Abstract
Mechanistic air pollution modeling is essential in air quality management, yet the extensive expertise and computational resources required to run most models prevent their use in many situations where their results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations - the air pollution outcome generally causing the largest monetized health damages-attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model and a variable spatial resolution computational grid to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. In comparisons run here, InMAP recreates comprehensive model predictions of changes in total PM2 5 concentrations with population-weighted mean fractional bias (MFB) of -17% and population-weighted R2 = 0.90. Although InMAP is not specifically designed to reproduce total observed concentrations, it is able to do so within published air quality model performance criteria for total PM2.5. Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2 5. InMAP can be trained to run for any spatial and temporal domain given the availability of appropriate simulation output from a comprehensive model. The InMAP model source code and input data are freely available online under an open-source license.
| Original language | English (US) |
|---|---|
| Article number | e0176131 |
| Journal | PloS one |
| Volume | 12 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2017 |
Bibliographical note
Funding Information:The authors thank Andrew Goodkind, Dylan Millet, Dev Millstein, David Paolella, Arvind Singh, and Vaughan Voller for feedback and input into model development, and John Michalakes for assistance with WRF-Chem performance tuning. We also thank the anonymous reviewers whose comments on previous versions of this manuscript have greatly improved its quality. We additionally acknowledge the University of Minnesota Institute on the Environment Initiative for Renewable Energy and the Environment Grants No. Rl-0026-09 and RO-0002-11, the US Department of Energy Award No. DE-EE0004397, and the US Department of Agriculture NIFA/AFRI Grant No. 2011-68005-30411 for funding; and the Minnesota Supercomputing Institute and the Department of Energy National Center for Computational Sciences Award No. DD-ATM007 for computational resources.
Publisher Copyright:
© 2017 Tessum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
