Public transit usage and air quality index during the COVID-19 lockdown

Comparative Assessment of the Impact of COVID-19 Lockdown on Air Quality: A Multinational Study of SARS-CoV-2 Hotspots

In response to the global COVID-19 pandemic, nations implemented lockdown measures to contain the virus. This study assessed air pollution levels during and after lockdowns, focusing on the following heavily affected locations: Oulu and Helsinki in Finland, Paris in France, Madrid in Spain, Milan in Italy, and Wuhan in China. Air Quality Index (AQI) data from these locations over two years were analyzed to understand the effects of lockdowns. The study compared COVID-19 lockdowns in these six cities with SARS-CoV-2 measurements using statistical methods. Variations in outdoor pollutants were evaluated through tests, revealing significant differences. Parametric analyses and regression were employed to study the impacts of lockdown measures on pollution and their relationships. The study comprehensively analyzed the effects of COVID-19 lockdowns on air quality, identifying differences, quantifying changes, and exploring patterns in each city. Pollutant correlations varied among cities during the lockdowns. Regression analysis highlighted the impact of independent variables on pollutants. Decreases in NO2 were observed in Helsinki, Madrid, Oulu, Paris, and Milan, reflecting reduced traffic and industrial activities. Reductions in PM2.5 and PM10 were noted in these cities and in Wuhan, except for O3 levels, which increased. The reduction in human activities improved air quality, particularly for NO2 and PM10. Regional variations underscore the need for tailored interventions. The study observed a substantial decrease in both PM2.5 and NO2 levels during the COVID-19 lockdowns, indicating a direct correlation between reduced human activities, such as transportation and industrial operations, and improved air quality. This underscores the potential impact of environmental measures and suggests the need for sustainable practices to mitigate urban pollution.

COVID-19, Cities and Inequality

COVID-19 has changed our lives and will likely leave a lasting imprint on our cities. This paper reviews how the pandemic has altered the way people commute, work, collaborate, and consume, especially its reflection on urban space and spatial inequality. We conceptualize these urban changes as structural transformation, accelerated transition, and temporal change. First, we have seen more structural transformation far exceeding scholars' earlier predictions, especially remote working and global supply chain restructuring. Second, COVID-19 has accelerated the processes of digitalization and sustainable transition. While COVID-19 has contributed to suburbanization and urban sprawl, it has also raised the significance of green spaces and the environment. Third, COVID-19 reduced human impact on the environment, which might be temporary. Last, the pandemic has also amplified the pre-existing inequalities in urban areas, created a more fragmented and segregated urban landscape, and expanded the scope of urban inequality research by connecting health inequality with environmental and socio-injustice. We further discuss the emergence of post-pandemic urban theories and identify research questions for future research.

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021. By distinguishing between weekday and weekend usage, we found different temporal patterns between trip volume and duration, and distinct spatial patterns of within- and between-region rides with respect to naturally separated regions. Overall, the results illustrate the resilience and the vital role of bike-sharing during the pandemic, consistent with previous observations on bike-sharing systems of a larger scale. Our study contributes to a comprehensive understanding of bike-sharing that calls for more research on smaller-scale systems under disruptive events such as the pandemic, which can greatly inform decision-makers from smaller sized cities and enable future studies to compare across different urban regions or modes of transportation.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

Trends in alcohol-impaired crashes in California, 2016 to 2021: A time series analysis for alcohol involvement and crash distribution among demographic subgroups

BACKGROUND

In 2020, the COVID-19 pandemic and control measures changed alcohol consumption in the United States (US) and globally. Before the pandemic, alcohol-impaired crashes contributed to approximately one-third of all road traffic crash injuries and fatalities nationally. We examined the impact of the COVID-19 pandemic on crashes and examined differences in alcohol-involved crashes across various subgroups.

METHODS

The University of California Berkeley Transportation Injury Mapping Systems provided information on all crashes reported to the California Highway Patrol from January 1, 2016 through December 31, 2021. Using autoregressive integrated moving average (ARIMA) models applied to weekly time series data, we estimated the effect of California's first mandatory statewide shelter-in-place order (March 19, 2020) on crashes per 100,000 population. We also examined crash subgroups according to crash severity, sex, race/ethnicity, age, and alcohol involvement.

RESULTS

In California, the mean crash rate per week before the pandemic (January 1, 2016-March 18, 2020) was 9.5 crashes per 100,000 population, and 10.3% of those were alcohol-involved. After the initiation of the COVID-19 stay-at-home order, the percentage of crashes that were alcohol-involved rose to 12.7%. Overall, the crash rate across California decreased significantly (-4.6 crashes per 100,000; 95% CI: -5.3, -3.9), including across all examined subgroups, with the greatest decrease among the least severe crashes. However, there was a 2.3% absolute increase in the proportion of crashes that were alcohol-involved (0.02 crashes per 100,000; 95% CI: 0.02, 0.03).

CONCLUSIONS

The initiation of a COVID-19 stay-at-home ordinance in California was associated with a substantial decrease in overall crash rates. While crashes have returned to pre-pandemic levels, alcohol-involved crashes remain elevated. The initiation of the stay-at-home order significantly increased alcohol-impaired driving, which has remained elevated.

How does COVID-19 lockdown affect air quality: Evidence from Lanzhou, a large city in Northwest China

Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's "morning peak" of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.

Modeling the resilience of urban mobility when exposed to the COVID-19 pandemic: A qualitative system dynamics approach

In December 2019, coronavirus disease (COVID-19) was detected in Wuhan, China. Due to the rapid spread of the disease, containment measures were adopted, which caused unprecedent shifts in individual mobility. Although some studies explored the impacts of the COVID-19 pandemic on travel patterns and resilience of transport systems based on different analysis techniques, there is a lack of studies addressing the impacts of the pandemic on the sustainability and resilience of urban mobility systems using in-depth and holistic methods, such as system dynamics. This research aims to characterize the dynamics present in urban mobility systems when exposed to pandemics and analyze the changes needed for systems to increase their resilience to pandemics using qualitative system dynamics modeling. The framework comprises the characterization of cause-and-effect relationships and the creation of systems' causal loop diagrams (CLD) in their basic state of functionality, when affected by pandemics, and still operating owing to its resilience. Our findings indicated that the CLD of a resilient system is driven by strategic preparedness and response plans, as well as research and development, which balance the spread of the pandemic and increase support on technological strengths and the activities performed from home.

Predictive modeling and analysis of air quality - Visualizing before and during COVID-19 scenarios

Quality air to breathe is the basic necessity for an individual and in recent times, emission from various sources caused by human activities has resulted in substantial degradation in the air quality. This work focuses to study the inadvertent effect of COVID-19 lockdown on air pollution. Pollutants' concentration before- and during- COVID-19 lockdown is captured to understand the variation in air quality. Firstly, multi-pollutant profiling using hierarchical cluster analysis of pollutants' concentration is performed that highlights the differences in the cluster compositions between before- and during-lockdown time periods. Results show that the particulate matter (PM10 and PM2.5) in air that formed the primary cluster before lock-down, came down to close similarity with other clusters during lockdown. Secondly, predicting air quality index (AQI) based on the forecasts of pollutants' concentration is performed using neural networks, support vector machine, decision tree, random forest, and boosting algorithms. The best-fitted models representing AQI is identified separately for before- and during-lockdown time periods based on its predictive power. While deterministic method reactively evaluates present AQI when current pollutants' concentration at a particular time and place are known, this study uses the best fitted data-driven model to determine future AQIs based on the forecasts of pollutant's concentration accurately (overall RMSE<0.1 for before lockdown scenario and <0.3 for during lockdown scenario). The study contributes to visualize the variation in pollutants' concentrations between the two scenarios. The results show that the reduced economic activities during lockdown period had led to the drop in concentration of PM10 and PM2.5 by 27% and 50% on an average. The findings of this study have practical and societal implications and serve as a reference mechanism for policymakers and governing bodies to revise their actions plans for regulating individual air pollutants in the atmospheric air.

Changes in Air Quality during the Period of COVID-19 in China

This paper revisits the heterogeneous impacts of COVID-19 on air quality. For different types of Chinese cities, we analyzed the different degrees of improvement in the concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) during COVID-19 by analyzing the predictivity of air quality. Specifically, we divided the sample into three groups: cities with severe outbreaks, cities with a few confirmed cases, and cities with secondary outbreaks. Ensemble empirical mode decomposition (EEMD), recursive plots (RPs), and recursive quantitative analysis (RQA) were used to analyze these heterogeneous impacts and the predictivity of air quality. The empirical results indicated the following: (1) COVID-19 did not necessarily improve air quality due to factors such as the rebound effect of consumption, and its impacts on air quality were short-lived. After the initial outbreak, NO2, CO, and PM2.5 emissions declined for the first 1-3 months. (2) For the cities with severe epidemics, air quality was improved, but for the cities with second outbreaks, air quality was first enhanced and then deteriorated. For the cities with few confirmed cases, air quality first deteriorated and then improved. (3) COVID-19 changed the stability of the air quality sequence. The predictability of the air quality index (AQI) declined in cities with serious epidemic situations and secondary outbreaks, but for the cities with a few confirmed cases, the AQI achieved a stable state sooner. The conclusions may facilitate the analysis of differences in air quality evolution characteristics and fluctuations before and after outbreaks from a quantitative perspective.

Understanding Air Quality Changes after Implementation of Mitigation Measures during a Pandemic: A Scoping Review of Literature in the United States

Traffic-related emissions continue to be a significant source of air pollution in the United States (US) and around the globe. Evidence has shown that previous policies implemented to restrict-traffic flows have affected air pollution levels. Thus, mitigation strategies associated with the COVID-19 pandemic that modified population-level mobility patterns provide a unique opportunity to study air pollution change across the US. For instance, to slow the spread of the pandemic, state and local governments started implementing various mitigation actions, including stay-at-home directives, social distancing measures, school closures, and travel restrictions. This scoping review aimed to summarize the existing evidence about how air quality changed through mitigation practices throughout the pandemic in the US. We found 66 articles that fit our inclusion criteria. Generally, the consolidated results revealed that nitrogen dioxide (NO2) and carbon monoxide (CO) decreased across the country. Studies observed mixed directions and magnitudes of change for fine and coarse particulate matter (PM2.5, PM10), ozone (O3), and sulfur dioxide (SO2). Few articles tried to explain this notable heterogeneity in air quality changes by associating contextual factors, such as mobility, traffic flow, and demographic factors. However, all studies agreed that the change in air pollution was nonuniform across the US and even varied within a city.

Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) has had severely disruptive impacts on transportation, particularly public transit. To understand metro ridership changes due to the COVID-19 pandemic, this study conducts an in-depth analysis of two Chinese megacities from January 1, 2020, to August 31, 2021. Generalized linear models are used to explore the impact of the COVID-19 pandemic on metro ridership. The dependent variable is the relative change in metro ridership, and the independent variables include COVID-19, socio-economic, and weather variables. The results suggested the following: (1) The COVID-19 pandemic has a significantly negative effect on the relative change in metro ridership, and the number of cumulative confirmed COVID-19 cases within 14 days performs better in regression models, which reflects the existence of the time lag effect of the COVID-19 pandemic. (2) Emergency responses are negatively associated with metro system usage according to severity and duration. (3) The marginal effects of the COVID-19 variables and emergency responses are larger on weekdays than on weekends. (4) The number of imported confirmed COVID-19 cases only significantly affects metro ridership in the weekend and new-normal-phase models for Beijing. In addition, the daily gross domestic product and weather variables are significantly associated with metro ridership. These findings can aid in understanding the usage of metro systems in the outbreak and new-normal phases and provide transit operators with guidance to adjust services.

Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data

The COVID-19 pandemic has given rise to a major impact on traffic mobility. To implement preventive measures and manage transportation, understanding the transformation of private driving behavior during the pandemic is critical. A data-driven forecasting model is proposed to estimate daily charging demand in the absence of the COVID-19 pandemic by leveraging electric vehicle (EV) charging data from four cities in China. It serves as a benchmark for quantifying the impact of the COVID-19 pandemic on EV charging demand. A vector autoregressive (VAR) model is then used to investigate the dynamic relationship between the changes in charging demand and potential influencing factors. Potential influencing factors are selected from three aspects: public health data, public concern, and the level of industrial activity. The results show that the magnitude of the decline in EV charging demand varied by city during the pandemic. Furthermore, COVID-19 related factors such as daily hospitalizations and national confirmed cases are the primary causes of the decline in charging demand. The research framework of this paper can be generalized to analyze the changes in other driving behaviors during the pandemic. Finally, three policy implications are proposed to assist other countries in dealing with similar events and to stimulate the recovery of the transport system during the post-pandemic period.

Air pollution and health impacts during the COVID-19 lockdowns in Grenoble, France

It is undeniable that exposure to outdoor air pollution impacts the health of populations and therefore constitutes a public health problem. Any actions or events causing variations in air quality have repercussions on populations' health. Faced with the worldwide COVID-19 health crisis that began at the end of 2019, the governments of several countries were forced, in the beginning of 2020, to put in place very strict containment measures that could have led to changes in air quality. While many works in the literature have studied the issue of changes in the levels of air pollutants during the confinements in different countries, very few have focused on the impact of these changes on health risks. In this work, we compare the 2020 period, which includes two lockdowns (March 16 - May 10 and a partial shutdown Oct. 30 - Dec. 15) to a reference period 2015-2019 to determine how these government-mandated lockdowns affected concentrations of NO₂, O₃, PM_2.5, and PM₁₀, and how that affected human health factors, including low birth weight, lung cancer, mortality, asthma, non-accidental mortality, respiratory, and cardiovascular illnesses. To this end, we structured 2020 into four periods, alternating phases of freedom and lockdowns characterized by a stringency index. For each period, we calculated (1) the differences in pollutant levels between 2020 and a reference period (2015-2019) at both background and traffic stations; and (2) the resulting variations in the epidemiological based relative risks of health outcomes. As a result, we found that relative changes in pollutant levels during the 2020 restriction period were as follows: NO₂ (-32%), PM_2.5 (-22%), PM₁₀ (-15%), and O₃ (+10.6%). The pollutants associated with the highest health risk reductions in 2020 were PM_2.5 and NO_2, while PM₁₀ and O₃ changes had almost no effect on health outcomes. Reductions in short-term risks were related to reductions in PM_2.5 (-3.2% in child emergency room visits for asthma during the second lockdown) and NO₂ (-1.5% in hospitalizations for respiratory causes). Long-term risk reductions related to PM_2.5 were low birth weight (-8%), mortality (-3.3%), and lung cancer (-2%), and to NO₂ for mortality (-0.96%). Overall, our findings indicate that the confinement period in 2020 resulted in a substantial improvement in air quality in the Grenoble area.

Presence of tourists and perceived safety from COVID-19 among local bus users: Evidence from a Mediterranean city

Since the start of COVID-19 pandemic, public transport has been signalled as a potential contagion hot-spot, leading to a generalised decrease in its use. However, public transport use is still being used and little is known about how the perception of loyal users is configured in contexts of influenza-like viruses such as SARS-CoV-2. The configuration of the perception of safety acquires a critical importance in urban contexts where the public transport system is used both by tourists and local users. The presence of strangers or higher crowding levels could impact the perception of safety among residents and their consequent travel behaviour. In the present study, we explored how the presence of tourists influences the configuration of the perception safety related to the transmission of COVID-19 on public transport of daily users. We used data from an ad-hoc survey conducted at the main bus stations and stops of the Tarragona Urban Area (Catalonia, Spain) between August and September 2020. This area includes the Costa Daurada coastal destination. The 2020 summer holiday season was characterised for the relaxation of mobility restrictions and the start of the second wave of COVID-19. Results show how the presence of tourists in buses negatively influenced the perception of safety of local users. However, this influence can be mostly explained to their prior perception of risk of contagion. These findings will be useful for policymakers and public transport managers in both the latter stages of the COVID-19 pandemic and future virus-related epidemics to maintain public transport ridership.

COVID-19 and transportation of India: influence on infection risk and greenhouse gas emissions

COVID-19 have significant impact on travel behaviour and greenhouse gases (GHG), especially for the most affected city in India, Mumbai metropolitan region (MMR). The present study attempts to explore the risk on different modes of transportation and GHG emissions (based on change in travel behavior) during peak/non-peak hours in a day by an online/offline survey for commuters in Indian metropolitan cities like MMR, Delhi and Bengaluru. In MMR, the probability of infection in car estimated to be 0.88 and 0.29 during peak and non-peak hour, respectively, considering all windows open. The risk of infection in public transportation system such as in bus (0.307), train (0.521), and metro (0.26) observed to be lower than in private vehicles. Furthermore, impact of COVID-19 on GHG emissions have also been explored considering three scenarios. The GHG emissions have been estimated for base (3.83-16.87 tonne), lockdown (0.22-0.48 tonne) and unlocking (2.13-9.30 tonne) scenarios. It has been observed that emissions are highest during base scenario and lowest during lockdown situation. This study will be a breakthrough in understanding the impact of pandemic on environment and transportation. The study shall help transport planners and decision makers to operate public transport during pandemic like situation such that the modal share of public transportation is always highest. It shall also help in regulating the GHG emissions causing climate change.

A Study on Flow Field Characteristics and Air Purifier with Barrier Effects

An air curtain machine is used in the entrances and exits of public places where air conditioners are used. The high-speed centrifugal or axial fan blows out the air, creating an airflow barrier to prevent air convection inside and outside, reducing air conditioning losses, and maintaining the indoor air quality by preventing dust, insects, and harmful gases from entering the room. Observation of the airflow behavior was conducted using CFD simulation, to explore whether it has a blocking effect, and the air curtain principle was applied to the air purification equipment. It is mainly composed of several rows of arrayed hole air outlets to form a multi-composite air wall. The airflow on the two sides, or below, can be blocked by the composite air wall and integrated into the main airflow, so that the air walls will not affect each other, and form a barrier effect to prevent infection. This research includes the measurement of impedance characteristics for three layers of filters made of different materials. These filters are used as the input characteristic parameters in the simulation analysis. Four scenarios are discussed, including the consultation room, hospital ward, quarantine station, and conference room. From the simulation results, it is known that when there are many people, the equipment can be set to high speed to increase the volume of air, forming a wind wall to effectively block airflow from the people in the chairs, reducing the risk of infection. Note that the rotation speed should not be too high. The air outlet equipment is susceptible to turbulent flow, which will make the airflow deviate from the expected direction and increase the possibility of mutual infection between adjacent people. Partitions can be used to block airflow to reduce the risk of infection.

Impact of COVID-19 Pandemic on Air Quality: A Systematic Review

With the emergence of the COVID-19 pandemic, several governments imposed severe restrictions on socio-economic activities, putting most of the world population into a general lockdown in March 2020. Although scattered, studies on this topic worldwide have rapidly emerged in the literature. Hence, this systematic review aimed to identify and discuss the scientifically validated literature that evaluated the impact of the COVID-19 pandemic and associated restrictions on air quality. Thus, a total of 114 studies that quantified the impact of the COVID-19 pandemic on air quality through monitoring were selected from three databases. The most evaluated countries were India and China; all the studies intended to evaluate the impact of the pandemic on air quality, mainly concerning PM10, PM2.5, NO2, O3, CO, and SO2. Most of them focused on the 1st lockdown, comparing with the pre- and post-lockdown periods and usually in urban areas. Many studies conducted a descriptive analysis, while others complemented it with more advanced statistical analysis. Although using different methodologies, some studies reported a temporary air quality improvement during the lockdown. More studies are still needed, comparing different lockdown and lifting periods and, in other areas, for a definition of better-targeted policies to reduce air pollution.

Effects of the COVID-19 pandemic on public bus occupancy and real-world tailpipe emissions of gaseous pollutants per passenger kilometer traveled

Public buses typically have less emission per passenger kilometer traveled (PKT) than private cars and motorcycles, and the emission benefit of public buses increases with ridership. However, the drop in public bus usage during the novel coronavirus (COVID-19) pandemic could lead to an increase in air pollutant emissions per PKT, making the emission benefits of public buses questionable. This study investigated the effects of the COVID-19 pandemic on public bus occupancy rates in Taichung City, Taiwan, and also compared real-world emissions per PKT of carbon monoxide (CO), total hydrocarbons (THC), nitric oxide (NO), and carbon dioxide (CO₂) of a public bus before and during the pandemic. Mean bus occupancy rates were 11–25% on different bus routes before the pandemic, indicating that only a fourth or less of the bus passenger capacity was utilized. During the pandemic, mean bus occupancy rates dropped to 4–15%. Moreover, the public bus was less polluting based on CO and THC emissions than the car and motorcycle, even at the low passenger occupancy rates observed during the pandemic. However, NO and CO₂ emissions per PKT of the bus were remarkably higher during the pandemic than those of the car and motorcycle. Furthermore, we estimated the break-even passenger occupancy rate for buses as 15%, which was the minimum threshold occupancy rate below which the buses would be more polluting than cars and motorcycles in terms of CO, THC, and CO₂ emissions per PKT. Our findings will help transport management authorities and policymakers to optimize bus route designs and frequencies and implement anti-pandemic measures to maximize the environmental benefits of the public bus transit systems.

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.

Spatial Correlation of Air Pollution and Its Causes in Northeast China

To understand the status of air pollution in northeastern China, we explore the structure of air pollution transmission networks and propose targeted policy recommendations. Using air pollution data from 35 cities in northeastern China for a total of 879 periods from 6 January 2015 to 3 June 2017, this paper used social network analysis (SNA) to construct a spatial association network of air pollution in the region, and analyzed the spatial association of air pollution among cities and its causes in an attempt to reveal the transmission path of air pollution in the region. The results show that inter-city air pollution in northeast China forms a complex and stable correlation network with obvious seasonal differences of "high in winter and low in summer". Different cities in the region play the roles of "spillover", "intermediary" and "receiver" of air pollution in the network. Small respirable particulate (PM2.5) pollution constitutes a significant component of air pollution in northeast China, which spreads from Liaoning province to Heilongjiang province via Jilin province. Therefore, regional joint pollution prevention and control measures should be adopted to combat the air pollution problem, and different treatment measures should be developed for different city "roles" in the pollution network, in order to fundamentally solve the air pollution problem in the region.