Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme...

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Modeling

Background:

• Accurate estimation of airborne pollutant emissions source information (strength and location) is vital for effective air pollution management and emergency response.
• The atmospheric dispersion scheme is a critical factor influencing the accuracy of source inversion models.
• Previous studies have not explored modifying atmospheric dispersion schemes to enhance source inversion performance.

Purpose of the Study:

• To develop and evaluate a novel approach for improving air pollutant source inversion performance by optimizing empirical dispersion schemes.
• To investigate the impact of optimizing dispersion coefficients on the accuracy of air pollutant concentration prediction and source parameter estimation.

Main Methods:

• A novel approach combining parameter sensitivity analysis and an optimization method was proposed.
• The dispersion coefficients (σy and σz) of the BRIGGS scheme were optimized under various atmospheric conditions.
• Optimized schemes were applied to air pollutant dispersion modeling and source inversion.

Main Results:

• Optimized dispersion schemes significantly improved air pollutant concentration prediction, with key statistical indices (|FB|, NMSE, FAC2, R) showing substantial enhancement.
• Source inversion accuracy for both source strength and location (x0) was significantly improved (∼271% and ∼121% improvement, respectively).
• The most significant improvements in source strength inversion accuracy were observed under unstable atmospheric conditions (stability classes A, B, C), with a 97.5% reduction in mean absolute relative deviation.

Conclusions:

• The proposed method offers a novel and versatile approach to enhance the performance of pollutant emission source estimation.
• Optimizing atmospheric dispersion schemes is a viable strategy to improve source inversion accuracy, providing more reliable data for air pollution management.
• This study advances the understanding of source inversion techniques and their application in real-world air quality scenarios.