Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Public Health

Background:

• Millions of premature deaths annually result from outdoor air pollution, particularly fine particulate matter (PM2.5).
• Effective policy design to mitigate these deaths requires high-resolution air quality modeling.
• Current models demand significant computational resources and expertise, limiting their use in high-burden regions.

Purpose of the Study:

• To adapt a reduced-complexity air quality model (InMAP) for global application, named Global InMAP.
• To create an open-source tool for simulating PM2.5 concentrations worldwide.
• To facilitate policy assessment for reducing air pollution-related mortality globally.

Main Methods:

• Adapted the InMAP model for global simulation, incorporating a variable resolution grid (4km urban to 500km remote).
• Evaluated Global InMAP performance against ground-level measurements and the GEOS-Chem chemical transport model.
• Assessed computational requirements, demonstrating feasibility on a desktop computer (2.6-8.4 hours per simulation).

Main Results:

• Global InMAP predicted total PM2.5 concentrations with a normalized mean error of 62% against measurements.
• The model reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%-121% for emission scenarios.
• The model's performance is deemed sufficient for initial policy assessment and scoping.

Conclusions:

• Global InMAP is a novel, open-source, reduced-complexity air quality model.
• It offers a scalable solution for simulating PM2.5, usable on standard computers.
• This tool can significantly aid policy development for reducing air pollution deaths globally, especially in low-resource settings.