Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China
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 PM2.5 pollution to a large extent. This study select...
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Elsevier
2023-07-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402304817X |
| _version_ | 1797771571290439680 |
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| author | Ju Wang Jiatong Han Tongnan Li Tong Wu Chunsheng Fang |
| author_facet | Ju Wang Jiatong Han Tongnan Li Tong Wu Chunsheng Fang |
| author_sort | Ju Wang |
| collection | DOAJ |
| description | 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentration. The more complex T and Q should be considered when formulating relevant emission measures. |
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| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T21:38:31Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
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| spelling | doaj.art-7c5b3f0c499f4248a6d14c3206ec68032023-07-27T05:56:49ZengElsevierHeliyon2405-84402023-07-0197e17609Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in ChinaJu Wang0Jiatong Han1Tongnan Li2Tong Wu3Chunsheng Fang4College of New Energy and Environment, Jilin University, Changchun, 130012, China; Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130012, China; Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130012, ChinaCollege of New Energy and Environment, Jilin University, Changchun, 130012, ChinaCollege of New Energy and Environment, Jilin University, Changchun, 130012, ChinaChina Coal Technology & Engineering Group Shenyang Engineering Company, Shenyang, Liaoning, ChinaCollege of New Energy and Environment, Jilin University, Changchun, 130012, China; Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130012, China; Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130012, China; Corresponding author. College of New Energy and Environment, Jilin University, Changchun, 130012, China.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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentration. The more complex T and Q should be considered when formulating relevant emission measures.http://www.sciencedirect.com/science/article/pii/S240584402304817XMeteorological variableWRF-CMAQPM2.5Impact analysisNorth China Plain |
| spellingShingle | Ju Wang Jiatong Han Tongnan Li Tong Wu Chunsheng Fang Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China Heliyon Meteorological variable WRF-CMAQ PM2.5 Impact analysis North China Plain |
| title | Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China |
| title_full | Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China |
| title_fullStr | Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China |
| title_full_unstemmed | Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China |
| title_short | Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China |
| title_sort | impact analysis of meteorological variables on pm2 5 pollution in the most polluted cities in china |
| topic | Meteorological variable WRF-CMAQ PM2.5 Impact analysis North China Plain |
| url | http://www.sciencedirect.com/science/article/pii/S240584402304817X |
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