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• W2791444549 abstract "Air pollution has become a major concern in cities worldwide. The present study explores the spatial-temporal patterns of PM2.5 (particles with an aerodynamic diameters ≤2.5μm) and the variation in the attainment rate (the number of cities attaining the national PM2.5 standard each day) at different time-scales based on PM2.5 concentrations. One-year of monitoring was conducted in 338 cities at or above the prefectural level in China. Spatial hot spots of PM2.5 were analyzed using exploratory spatial data analysis. Meteorological factors affecting PM2.5 distributions were analyzed. The results indicate the following: (1) Diurnal variations of PM2.5 exhibited a W-shaped trend, with the lowest value observed in the afternoon. The peak concentrations occurred after the ends of the morning and evening rush hours. (2) Out of 338 cities, 235 exceeded the national annual PM2.5 standards (≤35μg/m3), with slightly polluted (75-115μg/m3) cities occupying the greatest proportion. (3) The attainment rate showed an inverted U-shape, while there was a U-shaped pattern observed for daily and monthly mean PM2.5. (4) The spatial distribution of PM2.5 concentrations varied greatly, PM2.5 has significant spatial autocorrelation and clustering characteristics. Hot spots for pollution were mainly concentrated in the Beijing-Tianjin-Hebei area and neighboring regions, in part because of the large amount of smoke and dust emissions in this region. However, weather factors (temperature, humidity, and wind speed) also had an effect. In addition, southwest Xinjiang experienced heavy PM2.5 pollution that was mainly caused by the frequent occurrence of sandstorms, although no significant relationship was observed between PM2.5 and meteorological elements in this region." @default.
• W2791444549 created "2018-03-29" @default.
• W2791444549 creator A5024703341 @default.
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• W2791444549 date "2018-08-01" @default.
• W2791444549 modified "2023-09-29" @default.
• W2791444549 title "Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities" @default.
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• W2791444549 doi "https://doi.org/10.1016/j.scitotenv.2018.03.057" @default.
• W2791444549 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29529440" @default.
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