The impact of Chinese new year on air quality in north China based on machine learning

被引:1
作者
Ren, Yuchao [1 ]
Wang, Guoqiang [1 ]
Zhang, Qingzhu [1 ]
Tao, Chenliang [1 ]
Ji, Shuping [1 ]
Wang, Qiao [1 ]
Wang, Wenxing [1 ]
机构
[1] Shandong Univ, Environm Res Inst, Big Data Res Ctr Ecol & Environm, Qingdao 266003, Peoples R China
基金
中国国家自然科学基金;
关键词
Machine learning; Chinese new year; NO; 2; O-; 3; PM (2.5); METEOROLOGICAL NORMALIZATION; POLLUTION; PM2.5; FESTIVAL; SATELLITE; EMISSION; DIWALI; TRENDS;
D O I
10.1016/j.atmosenv.2024.120874
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The reduced economic and social activities during the Chinese New Year offer a unique opportunity to assess declines in anthropogenic emissions. However, limited research quantifies changes in PM2.5, NO2, and O3 concentrations during this period while accounting for meteorological conditions. This study utilized machine learning and the Time Warping-based K-Means method to evaluate the effectiveness of firework bans, the impact of emission reductions on pollutants during the Chinese New Year holiday, and the influence of meteorological conditions on pollutant concentrations during this period. Our findings reveal a significant reduction in emissions, with PM2.5 and NO2 concentrations decreasing by up to 24.76% and 33.39%, respectively, while O3 concentrations increased by up to 45%. Regions without firework bans saw peak PM2.5 levels on New Year's Eve. The ban has been effective, though signs of relaxation appeared in 2023. It is worth noting that pollution during the 2018 Chinese New Year holiday was more severe than before the holiday because the meteorological conditions before the holiday were favorable for pollutant dispersion, while unfavorable meteorological conditions during the holiday masked the emission reductions that occurred due to the holiday period. These results emphasize the significant role of meteorological conditions and the need for stricter emission controls beyond traffic restrictions or factory shutdowns to mitigate haze pollution during adverse weather.
引用
收藏
页数:8
相关论文
共 62 条
[11]   Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities [J].
Dai, Qili ;
Hou, Linlu ;
Liu, Bowen ;
Zhang, Yufen ;
Song, Congbo ;
Shi, Zongbo ;
Hopke, Philip K. ;
Feng, Yinchang .
GEOPHYSICAL RESEARCH LETTERS, 2021, 48 (11)
[12]   Novel Modeling of Task vs. Rest Brain State Predictability Using a Dynamic Time Warping Spectrum: Comparisons and Contrasts with Other Standard Measures of Brain Dynamics [J].
Dinov, Martin ;
Lorenz, Romy ;
Scott, Gregory ;
Sharp, David J. ;
Fagerholmand, Erik D. ;
Leech, Robert .
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2016, 10
[13]  
Fenech S., 2021, COVID-19-Related changes in NO2 and O3 concentrations and associated health effects in Malta, V3, DOI [10.3389/frsc.2021.631280, DOI 10.3389/FRSC.2021.631280]
[14]   The global impacts of COVID-19 lockdowns on urban air pollution: A critical review and recommendations [J].
Gkatzelis, Georgios, I ;
Gilman, Jessica B. ;
Brown, Steven S. ;
Eskes, Henk ;
Gomes, A. Rita ;
Lange, Anne C. ;
McDonald, Brian C. ;
Peischl, Jeff ;
Petzold, Andreas ;
Thompson, Chelsea R. ;
Kiendler-Scharr, Astrid .
ELEMENTA-SCIENCE OF THE ANTHROPOCENE, 2021, 9 (01)
[15]   COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas [J].
Grange, Stuart K. ;
Lee, James D. ;
Drysdale, Will S. ;
Lewis, Alastair C. ;
Hueglin, Christoph ;
Emmenegger, Lukas ;
Carslaw, David C. .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2021, 21 (05) :4169-4185
[16]   Using meteorological normalisation to detect interventions in air quality time series [J].
Grange, Stuart K. ;
Carslaw, David C. .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 653 :578-588
[17]   Random forest meteorological normalisation models for Swiss PM10 trend analysis [J].
Grange, Stuart K. ;
Carslaw, David C. ;
Lewis, Alastair C. ;
Boleti, Eirini ;
Hueglin, Christoph .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2018, 18 (09) :6223-6239
[18]   Impact of anthropogenic emission on air quality over a megacity - revealed from an intensive atmospheric campaign during the Chinese Spring Festival [J].
Huang, K. ;
Zhuang, G. ;
Lin, Y. ;
Wang, Q. ;
Fu, J. S. ;
Zhang, R. ;
Li, J. ;
Deng, C. ;
Fu, Q. .
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2012, 12 (23) :11631-11645
[19]   Impact of Aerosol-PBL Interaction on Haze Pollution: Multiyear Observational Evidences in North China [J].
Huang, Xin ;
Wang, Zilin ;
Ding, Aijun .
GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (16) :8596-8603
[20]   Relative importance of meteorological variables on air quality and role of boundary layer height [J].
Huang, Yaxuan ;
Guo, Bin ;
Sun, Haoxuan ;
Liu, Huijie ;
Chen, Song Xi .
ATMOSPHERIC ENVIRONMENT, 2021, 267 (267)