A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment--a North American case...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Geographic Information Systems (GIS)

Background:

• Accurate prediction of airborne pollutant concentrations is crucial for environmental management.
• Existing models often face challenges in integrating diverse data sources for spatial analysis.
• Local and regional scale air quality assessments require robust modeling frameworks.

Purpose of the Study:

• To present a novel GIS-based multi-source and multi-box modeling approach (GMSMB).
• To predict spatial concentration distributions of airborne pollutants on local and regional scales.
• To assess contributions from point- and area-source emissions.

Main Methods:

• Developed an extended multi-box model integrated with a multi-source and multi-grid Gaussian model within a GIS framework.
• Utilized GIS to integrate emission sources, air quality monitoring, and meteorological data.
• Quantitatively analyzed spatial variations in source distribution and meteorological conditions.

Main Results:

• Successfully predicted spatial concentration distributions for carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM2.5) in California.
• Achieved good agreement between modeling results and monitoring data.
• Demonstrated the model's effectiveness in air pollution assessment.

Conclusions:

• The GIS-based multi-source and multi-box modeling approach (GMSMB) provides an effective tool for air pollution assessment.
• The model supports air pollution control and management planning on both regional and local scales.
• Integration of diverse data within GIS enhances the accuracy of spatial air quality modeling.