InMAP: A model for air pollution interventions
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), whic...
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| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0176131 |
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(ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>InMAP: A model for air pollution interventions</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-04-19</date><risdate>2017</risdate><volume>12</volume><issue>4</issue><spage>e0176131</spage><epage>e0176131</epage><pages>e0176131-e0176131</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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. 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| subjects | Air Pollutants - analysis Air pollution Air Pollution - analysis Air Pollution - prevention & control Air quality Air quality management Air quality models Airborne particulates Atmosphere Atmospheric boundary layer Atmospheric pollution Atmospheric pollution emission reduction Computation Computer applications Computer Simulation Damage Earth Sciences Ecology and Environmental Sciences Emission standards Emissions Emissions control Engineering and Technology Environmental engineering Environmental impact Environmental justice Environmental Monitoring - statistics & numerical data ENVIRONMENTAL SCIENCES Health Humans Internet Models, Statistical Outdoor air quality Particulate matter Particulate Matter - analysis Particulate matter emissions Physical Sciences Pollution Pollution abatement Quality management Research and Analysis Methods Resource management Software Spatial resolution Time Factors Vehicle Emissions - analysis Vehicle Emissions - prevention & control |
| title | InMAP: A model for air pollution interventions |
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