Air pollution characteristics in China during 2015-2016: Spatiotemporal variations and key meteorological factors

Rui Li; Zhenzhen Wang; Lulu Cui; Hongbo Fu; Liwu Zhang; Lingdong Kong; Weidong Chen; Jianmin Chen

doi:10.1016/j.scitotenv.2018.08.181

Air pollution characteristics in China during 2015-2016: Spatiotemporal variations and key meteorological factors

Sci Total Environ. 2019 Jan 15:648:902-915. doi: 10.1016/j.scitotenv.2018.08.181. Epub 2018 Aug 16.

Authors

Rui Li¹, Zhenzhen Wang¹, Lulu Cui¹, Hongbo Fu², Liwu Zhang¹, Lingdong Kong¹, Weidong Chen³, Jianmin Chen⁴

Affiliations

¹ Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China.
² Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, PR China. Electronic address: fuhb@fudan.edu.cn.
³ Laboratoire de PhysicoChimie de l'Atmosphère Université du Littoral Côte d'Opale 189A, Av. Maurice Schumann, 59140 Dunkerque, France.
⁴ Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China. Electronic address: jmchen@fudan.edu.cn.

PMID: 30144758
DOI: 10.1016/j.scitotenv.2018.08.181

Abstract

With rapid economic development and urbanization, China has suffered from severe and persistent air pollution during the past years. In the work, the hourly data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ in all of the prefecture-level cities (336 cities) during 2015-2016 were collected to uncover the spatiotemporal variations and influential factors of these pollutants in China. The average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by 19.32%, 15.34%, 29.30%, 9.39%, and 8.00% from 2015 to 2016, suggesting the effects of efficient control measurements during this period. On the contrary, the O₃ concentration increased by 4.20% during the same period, which mainly owed to high volatile organic compounds (VOCs) loading. The concentrations of PM_2.5, PM₁₀, SO₂, CO and NO₂ showed the highest and the lowest ones in winter and summer, respectively. However, the O₃ concentration peaked in summer, followed by ones in spring and autumn, and presented the lowest one in winter. All of the pollutants exhibited significantly weekly and diurnal cycle in China. PM_2.5, PM₁₀, SO₂, CO and NO₂ presented the higher concentrations on weekdays than those at weekends, all of which showed the bimodal pattern with two peaks at late night (21:00-22:00) and in morning (9:00-10:00), respectively. However, the O₃ concentration exhibited the highest value around 15:00. The statistical analysis suggested that the PM_2.5, PM₁₀, and SO₂ concentrations were significantly associated with precipitation (Prec), atmosphere temperature (T), and wind speed (WS). The CO and NO₂ concentrations displayed the significant relationship with T, while the O₃ concentration was closely linked to the sunshine duration (Tsun) and relative humidity (RH). T and WS were major factors affecting the accumulation of PM and gaseous pollutants at a national scale. At a spatial scale, Prec and T played the important roles on the PM distribution in Northeast China, and the effect of Prec on CO concentration decreased from Southeast China to Northwest China. The results shown herein provide a scientific insight into the meteorology impacts on air pollution over China.

Keywords: Air pollution; China; Meteorological factors; Spatial econometric model.