首页> 中文期刊> 《中国地理科学:英文版》 >Spatiotemporal Variations and Influencing Factors Analysis of PM_(2.5) Concentrations in Jilin Province,Northeast China

Spatiotemporal Variations and Influencing Factors Analysis of PM_(2.5) Concentrations in Jilin Province,Northeast China

         

摘要

High PM_(2.5) concentrations and frequent air pollution episodes during late autumn and winter in Jilin Province have attracted attention in recent years. To describe the spatial and temporal variations of PM_(2.5) concentrations and identify the decisive influencing factors, a large amount of continuous daily PM_(2.5) concentration data collected from 33 monitoring stations over 2-year period from 2015 to 2016 were analyzed. Meanwhile, the relationships were investigated between PM_(2.5) concentrations and the land cover, socioeconomic and meteorological factors from the macroscopic perspective using multiple linear regressions(MLR) approach. PM_(2.5) concentrations across Jilin Province averaged 49 μg/m^3, nearly 1.5 times of the Chinese annual average standard, and exhibited seasonal patterns with generally higher levels during late autumn and over the long winter than the other seasons. Jilin Province could be divided into three kinds of sub-regions according to 2-year average PM_(2.5) concentration of each city. Most of the spatial variation in PM_(2.5) levels could be explained by forest land area, cultivated land area, urban greening rate, coal consumption and soot emissions of cement manufacturing. In addition, daily PM_(2.5) concentrations had negative correlation with daily precipitation and positive correlation with air pressure for each city, and the spread and dilution effect of wind speed on PM_(2.5) was more obvious at mountainous area in Jilin Province. These results indicated that coal consumption, cement manufacturing and straw burning were the most important emission sources for the high PM_(2.5) levels, while afforestation and urban greening could mitigate particulate air pollution. Meanwhile, the individual meteorological factors such as precipitation, air pressure, wind speed and temperature could influence local PM_(2.5) concentration indirectly.

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