ArticleAtmospheric research2021
Comparing different approaches for assessing the impact of COVID-19 lockdown on urban air quality in Reading, UK.
Article in Atmospheric research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Boat-shaped houses of the indigenous Li people on Hainan Island, China: plant resources and ecological adaptations.Journal of ethnobiology and ethnomedicine · 2025Article
- The Long-Term Impact of COVID-19 Lockdowns in Istanbul.International journal of environmental research and public health · 2022Article
- The effect of COVID-19 lockdown on atmospheric total particle numbers, nanoparticle numbers and mass concentrations in the UK.Atmospheric pollution research · 2022Article
- Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany.Atmospheric pollution research · 2022Article
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Authors and funding
3 authors.
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Abstract
Many studies investigated the impact of COVID-19 lockdown on urban air quality, but their adopted approaches have varied and there is no consensus as to which approach should be used. In this paper we compare three of the main approaches and assess their performance using both estimated and measured data from several air quality monitoring stations (AQMS) in Reading, Berkshire UK. The approaches are: (1) Sequential approach - comparing pre-lockdown and lockdown periods 2020; (2) Parallel approach - comparing 2019 and 2020 for the equivalent time of the lockdown period; and (3) Machine learning modelling approach - predicting pollution levels for the lockdown period using business as usual (BAU) scenario and comparing with the observations. The parallel and machine learning approaches resulted in relative higher reductions and both showed strong correlation (0.97) and less error with each other. The sequential approach showed less reduction in NO and NOx, showed positive gain in PM
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