Urban PM2.5 compliance strategy based on air quality and mathematical planning model
YANG Dan-dan1, WANG Ti-jian1, LI Shu1, SHU Lei1, XUE Zhi-gang2, CHAI Fa-he2
1. School of Atmospheric Science, Nanjing University, Nanjing 210023, China; 2. Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Abstract:In order to further reduce PM2.5 concentration and improve air quality, it is necessary to formulate a more scientific control strategy, while taking into account the reduction of pollutants and the cost-effectiveness of emission reduction. Based on the regional atmospheric environment model RegAEMS and mathematical planning model, this article used a multi-objective genetic algorithm to explore the optimal control strategy for urban atmospheric PM2.5 pollution. The method was applied to the PM2.5 concentration compliance plan of Linfen (14types of industry sources and 17regional sources), to achieve the largest pollutant emissions and dual-objective optimization with the least cost of abatement. The results showed that to achieve PM2.5 concentration target (75 μg/m3) under heavy pollution weather conditions where the average concentration of PM2.5 was close to 200 μg/m3, the maximum allowable emission of pollutants in Linfen was 356.7t/d, and the minimum emission reduction cost was 336million yuan. The emission reduction of NOx、SO2、NH3、VOCs and primary PM are 98.1, 49.9, 44.3, 155.7 and 105.5t/d, and the emission reduction costs were 11.7, 6.8, 6.2, 5.5 and 35million yuan. The industries with the greatest potential for reducing VOCs、NOx、PM2.5、NH3 and SO2 were coking、mobile、dust、agricultural and civil combustion sources, accounting for 21.6%、14.1%、11%、8.6% and 3.8% of the emission reduction of the 5pollutants in all industries. The steel industry had the highest emission reduction cost (39%). Xiangfen had the largest emission reduction and the highest emission reduction cost(72.18million yuan).
杨丹丹, 王体健, 李树, 束蕾, 薛志刚, 柴发合. 基于空气质量模式和数学规划模型的城市PM2.5达标策略——以临汾为例[J]. 中国环境科学, 2021, 41(8): 3493-3501.
YANG Dan-dan, WANG Ti-jian, LI Shu, SHU Lei, XUE Zhi-gang, CHAI Fa-he. Urban PM2.5 compliance strategy based on air quality and mathematical planning model. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(8): 3493-3501.
张泽宸.深圳市大气细颗粒物污染控制措施的成本效益分析[D]. 2017. Zhang Z C. Cost-benefit analysis of fine particulate pollution control in Shenzhen city[D]. 2017.
[2]
刘磊,胡非.基于多属性决策的区域大气复合污染多目标优化控制方法研究[J]. 气候与环境研究, 2019,24(4):407-416. Liu L, Hu F. Multi-attribute decision-based multi-objective optimization for regional atmospheric compound pollution control[J]. Climatic and Environmental Research, 2019,24(4):407-416.
[3]
薛文博,付飞.基于全国城市PM2.5达标约束的大气环境容量模拟.中国环境科学, 2014,34(10):2490-2496. Xue W B, Fu F. Modeling study on atmospheric environmental capacity of major pollutants constrained by PM2.5 compliance of Chinese cities[J]. China Environmental Science, 2014,34(10):2490-2496.
[4]
郝吉明,许嘉钰.我国京津冀和西北五省(自治区)大气环境容量研究.中国工程科学, 2017,19(4). Hao J M, Xu J Y. A Study of the Atmospheric Environmental Capacity of Jingjinji and of the Five Northwestern Provinces and Autonomous Regions in China. Strategic Study of CAE, 2017,19(4).
[5]
孙维,程小泉,王晖.合肥市冬季PM10污染特征及大气环境容量测算研究[J]. 气象与环境学报, 2017,33(2):80-86. Sun W, Chang X, et a1. Pollution characteristics of PM10 and its capacity calculation in the atmospheric environment over Hefei in winter[J]. Journal of Meteorology and Environment, 2017,33(2):80-86.
[6]
常嘉成,赵天良,谭成好,等.基于WRF-Chem模拟的玉溪市大气环境容量精细估算[J]. 环境科学学报, 2017,37(10):3876-3884. Chang J C, Zhao T L, Tan C H, et al. An elaborative assessment of atmospheric environmental capacity in Yuxi based on WRF-Chem modeling[J]. Acta Scientiae Circumstantiae, 2017,37(10):3876-3884.
[7]
Zhang F, Xing J, Zhou Y, Wang S, et al. Estimation of abatement potentials and costs of air pollution emissions in China[J]. Journal of Environmental Management, 2020,260.
[8]
方叠,钱跃东,王勤耕,等.区域复合型大气污染调控模型研究[J]. 中国环境科学, 2013,33(7):1215-1222. Fang D, Qian Y D, Wang Q G, et al. An optimization model for regionalcomplex air pollution control[J]. China Environmental Science, 2013,33(7):1215-1222.
[9]
胡炳清,柴发合,赵德刚,等.大气复合污染区域调控与决策支持系统研究[J]. 环境保护, 2015,43(5):43-47. Hu B Q, Chai F H, Zhao D G, et al. Study on the regional control and decision support system of complex air pollution[J]. Environmental Protection, 2015,43(5):43-47.
[10]
马丁, 陈文颖.中国钢铁行业技术减排的协同效益分析[J]. 中国环境科学, 2015,35(1):298-303. Ma D, Chen W Y. Analysis of the co-benefit of emission reduction measures in China's iron and steel industry[J]. China Environmental Science, 2015,35(1):298-303.
[11]
赵东阳,靳雅娜,张世秋.燃煤电厂污染减排成本有效性分析及超低排放政策讨论[J]. 中国环境科学, 2016,36(9):2841-2848. Zhao D Y, Jin Y N, Zhang S Q. Cost-effectiveness analysis of pollution emission reductionmeasures and ultra-low emission policies for coal-fired power plants[J]. China Environmental Science, 2016, 36(9):2841-2848.
[12]
高玉冰,毛显强, Gabriel Corsetti,等.城市交通大气污染物与温室气体协同控制效应评价[J]. 中国环境科学, 2014,34(11):2985-2992. Gao Y B, Mao X Q, Gabriel C, et al. Assessment of co-control effects for air pollutants and green house gases in urban transport:A case study in Urumqi[J]. China Environmental Science, 2014,34(11):2985-2992.
[13]
Zhang Q, Streets D G, Carmichael G R, et al. Asian emissions in 2006 for the NASA INTEX-B mission[J]. Atmos. Chem. Phys., 2009,9:5131-5153.
[14]
Wang T J, Hu Z Y, Xie M, et al. Atmospheric sulfur deposition onto different ecosystems over China[J]. Environmental Geochemistry & Health, 2004,26(2):169-177.
[15]
王德羿,王体健,韩军彩,等."2+26"城市大气重污染下PM2.5来源解析[J]. 中国环境科学, 2020,40(1):92-99. Wang De Y, Wang T J, Han J C, et al. Source apportionment of PM2.5 under heavy air pollution conditions in "2+26" cities[J]. China Environmental Science, 2020,40(1):92-99.
[16]
Xing J, Zhang F, Zhou Y, et al. Least-cost control strategy optimization for air quality attainment of Beijing-Tianjin-Hebei region in China[J]. Journal of Environmental Management, 2019,245.
[17]
Reis S, Nitter S, Friedrich R. Innovative approaches in integrated assessment modelling of European air pollution control strategies-implications of dealing with multi-pollutant multi-effect problems[J]. Environ. Model. Softw., 2005,20:1524-1531.
[18]
Yin C Q, Wang T J, Solmon F, et al. Assessment of direct radiative forcing due to secondary organic aerosol over China with a regional climate model[J]. Tellus B:Chemical and Physical Meteorology, 2015,67(1):24634-24653.
[19]
Stramer Y, Brenner A, Cohen S B, et al. Selection of a multi-stage system for biosolids management applying genetic algorithm[J]. Environ. Sci. Technol., 2010,44:5503-5508.
[20]
Huang J Y, Zhu Y, Kelly J T, et al. Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning[J]. Science of the Total Environment, 2020,722.