Impact of COVID-19 lockdown on NO2 and PM2.5 exposure inequalities in London, UK

Kazakos, Vasilis; Taylor, Jonathon; Luo, Zhiwen

doi:10.1016/j.envres.2021.111236

Cited by 19 publications

(9 citation statements)

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“…The second most influential policy was C5 "Close public transport", which explained 52.3% of the NO2 anomaly. This is consistent with many existing studies, which find that the transportation sector affected reductions in NO2 during the COVID-19 pandemic [10,11]. The third dominant factor is C1 "School closing", where school closure measures explained 46.4% of the NO2 anomaly.…”

Section: Quantifying Different Policy Effects On No2 Anomaliessupporting

confidence: 90%

“…The reduction in transport sector emissions is reported as the main cause for global NO2 anomalies [8]. London experienced a 50% decrease in NO2 during lockdown, with the highest NO2 emission reductions observed during morning rush hours due to the radical changes in routine life and commuting time [10]. Observations from other mega-cities, such as New York [11], Tokyo [12], Moscow [7] and Toronto [13], also showed a decrease in NO2 concentrations during lockdowns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence From Remote Sensing Integrated With Ground-Station Data

Kang¹,

Zhang²,

Zhang³

et al. 2023

Preprint

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Cities exposed their vulnerabilities during COVID-19 pandemic. Unprecedented policies restricted human activities but left an unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to address the key questions of urban development behind the restrictions with the goal of supporting sustainable transition. Data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, allowing us better to track the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The anomalies between the observations and predictions as the deviation were identified due to the policy interventions and quantified different effects using spatial stratified heterogeneity statistics. The top three restrictions showing dominant effects were workplace closures, restricted public transport usage, and school closures, accounting for 54.8%, 52.3%, and 46.4% of NO2 anomalies, respectively; and they are directly linked to the mismatch of employment and housing (deter-mining the commuting patterns), educational inequality and the long-term unsolved road con-gestion. Promoting the transformation of urban spatial structure will effectively alleviate air pollution.

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Section: Quantifying Different Policy Effects On No2 Anomaliessupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence From Remote Sensing Integrated With Ground-Station Data

Kang¹,

Zhang²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These findings apply to outdoor conditions; they may vary in indoor spaces due to distinct physicochemical interactions and environmental conditions in indoor locations [ 206 , 207 ]. Other studies found a decline in PM 2.5 and PM 10 from January to the end of May due to lockdown and broad limitations in nations [ 82 , 208 ].…”

Section: Scope and Long-term Prevalence Of Lockdownmentioning

confidence: 99%

Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly

Hassan

Mehmood

Lodhi

et al. 2022

IJERPH

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Air is a diverse mixture of gaseous and suspended solid particles. Several new substances are being added to the air daily, polluting it and causing human health effects. Particulate matter (PM) is the primary health concern among these air toxins. The World Health Organization (WHO) addressed the fact that particulate pollution affects human health more severely than other air pollutants. The spread of air pollution and viruses, two of our millennium’s most serious concerns, have been linked closely. Coronavirus disease 2019 (COVID-19) can spread through the air, and PM could act as a host to spread the virus beyond those in close contact. Studies on COVID-19 cover diverse environmental segments and become complicated with time. As PM pollution is related to everyday life, an essential awareness regarding PM-impacted COVID-19 among the masses is required, which can help researchers understand the various features of ambient particulate pollution, particularly in the era of COVID-19. Given this, the present work provides an overview of the recent developments in COVID-19 research linked to ambient particulate studies. This review summarizes the effect of the lockdown on the characteristics of ambient particulate matter pollution, the transmission mechanism of COVID-19, and the combined health repercussions of PM pollution. In addition to a comprehensive evaluation of the implementation of the lockdown, its rationales—based on topographic and socioeconomic dynamics—are also discussed in detail. The current review is expected to encourage and motivate academics to concentrate on improving air quality management and COVID-19 control.

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“…As of 29 March 2020, 214 and 199 countries (regions) across the world reported COVID-19 infections and deaths, respectively, with the coefficient of variation at the national scale of 4.20 and 3.82, and the Gini index of 0.87 and 0.88, respectively, suggesting that there is great variation in the global spread and distribution of COVID-19 virus. At the same time, Mizumoto [ 64 ] believes that there is a statistical correlation between health facilities and COVID-19 infection rate based on multiple regression analysis; Xie [ 65 ] holds that the quantity of hospital beds and medical workers has a positive impact on the spatial differentiation of COVID-19; Deguen [ 66 ], Pei [ 67 ], Ramirez [ 68 ], Kazakos et al [ 69 ] conclude that environmental pollution and meteorological conditions such as PM2.5, NOx and air temperature are in a significant correlation with the spatial distribution of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

The Geographical Distribution and Influencing Factors of COVID-19 in China

Zhang

Zhao

et al. 2022

TropicalMed

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The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector(Origin version,Beijing,PR China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

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Impact of COVID-19 lockdown on NO2 and PM2.5 exposure inequalities in London, UK

Cited by 19 publications

References 50 publications

Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence From Remote Sensing Integrated With Ground-Station Data

Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence From Remote Sensing Integrated With Ground-Station Data

Lockdown Amid COVID-19 Ascendancy over Ambient Particulate Matter Pollution Anomaly

The Geographical Distribution and Influencing Factors of COVID-19 in China

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