Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China

Ju Wang; Jiatong Han; Tongnan Li; Tong Wu; Chunsheng Fang

doi:10.1016/j.heliyon.2023.e17609

Impact analysis of meteorological variables on PM_2.5 pollution in the most polluted cities in China

Heliyon. 2023 Jun 24;9(7):e17609. doi: 10.1016/j.heliyon.2023.e17609. eCollection 2023 Jul.

Authors

Ju Wang^{1

2

3}, Jiatong Han¹, Tongnan Li¹, Tong Wu⁴, Chunsheng Fang^{1

2

3}

Affiliations

¹ College of New Energy and Environment, Jilin University, Changchun, 130012, China.
² Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130012, China.
³ Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130012, China.
⁴ China Coal Technology & Engineering Group Shenyang Engineering Company, Shenyang, Liaoning, China.

Abstract

With the continuous promotion of urbanization in China, air pollution problems have become increasingly prominent in recent years. Various factors, such as emissions, meteorology, and physical and chemical reactions, jointly affect the severity of PM_2.5 pollution to a large extent. This study selected five meteorological variables (planetary boundary layer height (PBLH), wind speed (WS), temperature(T), water vapor mixing ratio(Q), and precipitation (PCP)) for perturbation, and 21 different scenarios were set up. In this study, the effects of changes in a single meteorological variable on the pollutants produced in the area were represented by subtracting the baseline scenario (i.e., without perturbation of meteorological variables) simulated in January 2017 separately from each post-disturbance scenario. The results showed that Handan (HD) has the highest annual mean PM_2.5 concentration of 85.75 μg/m³ in 2017, while all cities in study area exceeded the secondary concentration limit of urban atmospheric particulate matter. The correlation coefficient (R) between the simulation values of models and the actual monitoring values ranges from 0.41 to 0.74, indicating good model performance and acceptable simulation errors. PBLH (±10%-±20%), WS(±10%-±20%), and PCP(±10%-±20%) all showed a single adverse effect among the five meteorological variables, meaning that a reduction in these three factors led to an increase in PM_2.5 concentrations. However, T (±1 K-±1.5 K) and Q (±10%-±20%) could indicate a positive impact under certain conditions. From the sensitivity calculations of single meteorological variables, it is clear that WS, PBLH, and PCP show a highly linear trend in all cities at the 0.01 level of significance. The hypothesis that T changes linearly in 10 cities in the study area is valid, while for Q, the hypothesis that Q changes linearly only occurs in Shijiazhuang and Baoding. When different meteorological variables are disturbed, there are significant spatial differences in the main affected areas of PM_2.5 concentrations. By discussing the impact of meteorological variable disturbance on air quality in critically polluted cities in China, this study identified the meteorological variables that can substantially affect PM_2.5 concentration. The more complex T and Q should be considered when formulating relevant emission measures.

Keywords: Impact analysis; Meteorological variable; North China Plain; PM2.5; WRF-CMAQ.