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Zhu Y, Bai Y, Xiong J, Zhao T, Xu J, Zhou Y, Meng K, Meng C, Sun X, Hu W. Mitigation Effect of Dense "Water Network" on Heavy PM 2.5 Pollution: A Case Model of the Twain-Hu Basin, Central China. TOXICS 2023; 11:169. [PMID: 36851044 PMCID: PMC9966530 DOI: 10.3390/toxics11020169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
The influence of the underlying surface on the atmospheric environment over rivers and lakes is not fully understood. To improve our understanding, this study targeted the Twain-Hu Basin (THB) in central China, with a unique underlying surface comprising a dense "water network" over rivers and lakes. In this study, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) was used to simulate the impact of this dense "water network" on a wintertime heavy PM2.5 pollution event in the THB. On this basis, the regulating effects of density and area of the lake groups, with centralized big lakes (CBLs) and discrete small lakes (DSLs), on PM2.5 concentrations over the underlying surface of the dense "water network" in the THB were clarified, and the relative contributions of thermal factors and water vapor factors in the atmospheric boundary layer to the variation of PM2.5 concentrations were evaluated. The results show that the underlying surface of dense "water networks" in the THB generally decreases the PM2.5 concentrations, but the influences of different lake-group types are not uniform in spatial distribution. The CBLs can reduce the PM2.5 concentrations over the lake and its surroundings by 4.90-17.68% during the day and night. The ability of DSLs in reducing PM2.5 pollution is relatively weak, with the reversed contribution between -5.63% and 1.56%. Thermal factors and water vapor-related factors are the key meteorological drivers affecting the variation of PM2.5 concentrations over the underlying surface of dense "water networks". The warming and humidification effects of such underlying surfaces contribute positively and negatively to the "purification" of air pollution, respectively. The relative contributions of thermal factors and water vapor-related factors are 52.48% and 43.91% for CBLs and 65.96% and 27.31% for DSLs, respectively. The "purification" effect of the underlying surface with a dense "water network" in the THB on regional air pollution highlights the importance of environmental protection of inland rivers and lakes in regional environmental governance. In further studies on the atmospheric environment, long-term studies are necessary, including fine measurements in terms of meteorology and the environment and more comprehensive simulations under different scenarios.
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Affiliation(s)
- Yan Zhu
- Hubei Meteorological Service Center, Wuhan 430205, China
- China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
| | - Yongqing Bai
- China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
| | - Jie Xiong
- China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
| | - Tianliang Zhao
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jiaping Xu
- Jiangsu Climate Center, Nanjing 210044, China
| | - Yue Zhou
- China Meteorological Administration Basin Heavy Rainfall Key Laboratory/Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
| | - Kai Meng
- Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Hebei Provincial Institute of Meteorological Sciences, Shijiazhuang 050021, China
| | - Chengzhen Meng
- Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Hebei Provincial Institute of Meteorological Sciences, Shijiazhuang 050021, China
| | - Xiaoyun Sun
- Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Weiyang Hu
- State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, Nanjing 210023, China
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