Influence of meteorological conditions on the air quality during the 2022 Winter Olympics in Beijing

Abstract

The 24th Winter Olympics was held in Beijing, and the air quality in the Beijing area has become the focus of the world’s attention. The Beijing government has taken a series of strict measures to control pollutant emissions during the Winter Olympics, which also provides us with a valuable opportunity to study the impact of meteorological conditions on pollutants. We defined November, December, January, February, and March as the polluted period in Beijing, and used the T-PCA method to divide the circulation types (CTs) affecting Beijing into six kinds (CT1-CT6). It was found that under the control of the western high pressure (CT1) and the northwest high pressure (CT4), the concentrations of PM2.5, NO2, SO2 and CO in Beijing were lower; while under the control of the northern high pressure (CT2), eastern high pressure (CT5), southeast high (CT3) and northeast low pressure (CT6), the concentration of PM2.5, NO2, SO2 and CO is higher. By analyzing the daily CTs, wind field and pollutant concentration changes in the Beijing area during the Beijing Winter Olympics, it was found that when two pollution events occurred during the Winter Olympics, the Beijing area was mainly prevailed by CT2, CT3, and CT6. Comparing the frequency of occurrence of six CTs during the 2022 Winter Olympics and the same period from 2014 to 2021, it was found that the proportion of CT1 and CT4 increased significantly during the Winter Olympics. Finally, the FLEXPART-WRF model was used to analyze the 48-h backward footprint distribution of pollutant particles in Beijing during the Winter Olympics. It further showed that the circulation in the Beijing area during the Winter Olympics was generally conducive to the dispersion of pollutants, and the air quality was better.

Synoptic Weather Patterns and Atmospheric Circulation Types of PM2.5 Pollution Periods in the Beijing-Tianjin-Hebei Region

“…Secondly, objective classification is based on an objective algorithm to calculate specific parameters by using basic meteorological data of different times and spaces and to judge the parameters according to specific criteria. It mainly includes T-mode principal component analysis (PCA) [15][16][17] and the Lamb-Jenkinson objective classification method [18,19]. Thirdly, objective classification relies on the quality of original data, and subjective and objective mixed classification has the advantages of simple operation and accurate judgment.…”

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

“…The socioeconomic factors influencing air quality include population [24,25], the economy [26][27][28][29], energy consumption [30][31][32], environmental policy [33], income inequality [34], land use [35,36], transportation infrastructure [37,38], etc., with even the large-scale sale of industrial land [39], increase in urbanization rate [40], and foreign direct investment [41] also increasing air pollution. Among the natural factors, meteorological elements such as temperature, precipitation, relative humidity, maximum wind speed, air pressure, and sunshine hours have an important association with air quality [42][43][44][45], and geographical environment elements such as topography [46,47], climate [48,49], and vegetation [50][51][52][53][54] also have an important impact on air quality. In some studies [13][14][15], cluster analysis, geographical concentration index, and spatial autocorrelation analysis are used to measure regional differences in air quality.…”