Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

In-depth analysis of ambient air pollution changes due to the COVID-19 pandemic in the Asian Monsoon region

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Reduction of the concentration of particulate material at a sampling point in Cusco city at the beginning of the pandemic

The pandemic produced by SARS-CoV-2 generated various impacts on public health, the environment and other anthropogenic activities. The purpose of this study was to evaluate the reduction of air pollution due to [Formula: see text] and [Formula: see text] particulate matter in Cusco city at the beginning of the pandemic. Social confinement in Peru began on March 16, 2020, until the end of June. These health measures caused strict isolation that resulted in a significant decrease in vehicle flow on the streets and avenues of the city of Cusco. In the first days of May, even during the time of confinement, we managed to measure air quality at a sampling point located on the campus of the Universidad Nacional de San Antonio Abad de Cusco; a reduction in air pollution due to particulate matter was observed. The evaluation was carried out using an high-volume (HiVol) 3000 particulate matter sampler and the mass of particulate matter adhered to the filters was determined by gravimetry. The concentrations of particulate matter [Formula: see text] and [Formula: see text] obtained pre-pandemic were compared with those recorded at the beginning of the pandemic. The results revealed a significant average reduction in the concentration of [Formula: see text] and [Formula: see text], reaching - 57.43% and - 59.52%, respectively, compared to pre-pandemic values. At the same time, its relationship with meteorological parameters and Google mobility data was evaluated and it was concluded that these parameters did not significantly affect the reduction of particulate matter. This study reveals the positive effects of the pandemic in reducing air pollution and the confinement measures had as a secondary effect on the decrease in air pollution in Cusco City.

Effect of lockdown and associated mobility changes amid COVID-19 on air quality in the Kathmandu Valley, Nepal

The COVID-19 pandemic caused a setback for Nepal, leading to nationwide lockdowns. The study analyzed the impact of lockdown on air quality during the first and second waves of the COVID-19 pandemic in the Kathmandu Valley. We analyzed 5 years of ground-based air quality monitoring data (2017-2021) from March to July and April to June for the first and second wave lockdowns, respectively. A significant decrease in PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) concentrations was observed during the lockdowns. The highest rate of decline in PM2.5 levels was observed during May and July compared to the pre-pandemic year. The PM2.5 concentration during the lockdown period remained within the WHO guideline limit and NAAQS for the maximum number of days compared to the lockdown window in the pre-pandemic years (2017-2019). Likewise, lower PM2.5 levels were observed during the second wave lockdown, which was characterized by a targeted lockdown approach (smart lockdown). We found a significant correlation of PM2.5 concentration with community mobility changes (i.e., walking, driving, and using public transport) from the Spearman correlation analysis. Lockdown measures restricted human mobility that led to a lowering of PM2.5 concentrations. Our findings can be helpful in developing urban air quality control measures and management strategies, especially during high pollution episodes.

The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China

Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃). A positive correlation might be indicated between air pollutants (PM_2.5, PM₁₀, and NO₂) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19

In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60-90%. The NO₂ concentration decreased by approximately 50%, while O₃ concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO₂ concentration decreased exponentially, whereas O₃ concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02351-7.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Impacts of the COVID-19 lockdown measures on coarse and fine atmospheric aerosol particles (PM) in the city of Rome (Italy): compositional data analysis approach

In the year 2020, Italy faced a pandemic due to the virus SARS-CoV-2 for short COVID-19. Following this pandemic, a national lockdown period was imposed and throughout the year 2020 various measures were taken by the government to limit the mobility of people and contain the mortality associated with COVID-19. In Italy, pandemic measures led to a reduction in anthropogenic activities and provided an unprecedented opportunity to evaluate the possible effects that restrictions on anthropogenic activities may have on the air quality. Two background site (i.e., Cipro and Cinecittà) and a traffic sites (i.e., Corso Francia) were studied in the city of Rome. PM₁₀ and PM_2.5 were considered for the years 2019 and 2020. Moreover, the vehicular mobility, the emission classes of the vehicles, and the people mobility were taken into consideration along with meteorological variables. A compositional data analysis was used to evaluate the effect of pandemic measures on the fine- and coarse-size fractions of PM in the three considered sites. The results showed that in the traffic site (i.e., Corso Francia site) in 2020, there was a reduction of fine-size fraction of PM of about 10% when compared to the data of 2019, whereas in the background site (i.e., Cinecittà site) in 2020 there was an increase of fine-size fraction of PM of about 14% when compared to the data of 2019. No variation in the coarse- and fine-size fractions of PM were observed at the background site Cipro. This study showed how, in an urban context, PM can be influenced by strong changes in people's habits and in vehicular mobility such as those recorded during the investigated period and due to pandemic lockdown measures.

Analysis of Particulate Matter Concentration Changes before, during, and Post COVID-19 Lockdown: A Case Study from Victoria, Mexico

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM2.5 and PM10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM2.5 and PM10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM2.5 and −51.61% for PM10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM2.5 and 100.00% for PM10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM2.5 and PM10, respectively. On the other hand, when comparing the concentrations of PM2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM10, a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs (x2 = 1.204) and between the DL-2021 and DL-2019 pairs (x2 = 0.372), with a p<0.000 for PM2.5, and the contrast between pairs of PM10 (DL) showed a significant difference between all pairs with p<0.01.

Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach

To reduce the spread of COVID-19, lockdowns were implemented in almost every single country in the world including Saudi Arabia. In this paper, the effect of COVID-19 lockdown on O₃, NO₂, and PM₁₀ in Makkah was analysed using air quality and meteorology data from five sites. Two approaches were employed: (a) comparing raw measured concentrations for the lockdown period in 2019 and 2020; and (b) comparing weather-corrected concentrations estimated by the machine learning approach with observed concentrations during the lockdown period. According to the first approach, the average levels of PM₁₀ and NO₂ decreased by 12% and 58.66%, respectively, whereas the levels of O₃ increased by 68.67%. According to the second approach, O₃ levels increased by 21.96%, while the levels of NO₂ and PM₁₀ decreased by 13.40% and 9.66%, respectively. The machine learning approach after removing the effect of changes in weather conditions demonstrated relatively less reductions in the levels of NO₂ and PM₁₀ and a smaller increase in the levels of O₃. This showed the importance of adjusting air pollutant levels for meteorological conditions. O₃ levels increased due to its inverse correlation with NO₂, which decreased during the lockdown period.

Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the "deweather" R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013-2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above -50% for NOx, around -10% for PM10 and below -5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.

Effect of Anti-COVID-19 Measures on Atmospheric Pollutants Correlated with the Economies of Medium-sized Cities in 10 Urban Areas of Grand Est Region, France

Using Sentinel-5P data, this study investigated the magnitude of change in the concentration of air pollutants (NO₂, HCHO, SO₂, O₃, CO, and aerosol index) in the air of ten cities and urban areas of the French region of Grand Est as a result of the first lockdown imposed between March 17, 2020 and May 11, 2020. The results showed that the air quality in the urban environments of Grand Est improved significantly compared to the same period in 2019 without lockdown. NO₂, O₃, aerosol index and CO were the pollutants that exhibited maximum reductions by an average of -33.98%, -5.94%, -26.82% and -0.66%, respectively (the observed maximum decreases were -54.7%, -7.7%, -13.1%, and -5.3%, respectively). The largest decrease occurred in the Public Establishments of Inter-municipal Cooperation (EPCI, in French: Établissement public de coopération intercommunale) areas of Eurométropole de Strasbourg, CA Colmar, and CA Mulhouse Alsace. The maximum decrease in air pollution first occurred in land cover classes close to cities, followed by built-up urban areas. In this study, a global depollution index known as the atmospheric clearance index (ACI) was developed, which involved several air pollution parameters, and quantitatively analyzed the decrease in contamination levels of the atmosphere in this region. In addition, the correlation between the novel ACI and other population and economic development indices was studied. The results indicated that there was a negative and statistically significant correlation between ACI and population density, gross domestic product, gross value added (GVA) at basic prices, number of employees, and active enterprises.

The Effect of Mobility on the Spread of COVID-19 in Light of Regional Differences in the European Union

The COVID-19 pandemic has spread rapidly all over the world, affecting many countries to varying degrees. In this study, an in-depth analysis of the factors influencing the spread of COVID-19 is offered mainly through big data in the European Union (EU) context. In doing so, the data of the first wave of the pandemic are assessed. Afterward, we evaluate the impacts of the COVID-19 spread in specific countries and regions. Based on the existing literature, mobility is recognized as a significant direct factor affecting disease transmission. The same applies to the case of COVID-19. However, compared with the analysis of mobility itself, this paper explores more profound reasons that affect mobility, ranging from policy and economy to geographical and transportation factors. Specifically, this paper studies nine EU countries based on their population density and the degree of impact of the epidemic in the first six months (February to July 2020) of the pandemic. Our study aims to illustrate how policies, economies, and geographical locations (including transportation factors) directly or indirectly affect the spread of the novel coronavirus by applying the SEIR model to analyze all selected countries’ big data. The key findings of this research are: (1) the timeliness of relevant policies and the effectiveness of government implementation indirectly limit the spread of the epidemic by reducing population mobility; (2) a better medical level would contribute to detect, isolate, and treat patients, and help control the epidemic; and (3) the large land borders and developed transportation between countries exacerbate the spread of the COVID-19. The paper contributes to ongoing research on COVID-19 by addressing the above points.