These models provide estimates of outdoor concentrations for six pollutants (four gases: O3, CO, SO2, NO2; two aerosols: PM10, PM2.5) throughout the contiguous U.S. Model estimates are annual-average values for years 1979 – 2015 (SO2, NO2), 1988 – 2015 (PM10), 1990-2015 (CO), 1999-2015 (PM2.5), and the average during May through September of the daily maximum 8-hour moving average for years 1979-2015 (O3). When downloading data, concentrations are listed as the variable "pred_weight"; units are micrograms per cubic meter for PM2.5 and PM10, parts per billion for ozone, SO2, and NO2, and parts per million for carbon monoxide. Data are available at national, state, county, census tract, and census block group levels.
Please see the README file for more information.
Decomposed annual concentrations of four spatial components ("long-range", "mid-range", "neighborhood", "near-source") for PM2.5 and NO2 are also available at the census block group level for years 2000 - 2015.
CACES participants have developed three models that estimate human health impacts from emissions of PM2.5, SO2, NOx, NH3, and VOCs: AP2, EASIUR, and InMAP. This page contains results from the three models in terms of economic damages caused by human exposure to air pollution as a function of the location of emissions ($ per U.S. metric ton).
The InMAP Source-Receptor Matrix (ISRM) estimates the air quality impacts of emissions released from any source location in the contiguous United States to any receptor location. Files linking the ISRM with US Census demographic data and the US EPA National Emissions Inventory are also available. More information on the ISRM is available here.
Contact email@example.com for any questions regarding the data or data download tool
These data are free and publicly available for non-commercial purposes, data may not be used for commercial purposes without prior permission.
Please cite as follows:
"This article includes [concentration/damages] estimates developed by the Center for Air, Climate and Energy Solutions using [v1 empirical models/AP2/EASIUR/InMAP] as described in [insert suggested citation]."
v1 empirical models: Kim S.-Y.; Bechle, M.; Hankey, S.; Sheppard, L.; Szpiro, A. A.; Marshall, J. D. 2020. “Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015: Role of prediction model parsimony in integrated empirical geographic regression.” PLoS ONE 15(2), e0228535. DOI: 10.1371/journal.pone.0228535
spatially-decomposed v1 empirical models: Wang Y.; Bechle, M.; Kim, S.-Y.; Adams, P. J.; Pandis, S. N.; Pope III, C. A.; Robinson, A. L.; Sheppard, L.; Szpiro, A. A.; Marshall, J. D. 2020. "Spatial decomposition analysis of NO2 and PM2.5 air pollution in the United States." Atmospheric Environment 241(15), 117470. DOI: 10.1016/j.atmosenv.2020.117470
AP2: Muller, N. Z. 2014. “Boosting GDP growth by accounting for the environment.” Science 345 (6199), 873-74. DOI: 10.1126/science.1253506
EASIUR: Heo, J.; Adams, P. J.; Gao, H. 2016. “Reduced-form modeling of public health impacts of inorganic PM2.5 and precursor emissions.” Atmospheric Environment 137, 80–89. DOI:10.1016/j.atmosenv.2016.04.026
InMAP/ISRM: Tessum, C. W.; Hill, J. D.; Marshall, J. D. 2017. “InMAP: A model for air pollution interventions.” PLoS ONE 12 (4), e0176131. DOI: 10.1371/journal.pone.0176131.