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Spatial-temporal characteristics and influencing mechanism of PM2.5 dry deposition in the Xiangjiang River Basin over the past two decades
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Ying DING1, Bin ZOU1, Shen-xin LI1, *, Yong XU1, 2, Ning LIU3, Fang ZHAO4, Jun CHEN4, Xiang-yu YOU5
China Environmental Science | 2025, 45(11) : 5921 - 5930
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China Environmental Science | 2025, 45(11): 5921-5930
• Air Pollution Control •
Spatial-temporal characteristics and influencing mechanism of PM2.5 dry deposition in the Xiangjiang River Basin over the past two decades
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Ying DING1, Bin ZOU1, Shen-xin LI1, *, Yong XU1, 2, Ning LIU3, Fang ZHAO4, Jun CHEN4, Xiang-yu YOU5
Affiliations
  • 1.School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
  • 2.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
  • 3.College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
  • 4.Changsha Ecological Environmental Monitoring Center of Hunan Province, Changsha 410001, China
  • 5.Hunan Research Academy of Environmental Science, Changsha 410004, China
Published: 2025-11-20
Outline
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Analyzing the spatiotemporal variation and influencing mechanisms of dry deposition is crucial for identifying pollution sources, atmospheric pollution control, soil environmental management, and agricultural pollution prevention. Focusing on the Xiangjiang River Basin, a vital grain producing region in China, this study estimated PM2.5 dry deposition fluxes using empirical parameterization formulas and multi-source data, including remote sensing PM2.5 concentrations, ERA5 reanalysis meteorological data, and ground-based monitoring. The long-term spatiotemporal characteristics of dry deposition across the basin and three zones delineated by three lines for land use (agricultural zones, ecological preservation zones, and urban development zones) were investigated. Furthermore, the random forest model was employed to quantify the contributions of influencing factors and elucidated the dominant mechanisms driving dry deposition. Results show thatthe dry deposition fluxes presented a spatial pattern of high in the north and central areas, low in the south and periphery. Urban development zones had the highest dry deposition flux (1.42g/m2), followed by agricultural zones (1.06g/m2) and ecological preservation zones (0.80g/m2). Since 2000, dry deposition fluxes have shown a declining trend at a rate of -0.017g/(m2·a), with significant decreases in high PM2.5 concentration areas. Residential emissions (2.43±0.04), air temperature (0.71±0.01), industrial emissions (0.69±0.02), and relative humidity (0.59±0.01) were identified as dominant factors influencing PM2.5 dry deposition. Considering the spatial heterogeneity of mechanisms in influencing factors, tailored atmospheric pollution control measures are recommended to achieve targeted pollution mitigation.

PM2.5  /  dry deposition  /  spatio-temporal distribution  /  influencing mechanism  /  Xiangjiang River Basin
丁莹, 邹滨, 李沈鑫, 徐勇, 刘宁, 赵芳, 陈军, 尤翔宇. 湘江流域近二十年大气PM2.5干沉降时空特征及影响机制解析. 中国环境科学, 2025 , 45 (11) : 5921 -5930 .
Ying DING, Bin ZOU, Shen-xin LI, Yong XU, Ning LIU, Fang ZHAO, Jun CHEN, Xiang-yu YOU. Spatial-temporal characteristics and influencing mechanism of PM2.5 dry deposition in the Xiangjiang River Basin over the past two decades[J]. China Environmental Science, 2025 , 45 (11) : 5921 -5930 .
Year 2025 volume 45 Issue 11
66
18
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Article Info
  • Receive Date:2025-04-09
  • Online Date:2026-10-08
  • Published:2025-11-20
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  • Received:2025-04-09
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Affiliations
    1.School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    2.College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
    3.College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
    4.Changsha Ecological Environmental Monitoring Center of Hunan Province, Changsha 410001, China
    5.Hunan Research Academy of Environmental Science, Changsha 410004, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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