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Grid computing method for atmospheric environmental capacity coupled with ventilation coefficient using CALPUFF

Tianxin Li^1,2, Xingyu Chen^1,2, Xiugui Wang^1,2

¹School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China.

Environmental Technology

|August 5, 2022

Summary

This summary is machine-generated.

This study introduces a grid computing method using CALPUFF and GIS to accurately estimate atmospheric environmental capacity, improving air pollution control strategies. The revised A-value method provides precise spatial management for atmospheric capacity.

Keywords:

Air pollutants air pollution control strategy atmospheric environmental capacity spatial distribution ventilation coefficient

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Area of Science:

Environmental Science
Atmospheric Chemistry
Geographic Information Systems (GIS)

Background:

Atmospheric pollution has shifted from localized to regional issues, necessitating effective control strategies.
Traditional A-value methods are inaccurate for small-scale atmospheric environmental capacity estimation, hindering targeted pollution control.
Existing methods struggle with administrative boundary limitations in managing air quality.

Purpose of the Study:

To develop an accurate grid computing method for estimating atmospheric environmental capacity at a fine scale.
To revise the A-value method using advanced modeling and spatial analysis tools.
To enable precise spatial management of atmospheric environmental capacity and inform differentiated emission reduction strategies.

Main Methods:

Utilized the CALPUFF modeling system and GIS spatial analysis tools for grid computing.
Employed meteorological data from the MM5 model to simulate pollutant distribution and ventilation coefficients.
Divided the research area into small partitions using ArcGIS and integrated underlying surface types.

Main Results:

The revised A-value method accurately estimated atmospheric environmental capacity, validated in Fengtai District, Beijing.
Simulation results for PM10 concentration showed good agreement with monitoring data (MRE: 7.05%-13.28%, RMSE: 11.62-17.89, R²: 0.84-0.90).
Developed four differentiated emission reduction schemes and suitable control strategies based on precise spatial management.

Conclusions:

The proposed grid computing method enhances the accuracy of atmospheric environmental capacity assessment.
This approach overcomes administrative boundary limitations, allowing management across natural geographic scales.
The method facilitates precise spatial management and the development of effective, localized air pollution control strategies.