Literature DB >> 33523881

Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns.

Zongbo Shi1, Congbo Song1, Bowen Liu2, Gongda Lu3, Jingsha Xu3, Tuan Van Vu4, Robert J R Elliott2, Weijun Li5, William J Bloss3, Roy M Harrison3.   

Abstract

The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2 concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2 concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3 increased by 2 to 30% (except for London), the total gaseous oxidant (O x = NO2 + O3) showed limited change, and PM2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.
Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

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Year:  2021        PMID: 33523881      PMCID: PMC7806219          DOI: 10.1126/sciadv.abd6696

Source DB:  PubMed          Journal:  Sci Adv        ISSN: 2375-2548            Impact factor:   14.136


  29 in total

1.  Air Pollution and Mortality in the Medicare Population.

Authors:  Qian Di; Yan Wang; Antonella Zanobetti; Yun Wang; Petros Koutrakis; Christine Choirat; Francesca Dominici; Joel D Schwartz
Journal:  N Engl J Med       Date:  2017-06-29       Impact factor: 91.245

2.  Using meteorological normalisation to detect interventions in air quality time series.

Authors:  Stuart K Grange; David C Carslaw
Journal:  Sci Total Environ       Date:  2018-10-28       Impact factor: 7.963

3.  Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19.

Authors:  Huang Zheng; Shaofei Kong; Nan Chen; Yingying Yan; Dantong Liu; Bo Zhu; Ke Xu; Wenxiang Cao; Qingqing Ding; Bo Lan; Zhouxiang Zhang; Mingming Zheng; Zewei Fan; Yi Cheng; Shurui Zheng; Liquan Yao; Yongqing Bai; Tianliang Zhao; Shihua Qi
Journal:  Sci Total Environ       Date:  2020-06-05       Impact factor: 7.963

4.  Loss of life expectancy from air pollution compared to other risk factors: a worldwide perspective.

Authors:  Jos Lelieveld; Andrea Pozzer; Ulrich Pöschl; Mohammed Fnais; Andy Haines; Thomas Münzel
Journal:  Cardiovasc Res       Date:  2020-09-01       Impact factor: 10.787

5.  Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak.

Authors:  Pengfei Wang; Kaiyu Chen; Shengqiang Zhu; Peng Wang; Hongliang Zhang
Journal:  Resour Conserv Recycl       Date:  2020-03-23       Impact factor: 10.204

6.  Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy.

Authors:  Daniele Fattorini; Francesco Regoli
Journal:  Environ Pollut       Date:  2020-05-04       Impact factor: 8.071

7.  Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic.

Authors:  Aurelio Tobías; Cristina Carnerero; Cristina Reche; Jordi Massagué; Marta Via; María Cruz Minguillón; Andrés Alastuey; Xavier Querol
Journal:  Sci Total Environ       Date:  2020-04-11       Impact factor: 7.963

8.  Drivers of improved PM2.5 air quality in China from 2013 to 2017.

Authors:  Qiang Zhang; Yixuan Zheng; Dan Tong; Min Shao; Shuxiao Wang; Yuanhang Zhang; Xiangde Xu; Jinnan Wang; Hong He; Wenqing Liu; Yihui Ding; Yu Lei; Junhua Li; Zifa Wang; Xiaoye Zhang; Yuesi Wang; Jing Cheng; Yang Liu; Qinren Shi; Liu Yan; Guannan Geng; Chaopeng Hong; Meng Li; Fei Liu; Bo Zheng; Junji Cao; Aijun Ding; Jian Gao; Qingyan Fu; Juntao Huo; Baoxian Liu; Zirui Liu; Fumo Yang; Kebin He; Jiming Hao
Journal:  Proc Natl Acad Sci U S A       Date:  2019-11-18       Impact factor: 11.205

9.  Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China.

Authors:  Tianhao Le; Yuan Wang; Lang Liu; Jiani Yang; Yuk L Yung; Guohui Li; John H Seinfeld
Journal:  Science       Date:  2020-06-17       Impact factor: 47.728

10.  Anthropogenic drivers of 2013-2017 trends in summer surface ozone in China.

Authors:  Ke Li; Daniel J Jacob; Hong Liao; Lu Shen; Qiang Zhang; Kelvin H Bates
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-31       Impact factor: 11.205

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  28 in total

1.  Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain.

Authors:  Weijie Liu; Yao Mao; Tianpeng Hu; Mingming Shi; Jiaquan Zhang; Yuan Zhang; Shaofei Kong; Shihua Qi; Xinli Xing
Journal:  Environ Res       Date:  2022-06-24       Impact factor: 8.431

2.  Variations in concentration and solubility of iron in atmospheric fine particles during the COVID-19 pandemic: An example from China.

Authors:  Lei Liu; Qiuhan Lin; Zhuoran Liang; Rongguang Du; Guizhen Zhang; Yanhong Zhu; Bing Qi; Shengzhen Zhou; Weijun Li
Journal:  Gondwana Res       Date:  2021-06-02       Impact factor: 6.151

3.  Global tropospheric ozone responses to reduced NO x emissions linked to the COVID-19 worldwide lockdowns.

Authors:  Kazuyuki Miyazaki; Kevin Bowman; Takashi Sekiya; Masayuki Takigawa; Jessica L Neu; Kengo Sudo; Greg Osterman; Henk Eskes
Journal:  Sci Adv       Date:  2021-06-09       Impact factor: 14.136

4.  Nitrogen oxides concentration and emission change detection during COVID-19 restrictions in North India.

Authors:  Prakhar Misra; Masayuki Takigawa; Pradeep Khatri; Surendra K Dhaka; A P Dimri; Kazuyo Yamaji; Mizuo Kajino; Wataru Takeuchi; Ryoichi Imasu; Kaho Nitta; Prabir K Patra; Sachiko Hayashida
Journal:  Sci Rep       Date:  2021-05-07       Impact factor: 4.379

5.  Multiple relationships between aerosol and COVID-19: A framework for global studies.

Authors:  Yaxin Cao; Longyi Shao; Tim Jones; Marcos L S Oliveira; Shuoyi Ge; Xiaolei Feng; Luis F O Silva; Kelly BéruBé
Journal:  Gondwana Res       Date:  2021-02-09       Impact factor: 6.051

6.  Exploring chemical changes of the haze pollution during a recent round of COVID-19 lockdown in a megacity in Northeast China.

Authors:  Yuan Cheng; Xu-Bing Cao; Jiu-Meng Liu; Qin-Qin Yu; Ying-Jie Zhong; Qiang Zhang; Ke-Bin He
Journal:  Chemosphere       Date:  2021-12-31       Impact factor: 7.086

7.  Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities.

Authors:  Qili Dai; Linlu Hou; Bowen Liu; Yufen Zhang; Congbo Song; Zongbo Shi; Philip K Hopke; Yinchang Feng
Journal:  Geophys Res Lett       Date:  2021-06-04       Impact factor: 4.720

8.  Cause analysis of PM2.5 pollution during the COVID-19 lockdown in Nanning, China.

Authors:  Zhaoyu Mo; Jiongli Huang; Zhiming Chen; Bin Zhou; Kaixian Zhu; Huilin Liu; Yijun Mu; Dabiao Zhang; Shanshan Wang
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379

9.  Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels.

Authors:  Sandra Ceballos-Santos; Jaime González-Pardo; David C Carslaw; Ana Santurtún; Miguel Santibáñez; Ignacio Fernández-Olmo
Journal:  Int J Environ Res Public Health       Date:  2021-12-18       Impact factor: 3.390

10.  Lessons from the COVID-19 air pollution decrease in Spain: Now what?

Authors:  Xavier Querol; Jordi Massagué; Andrés Alastuey; Teresa Moreno; Gotzon Gangoiti; Enrique Mantilla; José Jaime Duéguez; Miguel Escudero; Eliseo Monfort; Carlos Pérez García-Pando; Hervé Petetin; Oriol Jorba; Víctor Vázquez; Jesús de la Rosa; Alberto Campos; Marta Muñóz; Silvia Monge; María Hervás; Rebeca Javato; María J Cornide
Journal:  Sci Total Environ       Date:  2021-03-16       Impact factor: 7.963

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