Departments of Geographical Science, Harbin Normal University, Harbin, Heilongjiang, People's Republic of China.
J Air Waste Manag Assoc. 2021 Aug;71(8):964-973. doi: 10.1080/10962247.2021.1902423. Epub 2021 Apr 22.
Based on the monitoring data of atmospheric pollutants and the meteorological data in Harbin in 2017, the temporal spatial distribution characteristics of PM pollution and the relationships between PM concentration and meteorological factors in this region were analyzed. The PM concentration data and the meteorological data in 2017 were comprehensively analyzed by using ArcGIS and R. The results show that spatially, the PM concentration in the central districts of Harbin are high in the southeast and low in the northwest; temporally, PM 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 concentration, while temperature, wind direction, and wind speed are negatively correlated to PM mass concentration, and low wind speed and high relative humidity are major contributors to increase of PM concentration.: 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.
基于 2017 年哈尔滨大气污染物监测数据和气象资料，分析了该地区 PM 污染的时空分布特征及其与气象因子的关系。利用 ArcGIS 和 R 对 2017 年 PM 浓度数据和气象数据进行综合分析。结果表明，空间上，哈尔滨中心区 PM 浓度东南高西北低；时间上，秋冬 PM 污染最为严重，多次出现重度污染，且存在明显的“周末效应”，春夏季空气质量较好；整体上，相对湿度与 PM 浓度呈正相关，而温度、风向和风速与 PM 质量浓度呈负相关，低风速和高相对湿度是 PM 浓度增加的主要原因。: 亮点：利用大数据处理空气污染和气象数据。要点：秋冬季哈尔滨空气污染数据比春夏季严重，与气象因素密切相关。吸引力：利用大数据处理空气污染数据和气象数据，并使用 R 语言描述它们之间的关系。
