Analysis of temporal spatial distribution characteristics of PM2.5 pollution and the influential meteorological factors using Big Data in Harbin, China. (3rd August 2021)

• Record Type: Journal Article
• Authors: Luo, Yao; Liu, Shuo; Che, Lina; Yu, Yi
• Is Part Of: Journal of the Air & Waste Management Association. Volume 71:Number 8(2021)
• Page Start: 964
• Page End: 973
• Publication Date: 2021-08-03
• Subjects: Air -- Pollution -- Periodicals; Air quality management -- Periodicals; Hazardous wastes -- Management -- Periodicals; Air Pollution -- prevention & control -- Periodicals; Hazardous Waste -- prevention & control -- Periodicals; Waste Management -- Periodicals; 628.5305
• DOI: 10.1080/10962247.2021.1902423
• Languages: English
• ISSNs: 1047-3289

Abstract

ABSTRACT: Based on the monitoring data of atmospheric pollutants and the meteorological data in Harbin in 2017, the temporal spatial distribution characteristics of PM2.5 pollution and the relationships between PM2.5 concentration and meteorological factors in this region were analyzed. The PM2.5 concentration data and the meteorological data in 2017 were comprehensively analyzed by using ArcGIS and R. The results show that spatially, the PM2.5 concentration in the central districts of Harbin are high in the southeast and low in the northwest; temporally, PM2.5 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 PM2.5 concentration, while temperature, wind direction, and wind speed are negatively correlated to PM2.5 mass concentration, and low wind speed and high relative humidity are major contributors to increase of PM2.5 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.