[Differences in Pollution Characteristics Under the Southerly and Easterly Wind in Beijing].

In this study, the hourly meteorological factors and PM2.5 concentrations during 2014-2019 in Beijing were analyzed, in order to explore the characteristics of the prevailing wind direction of pollution, and the corresponding long-term tendency. During the study period, 67% of pollution in Beijing occurred under the influence of southerly and easterly wind, and pollution was most likely to occur in winter, followed by spring and autumn. The average pollution probability of winter, spring, autumn and summer was 45.2%, 34.1%, 32.1%, and 26.1% and 47.0%, 45.8%, 39.7%, and 29.6% for southerly and easterly wind, respectively. In Beijing, the southerly wind appeared more frequently, but the pollution occurrence probability was higher under the control of easterly wind, with the maximum difference of 11.7% (2.8%-18.6%) in spring and the minimum difference of 1.8% (-7.6%-13.9%) in winter. During the past six years, the pollution probability decreased at a rate of 4.6%-8.0% and 5.5%-7.9% per year under the southerly and easterly wind influence, respectively. This was clearly reflected in reduced moderate and above levels of pollution. An analysis of both the pollution and meteorological factors under the two wind directions indicates that the visibility, mixing layer height, wind speed, and the frequency of hourly wind speed greater than 3 m·s-1 were higher, and the relative humidity and dew point temperature were lower, when pollution occurred under the southerly wind, while the PM2.5 concentration of pollution was higher in winter and significantly lower in other seasons compared to that of the easterly wind. These findings show that when pollution occurred under the southerly wind, the carrying capacity and diffusion capacity of pollutants in the atmosphere was slightly better than that of the easterly wind, and the increased atmospheric water content under the easterly wind was more conducive to the maintenance and aggravation of pollution. Moreover, under the background of original emission levels, when adding urban heating in winter, the air mass transported by the southerly wind may be more conducive to increased PM2.5 concentration. Furthermore, pollution in Beijing tended to be an "easterly wind type" in spring, summer and autumn, but remained a "southerly wind type" in winter.

[Contribution Assessment of Meteorology Conditions and Emission Change for Air Quality Improvement in Beijing During 2014-2017].

During 2014-2017, the number of haze days and air pollution days declined year by year obviously in Beijing. The average mass concentrations of PM2.5, PM10, SO2, and NO2 also decreased with the alleviated pollution level. These decreases were more obvious during the heating period, especially in November and December. In order to analyze the reasons for the improvement of air quality, changes of the meteorological factors and emission-reduction have been discussed and quantified in this study. This work was based on the numerical simulation model WRF-CHEM and the large data mining technologies of k-nearest neighbor (KNN) and support vector machines (SVM). Meteorological observations indicated that the mean wind speed of 2017 increased by 7.9% compared with the last three years. The frequency of hourly wind speed higher than 3.4 m·s-1 was the highest (10.6%), and frequency of daily relative humidity higher than 70% was lowest (25.1%), in 2017. Meanwhile, the number of low wind days (daily wind speed lower than 2 m·s-1), environmental capacity, ventilation index, and height of the boundary layer showed that the diffusion conditions were better in the heating period of 2017 than those of 2014~2016, especially in November and December. The accumulated precipitation during the non-heating period was 558.3 mm in 2017, which is conducive to pollutant removal and wet deposition. Inter-annual changes of meteorological conditions are important to the air quality. A simulation for December 1~19 by WRF-CHEM during 2014-2017 was performed, and the results demonstrated that changes of meteorological conditions led to a reduction of the PM2.5 concentration of 2017 by 5%, 38%, and 25% compared with that of 2014-2016, respectively. However, it was not possible to quantify the specific contributions of meteorology conditions because of the lack of real emission reduction options. The KNN and SVM models are applied in this study based on the observed meteorology factors, haze days, and pollution days, and it was found that for the reduced haze days and heavy pollution days in 2017, 65.0% could be attributed to emission reduction and 35.0% was caused by improvement of the meteorological conditions.