InMAP: A model for air pollution interventions

PLoS One. 2017 Apr 19;12(4):e0176131. doi: 10.1371/journal.pone.0176131. eCollection 2017.


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.

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / analysis*
  • Air Pollution / prevention & control
  • Computer Simulation
  • Environmental Monitoring / statistics & numerical data*
  • Humans
  • Internet
  • Models, Statistical*
  • Particulate Matter / analysis*
  • Software
  • Time Factors
  • Vehicle Emissions / analysis*
  • Vehicle Emissions / prevention & control


  • Air Pollutants
  • Particulate Matter
  • Vehicle Emissions

Grant support

We received funding from the University of Minnesota Institute on the Environment Initiative for Renewable Energy and the Environment Grants No. Rl-0026-09 (JDM) and RO-0002-11 (JDH), the US Department of Energy Award No. DE-EE0004397 (JDH), the US Department of Agriculture NIFA/AFRI Grant No. 2011-68005 30411 (JDH), and the US Environmental Protection Agency Award No. R835873 (JDM, JDH) for funding; and the Minnesota Supercomputing Institute (JDM) and the Department of Energy National Center for Computational Sciences Award No. DD-ATM007 (JDH) for computational resources. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.