Potency of the pandemic on air quality: An urban resilience perspective-Reference-Cited by-同舟云学术

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Potency of the pandemic on air quality: An urban resilience perspective

Published:2022-01 Issue: Volume:805 Page:150248
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Feng Meili,Ren Jianfeng,He Jun,Chan Faith Ka Shun,Wu Chaofan

Funder

University of Nottingham

National Natural Science Foundation of China

Zhejiang Province Natural Science Foundation

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference61 articles.

1. Seeing COVID-19 through an urban lens;Acuto;Nat. Sustain.,2020

2. Air pollution in Ontario, Canada during the COVID-19 state of emergency;Adams;Sci. Total Environ.,2020

3. COVID-19 lockdown effects on air quality by NO2 in the cities of Barcelona and Madrid (Spain);Baldasano;Sci. Total Environ.,2020

4. Correlation between environmental pollution indicators and COVID-19 pandemic: a brief study in Californian context;Bashir;Environ. Res.,2020

5. Changes in US air pollution during the COVID-19 pandemic;Berman;Sci. Total Environ.,2020

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1. Assessing urban resilience to pandemics with a hybrid framework of planning, absorption, recovery, and adaptation abilities: A case study of Ahvaz, Iran;International Journal of Disaster Risk Reduction;2024-06

2. COVID-19 pandemic influence on perceived exposure to chemical substances in Latvia: data from a focus group discussion and the HBM4EU citizen survey;Frontiers in Public Health;2024-05-23

3. A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning;Science of The Total Environment;2023-11

4. Association of air pollution and synoptic weather impacts on COVID-19 transmission through in-situ and geospatial data;Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023);2023-09-21

5. Computational deep air quality prediction techniques: a systematic review;Artificial Intelligence Review;2023-08-16

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