Abstract：Objective To investigate the concentration level of PM2.5 andits relationship withmeteorological factors in Xi'an city in different seasons. Methods We collected the daily PM2.5 monitoring data and meteorological data in Xi'an city from 2016 to 2018. According to the daily value of the secondary standard limit (75 μg/m3) of the Chinese National Ambient Air QualityStandard (GB 3095-2012), the daily average concentration of PM2.5based on different seasons was evaluated.Pearson correlation analysis was performed to identify the relationship between the daily average concentration of PM2.5 and meteorological influencing factors in different seasons. The effects of meteorological factors on PM2.5 concentration in different seasons were evaluated by multiple linear regression analysis. The effects of meteorological factors in different seasons on the risk of PM2.5 exceeding the standard were analyzed by binary logistic regression analysis. Results The proportion of PM2.5 compliance days was found to be the highest in summer (100.00%), followed by spring (78.26%), autumn (70.33%) and winter (32.84%), and the differences were statistically significant (χ2=308.458, P<0.001). The median concentration of PM2.5 was found to be the highest in winter (102 μg/m3), followed by autumn (52 μg/m3), spring (50 μg/m3) and summer (30 μg/m3), and the differences were statistically significant (χ2=409.326, P<0.001). The meteorological factors correlated with the daily average concentration of PM2.5 in different seasons were dissimilar. The daily average concentration of PM2.5 in summer was positively correlated with the average temperature, maximum temperature and minimum temperature. The daily average concentration of PM2.5 in winter was positively correlated with the average temperature, minimum temperature, average relative humidity and minimum humidity. And negative correlations were found in other significantly correlated relationships. Multiple linear regression equations of the daily average PM2.5concentration and meteorological factors showed that thehighestcoefficient of determination after adjustment waswinter (0.436), followed by autumn (0.272), spring (0.241) and summer (0.083). Binary logistic regression equations of different seasons showed that the coefficient of determination from high to low was winter (0.547), autumn (0.360) and spring (0.340). Conclusions PM2.5 concentration in Xi'an was higher in winter than in other seasons. The meteorological factors affecting PM2.5 concentration in different seasons were unlike.
[1] 杨财平,马蓓蓓,余青,等.宜昌市 PM2.5的污染特征及其风险评价[J].实用预防医学,2017,24(10):1209-1212. [2] 严宙宇, 牟敬锋, 严燕, 等. 2017年深圳市大气PM2.5污染状况及其来源解析研究[J].实用预防医学,2019,26(10):1172-1176. [3] 杜新黎,李扬扬,白慧莉,等.西安市环境空气PM2.5污染现状及对策初探[J].中国环境监测,2013,29(6):44-48. [4] Liu L, Yang X, Liu H, et al.Spatial-temporal analysis of air pollution, climate change, and total mortality in 120 cities of China, 2012-2013[J].Front Public Health, 2016, 4:143. [5] 张淑平,韩立建,周伟奇,等.冬季 PM2.5的气象影响因素解析[J].生态学报,2016,36(24):7897-7907. [6] 李漫,高贵生,贺晓东,等.本溪市大气主要污染物与气象因素相关性[J].中国公共卫生,2018,34(1):127-130. [7] 卢小玲,陆春,龙莹,等.2014—2016年柳州市环境空气质量及污染物特征分析[J].现代预防医学,2017,44(20):3677-3681. [8] 易星月,刘春香,杨敏,等.成都市城区居民对雾霾天气知识行为及态度的调查与分析[J].实用预防医学,2016,23(5):578-580. [9] 环境保护部,国家质量监督检验检疫总局.环境空气质量标准:GB 3095-2012 [M].北京:中国标准出版社,2016:1-6. [10] 王军,许项发.西安雾霾成因分析及对策建议[J].新西部,2014(32):49-50. [11] Han LJ, Zhou WQ, Li WF, et al.Impact of urbanization level on urban air quality: a case of fine particles (PM2.5) in Chinese cities[J].Environ Pollut,2014,194:163-170. [12] 林承勇.PM2.5 浓度影响因素的探究[C].2016中国环境科学学会学术年会论文集(第3卷).北京:中国环境科学学会,2016:7. [13] 曾斤日,郭仲琪,姚云峰.深圳市宝安区2013年10月—2015年9月期间空气PM2.5污染情况分析及其趋势预测[J].现代预防医学,2017, 44(5):797-800.
2020, Vol. 27 
