy Solutions is a multidisciplinary, multi-institutional research center to address critical questions at the nexus of air, climate, and energy. The center design addresses overarching themes of regional differences, multiple pollutants, and development and dissemination of tools for air quality impact assessment. Novel measurement and modeling approaches will be applied to understand spatial and temporal differences in human exposures and health outcomes. We will investigate 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.

Mechanistic Models

Improving chemical transport models and developing reduced-complexity models for air quality and exposure assessment.



Field Measurements

Comprehensive measurements in three cities (Austin, TX; Oakland, CA; Pittsburgh, PA) to quantify factors influencing gradients in pollutant concentrations.



Empirical Models

Observation-based mapping of air pollution concentrations for the contiguous U.S. at high spatial resolution (~0.1 km) for exposure assessment.


Policy Scenarios & Outcomes

Investigation of policy scenarios using Project 1 models and a common policy framework.


Nationally representative, population-based, health studies.


Peter Adams

Neil Donahue

Ines Azevedo

Paulina Jaramillo

Jeremy Michalek

C. Arden Pope


H. Scott Matthews

Allen Robinson


Albert Presto

Spyros Pandis

Nick Muller

Rick Burnett

Majid Ezzati

Michael Brauer

Jason Hill

Dylan Millet

Steve Polasky

Jay Coggins

Joshua Apte

Julian Marshall


Steve Hankey


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.).

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.

Muller NZ. Environmental benefit-cost analysis and the national accounts. Journal of Benefit-Cost Analysis2017;1-40 [Epub ahead of print].

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.).

Tessum CW, Hill JD, Marshall JD. InMAP: a model for air pollution interventions. PloS ONE2017;12(4):e0176131 (26 pp.).

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.).

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.

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].

This website was developed under Assistance Agreement No. RD83587301 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by EPA. The views expressed on this website are solely those of The CACES Center and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned within this website.