PM[sub.2.5] Pollution in Six Major Chinese Urban Agglomerations: Spatiotemporal Variations, Health Impacts, and the Relationships with Meteorological Conditions

To investigate the spatiotemporal patterns of fine particulate matter (PM[sub.2.5] ) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM[sub.2.5] concentrations d...

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Veröffentlicht in:Atmosphere 2022-10, Vol.13 (10)
Hauptverfasser: Li, Zhuofan, Zhang, Xiangmin, Liu, Xiaoyong, Yu, Bin
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Sprache:eng
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Zusammenfassung:To investigate the spatiotemporal patterns of fine particulate matter (PM[sub.2.5] ) under years of control measures in China, a comprehensive analysis including statistical analysis, geographical analysis, and health impact assessment was conducted on millions of hourly PM[sub.2.5] concentrations data during the period of 2017–2020 in six typical major urban agglomerations. During the period of 2017–2020, PM[sub.2.5] concentrations in the Beijing–Tianjin–Hebei urban agglomeration (BTH-UA), Central Plains urban agglomeration (CP-UA), Yangtze River Delta urban agglomeration (YRD-UA), Triangle of Central China urban agglomeration (TC-UA), Chengdu–Chongqing urban agglomeration (CY-UA), and Pearl River Delta urban agglomeration (PRD-UA) decreased at a rate of 6.69, 5.57, 5.45, 3.85, 4.66, and 4.1 µg/m[sup.3] /year, respectively. PM[sub.2.5] concentration in BTH-UA decreased by 30.5% over four years, with an annual average of 44.6 µg/m[sup.3] in 2020. CP-UA showed the lowest reduction ratio (22.1%) among the six regions, making it the most polluted urban agglomeration. In southern BTH-UA, northeastern CP-UA, and northwestern TC-UA, PM[sub.2.5] concentrations with high levels formed a high–high agglomeration, indicating pollution caused by source emission in these areas was high and hard to control. Atmospheric temperature, pressure, and wind speed have important influences on PM[sub.2.5] concentrations. RH has a positive correlation with PM[sub.2.5] concentration in north China but a negative correlation in south China. We estimated that meteorological conditions can explain 16.7–63.9% of the PM[sub.2.5] changes in 129 cities, with an average of 33.4%, indicating other factors including anthropogenic emissions dominated the PM[sub.2.5] changes. Among the six urban agglomerations, PM[sub.2.5] concentrations in the CP-UA were most influenced by the meteorological change. Benefiting from the reduction in PM[sub.2.5] concentration, the total respiratory premature mortalities in six regions decreased by 73.1%, from 2017 to 2020. The CP-UA had the highest respiratory premature mortality in six urban agglomerations. We suggested that the CP-UA needs more attention and stricter pollution control measures.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos13101696