How do pollutants change post-pandemic? Evidence from changes in five key pollutants in nine Chinese cities most affected by the COVID-19.

作者: Qiang Wang , Xuan Yang

DOI: 10.1016/J.ENVRES.2021.111108

关键词:

摘要: Under the COVID-19 global pandemic, China has weakened large-scale spread of epidemic through lockdown and other measures. At same time, with recovery social production activities, become only country which achieves positive growth in 2020 major economies. It entered post pandemic period. These measures improved local environmental quality. However, whether this improvement can be sustained is also a problem that needs to solved. So, study investigated changes five air pollutants (PM2.5, PM10, NO2, SO2, CO) nine cities most severely affected by during We emphasized when analyzing quality epidemic, we must consider not impact day short-term changesbut cumulative lag effect sustainable development. Through combination qualitative quantitative methods, it found concentration decreased significantly compared situation before epidemic. PM10 NO2 are falling most, downs 39% 46% respectively. During period, pollutant concentrations response 3-7 days. More specifically, related single pollutants, on shows significant correlation delayed for seven In multiple usually highest 3-5 This means policy environment lasted Besides, Wuhan, Jingmen Jingzhou have seen obvious improvement. did last. rebounded, rates reached 44% 87% September. When period from 2017 2019, decline rate been slower, even higher than average previous years. The research contributes China's economic "green recovery" plan but provides references governance countries.

参考文章(48)
P. F. Stewart, A. N. Turner, S. C. Miller, Reliability, factorial validity, and interrelationships of five commonly used change of direction speed tests. Scandinavian Journal of Medicine & Science in Sports. ,vol. 24, pp. 500- 506 ,(2014) , 10.1111/SMS.12019
Douglas G. Bonett, Thomas A. Wright, Sample size requirements for estimating pearson, kendall and spearman correlations Psychometrika. ,vol. 65, pp. 23- 28 ,(2000) , 10.1007/BF02294183
Christophe Croux, Catherine Dehon, Influence functions of the Spearman and Kendall correlation measures Statistical Methods and Applications. ,vol. 19, pp. 497- 515 ,(2010) , 10.1007/S10260-010-0142-Z
Jerrold H. Zar, Significance Testing of the Spearman Rank Correlation Coefficient Journal of the American Statistical Association. ,vol. 67, pp. 578- 580 ,(1972) , 10.1080/01621459.1972.10481251
Jan Hauke, Tomasz Kossowski, Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data Quaestiones Geographicae. ,vol. 30, pp. 87- 93 ,(2011) , 10.2478/V10117-011-0021-1
Yuanzheng Cui, Weishi Zhang, Haijun Bao, Can Wang, Wenjia Cai, Jian Yu, David G. Streets, Spatiotemporal dynamics of nitrogen dioxide pollution and urban development: Satellite observations over China, 2005–2016 Resources Conservation and Recycling. ,vol. 142, pp. 59- 68 ,(2019) , 10.1016/J.RESCONREC.2018.11.015
Nicolai Skovbjerg Arildsen, Laura Martin de la Fuente, Anna Måsbäck, Susanne Malander, Ola Forslund, Päivi Kannisto, Ingrid Hedenfalk, Detecting TP53 mutations in diagnostic and archival liquid-based Pap samples from ovarian cancer patients using an ultra-sensitive ddPCR method. Scientific Reports. ,vol. 9, pp. 1- 8 ,(2019) , 10.1038/S41598-019-51697-6
A. Lorente, K. F. Boersma, H. J. Eskes, J. P. Veefkind, J. H. G. M. van Geffen, M. B. de Zeeuw, H. A. C. Denier van der Gon, S. Beirle, M. C. Krol, Quantification of nitrogen oxides emissions from build-up of pollution over Paris with TROPOMI. Scientific Reports. ,vol. 9, pp. 20033- ,(2019) , 10.1038/S41598-019-56428-5
Simiao Chen, Juntao Yang, Weizhong Yang, Chen Wang, Till Bärnighausen, COVID-19 control in China during mass population movements at New Year. The Lancet. ,vol. 395, pp. 764- 766 ,(2020) , 10.1016/S0140-6736(20)30421-9