同济大学学报（自然科学版）2026，Vol.54Issue(7)：1091-1103,13.DOI:10.11908/j.issn.0253-374x.25122
沿海城市气象特征对颗粒物与臭氧浓度的影响
Effect of Meteorological Characteristics on Particulate Matter and Ozone Concentrations in Coastal Cities
摘要
Abstract
In this study,based on particulate matter and ozone data from air quality monitoring stations in Shanghai near-sea,offshore and central urban area in 2023,the Pearson's correlation coefficient and XGBoost model are used to investigate the effect of meteorological characteristics and sea-land winds on PM2.5,PM10 and O3.The results indicate,pollutants exhibit clear seasonal variation,with the highest concentration of PM2.5 in winter,while O3 concentration peaks occurr from May to August,PM10 concentrations are the highest in April in coastal areas and in December in the central urban area.The occurrence of sea-land wind days at each site is central city(17 d)<offshore(47 d)<near-sea(73 d),sea-land wind days in offshore and near-sea areas are the most in summer,and the central urban area shows no significant seasonal variation.When the sea-land wind day occurs,particulate matter concentrations in coastal areas decrease,and O3 concentrations increase.The impact of sea-land winds on pollutants in the central urban area is relatively minor.Meteorological characteristics significantly influence pollutants in different seasons,the precipitation is significantly negatively correlated with PM2.5 and PM10 concentrations in spring and winter,the temperature and sunshine duration are positively correlated with O3 concentration,and the humidity is significantly negatively correlated with O3 concentration.The feature importance analysis of the XGBoost model indicates that the wind speed,near-surface air pressure,and temperature are the dominant meteorological characteristics for PM2.5 concentrations,the near-surface pressure contributes the most to PM10 concentration at each site,with the highest contribution of 74.7%to the central city,and the temperature has the most significant effect on O3 concentration at all sites.The simulation results of the XGBoost model show that PM2.5 and O3 are better,and the overall prediction accuracy shows the central city<offshore<near-sea.关键词
PM2.5/PM10/O3/海陆风/机器学习/XGBoost模型Key words
PM2.5/PM10/O3/sea-land wind/machine learning/XGBoost model分类
资源环境引用本文复制引用
李光明,陈淑慈,彭之光,朱珠..沿海城市气象特征对颗粒物与臭氧浓度的影响[J].同济大学学报（自然科学版）,2026,54(7):1091-1103,13.基金项目
上海市生态环境局科技项目(沪环科[2022]第24号) （沪环科[2022]第24号）
