摘要
当前我国沿海发达城市群大气臭氧(O_(3))污染严重,且污染的发生和发展受沿海气象条件影响明显.作为海岸附近典型的大气环流,海陆风对沿海地区大气污染物的生成和消散具有重要潜在贡献.本研究基于对2020年1月—2022年3月宁波沿海地区近地面空气质量和气象因子的连续监测,分析了海陆风对我国典型沿海城市大气O_(3)浓度及其时间变化的影响.同时,在区分有、无海陆风的不同情景下,利用线性回归和随机森林模型对O_(3)浓度进行了模拟预测.结果表明,海陆风可显著促进沿海地区大气O_(3)浓度的升高和污染事件的发生,海陆风日的O_(3)浓度明显高于无海陆风日.与无海陆风发生时段相比,春、夏、秋、冬四季海陆风发生时段的O_(3)超标率分别升高了19%、18%、38%和6%.海陆风天气下近地面大气相对静稳,污染物的局地循环和积聚是O_(3)浓度升高的主要原因.随机森林模型对O_(3)浓度的拟合效果明显优于线性回归模型,且全参数(气象因子和污染物)模型优于气象参数模型.在区分海陆风天气类型后,随机森林模型对沿海地区大气O_(3)浓度的预测精度得到了明显提升.在本研究分析的环境变量中,风向和气温分别是海陆风发生时段和无海陆风发生时段影响O_(3)浓度预测的主要气象变量,而二氧化氮(NO_(2))是影响O_(3)浓度的主要大气污染物.此外,细颗粒物(PM_(2.5))对沿海大气O_(3)浓度预测同样具有较大影响,宁波大气O_(3)污染防治需要综合考虑细颗粒物与O_(3)的协同作用.
Severe ozone(O_(3))pollution has been frequently observed in the developed coastal regions of China,where the air quality is strongly affected by local climate factors.As one of the typical atmospheric circulations in the coastal area,the sea-land breeze plays an important role in influencing the air quality of coastal regions.In this study,the impacts on O_(3)pollution of sea-land breeze were analyzed based on continuous monitoring of air quality and climate variables in the suburban coastal region of Ningbo City,China,over the period from January 1,2020 to March 31,2022.Besides,O_(3)prediction was made using both the linear regression and random forest models for the cases with and without sea-land breeze.The results showed that the sea-land breeze could significantly promote the occurrence of O_(3)pollution,with the exceeding rates of the maximum daily average 8h O_(3)concentrations(MDA8 O_(3))over the air quality standard(100μg·m^(-3))being 19%,18%,38%and 6%for spring,summer,autumn and winter,respectively.This was probably accounted for by the relatively stable climate conditions accompanied with the occurrence of sea-land breeze,which could promote the accumulation of air pollutants within relatively small spatial scales.Both the linear regression and random forest models had the best performances for the sea-land breeze case,which indicated that distinguishing the weather types could improve the predicting accuracy of O_(3)concentration.Compared with the linear regression model,the random forest model showed a better performance on O_(3)prediction.Furthermore,the random forest model based on both air pollutants and meteorological variables is superior to that based on meteorological variables alone.In this study,the major meteorological variables influencing the O_(3)modeling were found to be air temperature and wind direction for the cases with and without sealand breeze,respectively.Nitrogen dioxide(NO_(2))was the dominant atmospheric pollutant in affecting O_(3)prediction,while fine particulate matter(PM_(2.5))was also an influential air pollutant.The synergistic effect between O_(3)and fine particulate matter should be taken into consideration for the O_(3)pollution control in the coastal region of Ningbo.
作者
刘玉
蔡秋亮
佟磊
潘勇
呼斯乐
祝旭初
顾卓良
郑捷
吴坤
龚元均
何萌萌
肖航
LIU Yu;CAI Qiuliang;TONG Lei;PAN Yong;HU Sile;ZHU Xuchu;GU Zhuoliang;ZHENG Jie;WU Kun;GONG Yuanjun;HE Mengmeng;XIAO Hang(Center for Excellence in Regional Atmospheric Environment,Key Laboratory of Urban Environment and Health,Institute of Urban Environment,Chinese Academy of Sciences,Xiamen 361021;University of Chinese Academy of Sciences,Beijing 100049;Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control,Ningbo(Beilun)Zhongke Haixi Industrial Technology Innovation Center,Chinese Academy of Sciences,Ningbo 315800;Baise University,Baise 533000;Ningbo Beilun Environmental Monitoring Station,Ningbo 315800;College of Environment Sciences and Engineering,Peking University,Beijing 100871)
出处
《环境科学学报》
CAS
CSCD
北大核心
2023年第4期27-39,共13页
Acta Scientiae Circumstantiae
基金
国家自然科学基金(No.31300435,21976171)
广西重点研发计划(No.桂科AB21220063)
美丽中国生态文明建设科技工程专项(No.XDA23020301)。
关键词
臭氧
海陆风
随机森林
线性回归
预测
宁波
滨海郊区
O_(3)
sea-land breeze
random forest
linear regression
prediction
Ningbo
suburban coastal region
作者简介
刘玉(1997-),女,E-mail:yuliu@iue.ac.cn;责任作者:佟磊,E-mail:ltong@iue.ac.cn;责任作者:肖航,hxiao@iue.ac.cn。
