Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

Ma, Qihan; Wang, Jianbo; Xiong, Momiao; Zhu, Liye

doi:10.3390/rs15051295

Cited by 11 publications

(11 citation statements)

References 70 publications

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“…Our study offers a fresh perspective on assessing the socioeconomic effects of the COVID‐19 pandemic by demonstrating how nighttime light remote sensing can efficiently track changes in urban socioeconomic activities resulting from public health prevention and control measures. Consistent with the findings of many other researchers (Ma et al., 2023; Xue et al., 2022; Yang et al., 2021), our research indicates that pandemic control measures implemented in Zhengzhou from September to October 2022 significantly dampened economic activity. Our study stands as a valuable resource for China and other regions as they evaluate the effectiveness of pandemic prevention and control methods.…”

Section: Discussionsupporting

confidence: 92%

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Jiang,

Liu

2024

GeoHealth

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The global COVID‐19 outbreak severely hampered the growth of the global economy, prompting the implementation of the strictest prevention policies in China. Establishing a significant relationship between changes in nighttime light and COVID‐19 lockdowns from a geospatial perspective is essential. In light of nighttime light remote sensing, we evaluated the spatiotemporal dynamic effects of COVID‐19 city lockdowns on human activity intensity in the Zhengzhou region. Prior to the COVID‐19 outbreak, nighttime light in the Zhengzhou region maintained a significant growth trend, even under regular control measures. However, following the October 2022 COVID‐19 lockdown, nighttime light experienced a substantial decrease. In the central area of Zhengzhou, nighttime light decreased by at least 18% compared to pre‐lockdown levels, while in the sub‐center, the decrease was around 14%. The areas where nighttime light decreased the most in the central region were primarily within a 15 km radius, while in the sub‐center, the decrease was concentrated within a 5 km radius. These changes in both statistical data and nighttime light underscored the significant impact of the COVID‐19 lockdown on economic activities in the Zhengzhou region.

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Section: Discussionsupporting

confidence: 92%

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Jiang,

Liu

2024

GeoHealth

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“…Among these five air pollutants, NO 2 , which is primarily emitted by vehicle activities, decreased significantly, with a maximum decrease of 48.6% (Figure 2), which was consistent with previous studies in Jinan (54%; K. Li et al, 2022), Shanghai (50%; Q. Ma et al, 2023), the Beijing-Tianjin-Hebei region (65%; Ren et al, 2023), India (51%; Sekar et al, 2023), and Sri Lanka (82%; Pushpawela et al, 2023), etc. The most significant decrease in NO 2 was strongly associated with substantial restrictions on local traffic (Li & Lasenby, 2023;Llaguno-Munitxa & Bou-Zeid, 2023;Sekar et al, 2023;.…”

Section: Temporal Changes In Air Pollutant Concentrations In Differen...supporting

confidence: 90%

“…However, several studies have found that the lockdown measures do not significantly prevent air pollution (Le et al, 2020;P. Wang et al, 2020a;Mo et al, 2021;Song et al, 2021;Luo et al, 2022;Q. Ma et al, 2023;L.…”

Section: Introductionmentioning

confidence: 99%

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Impact of the Covid-19 Lockdown on Air Pollution in an Industrial City in Northeastern China

Wang,

Zhao

et al. 2023

Journal of Environmental Engineering and Landscape Management

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Many studies in China investigated how the lockdown following the coronavirus disease 2019 substantially affected air quality; however, few were conducted in Northeastern China. Here, the changes in six criteria air pollutants, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were investigated in Shenyang from January to May 2015–2020. Compared with the pre-lockdown, the mass concentrations of PM2.5, PM10, SO2, NO2, and CO during the lockdown decreased by 40.3% to 48.6%, indicating a positive impact of lockdown policies on reducing pollutant emissions. The responses of PM2.5, PM10, and CO to the lockdown measures in downtown areas were more sensitive than in the suburbs. However, the O3 concentration showed the opposite trend, attributed to the drop in NOx and particulate matters. Compared to the same period in 2015–2019, the proportion of days with good air quality increased from 63.2% to 77.2% during the lockdown and Shenyang experienced no severe pollution. Our results suggest that reducing human activities can improve air quality; however, coordinated control policies of O3, PM2.5, and NO2 are imperative.

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“…Human premature death from respiratory and cardiovascular disorders is closely correlated with both fine particulate matter (PM2.5) and particulate matter (PM10) [29]. According to a number of studies, tiny particulate matter (PM2.5) poses a greater risk than fine particulate matter (PM10).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Air Quality Index by Utilizing Several Meteorological Factors Using the ARIMAX Method (Case Study: Central Jakarta City)

Muzakki,

Putri,

Maruli

et al. 2024

jtik

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Today's society really pays attention to air quality because the impact of exposure to pollutants in the air is starting to be felt. PM 2.5 pollutants are very dangerous because their small size can penetrate the alveoli of human lungs. The value calculation of the Air Quality Index (AQI) is important to prepare mitigation and defensive measures to reduce the negative impact of air quality and as a basis for future policymaking. Several method comparisons have been carried out by researchers to predict AQI. However, researchers have not studied much regarding the use of meteorological factors in the form of average air temperature (°C), average air humidity (percent), and average wind speed (m/s) in forecasting AQI values, even though meteorological factors have a significant link, according to previous researchers. This research forecasts AQI using the ARIMAX method, which includes meteorological factors as exogenous variables, using daily AQI PM 2.5 data in Central Jakarta. The best modeling of the data is ARIMA (1,1,1) without X and ARIMAX (1,1,1). Based on the calculation of AIC, BIC, RMSE, and MAPE values, ARIMAX (1,1,1) modeling produces better forecasting, so it can be concluded that forecasting involving meteorological factors can make forecasting more precise. Predicting AQI using ARIMAX with upcoming meteorological factors is beneficial, as precise prediction results can assist in policy-making to prevent the adverse impacts of air quality on public health. In future research, other meteorological factors could be studied and combined with other modeling besides ARIMA.

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Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

Cited by 11 publications

References 70 publications

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

Impact of the Covid-19 Lockdown on Air Pollution in an Industrial City in Northeastern China

Forecasting the Air Quality Index by Utilizing Several Meteorological Factors Using the ARIMAX Method (Case Study: Central Jakarta City)

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