Qualitative and Quantitative Analyses of Meteorological Impacts on Fine Particle Pollution in Winters of Cold Region in China

Meteorological factors are the key drivers of air pollution. Stable weather conditions, the boundary layer height, and temperature inversion significantly influence the dispersion of particulate matter, which is also associated with the aerodynamic properties of particles. However, limited studies have been conducted on this topic in northeast China. This study investigates the influence of meteorological factors on PM2.5 pollution under cold weather conditions, employing both qualitative and quantitative methods. The key meteorological factors considered include temperature difference, relative humidity, wind speed and direction, the boundary layer height (BLH), and temperature inversion. The stable weather index (SWI) is introduced as a quantitative measure of the stability of weather based on data from the last five winters in a typical megacity of northeast China. The monthly PM2.5 concentrations recorded during the last five Februarys ranged from 59.79 μg/m3 to 85.68 μg/m3, with the highest daily concentration reaching 417 μg/m3. A new parameter, ‘temperature difference (ΔT)’, is defined in this study as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s. The temperature differences were found to have a significantly positive correlation with the differences in PM2.5 concentrations (p < 0.01). The results showed that PM2.5 pollution was associated with increased temperature, higher relative humidity, and lower wind speed, or any combination of these factors. The SWI explained 65% and 64% of the variances in air quality index (AQI) and PM2.5 pollution, respectively. When the predicted SWI exceeds 10, the likelihood of particle pollution increases. A lower BLH, in conjunction with a thicker inversion layer, contributes to the formation of severe particle pollution. In the early stages of a winter pollution episode in Harbin, the temperature inversion layer thickened and intensified, with the inversion top height reaching approximately 200 m. The boundary layer remained below 200 m, resulting in a poor vertical dispersion condition. PM2.5 pollution, therefore, is influenced by the combined effects of multiple meteorological factors. Our study quantitatively analyzed the characteristics of weather conditions and their impacts on air quality, which could provide scientific evidence for air pollution prediction and assist in making specific policy interventions, particularly for the upcoming ninth Asian Winter Games in Harbin in February 2025.

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Publication Details

Journal
Processes
Published
2024-12-01
DOI
https://doi.org/10.3390/pr12122713
Citations
2
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
0.75
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article

Qualitative and Quantitative Analyses of Meteorological Impacts on Fine Particle Pollution in Winters of Cold Region in China

Weiwei Song, Xiaoyu Cai, Jun Zhou, Mengying Wang et al.
2 citations
Processes
Atmospheric chemistry and aerosols
0.75
article

Qualitative and Quantitative Analyses of Meteorological Impacts on Fine Particle Pollution in Winters of Cold Region in China

Weiwei Song, Xiaoyu Cai, Jun Zhou, Mengying Wang, Ling Zhao, Yanan Sui, Min Zhang, Hao Sun, Qianqian Sun, Ngoc Diep Lai, Honglei Fu, Tianyuan Song, A Li
article en
2 citations

Abstract

Meteorological factors are the key drivers of air pollution. Stable weather conditions, the boundary layer height, and temperature inversion significantly influence the dispersion of particulate matter, which is also associated with the aerodynamic properties of particles. However, limited studies have been conducted on this topic in northeast China. This study investigates the influence of meteorological factors on PM2.5 pollution under cold weather conditions, employing both qualitative and quantitative methods. The key meteorological factors considered include temperature difference, relative humidity, wind speed and direction, the boundary layer height (BLH), and temperature inversion. The stable weather index (SWI) is introduced as a quantitative measure of the stability of weather based on data from the last five winters in a typical megacity of northeast China. The monthly PM2.5 concentrations recorded during the last five Februarys ranged from 59.79 μg/m3 to 85.68 μg/m3, with the highest daily concentration reaching 417 μg/m3. A new parameter, ‘temperature difference (ΔT)’, is defined in this study as the difference in temperature between two consecutive days, calculated by subtracting the previous day’s temperature from the current day’s. The temperature differences were found to have a significantly positive correlation with the differences in PM2.5 concentrations (p < 0.01). The results showed that PM2.5 pollution was associated with increased temperature, higher relative humidity, and lower wind speed, or any combination of these factors. The SWI explained 65% and 64% of the variances in air quality index (AQI) and PM2.5 pollution, respectively. When the predicted SWI exceeds 10, the likelihood of particle pollution increases. A lower BLH, in conjunction with a thicker inversion layer, contributes to the formation of severe particle pollution. In the early stages of a winter pollution episode in Harbin, the temperature inversion layer thickened and intensified, with the inversion top height reaching approximately 200 m. The boundary layer remained below 200 m, resulting in a poor vertical dispersion condition. PM2.5 pollution, therefore, is influenced by the combined effects of multiple meteorological factors. Our study quantitatively analyzed the characteristics of weather conditions and their impacts on air quality, which could provide scientific evidence for air pollution prediction and assist in making specific policy interventions, particularly for the upcoming ninth Asian Winter Games in Harbin in February 2025.

ProcessesVol. 12(12)
Northeast Agricultural University (CN), Harbin Institute of Technology (CN)
Openalex Percentile: Top 29%
Atmospheric chemistry and aerosols
0.75
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