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1.
Huan Jing Ke Xue ; 43(8): 3913-3922, 2022 Aug 08.
Article in Chinese | MEDLINE | ID: mdl-35971690

ABSTRACT

The dominant transportation and accumulation patterns of heavy PM2.5 pollution events over the Yangtze River middle basin were identified based on the obliquely rotated T-mode principal component analysis (PCT) method and the daily mean surface pressure. The heavy PM2.5 pollution events over the Yangtze River middle basin during 2015-2019 were divided into four patterns, namely, PCT1:high-pressure bottom transport pattern (number of days:41 d, accounting for 55.4% of the total heavy PM2.5 pollution days), PCT2:low-pressure convergence accumulation pattern (12 d, 16.2%), PCT3:high-pressure static stability accumulation pattern (11 d, 14.9%), and PCT4:high-pressure rear transport pattern (10 d, 13.5%). Regional transport patterns (PCT1 and PCT4) accounted for 69% of the total heavy PM2.5 pollution days and were the major pattern of heavy PM2.5 pollution in the Yangtze River middle basin. PCT1 occurred most frequently among the four patterns, accompanied with strong northerly winds, which could drive the rapid transportation of pollutants from the upstream areas and cause the explosive increase in PM2.5 over the Yangtze River middle basin. The PM2.5 pollution events in the transport corridor, including Xiangyang, Jingmen, and Jingzhou, exhibited a 12-hour lag feature. Most parts of northern China were the source of PM2.5, especially in central and northern Henan and western Shandong. The PCT4 transport pattern was featured by the low-level easterly winds, and the pollution level rose quickly. The PCT2 and PCT3 were characterized by the low ground wind speed, associated with the low-level horizontal convergence and subsidence. Such synoptic conditions were favorable for the accumulation of local PM2.5 pollution, and the pollution rise rate was slower, and the duration was longer than those of other patterns.


Subject(s)
Air Pollutants , Air Pollution , Air Pollutants/analysis , Air Pollution/analysis , China , Environmental Monitoring/methods , Particulate Matter/analysis , Rivers , Weather
2.
Huan Jing Ke Xue ; 42(10): 4669-4677, 2021 Oct 08.
Article in Chinese | MEDLINE | ID: mdl-34581109

ABSTRACT

To understand the recent characteristics of atmospheric environmental changes in the Twain-Hu(Hunan-Hubei) Basin, including the middle reaches of the Yangtze River, this paper uses near-surface PM2.5 and PM10 observational data for the Twain-Hu Basin in the winters of 2015 to 2019, combined with wind-speed, topography, the normalized difference vegetation index(NDVI), and other datasets. The results show that:① PM2.5 pollution occurred frequently in the winters of 2015-2019 in the Twain-Hu Basin, and Xiangyang and Jingmen in the western part of the basin, experience PM2.5 pollution on an average of 62 and 61 days in winter(PM2.5>75 µg·m-3). And the heavy pollution days in Xiangyang reached 19 more days(PM2.5>150 µg·m-3), indicating that the Twain-Hu Basin is an air pollution center in the middle reaches of the Yangtze River Basin; ② Spatially, pollution in the Twain-Hu Basin is heavier in the northwest than in the southeast, and in the urban agglomeration, which is mainly related to the regional transport of air pollutants by the winter monsoon and the high levels of emissions from urban areas; ③ A "U-shaped" nonlinear relationship was observed between near-surface wind speeds and PM2.5 and PM10 concentrations. The inflection points of PM2.5 and PM10 concentrations were 153 and 210 µg·m-3, respectively. This implies that the accumulation of local atmospheric particulate matter in the Twain-Hu Basin dominates light/medium pollution, while the regional transport of air pollutants dominates period of severe pollution; and ④ PM2.5 and PM10 in winter were significantly negatively correlated with terrain height and the NDVI, which reflects the atmospheric environmental effects of topography and urbanization.


Subject(s)
Air Pollutants , Air Pollution , Air Pollutants/analysis , Air Pollution/analysis , China , Cities , Environmental Monitoring , Particulate Matter/analysis , Seasons , Wind
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