期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 722, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2020.137701
关键词
Air pollution control strategies; Cost-benefit analysis; Multi-pollutant optimization; Genetic algorithm; Ozone; PM2.5
资金
- National Key Research and Development Program of China [2016YFC0207606, 2016YFC0207605]
- U.S. EPA Emission, Air quality, and Meteorological Modeling Support [EP-D-12-044]
- National Natural Science Foundation of China, China [21625701]
- Fundamental Research Funds for the Central Universities, China [D2160320, D6180330, D2170150]
- Natural Science Foundation of Guangdong Province, China [2017A030310279]
A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of similar to 1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O-3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 mu gm(-3)) and O-3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region. (C) 2020 Elsevier B.V. All rights reserved.
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