Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period

Vadrevu, Krishna Prasad; Eaturu, Aditya; Biswas, Sumalika; Lasko, Kristofer; Sahu, Saroj Kumar; Garg, J. K.; Justice, Chris

doi:10.1038/s41598-020-72271-5

Cited by 120 publications

(76 citation statements)

References 18 publications

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“…In this paper, the cumulative number of confirmed COVID-19 cases was taken as a variable, the spatial weight matrix based on geographical adjacency was selected, and the global Moran's I index, P test value, and Z statistical score were used to determine the number of confirmed cases with different types of neighbors ( Vadrevu et al, 2020 ).To investigate whether there is a spatial correlation between confirmed COVID-19 cases in China. ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, a retrospective spatio-temporal scanning statistical method was used to include a time dimension in the analysis and to determine when, where, and when the cluster occurred (and for how long) ( Vadrevu et al, 2020 ). Because COVID-19 is transmitted from person to person, and more cases are likely to occur in densely populated areas, we selected the Poisson model in SaTScan to calculate the population in each area.…”

Section: Methodsmentioning

confidence: 99%

“…Because COVID-19 is transmitted from person to person, and more cases are likely to occur in densely populated areas, we selected the Poisson model in SaTScan to calculate the population in each area. The base of the spatio-temporal scanning statistics is positioned around a possible center in the entire area, with the radius changing continuously from zero to a specified maximum ( Vadrevu et al, 2020 ). Due to the infectivity of the disease, the number of daily cases increases rapidly, and the time interval of this study is one day.…”

Section: Methodsmentioning

confidence: 99%

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Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

Wang¹,

Dong²,

Yang³

et al. 2021

International Journal of Infectious Diseases

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Objectives The purpose of this study was to explore the temporal and spatial characteristics of COVID-19 transmission and its influencing factors in China from January to October 2020. Methods About 81,000 COVID-19 confirmed case data, Baidu migration index data, air pollutants, meteorological data, and government response strictness index data were collected from 31 provincial-level regions (excluding Hong Kong, Macao, and Taiwan) and 337 prefecture-level cities. The spatio-temporal characteristics of COVID-19 were explored using spatial autocorrelation, hot spot, and spatio-temporal scanning statistics. At the same time, Spearman rank correlation analysis and multiple linear regression were used to explore the relationship between influencing factors and confirmed COVID-19 cases. Results The distribution of COVID-19 in China tends to be stable over time, with spatial correlation and obvious clustering regions. spatio-temporal scanning analysis showed that most COVID-19 high incidence months were from January to March at the beginning of the epidemic, and the area with the highest aggregation risk was Hubei Province (RR = 491.57), which was 491.57 times the aggregation risk of other regions. Among the meteorological variables, the daily average temperature, wind speed, precipitation, and new COVID-19 cases were negatively correlated. The air pollution concentration and migration index were positively correlated with new confirmed cases, and the government response strict index was strongly negatively correlated with confirmed COVID-19 cases. Conclusions Environmental temperature has a certain inhibitory effect on the transmission of COVID-19, and the air pollution concentration and migration index have a certain promoting effect on the transmission of COVID-19. The strict government response index indicates that the greater the intensity of government intervention, the fewer COVID-19 cases will occur.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

Wang¹,

Dong²,

Yang³

et al. 2021

International Journal of Infectious Diseases

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“…There is a growing literature studying and trying to estimate the impact of the pandemic on air pollution. Evidence of reductions in air pollution following the outbreak of the pandemic was found in many regions, such as Egypt ( Mostafa et al, 2021 ), the USA ( Liu et al, 2021 ), the UK ( Ropkins and Tate, 2021 ), and India ( Singh et al, 2020 ; Vadrevu et al, 2020 ). In the context of China, several recent studies found similar evidence that the variation in air pollution concentration in many regions of China dropped significantly during the pandemic ( Bao and Zhang, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The impacts of human migration and city lockdowns on specific air pollutants during the COVID-19 outbreak: A spatial perspective

Zeng

Bao

2021

Journal of Environmental Management

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The outbreak of COVID-19 continues to bring unprecedented shock to mankind's socioeconomic activities, and to the wider environment. China, as the early epicenter of the pandemic, locked down one-third of its cities in an attempt to prevent the rapid spread of the virus. Human migration patterns have subsequently been radically altered and many regions have seen perceived improvements in air quality during the lockdowns. This study empirically examines the relationship between human migration and air pollution and further evaluates the causal impacts of the lockdowns. A spatial econometric method and a spatial explicit counterfactual framework are employed in this study. The key findings are as follows: i) a considerable amount of variation in AQI, PM 10 , PM 2.5 , and NO 2 concentration can be explained by human migration but we fail to find suggestive evidence in the cases of SO 2 and CO; ii) the implementation of lockdown measures led to a significant reduction in AQI (18.1%), PM 2.5 (22.2%), NO 2 (20.5%), and PM 10 (10.7%), but has no meaningful impacts on SO 2, CO and O 3 levels; iii) further analysis indicates that the impacts of lockdown policies varied by control stringency and by regional heterogeneity. Our findings are of great importance for the Chinese government to create a stronger and more coherent framework in its efforts to tackle air pollution.

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“…Numerous studies around the world, as in China and East Asia [13][14][15], India [16][17][18], Southeast Asia [19,20], Europe [21][22][23][24][25][26], North America [27,28] and South America [29,30], have analyzed the effect of COVID-19 lockdowns in spring 2020 on concentrations of particulate matter (PM) and gaseous pollutants (NO x , CO, O 3 , SO 2 , NH 3 , etc.). All these studies agree on an unprecedented reduction of air pollution worldwide due to drastic limitations in traffic and industrial activity [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the COVID-19 Lockdown Effects on Spectral Aerosol Scattering and Absorption Properties in Athens, Greece

et al. 2021

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COVID-19 is evolving into one of the worst pandemics in recent history, claiming a death toll of over 1.5 million as of December 2020. In an attempt to limit the expansion of the pandemic in its initial phase, nearly all countries imposed restriction measures, which resulted in an unprecedented reduction of air pollution. This study aims to assess the impact of the lockdown effects due to COVID-19 on in situ measured aerosol properties, namely spectral-scattering (bsca) and absorption (babs) coefficients, black carbon (BC) concentrations, single-scattering albedo (SSA), scattering and absorption Ångström exponents (SAE, AAE) in Athens, Greece. Moreover, a comparison is performed with the regional background site of Finokalia, Crete, for a better assessment of the urban impact on observed differences. The study examines pre-lockdown (1–22 March 2020), lockdown (23 March–3 May 2020) and post-lockdown (4–31 May 2020) periods, while the aerosol properties are also compared with a 3–4 year preceding period (2016/2017–2019). Comparison of meteorological parameters in Athens, between the lockdown period and respective days in previous years, showed only marginal variation, which is not deemed sufficient in order to justify the notable changes in aerosol concentrations and optical properties. The largest reduction during the lockdown period was observed for babs compared to the pre-lockdown (−39%) and to the same period in previous years (−36%). This was intensified during the morning traffic hours (−60%), reflecting the large decrease in vehicular emissions. Furthermore, AAE increased during the lockdown period due to reduced emissions from fossil-fuel combustion, while a smaller (−21%) decrease was observed for bsca along with slight increases (6%) in SAE and SSA values, indicating that scattering aerosol properties were less affected by the decrease in vehicular emissions, as they are more dependent on regional sources and atmospheric processing. Nighttime BC emissions related to residential wood-burning were slightly increased during the lockdown period, with respect to previous-year means. On the contrary, aerosol and pollution changes during the lockdown period at Finokalia were low and highly sensitive to natural sources and processes.

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Spatial and temporal variations of air pollution over 41 cities of India during the COVID-19 lockdown period

Cited by 120 publications

References 18 publications

Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors

The impacts of human migration and city lockdowns on specific air pollutants during the COVID-19 outbreak: A spatial perspective

Assessment of the COVID-19 Lockdown Effects on Spectral Aerosol Scattering and Absorption Properties in Athens, Greece

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