The mediating effect of air quality on the association between human mobility and COVID-19 infection in China

The COVID-19 pandemic made it critical to limit the spread of the disease by enforcing human isolation, restricting travel and reducing social activities. Dramatic improvements to air quality, especially NO2, have often characterised places under COVID-19 restrictions. Air pollution measurements in Sydney in April 2019 and during the lockdown period in April 2020 show reduced daily averaged NO2 concentrations: 8.52 ± 1.92 and 7.85 ± 2.92 ppb, though not significantly so (p1~0.15) and PM2.5 8.91 ± 4.94 and 7.95 ± 2.64 µg m−3, again a non-significant difference (p1~0.18). Satellite imagery suggests changes that parallel those at ground level, but the column densities averaged over space and time, in false-colour, are more dramatic. Changed human mobility could be traced in increasing times spent at home, assessed from Google Mobility Reports and mirrored in decreased traffic flow on a major road, suggesting compliance with the restrictions. Electricity demand for the State of New South Wales was low under lockdown in early April 2020, but it recovered rapidly. Analysis of the uses of search terms: bushfires, air quality, haze and air pollution using Google Trends showed strong links between bushfires and pollution-related terms. The smoke from bushfires in late 2019 may well have added to the general impression of improved air quality during lockdown, despite only modest changes in the ground level measurements. This gives hints that successful regulation of air quality requires maintaining a delicate balance between our social perceptions and the physical reality.

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Impact on Air Quality of the COVID-19 Lockdown in the Urban Area of Palermo (Italy)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17207375 ◽

2020 ◽

Vol 17 (20) ◽

pp. 7375 ◽

Cited By ~ 2

Author(s):

Marcello Vultaggio ◽

Daniela Varrica ◽

Maria Grazia Alaimo

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Area ◽

Human Mobility ◽

Infected People ◽

National Crisis ◽

Global Pandemic ◽

Pm 10 ◽

The Impact

At the end of 2019, the first cases of coronavirus disease (COVID-19) were reported in Wuhan, China. Thereafter, the number of infected people increased rapidly, and the outbreak turned into a national crisis, with infected individuals all over the country. The COVID-19 global pandemic produced extreme changes in human behavior that affected air quality. Human mobility and production activities decreased significantly, and many regions recorded significant reductions in air pollution. The goal of our investigation was to evaluate the impact of the COVID-19 lockdown on the concentrations of the main air pollutants in the urban area of Palermo (Italy). In this study, the trends in the average concentrations of CO, NO2, O3, and PM10 in the air from 1 January 2020 to 31 July 2020 were compared with the corresponding average values detected at the same monitoring stations in Palermo during the previous five years (2015–2019). During the lockdown period (10 March–30 April), we observed a decrease in the concentrations of CO, NO2, and particulate matter (PM)10, calculated to be about 51%, 50%, and 45%, respectively. This confirms that air pollution in an urban area is predominantly linked to vehicular traffic.

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Machine Learning on the COVID-19 Pandemic, Human Mobility, and Air Quality: A Review

10.20944/preprints202103.0396.v1 ◽

2021 ◽

Author(s):

Md Mokhlesur Rahman ◽

Kamal Chandra Paul ◽

Md. Amjad Hossain ◽

G. G. Md. Nawaz Ali ◽

Md. Shahinoor Rahman ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Urban Form ◽

Human Mobility ◽

Virus Transmission ◽

Past Research ◽

Policy Implications ◽

Viral Spread ◽

Global Pandemic ◽

Review Study

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

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The Impact of The Government’s COVID-19 Restriction Policies On Human Mobility: Evidence From The United States

10.21203/rs.3.rs-1123806/v1 ◽

2021 ◽

Author(s):

Yuxun Zhou ◽

Mafizur Rahman Mohammad ◽

Khanam Rasheda ◽

Robert Taylor Brad

Keyword(s):

United States ◽

Structural Equation ◽

Human Mobility ◽

Negative Relationship ◽

The United States ◽

Mediating Effect ◽

Economic Environment ◽

Effect Decomposition ◽

Significant Negative Relationship ◽

The Impact

Abstract Purpose – In responding to COVID-19, governments around the world have imposed various restrictions with different levels of success. One important aspect of pandemic control is the willingness of individuals to stay home when possible. The purpose of this paper is to study the impact of government restrictions on human mobility in the United StatesMethodology/approach – Structural equation modelling is used to explore the issue. First, we use path regression analysis and factor analysis to identify the main factors that influence mobility. Second, we use total effect decomposition to investigate the deeper relationship between government restrictions and human mobility.Finding – Two important findings are revealed First, the economic environment is the fundamental and direct factor affecting human mobility. There is a significant negative relationship between economic environment and human mobility, meaning that where economic conditions are bad mobility is greater. Second, government restrictions and the scale of the pandemic do not directly affect human mobility. Government restriction indirectly influences human mobility through economic environment as a mediating variable. Therefore, the economic environment has a significant mediating effect.Originality/value – Existing literature lacks research on the mediating effect between government restrictions and human mobility. This paper provides new empirical evidence for the research topic by studying the mediating effect between government restrictions and human mobility. This provides policymakers with a more detailed picture of the processes through which policies operate.

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Changes in Air Quality during the COVID-19 Lockdown in Singapore and Associations with Human Mobility Trends

Aerosol and Air Quality Research ◽

10.4209/aaqr.2020.06.0303 ◽

2020 ◽

Vol 20 (8) ◽

pp. 1748-1758 ◽

Cited By ~ 3

Author(s):

Jiayu Li ◽

Federico Tartarini

Keyword(s):

Air Quality ◽

Human Mobility

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Classification and transformation of aerosols over selected Indian cities during reduced emissions under COVID-19 lockdown

10.5194/egusphere-egu21-8677 ◽

2021 ◽

Author(s):

Pradeep Attri ◽

Siddhartha Sarkar ◽

Devleena Mani

Keyword(s):

Air Quality ◽

Atmospheric Chemistry ◽

Indian Subcontinent ◽

Human Mobility ◽

Chemical Species ◽

Anthropogenic Emissions ◽

Eastern Region ◽

Vehicular Emissions ◽

Ambient Concentrations ◽

Indian Cities

An improved air quality around the globe and over India has been witnessed during the Covid-19 pandemic lockdown. Using surface observations of particulate matter and chemical species data and products from the MERRA-2 reanalysis &#197;ngstrom exponent (&#945;) and aerosol optical depth (AOD), this study documents the changes in atmospheric chemistry over the Indian subcontinent as a result of nationwide lockdown. Two major cities are selected in five Indian regions to cover a large spatial domain. A general shift from fine to coarse particle size, predominantly of dust type, in all regions is observed, which implies a lowered residence time of aerosol in the atmosphere during decreased anthropogenic emissions. For the studied period, Thiruvananthapuram is the cleanest city with marine origin aerosols and an average PM2.5 concentration of 7.69&#177;2.40&#181;g/m3 in the last phase of nationwide lockdown. Over Delhi and Ahmedabad, industrial and vehicular emission play important role in influencing the air quality. The diurnal variation of O3 and NO2 and their interdependency on each other vary over space and time, with the sharp nighttime O3 peak observed in the southern region for each lockdown phase. Biomass burning type aerosols decrease over the eastern region. In lockdown, NO2 also shows a significant correlation with population density (R2 = 0.75; p < 0.05), suggesting human mobility (and accordingly vehicular emissions) as the major contributor to NO2 concentration in the atmosphere. The results of present study did not find any relationship between the ambient concentrations of pollutants to the cumulative increase in COVID-19 cases. However, there is a significant relationship with O3 concentrations, and in turn with NO2, which can be associated with respiratory ailments.

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Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

IEEE Access ◽

10.1109/access.2021.3079121 ◽

2021 ◽

pp. 1-1

Author(s):

Md. Mokhlesur Rahman ◽

Kamal Chandra Paul ◽

Md. Amjad Hossain ◽

G. G. Md. Nawaz Ali ◽

Md. Shahinoor Rahman ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Human Mobility

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COVID-19 Higher Mortality in Chinese Regions With Chronic Exposure to Lower Air Quality

Frontiers in Public Health ◽

10.3389/fpubh.2020.597753 ◽

2021 ◽

Vol 8 ◽

Author(s):

Riccardo Pansini ◽

Davide Fornacca

Keyword(s):

Air Pollution ◽

Air Quality ◽

Viral Infections ◽

Human Mobility ◽

Public Health Issue ◽

High Carbon ◽

Pm 2.5 ◽

Multi Scale ◽

Pollution Exposure

We investigated the geographical character of the COVID-19 infection in China and correlated it with satellite- and ground-based measurements of air quality. Controlling for population density, we found more viral infections in those prefectures (U.S. county equivalent) afflicted by high Carbon Monoxide, Formaldehyde, PM 2.5, and Nitrogen Dioxide values. Higher mortality was also correlated with relatively poor air quality. When summarizing the results at a greater administrative level, we found that the 10 provinces (U.S. state equivalent) with the highest rate of mortality by COVID-19, were often the most polluted but not the most densely populated. Air pollution appears to be a risk factor for the incidence of this disease, despite the conventionally apprehended influence of human mobility on disease dynamics from the site of first appearance, Wuhan. The raw correlations reported here should be interpreted in a broader context, accounting for the growing evidence reported by several other studies. These findings warn communities and policymakers on the implications of long-term air pollution exposure as an ecological, multi-scale public health issue.

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