摘要
目的掌握西安市不同季节PM2.5浓度水平及其与气象因素的关系。方法收集2016—2018年西安市逐日PM2.5监测数据和气象数据,依据《环境空气质量标准》(GB 3095—2012)中日均二级浓度限值标准(75μg/m3),按照不同季节对PM2.5日均浓度进行分析评价。通过Pearson相关分析不同季节PM2.5日均浓度与气象影响因素的关系。通过多重线性回归评价不同季节各气象因素对PM2.5浓度的影响程度。通过二元logistc回归评估不同季节气象因素对PM2.5浓度超标风险的影响。结果各季节PM2.5达标天数占比由高到低依次为夏季(100.00%)>春季(78.26%)>秋季(70.33%)>冬季(32.84%),差异有统计学意义(χ2=308.458,P<0.001)。各季节PM2.5浓度中位数由高到低依次为冬季(102μg/m3)>秋季(52μg/m3)>春季(50μg/m3)>夏季(30μg/m3),差异有统计学意义(χ2=409.326,P<0.001)。不同季节与PM2.5日均浓度存在相关关系的气象因素不同。夏季PM2.5日均浓度同平均温度、最高温度和最低温度呈正相关关系;冬季PM2.5日均浓度同平均温度、最低温度、平均相对湿度和最小湿度呈正相关关系;其它显著性相关关系均呈负相关关系。多重线性回归方程调整后R2由高到低依次为冬季(0.436)>秋季(0.272)>春季(0.241)>夏季(0.083)。二元logistc回归方程R2由高到低依次为冬季(0.547)>秋季(0.360)>春季(0.340)。结论西安市冬季PM2.5浓度高于其它季节,不同季节影响PM2.5浓度的气象因素不同。
Objective To investigate the concentration level of PM2.5 and its relationship with meteorological 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 Quality Standard(GB 3095-2012), the daily average concentration of PM2.5 based 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 PM2.5 concentration and meteorological factors showed that the highest coefficient of determination after adjustment was winter( 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.
作者
孟昭伟
张同军
雷佩玉
常锋
MENG Zhao-wei;ZHANG Tong-jun;LEI Pei-yu;CHANG Feng(Institute for Environmental Health Research and Evaluation,Shaanxi Provincial Centre for Disease Control and Prevention,Xi’an,Shaanxi 710054,China)
出处
《实用预防医学》
CAS
2020年第8期934-937,共4页
Practical Preventive Medicine
基金
陕西省公共卫生检测监测服务平台(No.2016FWPT-12)。
