气象因素对西安市城区空气质量的影响  

Influences of meteorological factors on air quality of Xi'an city

作　　者：李琛[1,2,3,4] 刘瑾[1,2,3,4] 王彦民[1,2,3,4] 

机构地区：[1]陕西理工大学化学与环境科学学院,汉中723001 [2]陕西省秦巴地区生态与环境保护协同创新中心,汉中723001 [3]陕西省催化基础与应用技术重点实验室,汉中723001 [4]陕西理工大学秦巴地区生态环境保护研究所,汉中723001

出　　处：《干旱区资源与环境》2017年第3期83-88,共6页Journal of Arid Land Resources and Environment

基　　金：国家自然科学基金面上项目(21373132);陕西省秦巴山区生物资源综合开发协同创新中心自然科学项目(QBXT-Z(P)-15-8;QBXT-Z(P)-15-17);陕西理工学院科研计划项目(SLGKY15-26)资助

摘　　要：以西安市城区2014年1月1日~2015年12月31日空气质量监测数据和气象资料为基础,分析了气象因素对空气质量的影响。相关性分析表明:在各污染等级下,气温T和露点温度Td与气态污染物SO_2、CO、NO_2呈显著负相关,与O3呈显著正相关;4级污染时,露点温度Td与PM_(2.5)呈显著负相关,气温T与PM_(10)呈显著正相关;3、4级时,气温T与PM_(2.5)呈显著负相关。3级污染时,仅有气压趋势Pa对PM_(2.5)、PM_(10)、SO_2、CO存在显著相关性;4级及以上污染时,大气压P0、平均海平面大气压P与各污染物浓度间的显著相关性基本一致。4级污染时,湿度RH与PM_(10)和NO_2呈显著负相关,4级及以上污染时,RH与SO_2呈显著负相关。PM_(10)主成分回归模型通过了显著性检验、拟合优度很好且无多重共线性,CO、NO_2、PM_(2.5)、T、Td、RH对PM_(10)浓度存在显著影响。The influences of meteorological factors on air quality of Xihn were studied based on the environmental monitoring and meteorological data during January 1, 2014 - December 31, 2015. The correlation analysis suggests that： in or above slight air pollution level, the correlation between T （atmospheric temperature） , Td （dew point temperature） and SO2 , CO, NO2 were significant negative and 03 was significant positive. In moderate pollution level, the correlation between Td and PMz5 was significant negative while that of T and PM10 was significant positive. In slight and moderate pollution level, the correlation between T and PM2.5 was significant nega- tive. Pa （the trends of atmospheric pressure） showed significant correlation with PM25, PMl0, SO2 and CO when the air pollution was in lightly polluted level. The correlation between P0 （ the atmospheric pressure ） , P （ the mean sea level atmospheric pressure） with all the air pollutants had similar trends in or above moderate pollution level. In moderately polluted level, the correlation between RH（the humidity in the air） and PMl0 ,NO2was significant negative; however, that of RH and SO2 was significant negative in moderate, heavy and severe pollution level. The principal component regression model of PM10 passed the significant test. The goodness of fit was very good and there was no multicollinearity. CO, NO2, PM2.5, T, Td, and RH showed significant influence on PM10 concentration.

关 键 词：西安市 气象因素 污染等级 相关性分析 主成分回归模型 

分 类 号：X51[环境科学与工程—环境工程]