Concentration Characteristics and Correlations with Other Pollutants of Atmospheric Particulate Matter as Affected by Relevant Policies

Hong Song, Yuhang Dong, Jiayu Yang, Xin Zhang (), Xingxin Nie and Yuesheng Fan ()
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Hong Song: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yuhang Dong: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Jiayu Yang: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Xin Zhang: School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Xingxin Nie: School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yuesheng Fan: School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

IJERPH, 2023, vol. 20, issue 2, 1-13

Abstract: With the increase in global environmental pollution, it is important to understand the concentration characteristics and correlations with other pollutants of atmospheric particulate matter as affected by relevant policies. The data presented in this paper were obtained at monitoring stations in Xi’an, China, in the years from 2016 to 2020, and the spatial distribution characteristics of the mass and quantity concentrations of particulate matter in the atmosphere, as well as its correlation with other pollutants, were analyzed in depth. The results showed that the annual average concentrations of PM 10 and PM 2.5 decreased year by year from 2016 to 2020. The annual concentrations of PM 2.5 decreased by 20.3 μg/m 3 , and the annual concentrations of PM 10 decreased by 47.3 μg/m 3 . The days with concentrations of PM 10 exceeding the standards decreased by 82 days, with a decrease of 66.7%. The days with concentrations of PM 2.5 exceeding the standards decreased by 40 days, with a decrease of 35.4%. The concentration values of PM 10 and PM 2.5 were roughly consistent with the monthly and daily trends. The change in monthly concentrations was U-shaped, and the change in daily concentrations showed a double-peak behavior. The highest concentrations of particulate matter appeared at about 8:00~9:00 am and 11:00 pm, and they were greatly affected by human activity. The proportion of particles of 0~1.0 μm decreased by 1.94%, and the proportion of particles of 0~2.5 μm decreased by 2.00% from 2016 to 2020. A multivariate linear regression model to calculate the concentrations of the pollutants was established. This study provides a reference for the comprehensive analysis and control of air pollutants in Xi’an and even worldwide.

Keywords: particulate matter; distribution characteristics; Xi’an; correlation; model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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