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

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Evaluating Whether and How Public Health Event Information Frameworks Promote Pro-Environmental Behavior

The major public health emergencies (PHEs) represented by the COVID-19 pandemic, while posing a serious threat to human health, have led people to rethink about the harmonious relationship between humans and nature. It is worthy to explore whether and how the framework effect of event information can be used to turn crises into opportunities to promote public pro-environmental behavior (PEB). Through a pre-and post-test control experiment, this study took the COVID-19 pandemic as a case, to explore the effects of four PHE information frameworks on promoting PEB, coupled with two information loss-gain frameworks and two information content frameworks. The results showed that all four information frameworks contribute to the public PEB. However, there are differences: only the environmental gain information effect is significant for PEB in the private sphere. The environmental loss and health gain information are effective for PEB in organizations. However, in the public sphere, all four information frameworks significantly motivate PEB. Further factorial analysis revealed that the interaction between the information content and loss-gain framework was not significant, with the latter playing the dominant role. These findings provide a new approach to how to develop the information framework effect and turn crises into opportunities to promote public PEB in the context of major PHEs.

Short-term field research on air pollution within the boundaries of the large city in the Baltic region

Air quality in urban and suburban areas is strongly affected by the level of local urbanization, climatic conditions and industrial activity. Monitoring the main air pollutants such as nitrogen oxides, carbon monoxide and particulate matter may help control the most polluted areas of the site and take measures to reduce pollution. Uncontrolled emissions from other chemical pollutants, including volatile organic compounds and odorous contamination sources like ammonia, may cause both a chronic human disease and damage to flora and fauna. The conducted field research is aimed at determining air pollution within the areas of the large city (residential territory, recreation territory and the areas close to intense transport streets) polluted with the gaseous pollutants of varying nature (CO, NO₂, ozone, sulfur dioxide, VOC and NH₃) as well as particulate matter in different seasons of the year. Studies on Vilnius district air quality were carried out in 17 urban locations (sites) and based on two-phase measurements. The first phase was initiated in 2016-2017 and the second one took place in 2019-2020. It was observed that in the areas close to intense transport streets, the concentration of pollutants can increase more than 3 times, thus reaching up to 36.0 µg/m³ of PM10 (particulate matter) and up to 48.0 µg/m³ of nitrogen dioxide. During the summer period, ammonia concentrations can increase up to 3 times, reaching up to 11.0 µg/m³ from farming and/or industrial activities.

Lessons from a crisis-opportunities for lasting public health change from the COVID-19 pandemic

The coronavirus 2019 (COVID-19) global pandemic has wrought hardship and disrupted lives across all strata of humanity, giving rise to a variety of social, psychological, and medical challenges to individuals in almost every country in the world. Yet for all the difficulties the pandemic has inflicted, it has forced us to examine previously accepted practices at home, work, and society more broadly and has led to innovative changes in the way we communicate and collaborate. These novel approaches to contemporary challenges were devised primarily to allow continued productivity despite the need for social distancing, but have offered secondary advantages that could provide society with lasting benefits. In the following review, we outline three aspects of working life and public health which could experience lasting improvement on the back of lessons learnt from the current crisis.

Air pollution rebound and different recovery modes during the period of easing COVID-19 restrictions

Although COVID-19 lockdown policies have improved air quality in numerous countries, there is a lack of empirical evidence on the extent to which recovery has resulted in air pollution rebound, and the differences and similarities among regions' recovery modes during the period of easing COVID-19 restrictions. Here, we used daily air quality data and the recovery index constructed by a city-pair inflow index for 119 cities in China to quantify the impact of recovery on air pollution from March 2 to October 30, 2020. Findings show that recovery has significantly increased air pollution. When the recovery level increased by 10 %, the concentration of PM_2.5, SO₂, and NO₂ respectively deteriorated by 1.10, 0.33, 1.25 μg/m³, and the average growth rates of three air pollutants were about 3 %-6 %. Moreover, we used the counterfactual framework and time series clustering with wavelet transform to cluster the rebound trajectory of air pollution for 17 provinces into five recovery modes. Results show that COVID-19 has further intensified regional differentiations in economic development ability and green recovery trend. Three northwestern provinces dependent on their resource endowments belong to energy-intensive recovery mode, which have experienced a sharp rebound of air pollution for two months, thereby making green recovery more challenging to achieve. Three regions with a diversified industrial structure are in industrial-restructuring recovery mode, which has effectively returned to a normal level through adjusting industrial structure and technological innovation. Owing to local policies and the outbreak of COVID-19 in other countries, six provinces in policy-oriented and international trade-oriented recovery modes have not fully recovered to the level without COVID-19 until October 2020. The result highlights the importance of diversifying industrial structure, technological innovation, policy flexibility and industrial upgrading for different recovery modes to achieve long-term green recovery in the future.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021)

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO₂, O₃, PM₁₀, PM_2.5, and SO₂, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O₃). Pollution change in susceptibility is more likely to occur in NO₂ decrease and O₃ increase, but unsusceptibility is more likely to occur in CO and SO₂, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM₁₀ and PM_2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.

Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020

Fine particulate matter (PM2.5) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM2.5 distributions in Chinese cities, thus helping form better policies for future PM2.5 mitigation. Based on panel data of 332 cities, we analyzed the function of natural and anthropogenic factors to PM2.5 pollution by applying the geographically and temporally weighted regression (GTWR) model. We found that the PM2.5 concentration of 84.3% of cities decreased after lockdown. Spatially, in the winter of 2020, cities with high PM2.5 concentrations were mainly distributed in Northeast China, the North China Plain and the Tarim Basin. Higher temperature, wind speed and relative humidity were easier to promote haze pollution in northwest of the country, where enhanced surface pressure decreased PM2.5 concentrations. Furthermore, the intensity of trip activities (ITAs) had a significant positive effect on PM2.5 pollution in Northwest and Central China. The number of daily pollutant operating vents of key polluting enterprises in the industrial sector (VOI) in northern cities was positively correlated with the PM2.5 concentration; inversely, the number of daily pollutant operating vents of key polluting enterprises in the power sector (VOP) imposed a negative effect on the PM2.5 concentration in these regions. This work provides some implications for regional air quality improvement policies of Chinese cities in wintertime.

COVID-19 prevention, air pollution and transportation patterns in the absence of a lockdown

Recent studies demonstrate that air quality improved during the coronavirus pandemic due to the imposition of social lockdowns. We investigate the impact of COVID-19 on air pollution in the two largest cities in Taiwan, which were not subject to economic or mobility restrictions. Using a difference-in-differences approach and real-time data on air quality and transportation, we estimate that anthropogenic air pollution from local sources increased during working days and decreased during non-working days during the COVID-19 pandemic. This led to a 3-7 percent increase in CO, O₃, SO₂, PM₁₀ and PM_2.5. We demonstrate that the increase in air pollution resulted from a shift in preferred mode of travel away from public transportation and towards personal motor vehicles during working days. In particular, metro and shared bicycle usage decreased between 8 and 18 percent, on average, while automobile and scooter use increased between 11 and 21 percent during working days. Similar COVID-19 prevention behaviors in regions or countries emerging from lockdowns could likewise result in an increase in air pollution. Taking action to reduce the transmissibility of COVID-19 on metro cars, trains and buses could help policymakers limit the substitution of personal motor vehicles for public transit, and mitigate increases in air pollution when lifting mobility restrictions.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.