InMAP: A model for air pollution interventions

• DOI: 10.1371/journal.pone.0176131
• Publication Date: 2017-04-19T17:59:39Z
• Authors: Christopher W. Te...; Jason D. Hill; Julian D. Marshall
• Keywords: Air Pollutants; Internet; Models, Statistical; Time Factors; Science; Q; R; 01 natural sciences; 13. Climate action; Air Pollution; 11. Sustainability; Medicine; Humans; Computer Simulation; Particulate Matter; Software; Research Article; Environmental Monitoring; Vehicle Emissions; 0105 earth and related environmental sciences

Mechanistic air pollution modeling is essential in quality management, yet the extensive expertise and computational resources required to run most models prevent their use many situations where results would be useful. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative comprehensive estimating health impacts of emission reductions other potential interventions. estimates annual-average changes primary secondary fine particle (PM2.5) concentrations-the outcome generally causing largest monetized damages-attributable annual precursor emissions. leverages pre-processed physical chemical information from output a state-of-the-science transport model variable spatial resolution grid perform simulations that are several orders magnitude less computationally intensive than simulations. In comparisons here, recreates predictions total PM2.5 concentrations with population-weighted mean fractional bias (MFB) -17% R2 = 0.90. Although not specifically designed reproduce observed concentrations, it able do so within published performance criteria PM2.5. Potential uses include studying exposure, health, environmental justice shifts emissions can trained any temporal domain given availability appropriate simulation model. The source code input data freely available online under open-source license.