| Title: | Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning |
| Author(s): | Huang J; Zhu Y; Kelly JT; Jang C; Wang S; Xing J; Chiang PC; Fan S; Zhao X; Yu L; |
| Address: | "Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China. Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Southern Marine Science and Engineering Guangdong Laboratory, Sun Yat-Sen University, Zhuhai 519000, China. Electronic address: zhuyun@scut.edu.cn. US Environmental Protection Agency, Office Air Quality Planning & Standards, Research Triangle Park, NC 27711, USA. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China. Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10673, Taiwan; Carbon Cycle Research Center, National Taiwan University, 10672, Taiwan. Southern Marine Science and Engineering Guangdong Laboratory, Sun Yat-Sen University, Zhuhai 519000, China. Chinese Academy for Environmental Planning, Beijing 100012, China" |
| DOI: | 10.1016/j.scitotenv.2020.137701 |
| ISSN/ISBN: | 1879-1026 (Electronic) 0048-9697 (Print) 0048-9697 (Linking) |
| Abstract: | "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 ~10(35)). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM(2.5)) 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 PM(2.5) (< 35 mug m(-3)) and O(3) (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NO(x) (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM(2.5) goals, SO(2) 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" |
| Keywords: | Air pollution control strategies Cost-benefit analysis Genetic algorithm Multi-pollutant optimization Ozone Pm(2.5); |
| Notes: | "PubMed-not-MEDLINEHuang, Jinying Zhu, Yun Kelly, James T Jang, Carey Wang, Shuxiao Xing, Jia Chiang, Pen-Chi Fan, Shaojia Zhao, Xuetao Yu, Lian eng EPA999999/ImEPA/Intramural EPA/ Netherlands 2020/03/26 Sci Total Environ. 2020 Jun 20; 722:137701. doi: 10.1016/j.scitotenv.2020.137701. Epub 2020 Mar 6" |
