Methods Of Identifying Air Pollution Characteristics Based On Ambient Air Quality Monitor Data And Its Application

During the“Thirteenth Five-Year Plan”,the air quality in China improved significantly,but some key areas still faced serious air pollution problems.At the same time,the national air quality monitoring network had completed to comprehensively assess the air quality in various places.However,those data are insufficient for in-depth analysis of air pollution characteristics and causes.For realizing the analysis of air pollution characteristics and causes with high spatial and temporal resolution in countries or key areas,we use the Double Normalized Function（DNF）to analyze the structures of air pollution characteristics in the key area of Beijing-Tianjin-Hebei（BTH）and the surrounding area（e.g.,“2+26”cities）and 65 cities in China,India,and the Republic of Korea based on five ambient air quality parameters,SO₂,NO₂,CO,PM_2.5,and PM₁₀.The result shows as fellow.Based on five ambient air quality parameters,the DNF identifies eight air pollution characteristics（APC）:APC dominated by SO₂,NO₂,CO,PM_2.5,coarse particles,SO₂-CO,NO₂-CO,and PM_2.5-CO.The APC dominated by SO₂-CO can indicate the emission of the iron and steel industry,the APC dominated by NO₂-CO can indicate the emission of mobile sources.In the autumn and winter season of 2018-2019,the pollution characteristics of"2+26"cities have significant spatial differences.The K-Means analysis classifies four clusters.Cluster 1 has a high ratio of pollution characteristic dominated by NO₂,mainly distributed in Beijing,Tianjin,and Langfang.Cluster 2 has a high ratio of pollution characteristic dominated by PM_2.5,mainly distributed in the south of"2+26"cities,such as Puyang,Xinxiang,and Heze.Cluster 3 has a high ratio of pollution characteristic dominated by SO₂,mainly distributed in the belt of Taiyuan-Xingtai-Binzhou.Cluster4 has a high ratio of pollution characteristic dominated by SO₂-CO,mainly concentrated in the areas with high iron and steel production,e.g.Tangshan,Changzhi,and Handan.The ratio of APC dominated by NO₂ is the highest in China and India,and the coarse particle have highest ratio in India,indicating industry and mobile sources emissions are the major sources in China and the Republic of Korea,while fugitive dust and sand storms in India.The K-Means results show that 6,5,and 4 clusters are classified in China,India,and the Republic of Korea.The structures in most cities are consistent with the results of existing research.But this study provides a more comprehensive structure of pollution characteristics in remote areas of the three countries with few studies on the causes of pollution.The iron and steel emission in Yinchuan and Xining,coal burning in Guiyang,and mobile sources emissions in Lhasa have an important influence on air quality in west China.The coal-burning and industrial incomplete combustion emissions seriously influence the air quality in cities where not in the Gangetic Plains of India.Industrial and ship emissions change the air pollution structures in Ulsan and Busan,southwest of the Republic of Korea.Based on the ambient air quality with high spatial and temporal resolution,DNF analysis can identify the dominant pollution characteristics,analyze air pollution causes,and sources.Besides,this method not only has good applicability in countries facing different types of air pollution,but also results of the DNF are consist with exist result.Therefore,this method can be further applied to other countries in the world to analyze pollution characteristics’temporal and spatial evolution and provide suggestions for pollution control which have established air quality monitoring networks but the monitoring capacity of PM_2.5 components is insufficient.