2015~2021年京津冀典型城市PM_(2.5)和O_(3)污染趋势变化分析  

Trends Analysis of Fine Particulate Matter and Ozone Pollution in Typical Cities in the Beijing–Tianjin–Hebei Region during 2015–2021

作　　者：宿兴涛 冯静 安豪 李亚云 朱晓蕾 SU Xingtao;FENG Jing;AN Hao;LI Yayun;ZHU Xiaolei(People’s Liberation Army of China 61540,Beijing 100029;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029;Star Map Press,Beijing 100088)

机构地区：[1]中国人民解放军61540部队,北京100029 [2]中国科学院大气物理研究所大气边界层物理与大气化学国家重点试验室,北京100029 [3]星球地图出版社,北京100088

出　　处：《大气科学》2023年第5期1641-1653,共13页Chinese Journal of Atmospheric Sciences

基　　金：国防科技基础加强计划项目2021-JCJQ-JJ-1058。

摘　　要：大气细颗粒物(PM_(2.5))和臭氧(O_(3))的污染趋势变化受气象条件和污染源排放共同影响。基于国家空气环境监测数据和ERA5再分析资料,本文分析了京津冀地区典型城市(包括北京、天津、保定和石家庄)2015~2021年大气PM_(2.5)和O_(3)与气象因子间的关联性,并结合随机森林算法定量评估减排和气象条件对PM_(2.5)和O_(3)年际趋势变化的贡献。结果表明,除夏季外,日均PM_(2.5)与相对湿度和边界层高度均分别呈良好正和负相关;夏季日均PM_(2.5)与O_(3)和温度均呈良好正相关。分析PM_(2.5)和O_(3)不同污染类型天气中的气象条件变化特征发现,冬季和夏季PM_(2.5)单污染天均主要受不利大气扩散条件作用的影响。在PM_(2.5)和O_(3)双污染天,冬季强大气氧化性和高相对湿度条件是加剧PM_(2.5)污染的重要条件,而夏季高温和强太阳辐射条件是促进PM_(2.5)和O_(3)协同污染的重要气象条件。随机森林趋势分析发现,典型城市PM_(2.5)浓度呈下降趋势(−5.0~−10.8μg m^(−3)a^(−1)),减排主导了其趋势变化,相对贡献为84%~95%。O_(3)浓度在2015~2018年呈升高趋势,而在2018年后呈下降趋势(−3.4~−6.4μg m^(−3)a^(−1)),其中天津、保定和石家庄排放对趋势变化的相对贡献约为18%~34%,反映出近年减排措施对O_(3)污染治理产生有效作用。Atmospheric fine particulate matter(PM_(2.5))and ozone(O_(3))pollution are influenced by meteorological conditions and emissions from pollution sources.Based on the data from China’s national air quality monitoring stations and fifth-generation reanalysis data from the European Centre for Medium-Range Weather Forecasts,the relationships of atmospheric PM_(2.5)and O_(3)with major meteorological factors in typical cities in the Beijing–Tianjin–Hebei region,including Beijing,Tianjin,Baoding,and Shijiazhuang,during 2015–2021 were investigated in this study.Moreover,the contribution of emission reduction to the annual trend of PM_(2.5)and O_(3)was quantitatively evaluated using the random forest algorithm.Excluding summer,daily PM_(2.5)was positively and negatively correlated with relative humidity and boundary layer height,respectively.Meanwhile,daily PM_(2.5)was positively correlated with O_(3)and temperature in summer.Combined with the analysis of the variation characteristics of meteorological conditions associated with different concentration levels of PM_(2.5)and O_(3),it was found that adverse atmospheric diffusion conditions primarily affected the individual pollution days in terms of PM_(2.5)in winter and summer.On PM_(2.5)and O_(3)pollution days,strong atmospheric oxidation and high relative humidity conditions critically aggravated PM_(2.5)pollution in winter,while high temperature and strong solar radiation were important meteorological conditions responsible for increased PM_(2.5)and O_(3)co-pollution in summer.Quantitative trend analysis revealed that emission reduction was the driving factor leading to the annual change in PM_(2.5)and O_(3),and the contribution to the annual decrease in PM_(2.5)was 84%–95%.O_(3)showed an increasing trend from 2015 to 2018 but decreased after 2018(−3.4 to−6.4μg m^(−3) a^(−1)).Furthermore,the trend of O_(3)was overall consistent with the difference between the observed value and the prediction value obtained from the random forest algorithm.The contribu

关 键 词：PM_(2.5) 和O_(3) 协同污染 减排 气象条件 趋势分析 随机森林 

分 类 号：P456.8[天文地球—大气科学及气象学]