Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria

Etchie, Tunde Ogbemi; Etchie, Ayotunde Titilayo; Jauro, Aliyu; Pinker, Rachel T.; Swaminathan, Nedunchezhian

doi:10.1016/j.scitotenv.2021.145187

Cited by 17 publications

(9 citation statements)

References 94 publications

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“…Although particulate matter concentrations show short-term declines within three days after the lockdown policies were implemented, when compared to the measurements from different monitoring sites that acted as a control group, the lockdown effects were not as pronounced. The given results are in accordance also with a study by Etchie et al [ 33 ] who observes no effect on PM by the lockdown. A study that also used RF and meteorological normalization shows only a moderate decrease for PM 10 [ 73 ].…”

Section: Discussionsupporting

confidence: 93%

“…A NN approach was used to investigate whether changes in air quality in Nigeria were caused primarily by the lockdown. In this case, monthly average values of ground-level fine aerosol optical depth (AODf) across Nigeria from 2001 to 2020 were used [ 33 ]. Another method for the assessment of air pollution during the lockdown period is the difference-in-differences (DID) model.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

Lovrić

Antunović²,

Šunić

et al. 2022

IJERPH

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In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models’ predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the “new normal”. An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020—compared to the same period in 2018 and 2019. No significant changes were observed for the “new normal” as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

Lovrić

Antunović²,

Šunić

et al. 2022

IJERPH

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“…A study was conducted to analyse the ambient air quality for Mumbai and Delhi and it was found that there was a substantial decrease in the emission of NO 2 , PM 10 and NO 2 was recorded during the lockdown time [90] . As the lockdown was implemented most of the activities and vehicular movement was restricted to a considerable extent helping the air quality to improve [91] , [92] . However, through various measures, if the air quality could be improved it would help in decreasing the number of cases of asthma, heart problems, respiratory disorder etc.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 pandemic in Uttarakhand, India: Environmental recovery or degradation?

Nandan

Siddiqui

Singh

et al. 2021

Journal of Environmental Chemical Engineering

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The human coronavirus disease-2019 (COVID-19) caused by SARS-CoV-2 is now a global pandemic. Personal hygiene such as hand-washing, the use of personal protective equipment, and social distancing via local and national lockdowns are used to reduce the risk of transmission of SARS-CoV-2. COVID-19 and the associated lockdowns may have significant impacts on environmental quality and ergonomics. However, limited studies exists on the impacts of COVID-19 and the associated lockdowns on environmental quality and ergonomics in low-income settings. Therefore, the present study investigated the impacts of the COVID-19 outbreak on socioeconomics, ergonomics and environment (water quality, air quality and noise) in Uttrakhand, India. Approximately 55% of respondents experienced headaches, and the other common health-related issue was back pain, with 45% of respondents having problems with their backs. Water and air quality significantly improved during the lockdown relative to the pre-lockdown period, but was observed to return to their previous characteristics afterwards. Lockdowns significant increased the concentration of indoor air pollutants while noise pollution levels significantly declined. In summary, lockdowns have adverse impacts on ergonomics, resulting in work-related human health risks. The impacts of lockdowns on environmental quality are mixed: temporary improvements on water and air quality, and noise reduction were observed, but indoor air quality deteriorated. Therefore, during lockdowns there is a need to minimize the adverse environmental and ergonomic impacts of lockdowns while simultaneously enhancing the beneficial impacts.

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“…Only few papers have evaluated the lockdown effects while accounting for factors, like seasonality, air quality improvement trends, the weather conditions, and the local characteristic of the investigated area ( Etchie et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the lockdown effects due to the COVID-19 on air pollution in Brescia (Lombardy)

Bontempi

Carnevale

Cornelio

et al. 2022

Environmental Research

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Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria

Cited by 17 publications

References 94 publications

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

COVID-19 pandemic in Uttarakhand, India: Environmental recovery or degradation?

Analysis of the lockdown effects due to the COVID-19 on air pollution in Brescia (Lombardy)

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