Analysis of temporal spatial distribution characteristics of PM_2.5 pollution and the influential meteorological...

Based on the monitoring data of atmospheric pollutants and the meteorological data in Harbin in 2017, the temporal spatial distribution characteristics of PM_2.5 pollution and the relationships between PM_2.5 concentration and meteorological factors in this region were analyzed. The PM_2.5 concentration data and the meteorological data in 2017 were comprehensively analyzed by using ArcGIS and R. The results show that spatially, the PM_2.5 concentration in the central districts of Harbin are high in the southeast and low in the northwest; temporally, PM_2.5 pollution is most serious in autumn and winter, with multiple spells of heavy pollution and an obvious "weekend effect", while the air quality is better in spring and summer; overall, relative humidity is positively correlated to PM_2.5 concentration, while temperature, wind direction, and wind speed are negatively correlated to PM_2.5 mass concentration, and low wind speed and high relative humidity are major contributors to increase of PM_2.5 concentration.Implications: Highlight: The use of big data to deal with the data of air pollution and meteorology.Key points: The air pollution data of Harbin in autumn and winter is more serious than that in spring and summer, and is closely related to meteorological factors. Attraction: Big data is used to process air pollution data and meteorological data, and R language is used to describe the relationship between them.