摘要
采用KZ滤波法、多元逐步回归法和小波相干性分析法,从不同时间尺度探究了唐山市2015~2022年间PM_(2.5)、PM_(10)和O_(3)的演变特征,并有效区分和定量估算了污染源排放和气象因素对污染物浓度的贡献,揭示了气象因素对污染物不同尺度的影响,以及颗粒物和O_(3)之间的协同作用机制.结果表明:研究期间唐山市颗粒物PM_(2.5)和PM_(10)的浓度长期分量均呈现显著下降趋势,季节分量和短期分量均呈现不同程度的周期波动.O_(3)浓度长期分量变化幅度较小,其季节分量和短期分量均在每年5~7月之间有明显变化趋势.颗粒物PM_(2.5)和PM_(10)浓度的长期分量变化主要由源排放因素控制,且源排放贡献占90%以上,而O_(3)浓度的长期分量变化则由源排放和气象因素共同控制,且其贡献比例约为2:3.气象因素温度、相对湿度、地表垂直风速和降水量对PM_(2.5)主要表现为小时间尺度的正向作用和大时间尺度的负向作用.温度和短波辐射强度对O_(3)主要呈正向影响,而PM_(2.5)、PM_(10)和O_(3)之间存在小时间尺度的正向影响和大时间尺度的负向作用.
The methods of Kolmogorov-Zurbenko filtering,stepwise multiple linear regression and wavelet coherence analysis were used to explore the evolution characteristics of PM_(2.5),PM_(10) and O_(3)at different time scales from 2015 to 2022 in Tangshan City,and to effectively distinguish and quantitatively estimate the contributions of pollution source emissions and meteorological factors to pollutant concentrations.Meanwhile,the impacts of meteorological factors on pollutants at different time scales and the synergistic mechanism between PM_(2.5),PM_(10) and O_(3)were revealed.The long-term component of PM_(2.5) and PM_(10) concentrations in Tangshan showed a significant downward trend during the study period,and the seasonal component and short-term component showed different periodic-fluctuations.The long-term component of O_(3)concentration slightly changed,and its seasonal component and short-term component changed dramatically between May and July every year.The variations of long-term components of PM_(2.5) and PM_(10) were mainly controlled by source emission factors,which accounted for more than 90%,while the long-term component of O_(3)was controlled by source emission and meteorological factors simultaneously,with the contribution ratio being about 2:3.Meteorological factors such as temperature,relative humidity,surface vertical wind speed and precipitation presented positive effects on PM_(2.5) at small time scales and negative effects at large time scales.Temperature and shortwave radiation intensity had positive effects on O_(3),however,coherence between PM_(2.5),PM_(10) and O_(3)displayed positive effects at small time scales and negative effects at large time scales.
作者
韩力慧
兰童
程水源
王慎澳
田健
齐超楠
肖茜
王海燕
韩登越
王迎澳
HAN Li-hui;LAN Tong;CHENG Shui-yuan;WANG Shen-ao;TIAN Jian;QI Chao-nan;XIAO Qian;WANG Hai-yan;HAN Deng-yue;WANG Ying-ao(Faculty of Environment and Life,Beijing University of Technology,Beijing 100124 China;Key Laboratory of Beijing on Regional Air Pollution Control,Beijing 100124,China)
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2024年第3期1185-1194,共10页
China Environmental Science
基金
国家重点研发计划(2018YFC0213203)
大气重污染成因与治理攻关项目(DQGG0302-01)。
关键词
颗粒物
O_(3)
KZ滤波
小波相干性
贡献
影响因素
particulate matter
O_(3)
KZ filter
wavelet coherence
contribution
influencing factors
