Marginal Social Costs of Emissions in the United States
The Estimating Air pollution Social Impact Using Regression (EASIUR) model is an easy-to-use tool estimating the social cost (or public health cost) of emissions in the United States. The EASIUR model was derived using regression on a large dataset created by CAMx, a state-of-the-art chemical transport model. The EASIUR closely reproduce the social costs of emissions predicted by full CAMx simulations but without the high computational costs.
Your Guide to Using EASIUR
Methods and Applications
EASIUR marginal social costs [2010 USD/metric ton] are provided in four formats.
(Last updated: 8/21/2015)
The 148×112 grid version is recommended unless your emissions information is limited to county resolution. This county version was generated by area-weighted averaging EASIUR grid cells that overlap each county.
What is the difference between EASIUR and APSCA?
For a unit emission, EASIUR provides total air pollution health damages across all downwind “receptor” locations without resolving where those damages occur. It is suitable for a wide variety of benefit-cost analyses where only total damages are required.
APSCA is a source-receptor model that goes beyond EASIUR by providing data about where the health damages occur. Analyses that require spatially resolved damage estimates will need to use APSCA rather than EASIUR.
Download the following Google Drive folder for the complete set of APSCA and EASIUR in HDF5 format.
This folder includes a tutorial (easiur_apsca.html) about using APSCA and EASIUR programmatically in Python. APSCA and EASIUR can be used similarly in other languages and tools because they are provided in the HDF5 format.
County-level source contribution estimates in 2005 are generated using the 2005 National Emissions Inventory.
Comparison of EASIUR to AP2. (Last updated: 1/22/2016)
62 gb total
EASIUR also estimated intake fractions (ppm). Let us know if needed.
Marginal Social Costs at the point of Ground-Level Emissions
This figure was estimated by EASIUR.
This work was supported by the Center for Climate and Energy Decision Making (SES-0949710) through a cooperative agreement between the National Science Foundation and Carnegie Mellon University.
Please let us (easiur at barney.ce.cmu.edu) know if EASIUR is used for your research.
1. The Easiur Model
Jinhyok Heo, Peter J. Adams, H. Gao, "Reduced-form modeling of public health impacts of inorganic PM2.5 and precursor emissions", Atmospheric Environment, 137, 80–89, 2016. doi:10.1016/j.atmosenv.2016.04.026
Correction: Eq. (S3) should be corrected as follows:
Jinhyok Heo, Peter J. Adams, H. Gao, "Public Health Costs of Primary PM2.5 and Inorganic PM2.5 Precursor Emissions in the United States", Environmental Science & Technology, 50 (11), 6061–6070, 2016.doi:10.1021/acs.est.5b06125
2. Papers Citing the EASIUR Model
Seth Borenstein, "AP analysis: Dozens of deaths likely from VW pollution dodge", Associated Press, 2015.