InMAP: A model for air pollution interventions.

Citation data:

PloS one, ISSN: 1932-6203, Vol: 12, Issue: 4, Page: e0176131

Publication Year:
2017
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PMID:
28423049
DOI:
10.1371/journal.pone.0176131
Author(s):
Christopher W. Tessum, Jason D. Hill, Julian D. Marshall, Juan A. Añel
Publisher(s):
Public Library of Science (PLoS), Figshare
Tags:
Biochemistry, Genetics and Molecular Biology, Agricultural and Biological Sciences, Biotechnology, Science Policy, 59999 Environmental Sciences not elsewhere classified, 69999 Biological Sciences not elsewhere classified, 80699 Information Systems not elsewhere classified, population-weighted r 2, air quality models, annual-average pm 2.5, inmap model source code, air quality management, air pollution health impacts, air pollution interventions mechanistic air pollution modeling, pm 2.5, inmap leverages pre-processed, air quality model performance criteria, pm 2.5 concentrations, mfb, state-of-the-science chemical transport model, inmap estimates annual-average changes
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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.

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