Impacts of the COVID-19 responses on traffic-related air pollution in a Northwestern US city

Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP exposure.

The impact of low emission zones on personal exposure to ultrafine particles in the commuter environment

Auckland is a city with limited industrial activity, road traffic being the dominant source of air pollution. Thus, the time periods when social contact and movement in Auckland were severely curtailed due to COVID-19 restrictions presented a unique opportunity to observe impacts on pedestrian exposure to air pollution under a range of different traffic flow scenarios, providing insights into the impacts of potential future traffic calming measures. Pedestrian exposure to ultrafine particles (UFPs), was measured using personal monitoring along a customised route through Central Auckland during different COVID-19-affected traffic flow conditions. Results showed that reduced traffic flows led to statistically significant reductions in average exposure to UFP under all traffic reduction scenarios (TRS). However, the size of the reduction was variable in both time and place. Under the most stringent TRS (traffic reduction of 82 %), median ultrafine particle (UFP) concentrations reduced by 73 %. Under the less stringent scenario, the extent of reduction varied in time and space; a traffic reduction of 62 % resulted in a 23 % reduction in median UFP concentrations in 2020 but in 2021 similar traffic reductions led to a decrease in median UFP concentrations of 71 %. Under all scenarios, the magnitude of the impact of traffic reductions on UFP exposure varied along the route, with areas dominated by emissions from construction and ferry/port activities showing little correlation between traffic flow and exposure. Shared traffic spaces, previously pedestrianised, also recorded consistently high concentrations with little variability observed. This study provided a unique opportunity to assess the potential benefits and risks of such zones and to help decision-makers evaluate future traffic management interventions (such as low emissions zones). The results suggest that controlled traffic flow interventions can result in a significant reduction in pedestrian exposure to UFPs, but that the magnitude of reductions is sensitive to local-scale variations in meteorology, urban land use and traffic flow patterns.

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NO_x), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4-69%, 14-65%, and 21-97% of BC, NO_x, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26-46%, 17-63%, and -7-18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NO_x to CO and BC to NO_x emission ratios, which were between 0.21-0.32 μg m^-3 NO_x (μg m^-3 CO)^-1 and 0.013-0.018 μg m^-3 BC (μg m^-3 NO_x)^-1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes.Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Impact of traffic on air pollution in a mid-sized urban city during COVID-19 lockdowns

In this study, we evaluated the changes in air pollutant concentrations around Milwaukee, WI, during and after lockdown due to the COVID-19 pandemic for a period of 126 days. Measurements of particulate matter (PM1, PM2.5, and PM10), NH3, H2S, and O3 + NO2, were made on a 74-km route of arterial and highway roads from April to August 2020 using a Sniffer 4D sensor mounted to a vehicle. Traffic volume during measurement periods were estimated from smartphone-based traffic data. From lockdown (March 24, 2020-June 11, 2020) to post-lockdown (June 12, 2020-August 26, 2020) median traffic volume increased roughly 30-84%, depending upon the road type. In addition, increases in mean concentrations of NH3 (277%), PM (220-307%), and O3 + NO2 (28%) were also observed. For both traffic and air pollutants, abrupt changes in the data were observed mid-June, shortly after lockdown measures were lifted in Milwaukee County. Indeed, traffic was able to explain up to 57% of PM, 47% of NH3, and 42% of O3 + NO2 variance in pollutant concentrations on arterial and highway road segments. Two arterial roads that did not have statistically significant changes in traffic patterns during the lockdown exhibited no statistically significant trends between traffic and air quality parameters. This study demonstrated that COVID-19 lockdowns in Milwaukee, WI, caused significant decreases in traffic, which in turn had a direct impact on air pollutants. It also highlights the need for traffic volume and air quality data at relevant spatial and temporal scales for accurately assessing source apportionment of combustion-based air pollutants, which cannot be captured with typical ground-based sensor systems.

The long-term impact of coronavirus disease 2019 on environmental health: a review study of the bi-directional effect

BACKGROUND

When health systems worldwide grapple with the coronavirus disease 2019 (COVID-19) pandemic, its effect on the global environment is also a significant consideration factor. It is a two-way process where the pre-COVID climate factors influenced the landscape in which the disease proliferates globally and the consequences of the pandemic on our surroundings. The environmental health disparities will also have a long-lasting effect on public health response.

MAIN BODY

The ongoing research on the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and COVID-19 must also include the role of environmental factors in the process of infection and the differential severity of the disease. Studies have shown that the virus has created positive and negative ramifications on the world environment, especially in countries most critically affected by the pandemic. Contingency measures to slow down the virus, such as self-distancing and lockdowns have shown improvements in air, water, and noise quality with a concomitant decrease in greenhouse gas emissions. On the other hand, biohazard waste management is a cause for concern that can result in negative effects on planetary health. At the peak of the infection, most attention has been diverted to the medical aspects of the pandemic. Gradually, policymakers must shift their focus to social and economic avenues, environmental development, and sustainability.

CONCLUSION

The COVID-19 pandemic has profoundly impacted the environment, both directly and indirectly. On the one hand, the sudden halt in economic and industrial activities led to a decrease in air and water pollution, as well as a reduction in greenhouse gas emissions. On the other hand, the increased use of single-use plastics and a surge in e-commerce activities have had negative effects on the environment. As we move forward, we must consider the pandemic's long-term impacts on the environment and work toward a more sustainable future that balances economic growth and environmental protection. The study shall update the readers on the various facets of the interaction between this pandemic and environmental health with model development for long-term sustainability.

Role of vehicular emissions in urban air quality: The COVID-19 lockdown experiment

While the decrease in air pollutant concentration during the COVID-19 lockdown is well documented, neighborhood-scale and multi-city data have not yet been explored systematically to derive a generalizable quantitative link to the drop in vehicular traffic. To bridge this gap, high spatial resolution air quality and georeferenced traffic datasets were compiled for the city of London during three weeks with significant differences in traffic. The London analysis was then augmented with a meta-analysis of lower-resolution studies from 12 other cities. The results confirm that the improvement in air quality can be partially attributed to the drop of traffic density, and more importantly quantifies the elasticity (0.71 for NO₂ & 0.56 for PM_2.5) of their linkages. The findings can also inform on the future impacts of the ongoing shift to electric vehicles and micro-mobility on urban air quality.

Impact of the COVID-19 pandemic on air pollution from jet engines at airports in central eastern China

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NO_x) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.

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

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.

Changes in Ultrafine Particle Concentrations near a Major Airport Following Reduced Transportation Activity during the COVID-19 Pandemic

Mobility reductions following the COVID-19 pandemic in the United States were higher, and sustained longer, for aviation than ground transportation activity. We evaluate changes in ultrafine particle (UFP, Dp < 100 nm, a marker of fuel-combustion emissions) concentrations at a site near Logan Airport (Boston, Massachusetts) in relation to mobility reductions. Several years of particle number concentration (PNC) data prepandemic [1/2017-9/2018] and during the state-of-emergency (SOE) phase of the pandemic [4/2020-6/2021] were analyzed to assess the emissions reduction impact on PNC, controlling for season and wind direction. Mean PNC was 48% lower during the first three months of the SOE than prepandemic, consistent with 74% lower flight activity and 39% (local)-51% (highway) lower traffic volume. Traffic volume and mean PNC for all wind directions returned to prepandemic levels by 6/2021; however, when the site was downwind from Logan Airport, PNC remained lower than prepandemic levels (by 23%), consistent with lower-than-normal flight activity (44% below prepandemic levels). Our study shows the effect of pandemic-related mobility changes on PNC in a near-airport community, and it distinguishes aviation-related and ground transportation source contributions.

Significant reduction of ultrafine particle emission fluxes to the urban atmosphere during the COVID-19 lockdown

The worldwide restrictions of social contacts that were implemented in spring 2020 to slow down infection rates of the SARS-CoV-2 virus resulted in significant modifications in mobility behaviour of urban residents. We used three-year eddy covariance measurements of size-resolved particle number fluxes from an urban site in Berlin to estimate the effects of reduced traffic intensity on particle fluxes. Similar observations of urban surface-atmosphere exchange of size-resolved particles that focus on COVID-19 lockdown-related effects are not available, yet. Although the site remained a net emission source for ultrafine particles (UFP, D_p < 100 nm), the median upward flux of ultrafine particles (F_UFP) decreased from 8.78 × 10⁷ m^-2 s^-1 in the reference period to 5.44 × 10⁷ m^-2 s^-1 during the lockdown. This was equivalent to a relative reduction of -38 % for median F_UFP, which was similar to -35 % decrease of road traffic intensity in the flux source area during that period. The size-resolved analysis demonstrated that, on average, net deposition of UFP occurred only during night when particle emission source strength by traffic was at its minimum, whereas accumulation mode particles (100 nm < D_p < 200 nm) showed net deposition also during daytime. The results indicate the benefits of traffic reductions as a mitigation strategy to reduce UFP emissions to the urban atmosphere.

Widespread Wildfires Over the Western United States in 2020 Linked to Emissions Reductions During COVID-19

Widespread wildfires struck the western United States in 2020, damaging properties and threating human lives. Meanwhile, the COVID-19 pandemic spread across the globe, which disrupted human activities. Here, we investigate the effects of the emissions reductions during the pandemic on fire weather in 2020 over the western United States by using an earth system model together with observations. We show that reductions in aerosols dominate the increases in wildfire risks, whereas greenhouse gas decrease counteracts this influence. The aerosol emissions reductions increased surface air temperature and decreased precipitation and relative humidity due to a weakened moisture transport, which explains one-third of the observed increase in wildfire risks during August-November over the western United States in 2020. This study suggests that COVID-19-related emissions reductions have an unexpected influence on wildfires, highlighting a different but important role of human activities in affecting wildfire risks.

Autoantibodies and autoimmune disorders in SARS-CoV-2 infection: pathogenicity and immune regulation

Coronavirus disease 2019 (COVID-19) is an infectious disease associated with the respiratory system caused by the SARS-CoV-2 virus. The aim of this review article is to establish an understanding about the relationship between autoimmune conditions and COVID-19 infections. Although majority of the population have been protected with vaccines against this virus, there is yet a successful curative medication for this disease. The use of autoimmune medications has been widely considered to control the infection, thus postulating possible relationships between COVID-19 and autoimmune diseases. Several studies have suggested the correlation between autoantibodies detected in patients and the severity of the COVID-19 disease. Studies have indicated that the SARS-CoV-2 virus can disrupt the self-tolerance mechanism of the immune system, thus triggering autoimmune conditions. This review discusses the current scenario and future prospects of promising therapeutic strategies that may be employed to regulate such autoimmune conditions.

NOx and O3 Trends at U.S. Non-Attainment Areas for 1995-2020: Influence of COVID-19 Reductions and Wildland Fires on Policy-Relevant Concentrations

We analyzed NO₂ and O₃ data from 32 U.S. non-attainment areas (NAAs) for 1995-2020. Since 1995, all regions have shown steady reductions in NO₂ and the weekend-weekday pattern indicates that the O₃ production regime in most NAAs has transitioned to a NO_x-limited regime, while a few NAAs remain NO_x-saturated. In the eastern U.S., all NAAs have made steady progress toward meeting the current (70 ppb) O₃ standard, but this is less true in midwestern and western NAAs, with most showing little improvement in peak O₃ concentrations since about 2010. Due to COVID-19 restrictions, NO₂ concentrations were substantially reduced in 2020. In the eastern NAAs, we see significant reductions in both NO₂ and peak O₃ concentrations. In the midwestern U.S., results were more variable, with both higher and lower O₃ values in 2020. In the western U.S. (WUS), we see variable reductions in NO₂ but substantial increases in O₃ at most sites, due to the influence from huge wildland fires. The recent pattern over the past decade shows that the large amount of wildland fires has a strong influence on the policy-relevant O₃ metric in the WUS, and this is making it more difficult for these regions to meet the O₃ standard.

Changes in Sewage Sludge Chemical Signatures During a COVID-19 Community Lockdown, Part 1: Traffic, Drugs, Mental Health, and Disinfectants

The early months of the COVID-19 pandemic and the associated shutdowns disrupted many aspects of daily life and thus caused changes in the use and disposal of many types of chemicals. While records of sales, prescriptions, drug overdoses, and so forth provide data about specific chemical uses during this time, wastewater and sewage sludge analysis can provide a more comprehensive overview of chemical changes within a region. We analyzed primary sludge from a wastewater-treatment plant in Connecticut, USA, collected March 19 to June 30, 2020. This time period encompassed the first wave of the pandemic, the initial statewide stay at home order, and the first phase of reopening. We used liquid chromatography-high-resolution mass spectrometry and targeted and suspect screening strategies to identify 78 chemicals of interest, which included pharmaceuticals, illicit drugs, disinfectants, ultraviolet (UV) filters, and others. We analyzed trends over time for the identified chemicals using linear trend analyses and multivariate comparisons (p < 0.05). We found trends related directly to the pandemic (e.g., hydroxychloroquine, a drug publicized for its potential to treat COVID-19, had elevated concentrations in the week following the implementation of the US Emergency Use Authorization), as well as evidence for seasonal changes in chemical use (e.g., increases for three UV-filter compounds). Though wastewater surveillance during the pandemic has largely focused on measuring severe acute respiratory syndrome-coronavirus-2 RNA concentrations, chemical analysis can also show trends that are important for revealing the public and environmental health effects of the pandemic. Environ Toxicol Chem 2022;41:1179-1192. © 2021 SETAC.

Changes in Sewage Sludge Chemical Signatures During a COVID-19 Community Lockdown, Part 1: Traffic, Drugs, Mental Health, and Disinfectants

The early months of the COVID-19 pandemic and the associated shutdowns disrupted many aspects of daily life and thus caused changes in the use and disposal of many types of chemicals. While records of sales, prescriptions, drug overdoses, and so forth provide data about specific chemical uses during this time, wastewater and sewage sludge analysis can provide a more comprehensive overview of chemical changes within a region. We analyzed primary sludge from a wastewater-treatment plant in Connecticut, USA, collected March 19 to June 30, 2020. This time period encompassed the first wave of the pandemic, the initial statewide stay at home order, and the first phase of reopening. We used liquid chromatography-high-resolution mass spectrometry and targeted and suspect screening strategies to identify 78 chemicals of interest, which included pharmaceuticals, illicit drugs, disinfectants, ultraviolet (UV) filters, and others. We analyzed trends over time for the identified chemicals using linear trend analyses and multivariate comparisons (p < 0.05). We found trends related directly to the pandemic (e.g., hydroxychloroquine, a drug publicized for its potential to treat COVID-19, had elevated concentrations in the week following the implementation of the US Emergency Use Authorization), as well as evidence for seasonal changes in chemical use (e.g., increases for three UV-filter compounds). Though wastewater surveillance during the pandemic has largely focused on measuring severe acute respiratory syndrome-coronavirus-2 RNA concentrations, chemical analysis can also show trends that are important for revealing the public and environmental health effects of the pandemic. Environ Toxicol Chem 2022;41:1179-1192. © 2021 SETAC.

Measuring the effect of fireworks on air quality in Minneapolis, Minnesota

Abstract

Air quality was measured before, during, and after a 4th of July fireworks display in downtown Minneapolis, Minnesota using a mix of low-cost sensors (CO, CO2, and NO) for gases and portable moderate cost instruments for particle measurements (PM2.5, lung deposited surface area, and number weighted particle size distributions). Meteorological conditions—temperature, humidity, and vertical temperature profile were also monitored. Concentrations of particles and most gaseous species peak between 10 pm and midnight on July 4th, decrease in the middle of the night but increase again and by between 6 and 7 am reach concentrations as high or higher than during fireworks. This overnight increase is likely due to a temperature inversion trapping emissions. Between 10 pm and midnight on July 4th the measures of particle concentration increase by 180–600% compared to the same period on July 3rd. Particle size distributions are strongly influenced by fireworks, shifting from traffic-like bimodal distributions before to a nearly unimodal distribution dominated by a large accumulation mode during and after. The shape of the size distribution measured during the early morning peak is nearly identical to that observed during fireworks, suggesting that the early morning peak is mainly due to trapped fireworks emissions not early morning traffic. Gaseous species are less strongly influenced by fireworks than particles. Comparing measurements made between 10 pm and midnight on July 4th and the same period on July 3rd, the concentration of CO increases 32% while the CO2 increases only 2% but increases by another 15% overnight. The NO concentration behaves oddly, decreasing during fireworks, but then recovering the next morning, more than doubling overnight. Our measurements of CO, NO, and PM2.5 are compared with those made at the nearest (~ 2 km away) Minnesota Pollution Control Agency Air Monitoring Station. Their NO results are quite different from ours with much lower concentrations before fireworks, a distinct peak during, followed by a strong overnight increase and an early morning peak somewhat similar in shape and concentration to ours. These differences are likely due mainly to malfunction of our low-cost NO sensor. Concentrations of CO and PM2.5 track ours within 25% but peak shapes are somewhat different, which is not unexpected given the spatial separation of the measurements.

Article highlights

Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing-Tianjin-Hebei (BTH), Chengdu-Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.

Adapting Urban Transport Planning to the COVID-19 Pandemic: An Integrated Fermatean Fuzzy Model

The critical worldwide problem of adapting urban transport planning to COVID-19 is for the first time comprehensively addressed and solved in this study. It primarily aims to help transport planners increase the resilience of transport systems. Firstly, a multi-level decision-making hierarchy structure based on four main criteria and 17 sub-criteria is introduced for relevant stakeholders to provide a practical framework for assessing existing transport plans. Then, a three-stage integrated Fermatean fuzzy model for adapting urban transport planning to the pandemic is presented. The model hybridizes the method based on the removal effects of criteria (MEREC) and combined compromise solution (CoCoSo) method into a unique methodological framework under the Fermatean fuzzy environment. A case study provides decision-making guidelines on how to adapt transport plans to COVID-19 in the real-world context of Belgrade, Serbia. The research findings show that the pandemic significantly changed the priorities of transport planning strategies and measures. "Non-motorized travel" is now the best alternative since its numerous short-term measures lead to better transport service. The major advantages of the introduced model are higher flexibility and a more precise fusion of experts' preference information. The integrated Fermatean fuzzy model could be used for adapting other emerging problems to COVID-19.

Impacts of COVID-19 on Air Quality through Traffic Reduction

In 2020, the first case of COVID-19 was confirmed in Korea, and social distancing was implemented to prevent its spread. This reduced the movement of people, and changes in air quality were expected owing to reduced emissions. In the present paper, the impact of traffic volume change caused by COVID-19 on air quality in Seoul, Korea, is examined. Two regression analyses were performed using the generalized additive model (GAM), assuming a Gaussian distribution; the relationships between (1) the number of confirmed COVID-19 cases in 2020-2021 and the rate of change in the traffic volume in Seoul, and (2) the traffic volume and the rate of change in the air quality in Seoul from 2016 to 2019 were analyzed. The regression results show that traffic decreased by 0.00431% per COVID-19 case; when traffic fell by 1%, the PM₁₀, PM_2.5, CO, NO₂, O₃, and SO₂ concentrations fell by 0.48%, 0.94%, 0.39%, 0.74%, 0.16%, and -0.01%, respectively. This mechanism accounts for air quality improvements in PM₁₀, PM_2.5, CO, NO₂, and O₃ in Seoul during 2020-2021. From these results, the majority of the reduction in pollutant concentrations in 2020-2021 appears to be the result of a long-term declining trend rather than COVID-19.

Changes in air quality in-taxis and in working conditions of taxi drivers pre- and post-lockdown, during the COVID-19 pandemic in the Paris area

We evaluated the impact of the lockdown restriction measures in the Paris area on the variation of in-vehicle ultrafine particle (UFP) and black carbon (BC) concentrations between the pre- and post-lockdown period and professional drivers' working conditions and practices. The study was conducted with 33 taxi drivers. UFP and BC were measured inside their vehicles with DiSCmini^® and microAeth^® , respectively, on two typical working days pre- and post-lockdown. Job characteristics were self-reported. Our results showed that post-lockdown, both the number of clients and journey duration significantly decreased. Taxi drivers opened their windows significantly more and reduced the use of air recirculation. UFP decreased significantly by 32% and BC by 31% post-lockdown, with a weaker positive correlation compared to pre-lockdown. The reduction of in-vehicle UFP was due mainly to the reduction of traffic flow and ventilation settings, though the latter probably varied according to traffic conditions. The variation of in-vehicle BC also tended to be related to the decrease in traffic flow post-lockdown. We emphasize the role of traffic emissions on in-vehicle air pollution and that preventive measures such ventilation settings would help to minimize the exposure of professional drivers and passengers to air pollutants.

Driving factors to air pollutant reductions during the implementation of intensive controlling policies in 2020 in Ulsan, South Korea

Evaluation for the controlling policy's effectiveness to mitigate criteria air pollutants (CAPs) in South Korea during December 1, 2019-March 31, 2020 is difficult because of its coincidence with the COVID-19 social distancing. In this study, we differentiated the influence of three major driving factors (intensive controlling policy by the government, meteorological conditions, and social distancing) to the CAP variation in Ulsan, the largest industrial city in South Korea. In 2013-2019, the concentrations of PM_2.5 (2015-2019), PM₁₀, SO₂, and NO₂ decreased by 6.7, 1.6, 4.2, and 3.3%/year, respectively, whereas the O₃ concentration slightly increased by 0.7%/year. Trend analysis was used to predict the CAP concentrations before (January 1-February 21) and during (February 22-March 31) the social distancing in 2020. The difference between the measured and predicted concentrations was designated as the contribution of the three factors. The controlling policy was the most important driver of the CAP reductions. In particular, its contributions were 94.1% (January 1-February 21) and 87.4% (February 22-March 31) to the PM_2.5 decrease. The change in meteorological conditions considerably affected the CAP reductions, with the highest contributions of 35.2% (January 1-February 21) and 39.2% (February 22-March 31) to the O₃ decrease. On February 22-March 31, the effects of social distancing were 1.6, 0.6, 1.3, and 1.4% to the reduction of SO₂, NO₂, PM₁₀, and PM_2.5, respectively. Overall, a decrease in the CAP concentrations was apparent during January-March 2020 in Ulsan primarily due to the intensive controlling policies, not by the COVID-19 social distancing.

Impact of COVID-19 Pandemic Preventing Measures and Meteorological Conditions on the Atmospheric Air Composition in Moscow in 2020

Changes in the atmospheric composition during different periods of 2020 in Moscow which were associated with the COVID-19 pandemic preventing measures as well as corresponding pollutant emission reduction, are investigated. Surface concentrations of nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), aerosol fraction (PM₁₀), and meteorological parameters during different periods of 2020 were compared with similar data for the previous five years. The analysis of ground-based measurements, as well as of high-resolution satellite distributions of CO and NO₂ indicated that the concentration of major pollutants and its spatial distribution in the Moscow region were significantly affected by both restrictive measures and abnormal meteorological conditions in 2020.

Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States

Lockdown measures implemented in response to the COVID-19 pandemic produced sudden behavioral changes. We implement counterfactual time series analysis based on seasonal autoregressive integrated moving average models (SARIMA), to examine the extent of air pollution reduction attained following state-level emergency declarations. We also investigate whether these reductions occurred everywhere in the US, and the local factors (geography, population density, and sources of emission) that drove them. Following state-level emergency declarations, we found evidence of a statistically significant decrease in nitrogen dioxide (NO2) levels in 34 of the 36 states and in fine particulate matter (PM2.5) levels in 16 of the 48 states that were investigated. The lockdown produced a decrease of up to 3.4 µg/m3 in PM2.5 (observed in California) with range (- 2.3, 3.4) and up to 11.6 ppb in NO2 (observed in Nevada) with range (- 0.6, 11.6). The state of emergency was declared at different dates for different states, therefore the period "before" the state of emergency in our analysis ranged from 8 to 10 weeks and the corresponding "after" period ranged from 8 to 6 weeks. These changes in PM2.5 and NO2 represent a substantial fraction of the annual mean National Ambient Air Quality Standards (NAAQS) of 12 µg/m3 and 53 ppb, respectively. As expected, we also found evidence that states with a higher percentage of mobile source emissions (obtained from 2014) experienced a greater decline in NO2 levels after the lockdown. Although the socioeconomic restrictions are not sustainable, our results provide a benchmark to estimate the extent of achievable air pollution reductions. Identification of factors contributing to pollutant reduction can help guide state-level policies to sustainably reduce air pollution.

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO₂ has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO₂ mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m³) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

Exposure to ultrafine particles while walking or bicycling during COVID-19 closures: A repeated measures study in Copenhagen, Denmark

Ultrafine particles (UFP; particulate matter <0.1 μm diameter) emitted from motorized traffic may be highly detrimental to health. Active mobility (walking, bicycling) is increasingly encouraged as a way to reduce traffic congestion and increase physical activity levels. However, it has raised concerns of increased exposure to UFP, due to increased breathing rates in traffic microenvironments, immediately close to their source. The recent Coronavirus Disease 2019 (COVID-19) societal closures reduced commuting needs, allowing a natural experiment to estimate contributions from motorized traffic to UFP exposure while walking or bicycling. From late-March to mid-July 2020, UFP was repeatedly measured while walking or bicycling, capturing local COVID-19 closure ('Phase 0') and subsequent phased re-opening ('Phase 1', '2', '2.1' & '3'). A DiSCmini continuously measured particle number concentration (PNC) in the walker/bicyclist's breathing zone. PNC while walking or bicycling was compared across phased re-openings, and the effect of ambient temperature, wind speed and direction was determined using regression models. Approximately 40 repeated 20-minute walking and bicycling laps were made over 4 months during societal re-opening phases related to the COVID-19 pandemic (late-March to mid-July 2020) in Copenhagen. Highest median PNC exposure of both walking (13,170 pt/cm³, standard deviation (SD): 3560 pt/cm³) and bicycling (21,477 pt/cm³, SD: 8964) was seen during societal closures (Phase 0) and decreased to 5367 pt/cm³ (SD: 2949) and 8714 pt/cm³ (SD: 4309) in Phase 3 of re-opening. These reductions in PNC were mainly explained by meteorological conditions, with most of the deviation explained by wind speed (14-22%) and temperature (10-13%). Highest PNC was observed along major roads and intersections. In conclusion, we observed decreases in UFP exposure while walking and bicycling during societal re-opening phases related to the COVID-19 pandemic, due largely to meteorological factors (e.g., wind speed and temperature) and seasonal variations in UFP levels.

Traffic-induced atmospheric pollution during the COVID-19 lockdown: Dispersion modeling based on traffic flow monitoring in Turin, Italy

The COVID-19 pandemic, as a worldwide threat to public health, has led many governments to impose mobility restrictions and adopt partial or full lockdown strategies in many regions to control the disease outbreak. Although these lockdowns are imposed to save public health by reducing the transmission of the virus, rather significant improvements of the air quality in this period have been reported in different areas, mainly as a result of the reduction in vehicular trips. In this research, the city of Turin in the northern part of Italy has been considered as the study area, because of its special meteorology and geographic location in one of the most polluted regions in Europe, and also its high density of vehicular emissions. A Lagrangian approach is applied to illustrate and analyze the effect of imposing full lockdown restrictions on the reduction of traffic-induced air pollution in the city. To do this, the real-time traffic flow during the lockdown period is recorded, and by utilizing CALPUFF version 7, the dispersion of PM_2.5, Total Suspended Particulate (TSP), Benzo(a)pyrene (BaP), NO_x, and Black Carbon (BC) emitted from all circulating vehicles during and before the lockdown period are compared. Results indicate that the concentration of pollutants generated by road traffic sources (including passenger cars, busses, heavy-duty vehicles, light-duty vehicles, mopeds, and motorcycles) reduced at least 70% (for PM_2.5) up to 88.1% (for BaP) during the studied period. Concentration maps show that the concentration reduction varied in different areas of the town, mainly due to the characteristics and strength of the emission sources and the geophysical features of the area.

Air quality changes in a Central European city during COVID-19 lockdown

A comparison of mobile and stationary air quality measurements in Lublin, Poland during the COVID-19 lockdown in 2020 and in a comparable period in 2017 has demonstrated that a substantial decrease of the traffic intensity by more than 50%, especially during certain times of the day in the lockdown period has only been partially reflected in the air quality improvement in the city. Mobile measurements carried out during six runs within a 24-hour period in 2017 and 2020 indicated a decrease of the average PM_2.5 and PM₁₀ concentrations by ~ 30% and ~14%, respectively. In turn, stationary measurement results obtained for the same periods demonstrated their increase by respectively ~35% and ~106% and a decrease in the average NO₂, NO_x, C₆H₆ and CO concentrations. This could have been impacted by meteorological factors and emissions from other, non-traffic-related sources, mainly from residential coal burning. The changes in the vehicle fleet structure could also have played a role.

Spatial-Temporal Patterns of Air Pollutant Emissions From Landing and Take-Off Cycles in the Yangtze River Delta of China During the COVID-19 Outbreak

The global aviation industry has been experiencing catastrophic disruption since the beginning of 2020 due to the unprecedented impact of the COVID-19 pandemic on air traffic. Although the decline in regular commercial air travel has caused tremendous economic loss to aviation stakeholders, it has also led to the reduction in the amount of recorded air pollutants. Most of the aircraft emissions are released during the cruise phase of flight, however they have relatively small impact on humans due to the fact that those emissions are released directly into the upper troposphere and lower stratosphere. Therefore, the scope of this study is to investigate the ground-level aircraft emissions from landing and take-off (LTO) cycles, as they have a greater influence on the ambient environment of the airports in a specific region. In this paper, we study the variation of typical air pollutant concentrations (i.e., HC, CO, and NO_x) from the LTO cycles during the outbreak of COVID-19 pandemic in both temporal and spatial scales. These ground-level emissions are estimated for the 22 airports in the Yangtze River Delta, China. The results indicate that the variation pattern of the three air pollutants were significantly influenced by the dramatic onset of the COVID-19 pandemic, as well as the pertinent policies to suppress the spread of the virus. The results also reveal non-uniform distribution of the emission quantified at different airports. It is noticeable that the emission quantity generally declined from the east coast to the central and western part of the research region. Furthermore, discrepancies in the target markets also create disparities in the variation pattern of the emissions at different airports under the context of COVID-19.

Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe

The lockdown and related measures implemented by many European countries to stop the spread of the SARS-CoV-2 virus (COVID-19) pandemic have altered the economic activities and road transport in many cities. To rigorously evaluate how these measures have affected air quality in Europe, we developed Bayesian spatio-temporal (BST) models that assess changes in the surface nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) concentration across the continent. We fitted BST models to measurements of the two pollutants in 2020 using a lockdown indicator covariate, while accounting for the spatial and temporal correlation present in the data. Since other factors, such as weather conditions, local combustion sources and/or land surface characteristics may contribute to the variation of pollutant concentrations, we proposed two model formulations that allowed the differentiation between the variations in pollutant concentrations due to seasonality from the variations associated to the lockdown policies. The first model compares the changes in 2020, with the ones during the same period in the previous five years, by introducing an offset term, which controls for the long-term average concentrations of each pollutant during 2014–2019. The second approach models only the 2020 data, but adjusts for confounding factors. The results indicated that the latter can better capture the lockdown effect. The measures taken to tackle the virus in Europe reduced the average surface concentrations of NO₂ and PM_2.5 by 29.5% (95% Bayesian credible interval: 28.1%, 30.9%) and 25.9% (23.6%, 28.1%), respectively. To our knowledge, this research is the first to account for the spatio-temporal correlation present in the monitoring data during the pandemic and to assess how it affects estimation of the lockdown effect while accounting for confounding. The proposed methodology improves our understanding of the effect of COVID-19 lockdown policies on the air pollution burden across the continent.

Changes in air quality in Mexico City, London and Delhi in response to various stages and levels of lockdowns and easing of restrictions during COVID-19 pandemic

The impacts of COVID-19 lockdown restrictions have provided a valuable global experiment into the extent of improvements in air quality possible with reductions in vehicle movements. Mexico City, London and Delhi all share the problem of air quality failing WHO guideline limits, each with unique situations and influencing factors. We determine, discuss and compare the air quality changes across these cities during the COVID-19, to understand how the findings may support future improvements in their air quality and associated health of citizens. We analysed ground-level PM₁₀, PM_2.5, NO₂, O₃ and CO changes in each city for the period 1st January to August 31, 2020 under different phases of lockdown, with respect to daily average concentrations over the same period for 2017 to 2019. We found major reductions in PM₁₀, PM_2.5, NO₂ and CO across the three cities for the lockdown phases and increases in O₃ in London and Mexico City but not Delhi. The differences were due to the O₃ production criteria across the cities, for Delhi production depends on the VOC-limited photochemical regime. Levels of reductions were commensurate with the degree of lockdown. In Mexico City, the greatest reduction in measured concentration was in CO in the initial lockdown phase (40%), in London the greatest decrease was for NO₂ in the later part of the lockdown (49%), and in Delhi the greatest decrease was in PM₁₀, and PM_2.5 in the initial lockdown phase (61% and 50%, respectively). Reduction in pollutant concentrations agreed with reductions in vehicle movements. In the initial lockdown phase vehicle movements reduced by up to 59% in Mexico City and 63% in London. The cities demonstrated a range of air quality changes in their differing geographical areas and land use types. Local meteorology and pollution events, such as forest fires, also impacted the results.

Improvement of atmospheric pollution in the capital cities of US during COVID-19

The spread of COVID-19 during 2020 impacted the whole world and still affecting the lives of people living in some parts of the world. The spread of this epidemic started in the US in late March 2020 and became a major issue in April due to an outburst of COVID-19 cases. Most of the countries in the world imposed complete to partial lockdown, but in the US, few states imposed lockdowns. Even after the advisory of the various Government department, the mobility data suggest that there was an enhancement (10–15%) in mobility during March 2020. Later sudden drop in mobility was observed during April 2020. The fall in aerosols optical depth (AOD), particulate matter concentration, NO_2, and Ozone are observed along with the positive shifts in the SO₂. In some of the states, AOD shows pronounced decline during May and June (5–40.90%), in the month of  May more than 80% decline was observed compared to the month of June 2020. In the month of April 2020, up to 73.64% decline was observed in NO_2, and 70–99% in the months of May and June 2020. We found a good relationship between the mobility data and improvement in the air quality of the US. The changes were not significant compared to other countries in the world due to scattered lockdown policy, but in the US a pronounced change is observed during April month compared to March and May.

The Burden of Disease Due to Road Traffic Noise in Hesse, Germany

Road-traffic-noise exposition is widespread in Germany and can have harmful health effects. As guidance for informed decision-making, we estimated the environmental burden of disease attributable to road-traffic noise in Hesse, Germany as disability-adjusted life-years (DALYs). Using detailed road-traffic-noise exposure data provided by the Hessian Agency for Nature Conservation, Environment, and Geology (HLNUG), we calculated the DALYs due to road-traffic noise > 40 dB(A) L_24h (unweighted average 24 h noise level) and other noise metrics for endpoints with known dose-response functions and evidence in the literature (NORAH-study on disease risks and WHO reviews): cardiovascular disease, depressive disorders, road-traffic annoyance, and sleep disturbance. We calculated the population-attributable fractions (PAF) for road-noise-related cardiovascular disease (hypertensive heart disease, ischemic heart disease, and stroke) and depressive disorders in the population using published relative risk estimates. We multiplied the PAFs with the Hessian proportion of the 2015 WHO DALY estimates for Germany in people aged ≥ 40 years. For high annoyance and high sleep disturbance, we used published dose-response functions to determine the burden for residents of all ages. For Hesse, we found a total of 26,501 DALYs attributable to road-traffic noise or 435 DALY per 100,000 persons for the reference year, 2015. Further, we estimated that a hypothetic uniform road-traffic-noise reduction of 3 dB would prevent 23% of this burden of disease.

COVID-19 lockdown and particle exposure of road users

INTRODUCTION

In 2020, due to the outbreak of COVID-19, there has been an unprecedented decrease in road traffic in almost all urbanized areas around the globe. This has undoubtedly affected the ambient air quality.

METHODS

In this study mobile and fixed-site measurements of aerosol particle concentrations in the ambient air in one of the busiest streets in Lublin, a mid-sized city in Central Europe (Poland) during the COVID-19 lockdown in the spring of 2020 were performed. Based on the measurements particle doses received by road users during different times of the day were assessed. The obtained results were compared with corresponding pre-COVID-19 measurements also performed in the spring which were available only from 2017.

RESULTS

During lockdown the mass concentration of traffic-related submicrometer PM1 particles and number concentration of ultrafine PN0.1 particles was significantly reduced. This resulted in a decrease of doses inhaled by road users as well as of particle doses deposited in their respiratory tracks. The greatest reductions of respectively over 2 times and over 5 times were observed during the day for total particles and traffic-related particles. Smaller reductions indicating the existence of relatively intensive non-traffic emissions were reported at night.

CONCLUSIONS

Substantial decrease in traffic intensity in the city caused by lockdown restrictions resulted in a significant reduction in the concentration of vehicle-generated particles in the ambient air. This in turn could have resulted in smaller doses inhaled by the inhabitants, specifically road users, which should have a positive impact on their health.

Threshold effects of COVID-19-confirmed cases on change in pollutants changes: evidence from the Chinese top ten cities

A more comprehensive understanding of the impact of the COVID-19 pandemic on changes in pollution could serve us to better deal with the environmental challenges caused by the pandemic. Existing studies mainly focused on the linear impact of the pandemic on the pollutants without considering the impact of other factors. To fill the research gap, the nonlinear relationship between pandemic and pollutants with considering the temperature factor was explored by developing panel threshold regression approach. In the proposed approach, the number of confirmed cases was set as explanatory variable, concentrations of NO2 and PM2.5 were set as explained variables, temperature was used as threshold variable, and other air pollution indicators were used as control variables. The results showed that there is a threshold effect between the changes in confirmed COVID-19 cases and the concentrations of PM2.5 and NO2, confirming the impact of the pandemic on pollutions was nonlinear. The results also show that the negative impact of pandemic on pollution increased when the temperature was rising. This work had theoretical and practical significance. The nonlinear research perspective of this article provided a methodological reference for exploring the relationship between epidemic and pollutant-related variables. Furthermore, this study expanded the scope of application of the threshold panel regression model and enriched the quantitative analysis of epidemics and pollutants.

Effect of COVID-19-Related Lockdown οn Hospital Admissions for Asthma and COPD Exacerbations: Associations with Air Pollution and Patient Characteristics

We conducted a retrospective observational study to assess the hospitalization rates for acute exacerbations of asthma and COPD (chronic obstructive pulmonary disease) during the first imposed lockdown in Athens, Greece. Patient characteristics and the concentration of eight air pollutants [namely, NO (nitrogen monoxide), NO2 (nitrogen dioxide), CO (carbon monoxide), PM2.5 (particulate matter 2.5), PM10 (particulate matter 10), O3 (ozone), SO2 (sulfur dioxide) and benzene] were considered. A total of 153 consecutive hospital admissions were studied. Reduced admissions occurred in the Lockdown period compared to the Pre-lockdown 2020 (p < 0.001) or the Control 2019 (p = 0.007) period. Furthermore, the concentration of 6/8 air pollutants positively correlated with weekly hospital admissions in 2020 and significantly decreased during the lockdown. Finally, admitted patients for asthma exacerbation during the lockdown were younger (p = 0.046) and less frequently presented respiratory failure (p = 0.038), whereas patients with COPD presented higher blood eosinophil percentage (p = 0.017) and count (p = 0.012). Overall, admissions for asthma and COPD exacerbations decreased during the lockdown. This might be partially explained by reduction of air pollution during this period while medical care avoidance behavior, especially among elderly patients cannot be excluded. Our findings aid in understanding the untold impact of the pandemic on diseases beyond COVID-19, focusing on patients with obstructive diseases.

The effect of environmental diesel exhaust pollution on SARS-CoV-2 infection: The mechanism of pulmonary ground glass opacity

Diesel exhaust particles (DEP) are the major components of atmospheric particulate matter (PM) and chronic exposure is recognized to enhance respiratory system complications. Although the spread of SARS-CoV-2 was found to be associated with the PMs, the mechanism by which exposure to DEP increases the risk of SARS-CoV-2 infection is still under discussion. However, diesel fine PM (dPM) elevate the probability of SARS-CoV-2 infection, as it coincides with the increase in the number of ACE2 receptors. Expression of ACE2 and its colocalized activator, transmembrane protease serine 2 (TMPRSS2) facilitate the entry of SARS-CoV-2 into the alveolar epithelial cells exposed to dPM. Thus, the coexistence of PM and SARS-CoV-2 in the environment augments inflammation and exacerbates lung damage. Increased TGF-β1 expression due to DEP accompanies the proliferation of the extracellular matrix. In this case, "multifocal ground-glass opacity" (GGO) in a CT scan is an indication of a cytokine storm and severe pneumonia in COVID-19.

How changes in human activities during the lockdown impacted air quality parameters: A review

The health emergency linked to the spread of COVID-19 has led to important reduction in industrial and logistics activities, as well as to a drastic changes in citizens' behaviors and habits. The restrictions on working activities, journeys and relationships imposed by the lockdown have had important consequences, including for environmental quality. This review aims to provide a structured and critical evaluation of the recent scientific bibliography that analyzed and described the impact of lockdown on human activities and on air quality. The results indicate an important effect of the lockdown during the first few months of 2020 on air pollution levels, compared to previous periods. The concentrations of particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide have decreased. Tropospheric ozone, on the other hand, has significantly increased. These results are important indicators that can become decision drivers for future policies and strategies in industrial and logistics activities (including the mobility sector) aimed at their environmental sustainability. The scenario imposed by COVID-19 has supported the understanding of the link between the reduction of polluting emissions and the state of air quality and will be able to support strategic choices for the future sustainable growth of the industrial and logistics sector.

Impact of COVID-19 Social Distancing Policies on Traffic Congestion, Mobility, and NO2 Pollution

Lockdowns implemented during the COVID-19 pandemic were utilized to evaluate the associations between “social distancing policies” (SDPs), traffic congestion, mobility, and NO2 air pollution. Spatiotemporal linear mixed models were used on city-day data from 22 US cities to estimate the associations between SDPs, traffic congestion and mobility. Autoregressive integrated moving average models with Fourier terms were then used on historical data to forecast expected 2020 NO2. Time series models were subsequently employed to measure how much reductions in local traffic congestion were associated with lower-than-forecasted 2020 NO2. Finally, the equity of NO2 pollution was assessed with community-level sociodemographics. When cities’ most stringent SDPs were implemented, they observed a 23.47 (95% CI: 18.82–28.12) percent reduction in average daily congestion and a 13.48 (95% CI: 10.36–16.59) percent decrease in average daily mobility compared to unrestricted days. For each standard deviation (8.38%) reduction in local daily congestion, average daily NO2 decreased by 1.37 (95% CI: 1.24–1.51) parts per billion relative to its forecasted value. Citizenship, education, and race were associated with elevated absolute NO2 pollution levels but were not detectibly associated with reductions in 2020 NO2 relative to its forecasted value. This illustrates the immediate behavioral and environmental impacts of local SDPs during the COVID-19 pandemic.

Field measurements of PM2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%-78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h-1 to 1.41 (3.18) h-1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h-1 to 0.49 (0.47) h-1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

The impact of the SARS-CoV-2 pandemic on referral characteristics in a national tertiary spinal injuries unit

Background

The SARS-CoV-2 pandemic has had profound implications on healthcare institutions.

Aims

This study aims to assess and compare referral patterns during COVID-19 to corresponding dates for the preceding 3 years (2017–2019), in order to preemptively coordinate the logistics of the surgical unit for similar future experiences.

Methods

Retrospective review for our institution, a national tertiary referral centre for spine pathology. Two distinct time-points were chosen to represent the varied levels of social restriction during the current pandemic: (i) study period 1 (SP1) from 11 November 2020 to 08 June 2020 represents a national lockdown, and (ii) study period 2 (SP2) from 09 June 2020 to 09 September 2020 indicates an easing of restrictions. Both periods were compared to corresponding dates (CP1: 11 March–08 June and CP2 09 June–09 September) for the preceding 3 years (2017–2019). Data collected included age, gender, and mechanism of injury (MOI) for descriptive analyses. MOIs were categorised into disc disease, cyclist, road-traffic-accident (RTA), falls < 2 m, falls > 2 m, malignancy, sporting injuries, and miscellaneous.

Results

All MOI categories witnessed a reduction in referral numbers during SP1: disc disease (−29%), cyclist (−5%), RTAs (−66%), falls < 2 m (−39%), falls > 2 m (−17%), malignancy (−33%), sporting injuries (−100%), and miscellaneous (−58%). Four of 8 categories (RTAs, falls < 2 m, malignancy, miscellaneous) showed a trend towards return of pre-lockdown values during SP2. Two categories (disc disease, falls > 2 m) showed a further reduction (−34%, −27%) during SP2. One category (sporting injuries) portrayed a complete return to normal values during SP2 while a notable increase in cyclist-related referrals was witnessed (+ 63%) when compared with corresponding dates of previous years.

Conclusion

Spinal injury continues to occur across almost all categories, albeit at considerably reduced numbers. RTAs and falls remained the most common MOI. Awareness needs to be drawn to the reduction of malignancy-related referrals to dissuade people with such symptoms from avoiding presentation to hospital over periods of social restrictions.

US COVID-19 Shutdown Demonstrates Importance of Background NO2 in Inferring NOx Emissions From Satellite NO2 Observations

Satellite nitrogen dioxide (NO2) measurements are used extensively to infer nitrogen oxide emissions and their trends, but interpretation can be complicated by background contributions to the NO2 column sensed from space. We use the step decrease of US anthropogenic emissions from the COVID-19 shutdown to compare the responses of NO2 concentrations observed at surface network sites and from satellites (Ozone Monitoring Instrument [OMI], Tropospheric Ozone Monitoring Instrument [TROPOMI]). After correcting for differences in meteorology, surface NO2 measurements for 2020 show decreases of 20% in March-April and 10% in May-August compared to 2019. The satellites show much weaker responses in March-June and no decrease in July-August, consistent with a large background contribution to the NO2 column. Inspection of the long-term OMI trend over remote US regions shows a rising summertime NO2 background from 2010 to 2019 potentially attributable to wildfires.

Changes in criteria air pollution levels in the US before, during, and after Covid-19 stay-at-home orders: Evidence from regulatory monitors

The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental Protection Agency for criteria pollutants (PM_2.5, ozone, NO₂, CO, PM₁₀) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM_2.5 levels were slightly higher (~10% of its multi-year interquartile range [IQR]) than expected; average ozone, NO₂, CO, and PM₁₀ levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO₂, CO: 3 weeks after; PM₁₀: 2 weeks after); but, by 5-6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM_2.5, ozone, CO, and PM₁₀, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO₂, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM_2.5 levels across the US beyond their normal range of variability; for ozone, NO₂, CO, and PM₁₀ concentrations were lowered but the reduction was modest and transient.

COVID-19 and the Environment, Review and Analysis

We reviewed studies linking COVID-19 cases and deaths with the environment, focusing on relationships with air pollution. We found both short- and long-term observational relationships with a range of regulated pollutants, although only two studies considered both cases (i.e., infections) and deaths within a common analytical framework. Most of these studies were limited to a few months of the pandemic period. Statistically significant relationships were found more often for PM2.5 and NO2 than for other regulated pollutants, but no rationale was suggested for such short-term relationships; latency was seldom considered for long-term relationships. It was also unclear whether confounding had been adequately controlled in either type of study. Studies of air quality improvement following lockdowns found more robust relationships with local (CO, NO2) rather than regional (PM2.5, O3) pollutants, but meteorological confounding was seldom considered. Only one of seven studies of airborne virus transmission reported actual measurements. Overall, we found the existing body of literature to be more suggestive than definitive. Due to these various deficiencies, we assembled a new state-level database of cumulative COVID-19 cases and deaths through March 2021 with a range of potential predictor variables and performed linear regression analyses on various combinations. As single predictors, we found significant (p < 0.05) relationships between cumulative cases and household crowding (+), education (−), face-mask usage (−), or voting Republican (+). For cumulative deaths, we found significant relationships with education (−), black race (+), or previous levels of PM2.5 (+). NOx (+), and elemental carbon (EC, +). We found no relationships between long-term air quality and cumulative COVID-19 cases. Our associations linking air pollution with COVID-19 mortality were not statistically different from those for all-cause mortality in previous studies. In multiple mortality regressions combining air pollution, race, and education, NOx and EC remained significant but PM2.5 did not. We concluded that the current worldwide emphasis on PM2.5 is misplaced. We predicted air pollutant effects of a few percentage points, but individual differences between races, political identification, and post-graduate education were of the order of factors of 2 to 4. In general, the factors predicting infection were personal and related to COVID-19 exposure, while those predicting subsequent mortality tended to be more situational and related to geography. Overall, we concluded that how you live is more important than where you live.

Assessing the impact of COVID-19 pandemic on urban transportation and air quality in Canada

The global outbreak and spread of COVID-19 had a significant impact on the environment of urban areas. This study aimed to provide a new insight into the urban transportation and air pollutant emission of representative Canadian cities impacted by this pandemic. The consumption of urban transportation fuel was analyzed and the corresponding CO₂ emissions was evaluated. The changes in urban traffic volume and air pollutant concentrations before and after the outbreak of this pandemic was investigated. Due to the lockdown after the outbreak of COVID-19, the domestic consumption of motor gasoline and estimated CO₂ emissions from urban vehicles in Canada has continuously decreased with a lowest level in April 2020, and rebounded in May 2020. It will still take a long time to recover to pre-pandemic levels because of the upcoming second wave of pandemic and further change. The Air Quality Health Index (AQHI), level of urban congestion and concentration level of NO₂ and CO had strong relevance with the COVID-19 period while SO₂ did not show significant relation. The comprehensive analysis of changing fuel consumptions, traffic volume and emission levels can help the government assess the impact and make corresponding strategy for such a pandemic in the future.

Energy consumption of using HEPA-based portable air cleaner in residences: A monitoring study in Seattle, US

Portable air cleaners (PACs), offering both auto and manual (adjustable) operation modes, are commonly used in residences. Compared with adjustable mode, auto mode's advantage of reducing indoor PM2.5 has been previously demonstrated. This study examines the energy consumption of such PACs in six residences recruited in Seattle, United States, and compares the power consumption between auto and adjustable modes. Each residence went through a one-week-long PAC filtration session under auto and adjustable modes, respectively. PAC power consumption, indoor PM2.5, temperature, and relative humidity (RH) were measured at 10-second intervals in each residence. A linear mixed-effects regression (LMER) model was used to compare the PAC power consumption between the two modes after adjusting for indoor PM2.5, temperature, and RH. Results show that the mean (standard deviation) PAC power consumption under adjustable and auto modes were 7.0 (3.5) and 6.8 (2.6) W, respectively. The average monthly energy consumption of continuous PAC operation was estimated to be ~5 kWh for both modes. Based on the LEMR model, PAC power consumption under auto mode was approximately 3% larger than that under adjustable mode, after adjusting for living-room PM2.5, temperature, and RH levels. The implications for PAC operation mode selection in residential environments were discussed.

Inter- and intra-city comparisons of PM2.5 concentration changes under COVID-19 social distancing in seven major cities of South Korea

The COVID-19 pandemic has prompted governments around the world to impose mitigation strategies of unprecedented scales, typically involving some form of restrictions on social activities and transportation. The South Korean government has been recommending a collection of guidelines now known as social distancing, leading to reduced human activities. This study analyzes changes in the concentrations of fine particulate matter (PM_2.5) during the 30-day periods before and since the start of social distancing on 29 February 2020 using measurement data from air quality monitoring stations at various locations of the seven major cities of South Korea, namely, Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, and Ulsan. All seven cities experienced decreased levels of PM_2.5 concentration by up to 25% and smaller fluctuations during the period of social distancing. Inter-city comparisons show that the PM_2.5 concentration changes are positively correlated with the city-wide PM_2.5 emission fractions for mobile sources and negatively correlated with the city-wide PM_2.5 emission fractions for combustion and industrial process sources. In addition, the meteorological influences favorable for transboundary pollutant transport have weakened during the period under COVID-19 social distancing. Intra-city comparisons show that decreases in the intra-city variability of PM_2.5 concentration were larger in coastal cities than in inland cities. Comparisons between the inter- and intra-city variabilities in the PM_2.5 concentration changes under social distancing highlight the importance of taking into account intra-city variabilities in addition to inter-city variabilities.

Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package

Ningbo is a major coastal city in the Yangtze River Delta region, China, with the largest cargo capacity in the world. We conducted a field campaign in Ningbo to measure the impact of the COVID-19 lockdown on air pollutants including NO2, O3 and CO from 21 January to 23 March 2020, using a home-made low-cost sensor package. The average concentrations of NO2, O3 and CO were observed to be 7.2, 37.5 and 648.5 ppb, respectively, during the lockdown. Compared with the previous year, the concentrations of NO2 and CO decreased by 63.1% and 6.9%, while the concentration of O3 increased by 37.9%. The significant reduction of NO2 concentration may be attributed to the reduced emissions of freighters and heavy trucks with lower port cargo throughput, which led to a decrease of NO concentration. The increase of O3 concentration was probably due to the lower titration of O3 by NO. After the lockdown, the concentrations of O3 and NO2 increased by 15.5% and 143.1%, respectively, compared with those during the lockdown. The temporal variations of the concentrations of NO2, O3 and CO measured by the sensor package were coincident with those obtained by the reference apparatus, which proves the sensor package to be suitable for air quality monitoring in field campaigns. This is the first time that a dramatic decrease in NO2 concentration in a coastal city due to a lockdown has been reported.

Distinct Ultrafine Particle Profiles Associated with Aircraft and Roadway Traffic

The Mobile ObserVations of Ultrafine Particles study was a two-year project to analyze potential air quality impacts of ultrafine particles (UFPs) from aircraft traffic for communities near an international airport. The study assessed UFP concentrations within 10 miles of the airport in the directions of aircraft flight. Over the course of four seasons, this study conducted a mobile sampling scheme to collect time-resolved measures of UFP, CO₂, and black carbon (BC) concentrations, as well as UFP size distributions. Primary findings were that UFPs were associated with both roadway traffic and aircraft sources, with the highest UFP counts found on the major roadway (I-5). Total concentrations of UFPs alone (10-1000 nm) did not distinguish roadway and aircraft features. However, key differences existed in the particle size distribution and the black carbon concentration for roadway and aircraft features. These differences can help distinguish between the spatial impact of roadway traffic and aircraft UFP emissions using a combination of mobile monitoring and standard statistical methods.