Effect of restricted emissions during COVID-19 on air quality in India

Variations in air pollution before, during and after the COVID-19 lockdown in Peruvian cities

The high concentrations of air pollutants in Peru remain a persistent problem, significantly impacting public health. Understanding the extent to which the COVID-19 lockdown affected these contaminants is crucial. To determine variations in NO2, O3, CO, and SO2 concentrations in 10 Peruvian cities before, during, and after lockdown. A comparative ecological study was conducted in urban areas of 10 major Peruvian cities using the Google Earth Engine (GEE) platform. Data on atmospheric pollutant concentrations were extracted from the Sentinel-5P/TROPOMI satellite images for the period between March 16 and June 30, across the years 2019, 2020, 2021, and 2022, for comparative analysis. The Wilcoxon test was used to evaluate changes between the study periods. We included 10 urban cities located across three geographic regions of Peru. Most urban cities experienced a decrease in NO2 concentrations and an increase in O3 and CO levels during the lockdown, while SO2 concentrations remained relatively constant. The lockdown has caused variations in NO2, O3 and CO concentrations. Future studies with accurate data on air pollutant concentrations are needed to ensure targeted and effective interventions.

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Following the COVID-19 restrictions, there was a sharp decline in global air quality and related environmental metrics. Due to the limited availability of in situ atmospheric data in Bangladesh, this study collected data on various air pollutants (NO2, SO2, CO, and PM2.5), greenhouse gases (CO2, CH4, and O3), as well as meteorological variables like Land Surface Temperature (LST), Relative Humidity (RH), Precipitation, surface albedo and Aerosol Optical Depth (AOD) from different datasets by Google Earth Engine (GEE), the International Energy Agency (IEA), NASA Giovanni, and NASA Power Access Viewer, covering periods before (2019), during (2020), and after (2021-2023) the COVID-19 lockdown in Bangladesh. GIS-based assessment alongside Principal Component Analysis (PCA) has been performed to explore the patterns, trends and correlations among the observed variables. Results showed in 2020 compared to 2019, NO2, SO2, CO, PM2.5, and CO2 concentrations decreases by 1.94, 16.67, 1.95, 2.08, and 6 %, respectively, while CH4 and O3 continued to rise. Meteorological variables exhibited a 0.16 °C decreases in LST, 6.4 % increases in RH, a 6 % reduction in AOD, and 6.36 % declines in surface albedo. Post-lockdown in 2021, air pollutants surged (NO2, SO2, CO, and PM2.5 increases by 17.3, 23.6, 0.6, and 8.3 %, respectively), with CO2, LST, and AOD rising by 8.5 %, 0.13 °C, and 8.3 %, and a slight 0.46 % decrease in RH compared to 2019 due to resuming more economic activities, transportation and industrial production works. The years 2022-2023 saw slight improvements in most variables except CH4, though concentrations did not revert to those of 2019. The findings of correlation coefficients revealed that pollutants and GHG are highly correlated with the meteorological variables specially with RH. This study underscores the substantial shifts in atmospheric variables from pre-to post-lockdown periods, offering valuable insights for more effective management of the greenhouse effect and air pollution control strategies.

Cross-talk between antioxidant production and secondary metabolite biosynthesis under combined effects of ozone stress and nitrogen amendments: A case study of lemongrass

The present experiment was done to study the interactive effects of soil nitrogen (N) amendments and elevated ozone (O3) (N-O3) on a medicinal plant, lemongrass [Cymbopogon flexuosus (Steud.) (Wats.)]. The experiment used two doses of inorganic soil nitrogen (N1, recommended and N2, 1.5-times recommended dose) in open-top chambers under ambient and elevated (ambient + 15 ppb and ambient + 30 ppb) O3 conditions. To analyze various characteristics, samples were collected at 45 and 90 days after transplantation (DAT). Additionally, at 110 days after transplantation (DAT), the metabolite contents of the leaves and essential oils were analyzed. The present study aims to investigate the mechanistic approach involving the crosstalk between antioxidant production and secondary metabolite biosynthesis in lemongrass upon N-O3 interactions. The present experiment showed that N amendments can be an efficient measure to manage O3 injury in plants, along with ensuring a balance between primary and secondary metabolic pathways, thus sustaining the plant defense and production of bioactive compounds, simultaneously. Under N-O3, not only the Halliwell asada pathway was stimulated resulting in the increased activities and concentrations of antioxidant pools; the shikimate, phenylpropanoid and mevalonic acid pathways were also invigorated, producing more number and contents of secondary metabolites (SMs), compared with plants that were not treated with N doses. This study suggests that soil nitrogen amendments will improve the therapeutic qualities of lemongrass, along with the strengthening of its antioxidant machinery, upon exposure to O3 stress.

Mapping air quality trends across 336 cities in India: Insights from three decades of monitoring (1987-2019)

Over a span of 34 years (1987-2019), an in-depth analysis of PM10, SO2, and NO2 trends across India was conducted using data from the National Ambient Air Quality Monitoring Programme's manual monitoring stations in 336 cities. The study encompassed six geographical regions over three time blocks, revealing a correlation between the expansion of monitoring networks and the nation's economic growth. Regions like the densely populated Indo-Gangetic Plains (IGP) and Central India consistently hosted more monitoring stations, while the Himalayan and Northeast regions saw substantial increases from initial scarcity. SO2 concentrations showed a declining trend, while NO2 levels remained relatively stable with intermittent fluctuations. Conversely, national average PM10 concentrations exhibited an upward trajectory, notably spiking by 128 % between 2006 and 2009 due to economic activities, construction, network expansion, the 2009 drought, and heightened coal consumption. Spatially, pollutant concentrations across three blocks demonstrated improved SO2 levels, several cities exceeding NO2 standards, and persistently high PM10 levels in the IGP. PM10 levels in block 3 were lower than in block 2, reflecting effective policy interventions. State rankings, however, did not consistently reflect pollutant trends across blocks. Regionally, the IGP consistently had the highest PM10 concentrations, while the Northeast recorded the lowest. Population-weighted exposure levels indicated an overall increase in public exposure to PM10. Analysis of major city per region aligned with national trends, as evidenced by Delhi (IGP), Guwahati (Northeast), Vadodara (Northwest), and Bhopal (Central) showing increased PM10 concentrations since 2006, followed by intermittent declines. In contrast, Shimla (Himalayan) and Chennai (Southern) exhibited distinct patterns. Major industrial cities such as Parwanoo, Bongaigaon, Angul and Talcher, and Visakhapatnam mirrored national trends, with PM10 levels rising since 2009, highlighting the significant impact of industrial activities on air quality. This research underscores the need for targeted, effective mitigation strategies based on spatial and temporal pollutant trends.

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach

The emergence of the COVID-19 pandemic in 2020 led to the implementation of legal restrictions on individual activities, significantly impacting traffic and air pollution levels in urban areas. This study employs a state-space intervention method to investigate the effects of three major COVID-19 lockdowns in March 2020, November 2020, and January 2021 on London's air quality. Data were collected from 20 monitoring stations across London (central, ultra-low emission zone, and greater London), with daily measurements of NOx, PM10, and PM2.5 for four years (January 2019-December 2022). Furthermore, the developed model was adjusted for seasonal effects, ambient temperature, and relative humidity. This study found significant reductions in the NOx levels during the first lockdown: 49% in central London, 33% in the ultra-low emission zone (ULEZ), and 37% in greater London. Although reductions in NOx were also observed during the second and third lockdowns, they were less than the first lockdown. In contrast, PM10 and PM2.5 increased by 12% and 1%, respectively, during the first lockdown, possibly due to higher residential energy consumption. However, during the second lockdown, PM10 and PM2.5 levels decreased by 11% and 13%, respectively, and remained unchanged during the third lockdown. These findings highlight the complex dynamics of urban air quality and underscore the need for targeted interventions to address specific pollution sources, particularly those related to road transport. The study provides valuable insights into the effectiveness of lockdown measures and informs future air quality management strategies.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Increased global cropland greening as a response to the unusual reduction in atmospheric PM₂.₅ concentrations during the COVID-19 lockdown period

The devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017-2019) in India (10-20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05-0.1, 0.05, 0.05 and 0.05-0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1-20%) and maize (1-10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs).

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 μg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 μg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

Exploring the association between long-term MODIS aerosol and air pollutants data across the Northern Great Plains through machine learning analysis

Aerosol optical depth (AOD) and Ångström exponent (AE) are the major environmental indicators to perceive air quality and the impact of aerosol on climate change and health as well as the global atmospheric conditions. In the present study, an average of AOD and AE data from Tera and Aqua satellites of MODIS sensors has been investigated over 7 years i.e., from 2016 to 2022, at four locations over Northern Great Plains. Both temporal and seasonal variations over the study periods have been investigated to understand the behavior of AOD and AE. Over the years, the highest AOD and AE were observed in winter season, varying from 0.75 to 1.17 and 1.30 to 1.63, respectively. During pre-monsoon season, increasing trend of AOD varying from 0.65 to 0.95 was observed from upper (New Delhi) to lower (Kolkata) Gangetic plain, however, during monsoon and post-monsoon a reverse trend varying from 0.85 to 0.65 has been observed. Seasonal and temporal aerosol characteristics have also been analyzed and it has been assessed that biomass burning was found to be the major contributor, followed by desert dust at all the locations except in Lucknow, where the second largest contributor was dust instead of desert dust. During season-wise analysis, biomass burning was also found to be as the major contributor at all the places in all the seasons except New Delhi and Lucknow, where dust was the major contributor during pre-monsoon. A boosting regression algorithm was done using machine learning to explore the relative influence of different atmospheric parameters and pollutants with PM2.5. Water vapor was assessed to have the maximum relative influence i.e., 51.66 % followed by CO (21.81 %). This study aims to help policy makers and decision makers better understand the correlation between different atmospheric components and pollutants and the contribution of different types of aerosols.

Quantifying the impact of urban road traffic on air quality: activity pre-pandemic and during partial and full lockdowns

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown.

Assessing the spatial distribution of odor at an urban waterfront using AERMOD coupled with sensor measurements

Impressions of a place are partly formed by smell. The urban waterfronts often leave a rather poor impression due to odor pollution, resulting in recurring complaints. The nature of such complaints can be subjective and vague, so there is a growing interest in quantitative measurements of emissions to explore the causes of malodorous influence. In the present work, an air quality monitor with an H2S sensor was employed to continuously measure emissions of malodors at 1-min resolution. H2S is often considered to be the predominant odorous substance from sludge and water bodies as it is readily perceptible. The integrated means of concentration from in situ measurements were combined with the AERMOD dispersion model to reveal the spatial distribution of odor concentrations and estimate the extent of odor-prone areas at a daily time step. Year-long observations showed that the diurnal profile exhibits a positively skewed distribution. Meteorology plays a vital role in odor dispersion; the degree of dispersion was explored on a case-by-case basis. There is a greater likelihood of capturing the concentration peaks at night (21:00 to 6:00) as the air is more stable then with less tendency for vertical mixing but favors a horizontal spread. This study indicates that malodors are changeable in time and space and establishes a new approach to using H2S sensor data and resolves a long-standing question about odor in Hong Kong.Implications: this study establishes a new approach combining dispersion model with novel H2S sensor data to understand the characteristics and pattern of odor emanated from the urban waterfront in Hong Kong. The sensor has dynamic concentration range to detect the episodic level of H2S and low level at background conditions. It provides more complete information in relation to odor annoyance, as well as quantitative information useful for odor regulation.

Air pollution trend in Chandigarh during 2019-2022: status and influence of meteorological factors

The degradation of ambient air quality is a pressing global concern, and India, as a developing nation, has witnessed a rapid surge in industrial activities in recent decades. This surge has resulted in numerous Indian cities ranking among the world's most polluted urban areas. Chandigarh, strategically positioned within the Indo-Gangetic plains (IGP), has not escaped this distressing trend, experiencing a significant spike in air pollution levels. This study focuses on comprehending and addressing the air quality issues in Chandigarh, shedding light on the evolution of air pollution trends and their dependence on meteorological factors. Notably, the study reveals that, with the exception of O3, pollutant concentrations surge during the rice stubble burning season. These pollutants persist in the atmosphere for prolonged periods, exacerbating the situation during winter due to lower temperatures and heightened use of fossil fuels for heating by low-income households. In contrast, the wheat stubble burning period does not significantly impact pollutant concentrations. The study also identifies a spring peak in surface O3 concentrations, attributed to favorable high temperatures that promote the photochemical reactions responsible for this phenomenon, a distinctive feature in South Asia and the Himalayas. An examination of the connection between pollutant concentrations and meteorological parameters underscores that elevated pollutant levels, except for CO, are linked to lower relative humidity and temperatures. This suggests that current development patterns have contributed to the escalation of air pollution in Chandigarh, necessitating urgent interventions to preserve the city's aesthetics and the health of its residents. Furthermore, to model and monitor pollutant behavior in Chandigarh, more extensive and extended studies are imperative. Both short-term and long-term investigations into the environmental and health impacts of air pollutants, including primary and secondary pollutants, are of paramount importance. These endeavors are essential for the well-being of both the environment and the population of Chandigarh.

The Diwali festival: short-term high effect of fireworks emissions on particulates and their associated empirically calculated health risk assessment at Bhubaneswar city

This article elucidates the role of the short-term combustion of firecrackers and sparklers as a significant source of atmospheric pollutants that deteriorate ambient air quality and increase health risks during the popular Diwali festival. The study was conducted at Bhubaneswar during the festive celebration in early November 2021 (4th Nov) and late October 2022 (24th Oct) to assess the level of particulates (PM2.5 and PM10 mass concentration) and the relative health risks associated with them. PM2.5 (113.83 µg/m3) and PM10 (204.32 µg/m3) showed significant rises on D-day at all seven different sites that exceeded the NAAQS in 2021. From 2021 to 2022, an overall decrease in PM2.5 (41%) and PM10 (36%) was observed. On D-day, the total concentration of quantified metals in PM2.5 and PM10 were found to be 4.83 µg/m3 5.97 µg/m3 (2021) and 5.08 µg/m3 5.18 µg/m3 (2022) respectively. The AQI during both years (2021-2022) was found to be high for PM2.5 (unhealthy) and PM10 (moderate), but it was markedly good for all other pollutants on the scale. The overall population in the study area were under a significant health risk was observed in the overall population as PM surpassed the threshold concentration amid the festivities for consecutive years, with PM2.5 being more potent than PM10. The total excess health risk in 2022 was found to be decreased lower by ~ 88% from 2021 on D-day. But, metal exposure (through inhalation) in children were more compared to the adults for both the years. However, the exposure risk of both children and adults were high in the year 2022 with inhalation of metals like K, Al, Ba, Fe and Ca found in higher concentration and directly emitted from the firecrackers.

Drastic mobility restrictions during SARS-CoV-2 pandemic: an opportunity to learn about constraints on the way to a pollution-free city

Road traffic is one of the main sources of pollution in modern cities. If there is a desire to move towards healthier cities, it may be necessary to modify the current model of mobility. The SARS-CoV-2 pandemic, together with the measures applied by most governments in the world to control the mobility of citizens, offered a unique opportunity to assess the changes in pollution levels after a drastic reduction in road traffic. In this study, air and noise pollution levels and road traffic flow were analyzed in the city of Cáceres, Spain, before and during the state of emergency imposed by the Spanish government. The values obtained were compared with the quality limits established by both the Spanish government and the World Health Organization (WHO). A traffic noise prediction model has been employed to evaluate the acoustic impact resulting from the reduction in traffic flow. As a result of this study, it was found that air pollution was indeed reduced due to the mobility restrictions imposed to control the pandemic, but that the WHO's recommendations for the values of the day-evening-night noise indicator (Lden) and the night-time noise indicator (Ln) for road traffic noise, which should not be exceeded, were not met. These findings highlight the need to review current urban mobility models if the WHO's recommendations are to be reached with regard to reducing the effects of exposure of the population to urban noise.

Better understanding the behavior of air pollutants at shutdown times - results of a short full lockdown

Numerous studies have evaluated the effects of lockdowns during the COVID-19 pandemic, but most of them have concerned large cities and regions. This study aimed to evaluate the dynamics of air pollutants during and after the implementation of a short lockdown in the medium-sized city of Pelotas, Brazil, using hourly measurements of pollutants. The evaluation period included in this study was between August 9th and 12th, 2020. A machine learning model was used to investigate the expected behavior against what was observed during the study period. All pollutants presented a gradual reduction until a dynamic plateau established 48 hours after the start of the lockdown: NO2 (↓4%), O3 (↓34%), SO2 (↓24%), CO (↓48%), PM10 (↓82%) and PM2.5 (↓82%). At the end of the restriction measures, the PM10 and PM2.5 levels continued to decline beyond expectations. Our findings show that these measures can positively affect the air quality in medium-sized cities.

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.

Assessment of meteorological parameters on air pollution variability over Delhi

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.

Tracing air pollution changes (CO, NO2, SO2, and HCHO) using GEE and Sentinel 5P images in Ahvaz, Iran

The first case of COVID-19 in Iran was reported on February 25, 2020, leading in the implementation of a government-mandated lockdown as the virus gradually spread to different cities. The objective of this study was to evaluate the impact of the COVID-19 pandemic on air quality in Ahvaz city by utilizing Sentinel 5 images and the Google Earth Engine (GEE) platform. Specifically, the concentrations of air pollutants, including CO, NO2, SO2, and HCHO, during the COVID-19 pandemic from May 10 to June 01, 2021, were examined. Also, they were compared to the same period in 2019. Additionally, the influence of meteorological parameters, such as wind speed and precipitation, on pollutant concentrations during the pandemic and in the pre-pandemic year of 2019 were investigated. The results revealed a significant decrease in the concentrations of NO2 (13.7%), CO (6.1%), SO2 (28%), and HCHO (9.5%) in Ahvaz during the study period in 2021 compared to the same period in 2019. Statistical analyses indicated no significant changes in wind speed and precipitation between the COVID-19 pandemic and the pre-pandemic period in 2019. Therefore, the impact of these parameters on the observed changes in pollutant concentrations can be disregarded. It is clear that the COVID-19 epidemic and the subsequent lockdown measures, including traffic restrictions and business closures, played a crucial role in significantly reducing air pollutant concentrations in Ahvaz.

Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model

Air pollution has emerged as a significant environmental challenge at the global level, and India is majorly affected by it. Numerous emission sources, such as automobiles, industries, fuel-burning for household and commercial activities, and dust due to construction activities, are responsible for air pollution. The lockdown in India which was clamped for controlling the spread of virulent disease also brought down the level of pollutants in air significantly. The proposed approach deals with the application of the hybrid model of Daubechies discrete wavelet decomposition (Db-DWD) and the autoregressive integrated moving average (ARIMA) model for modeling and forecasting the chaotic data of air quality index (PM2.5) from the three most polluted cities (Agra, New Delhi, and Varanasi) in India for pre and within lockdown periods. The estimated outputs of the component series are then reconstructed to obtain the final forecast of the AQI data. The statistical evaluation compares the performance of the simple ARIMA model and the joint Db-DWD-ARIMA model. Also, the coupled model has been applied for forecasting efficacy with Daubechies mother wavelet of orders 5, 8, 10, and 12. The hybrid model reduced forecasting errors and improved accuracy significantly. Secondly, the forecasting efficiencies in this hybrid model have enhanced with the increase in wavelet order. This study will help to assess and take appropriate steps to control air pollution levels and to monitor the growing air pollutants, which will be significant for our existence.

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

INTRODUCTION

Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown's impact on COVID-19 cases and associated healthcare costs.

METHODS

Using daily case data for 84 days (9 March-31 May 2020), we modeled the epidemic's trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R₀), daily reproduction number (R_e), and case doubling time. Based on pre-restriction and Phase 1 R₀, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis.

RESULTS

The estimated median R₀ during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, -15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, -204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83).

CONCLUSION

Maharashtra's early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

Measurement of fine particle concentrations and estimation of air quality index (AQI) over northeast Douala, Cameroon

Due to absence of data on air quality monitoring and pollutant emissions in Douala, a measurement campaign along the principal street passage to the college grounds was started. Using the OC 300 Laser Dust Particle, fine particle concentrations are monitored during 1 week from Monday to Sunday. The instrument used detects four different sizes of particles: PM10, PM5, PM2.5, and PM1. The daily average concentrations measured ranged from 9.47 ± 0.26 to 50.14 ± 2.42 µg·m-3 for PM1.0; 13.13 ± 0.38 to 86.65 ± 3.96 µg·m-3 for PM2.5; 13.60 ± 0.40 to 100.56 ± 4.20 µg·m-3 for PM5; and 14.52 ± 0.42 to 114.59 ± 4.60 µg·m-3 for PM10. Exceptions made from PM5 and PM1.0 which were not in relation to the WHO (World Health Organization) guideline values, the level of PM10 and PM2.5 is higher than the WHO standards. The air quality index (AQI) is between very poor and poor during this measurement campaign, indicating that residents of the study region are highly exposed. Through the use of correlation studies, it has been demonstrated that the predominant source of fine particles in the studied region is vehicular activity. As a result, traffic density is the most significant factor causing the different air pollution levels seen in the tested areas.

Impact of COVID-19 restrictions on the concentration and source apportionment of atmospheric ammonia (NH3) across India

The wide spread of the coronavirus disease (COVID-19) has significantly influenced human activities around the world, providing a unique opportunity to investigate the response of air pollution to anthropogenic emission reduction. Compared with numerous studies on conventional air pollutants, atmospheric ammonia (NH₃) that has matched sources from both anthropogenic and natural emissions has been rarely investigated. Here we assess impacts of the COVID-19 lockdown on ambient NH₃ variation across India, one of the most severe NH₃ pollution region in the world. The role of meteorology in shaping emission contribution to NH₃ pollution and respective contribution of each emission source to ambient NH₃ before and after the COVID-19 outbreak were investigated using the XGBoost algorithm coupled with WRF-Chem model. Results showed that ambient NH₃ concentrations in the seven major cities (Hyderabad, Bengaluru, Chennai, Delhi, Lucknow, Kolkata and Mumbai) decreased by 2.1-53.8 % whereas in Ahmedabad increased by 20.3 % during the COVID-19 lockdown period. Obvious decrease in NH₃ in Indo-Gangetic Plain (-17.1 %) was mainly driven by favorable meteorology, whereas the slight decline in NH₃ in South India was mainly driven by epidemic-related emission control (-8.56 %). Source appointment results showed that the contribution of industrial emission (Ind) to ambient NH₃ in most megacities showed a decreasing trend (between 11 % and 26 %) during the lockdown period. However, the reduction effect was mostly compensated by increasing contributions (15-25 %) of residential emission (Res) or agricultural soil emission (Ags). Particularly, in Ahmedabad and Lucknow ambient NH₃ increased by 20.3 % and 12 % during the lockdown period, the reduction effect of Ind on ambient NH₃ (-23 % and -11 %, respectively) was absolutely compensated by enhanced contribution of Res (24 %) and Ags (12 %), respectively. Our results highlight the importance of eliminating residential and agricultural NH₃ emissions especially in North India.

Impact of the emergency response to COVID-19 on air quality and its policy implications: Evidence from 290 cities in China

The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O₃ concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone.

Biological effects of particulate matter samples during the COVID-19 pandemic: a comparison with the pre-lockdown period in Northwest Italy

In 2020, during the COVID-19 pandemic, containment measures were applied inducing potential changes in air pollutant concentrations and thus in air toxicity. This study evaluates the role of restrictions on biological effects of particulate matter (PM) in different Northwest Italy sites: urban background, urban traffic, rural, and incinerator. Daily PM samples collected in 2020 were pooled according to restrictions: January/February (no restrictions), March and April (first lockdown), May/June and July/August/September (low restrictions), October/November/December (second lockdown). The 2019 samples (pre-pandemic period) were pooled as 2020 for comparison. Pools were extracted with organic solvents and extracts were tested to assess cytotoxicity (WST-1 assay) and genotoxicity (comet assay) on BEAS-2B cells, mutagenicity (Ames test) on TA98 and TA100 Salmonella typhimurium strains, and estrogenic activity (gene reporter assay) on MELN cells. Pollutant concentrations were also analyzed (PM10, PM2.5, polycyclic aromatic hydrocarbons). No difference was observed for PM and polycyclic aromatic hydrocarbon concentrations between 2020 and 2019. During lockdown months (2020), PM cytotoxicity/genotoxicity was significantly lower in some sites than during 2019, while considering PM mutagenicity/estrogenic activity some differences were detected but without statistical significance. PM extract effects decreased in some sites during 2020; this may be due to lockdowns that reduced/modified pollutant emissions and may be related also to complex PM origin/formation and to meteorological conditions. In conclusion, the study confirms that PM biological effects cannot be assessed considering only the PM concentration and suggests to include a battery of bioassay for air quality monitoring in order to protect human health from air pollution effects.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11869-023-01381-6.

Characteristics and sources of carbonaceous aerosols in a semi-arid city: Quantifying anthropogenic and meteorological impacts

Carbonaceous aerosols have great adverse impacts on air quality, human health, and climate. However, there is a limited understanding of carbonaceous aerosols in semi-arid areas. The correlation between carbonaceous aerosols and control measures is still unclear owing to the insufficient information regarding meteorological contribution. To reveal the complex relationship between control measures and carbonaceous aerosols, offline and online observations of carbonaceous aerosols were conducted from October 8, 2019 to October 7, 2020 in Hohhot, a semi-arid city. The characteristics and sources of carbonaceous aerosols and impacts of anthropogenic emissions and meteorological conditions were studied. The annual mean concentrations (± standard deviation) of fine particulate matter (PM_2.5), organic carbon (OC), and elemental carbon (EC) were 42.81 (±40.13), 7.57 (±6.43), and 2.25 (±1.39) μg m^-3, respectively. The highest PM_2.5 and carbonaceous aerosol concentrations were observed in winter, whereas the lowest was observed in summer. The result indicated that coal combustion for heating had a critical role in air quality degradation in Hohhot. A boost regression tree model was applied to quantify the impacts of anthropogenic emissions and meteorological conditions on carbonaceous aerosols. The results suggested that the anthropogenic contributions of PM_2.5, OC, and EC during the COVID-19 lockdown period were 53.0, 15.0, and 2.36 μg m^-3, respectively, while the meteorological contributions were 5.38, 2.49, and -0.62 μg m^-3, respectively. Secondary formation caused by unfavorable meteorological conditions offset the emission reduction during the COVID-19 lockdown period. Coal combustion (46.4% for OC and 35.4% for EC) and vehicular emissions (32.0% for OC and 50.4% for EC) were the predominant contributors of carbonaceous aerosols. The result indicated that Hohhot must regulate coal use and vehicle emissions to reduce carbonaceous aerosol pollution. This study provides new insights and a comprehensive understanding of the complex relationships between control strategies, meteorological conditions, and air quality.

Toxicological Effects of Fine Particulate Matter (PM2.5): Health Risks and Associated Systemic Injuries-Systematic Review

Previous studies focused on investigating particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) have shown the risk of disease development, and association with increased morbidity and mortality rates. The current review investigate epidemiological and experimental findings from 2016 to 2021, which enabled the systemic overview of PM2.5's toxic impacts on human health. The Web of Science database search used descriptive terms to investigate the interaction among PM2.5 exposure, systemic effects, and COVID-19 disease. Analyzed studies have indicated that cardiovascular and respiratory systems have been extensively investigated and indicated as the main air pollution targets. Nevertheless, PM2.5 reaches other organic systems and harms the renal, neurological, gastrointestinal, and reproductive systems. Pathologies onset and/or get worse due to toxicological effects associated with the exposure to this particle type, since it can trigger several reactions, such as inflammatory responses, oxidative stress generation and genotoxicity. These cellular dysfunctions lead to organ malfunctions, as shown in the current review. In addition, the correlation between COVID-19/Sars-CoV-2 and PM2.5 exposure was also assessed to help better understand the role of atmospheric pollution in the pathophysiology of this disease. Despite the significant number of studies about PM2.5's effects on organic functions, available in the literature, there are still gaps in knowledge about how this particulate matter can hinder human health. The current review aimed to approach the main findings about the effect of PM2.5 exposure on different systems, and demonstrate the likely interaction of COVID-19/Sars-CoV-2 and PM2.5.

Aerosol demasking enhances climate warming over South Asia

Anthropogenic aerosols mask the climate warming caused by greenhouse gases (GHGs). In the absence of observational constraints, large uncertainties plague the estimates of this masking effect. Here we used the abrupt reduction in anthropogenic emissions observed during the COVID-19 societal slow-down to characterize the aerosol masking effect over South Asia. During this period, the aerosol loading decreased substantially and our observations reveal that the magnitude of this aerosol demasking corresponds to nearly three-fourths of the CO2-induced radiative forcing over South Asia. Concurrent measurements over the northern Indian Ocean unveiled a ~7% increase in the earth's surface-reaching solar radiation (surface brightening). Aerosol-induced atmospheric solar heating decreased by ~0.4 K d-1. Our results reveal that under clear sky conditions, anthropogenic emissions over South Asia lead to nearly 1.4 W m-2 heating at the top of the atmosphere during the period March-May. A complete phase-out of today's fossil fuel combustion to zero-emission renewables would result in rapid aerosol demasking, while the GHGs linger on.

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

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

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO₂ column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO₂ and particulate material (PM; coarse: PM₁₀ and fine: PM_2.5)]. For this purpose, tropospheric NO₂ obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM₁₀, most stations showed correlations lower than 0.2, which were not significant. The results for PM_2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO₂ proved to be a good predictor for NO₂ concentrations at ground level. Considering all stations with NO₂ measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO₂ throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO₂) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO₂). Our results demonstrate that Tropospheric NO₂ column densities can serve as good predictors of NO₂ concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Source apportionment of PM2.5 before and after COVID-19 lockdown in an urban-industrial area of the Lisbon metropolitan area, Portugal

The lockdowns held due to the COVID-19 pandemic conducted to changes in air quality. This study aimed to understand the variability of PM_2.5 levels and composition in an urban-industrial area of the Lisbon Metropolitan Area and to identify the contribution of the different sources. The composition of PM_2.5 was assessed for 24 elements (by PIXE), secondary inorganic ions and black carbon. The PM_2.5 mean concentration for the period (December 2019 to November 2020) was 13 ± 11 μg.m^-3. The most abundant species in PM_2.5 were BC (19.9%), SO₄ ^2- (15.4%), NO₃ ^- (11.6%) and NH₄ ⁺ (5.3%). The impact of the restrictions imposed by the COVID-19 pandemic on the PM levels was found by comparison with the previous six years. The concentrations of all the PM_2.5 components, except Al, Ba, Ca, Si and SO₄ ^2-, were significantly higher in the winter/pre-confinement than in post-confinement period. A total of seven sources were identified by Positive Matrix Factorisation (PMF): soil, secondary sulphate, fuel-oil combustion, sea, vehicle non-exhaust, vehicle exhaust, and industry. Sources were greatly influenced by the restrictions imposed by the COVID-19 pandemic, with vehicle exhaust showing the sharpest decrease. Secondary sulphate predominated in summer/post-confinement. PM_2.5 levels and composition also varied with the types of air mass trajectories.

Assessment of NO2 concentrations over industrial state Jharkhand, at the time frame of pre, concurrent, and post-COVID-19 lockdown along with the meteorological behaviour: an overview from satellite and ground approaches

Burning of fossil fuels in the form of coal or gasoline in thermal power plants, industries, and automobiles is a prime source of nitrogen dioxide (NO₂), a major air pollutant causing health problems. In this paper, spatio-temporal unevenness of NO₂ concentrations via both spaceborne Sentinel-5P and ground-based in situ data have been studied for the period of 2017-2021. Annual and seasonal distribution of TROPOMI-NO₂ depict consistency over the Jharkhand region, highlighting six hotspot regions. As compared to 2019, a notable dip of 11% in the spatial annual average TROPOMI-NO₂ was achieved in 2020, which were elevated again by 22% in 2021 as the lockdown gradually goes out of the picture. Among eight ground-monitoring stations, Tata and Golmuri stations always displayed a higher level of TROPOMI-NO₂ ranges up to 15.2 ×10¹⁵molecules.cm^-2 and 16.9 ×10¹⁵molecules.cm^-2 respectively, as being located in the highly industrialised district of Jamshedpur. A big percentage reduction of up to 30% in TROPOMI-NO₂ has been reported in Jharia and Bastacola stations in Dhanbad in the lockdown phase of 2020 compared to 2019. Good agreement between TROPOMI-NO₂ and surface-NO₂ has been achieved with R = 0.8 and R = 0.71 during winter and post-monsoon respectively. Among four meteorological parameters, TROPOMI-NO₂ was majorly found to be influenced by precipitation, having R = 0.6-0.8 for almost all stations. More advanced satellite algorithms and ground-based data may be used to estimate NO₂ in places where monitoring facilities are limited and thus can help in air pollution control policy.

Impact of COVID-19 pandemic on cultural ecosystem services from urban green spaces: a case from English Bazar Urban Agglomeration, Eastern India

Urban green space (UGS) provides multiple cultural ecosystem services (CES) and enhances people's physical and mental well-being. The importance of UGS was more critical during crisis periods such as the COVID-19 pandemic. Recently, UGS has been under severe threats due to rapid urban expansion and climate change. Particularly, the cities in developing countries have experienced a dramatic loss of green spaces due to land use and land cover (LULC) change. This study aims to assess the impact of the COVID-19 pandemic on CES perception obtained from two public UGS, i.e., Subhankar Sishu Uddyan (SSU) and Bandh Road (BR) in English Bazar Urban Agglomeration (EBUA), Eastern India. A survey was performed to assess the valuation of CES, CES obtained from UGS, and the use of CES difference during partial (unlock phases) and after lockdown periods. The non-parametric tests were carried out to understand the significant differences between CES and UGS perceived by the respondents. Explanatory factor analysis was also performed to find the association among CES for each UGS. The results reported substantial variations of UGS use during partial and after lockdown periods. During the after-lockdown period, the UGS mainly were used for social relations (51%), spending time with relatives and friends (25%), spending time with children (11%) in SSU and walking (25%), spending time with relatives and friends (21%), and physical activities (17%) in BR. During the partial lockdown period, a significant difference between SSU and BR was recorded from social relations and health value among all CES. After the full lockdown period, a significant difference was observed in recreation, social relations, health, and aesthetic value. The frequency and duration of UGS use during partial lockdown periods substantially increased. About 12% of the total visitor visited UGS daily after the lockdown, whereas it was 4% during the partial lockdown. The study's findings will assist urban planners and policymakers in understanding the need for UGS to promote the well-being of the citizens.

Ultraviolet-B radiation in relation to agriculture in the context of climate change: a review

Over the past few decades, the amount of ultraviolet-B radiation (UV-B) reaching the earth's surface has been altered due to climate change and stratospheric ozone dynamics. This narrow but highly biologically active spectrum of light (280-320 nm) can affect plant growth and development. Depletion of ozone and climate change are interlinked in a very complicated manner, i.e., significantly contributing to each other. The interaction of climate change, ozone depletion, and changes in UV-B radiation negatively affects the growth, development, and yield of plants. Furthermore, this interaction will become more complex in the coming years. The ozone layer reduction is paving a path for UV-B radiation to impact the surface of the earth and interfere with the plant's normal life by negatively affecting the plant's morphology and physiology. The nature and degree of the future response of the agricultural ecosystem to the decreasing or increasing UV-B radiation in the background of climate change and ozone dynamics are still unclear. In this regard, this review aims to elucidate the effects of enhanced UV-B radiation reaching the earth's surface due to the depletion of the ozone layer on plants' physiology and the performance of major cereals.

Investigating impacts of COVID-19 on urban mobility and emissions

The COVID-19 pandemic has severely impacted human activities in a way never documented in modern history. The prevention policies and measures have abruptly changed well-established urban mobility patterns. In this context, we exploit different sources of urban mobility data to gain insights into the effects of restrictive policies on the daily mobility and exhaust emissions in pandemic and post-pandemic periods. Manhattan, the most densely populated borough in New York City, is chosen as the study area. We collect data generated by taxis, sharing bikes, and road detectors between 2019 and 2021, and estimate exhaust emissions using the COPERT (Computer Programme to calculate Emissions from Road Transport) model. A comparative analysis is conducted to identify important changes in urban mobility and emission patterns, with a particular focus on the lockdown period in 2020 and its counterparts in 2019 and 2021. The results of the paper fuel the discussion on urban resilience and policy-making in a post pandemic world.

Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis

BACKGROUND

The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India.

METHOD

District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), Ozone (O₃), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19.

RESULTS

Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O₃, and NO₂ in India.

CONCLUSIONS

COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Japanese urban household carbon footprints during early-stage COVID-19 pandemic were consistent with those over the past decade

As urbanization accelerates worldwide, substantial energy and services are required to meet the demand from cities, making cities major contributors to adverse environmental consequences. To bridge the knowledge gap in the absence of fine-grained city-level climate protection measures due to data availability and accuracy, this study provides a detailed carbon emission inventory for analyzing the monthly fluctuations based on citizens' daily consumption behaviors. Here, carbon emissions embodied in approximately 500 household consumption items were calculated in 47 prefectural-level cities in Japan from 2011 to June 2021. We analyzed the results considering the regional, seasonal, demand, and emission way-specific aspects, and compared the emission before and during the COVID-19 pandemic. Notably, the carbon footprints during the pandemic were consistent with the previous level despite downtrends in specific categories. This study provides an example of utilizing city-level emission data to improve household green consumption behavior as references for enriching city-level decarbonization paths.

Black carbon over tropical Indian coast during the COVID-19 lockdown: inconspicuous role of coastal meteorology

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59' 26.5″ N, 80° 13' 51.8″ E) on the eastern coast of India during January-June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene.

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.

Graphic Abstract

Impact of mobility restrictions on NO2 concentrations in key Latin American cities during the first wave of the COVID-19 pandemic

Between March and June 2020, activity in the major cities of Latin America declined due to containment efforts implemented by local governments to avoid the rapid spread of COVID-19. Our study compared 2020 with the previous year and demonstrated a considerable drop in tropospheric NO₂ levels obtained by the SENTINEL 5P satellite in major Latin American cities. Lima (47.5%), Santiago (36.1%), São Paulo (27%), Rio de Janeiro (23%), Quito (18.6%), Bogota (17.5%), Buenos Aires (16.6%), Guayaquil (15.3%), Medellin (14.2%), La Paz (9.5%), Belo Horizonte (7.8%), Mexico (7.6%) and Brasilia (5.9%) registered statistically significant decreases in NO₂ concentrations during the study period. In addition, we analyzed mobility data from Google and Apple reports as well as meteorological information from atmospheric reanalysis data along with satellite fields between 2011 and 2020, and performed a refined multivariate analysis (non-negative matrix approximation) to show that this decrease was associated with a reduction in population mobility rather than meteorological factors. Our findings corroborate the argument that confinement scenarios may indicate how air pollutant concentrations can be effectively reduced and managed.

On the transition of major pollutant and O3 production regime during Covid-19 lockdowns

Lockdowns enforced amid the pandemic facilitated the evaluation of the impact of emission reductions on air quality and the production regime of O₃ under NOx reduction. Analysis of space-time variation of various pollutants (PM10, PM2.5, NOx, CO, O₃ and VOC or TNMHC) through the lockdown phases at eight typical stations (Urban/Metro, Rural/high vegetation and coastal) is carried out. It reveals how the major pollutant (PM10 or PM2.5 or O₃, or CO) differs from station to station as lockdowns progress depending on geography, land-use pattern and efficacy of lockdown implementation. Among the stations analyzed, Delhi (Chandnichowk), the most polluted (PM10 = 203 μgm^-3; O₃ = 17.4 ppbv) in pre-lockdown, experienced maximum reduction during the first phase of lockdown in PM2.5 (-47%), NO₂ (-40%), CO (-37%) while O₃ remained almost the same (2% reduction) to pre-lockdown levels. The least polluted Mahabaleshwar (PM10 = 45 μgm^-3; O₃ = 54 ppbv) witnessed relatively less reduction in PM2.5 (-2.9%), NO₂ (-4.7%), CO (-49%) while O₃ increased by 36% to pre-lockdown levels. In rural stations with lots of greenery, O₃ is the major pollutant attributed to biogenic VOC emissions from vegetation besides lower NO levels. In other stations, PM2.5 or PM10 is the primary pollutant. At Chennai, Jabalpur, Mahabaleshwar and Goa, the deciding factor of Air Quality Index (AQI) remained unchanged, with reduced values. Particulate matter, PM10 decided AQI for three stations (dust as control component), and PM2.5 decided the same for two but within acceptable limits for stations. Improvement of AQI through control of dust would prove beneficial for Chennai and Patiala; anthropogenic emission control would work for Chandani chowk, Goa and Patiala; emission control of CO is required for Mahabaleshwar and Thiruvanathapuram. Under low VOC/NOx ratio conditions, O₃ varies with the ratio, NO/NO₂, with a negative (positive) slope indicating VOC-sensitive (NOx-sensitive) regime. Peak O₃ isopleths as a function of NOx and VOC depicting distinct patterns suggest that O₃ variation is entirely non-linear for a given NOx or VOC.

Concentration and size distribution of atmospheric particles in southern Italy during COVID-19 lockdown period

Many countries imposed lockdown (LD) to limit the spread of COVID-19, which led to a reduction in the emission of anthropogenic atmospheric pollutants. Several studies have investigated the effects of LD on air quality, mostly in urban settings and criteria pollutants. However, less information is available on background sites, and virtually no information is available on particle number size distribution (PNSD). This study investigated the effect of LD on air quality at an urban background site representing a near coast area in the central Mediterranean. The analysis focused on equivalent black carbon (eBC), particle mass concentrations in different size fractions: PM_2.5 (aerodynamic diameter D_a < 2.5 μm), PM₁₀ (D_a < 10 μm), PM_10-2.5 (2.5 < D_a < 10 μm); and PNSD in a wide range of diameters (0.01-10 μm). Measurements in 2020 during the national LD in Italy and period immediately after LD (POST-LD period) were compared with those in the corresponding periods from 2015 to 2019. The results showed that LD reduced the frequency and intensity of high-pollution events. Reductions were more relevant during POST-LD than during LD period for all variables, except quasi-ultrafine particles and PM_10-2.5. Two events of long-range transport of dust were observed, which need to be identified and removed to determine the effect of LD. The decreases in the quasi-ultrafine particles and eBC concentrations were 20%, and 15-22%, respectively. PM_2.5 concentration was reduced by 13-44% whereas PM_10-2.5 concentration was unaffected. The concentration of accumulation mode particles followed the behaviour of PM_2.5, with reductions of 19-57%. The results obtained could be relevant for future strategies aimed at improving air quality and understanding the processes that influence the number and mass particle size distributions.

Improved air quality leads to enhanced vegetation growth during the COVID-19 lockdown in India

The direct effect of pandemic induced lockdown (LD) on environment is widely explored, but its secondary impacts remain largely unexplored. Therefore, we assess the response of surface greenness and photosynthetic activity to the LD-induced improvement of air quality in India. Our analysis reveals a significant improvement in air quality marked by reduced levels of aerosols (AOD, -19.27%) and Particulate Matter (PM 2.5, -23%) during LD (2020)from pre-LD (March-September months for the period 2017-2019). The vegetation exhibits a positive response, reflected by the increase in surface greenness [Enhanced Vegetation Index (EVI, +10.4%)] and photosynthetic activity [Solar Induced Fluorescence (SiF, +11%)], during LD from pre-LD that coincides with two major agricultural seasons of India; Zaid (March-May) and Kharif (June-September). In addition, the croplands show a higher response [two-fold in EVI (14.45%) and four-fold in SiF (17.7%)] than that of forests. The prolonged growing period (phenology) and high rate of photosynthesis (intensification) led to the enhanced greening during LD owing to the reduced atmospheric pollution. This study, therefore, provides new insights into the response of vegetation to the improved air quality, which would give ideas to counter the challenges of food security in the context of climate pollution, and combat global warming by more greening.

Urban policy interventions to reduce traffic-related emissions and air pollution: A systematic evidence map

BACKGROUND

Urban areas are hot spots for human exposure to air pollution, which originates in large part from traffic. As the urban population continues to grow, a greater number of people risk exposure to traffic-related air pollution (TRAP) and its adverse, costly health effects. In many cities, there is a need and scope for air quality improvements through targeted policy interventions, which continue to grow including rapidly changing technologies.

OBJECTIVE

This systematic evidence map (SEM) examines and characterizes peer-reviewed evidence on urban-level policy interventions aimed at reducing traffic emissions and/or TRAP from on-road mobile sources, thus potentially reducing human exposures and adverse health effects and producing various co-benefits.

METHODS

This SEM follows a previously peer-reviewed and published protocol with minor deviations, explicitly outlined here. Articles indexed in Public Affairs Index, TRID, Medline and Embase were searched, limited to English, published between January 1, 2000, and June 1, 2020. Covidence was used to screen articles based on previously developed eligibility criteria. Data for included articles was extracted and manually documented into an Excel database. Data visualizations were created in Tableau.

RESULTS

We identified 7528 unique articles from database searches and included 376 unique articles in the final SEM. There were 58 unique policy interventions, and a total of 1,139 unique policy scenarios, comprising these interventions and different combinations thereof. The policy interventions fell under 6 overarching policy categories: 1) pricing, 2) land use, 3) infrastructure, 4) behavioral, 5) technology, and 6) management, standards, and services, with the latter being the most studied. For geographic location, 463 policy scenarios were studied in Europe, followed by 355 in Asia, 206 in North America, 57 in South America, 10 in Africa, and 7 in Australia. Alternative fuel technology was the most frequently studied intervention (271 times), followed by vehicle emission regulation (134 times). The least frequently studied interventions were vehicle ownership taxes, and studded tire regulations, studied once each. A mere 3 % of studies addressed all elements of the full-chain-traffic emissions, TRAP, exposures, and health. The evidence recorded for each unique policy scenario is hosted in an open-access, query-able Excel database, and a complementary interactive visualization tool. We showcase how users can find more about the effectiveness of the 1,139 included policy scenarios in reducing, increasing, having mixed or no effect on traffic emissions and/or TRAP.

CONCLUSION

This is the first peer-reviewed SEM to compile international evidence on urban-level policy interventions to reduce traffic emissions and/or TRAP in the context of human exposure and health effects. We also documented reported enablers, barriers, and co-benefits. The open-access Excel database and interactive visualization tool can be valuable resources for practitioners, policymakers, and researchers. Future updates to this work are recommended.

PROTOCOL REGISTRATION

Sanchez, K.A., Foster, M., Nieuwenhuijsen, M.J., May, A.D., Ramani, T., Zietsman, J. and Khreis, H., 2020. Urban policy interventions to reduce traffic emissions and traffic-related air pollution: Protocol for a systematic evidence map. Environment international, 142, p.105826.

Weekend effect of air pollutants in small and medium-sized cities: The role of policies stringency to COVID-19 containment

Although the pattern of air pollutants has been extensively studied during the COVID-19 pandemic, the weekend effect has been rarely investigated. In order to understand the impact of policies stringency as well as the interruption-recovery pattern, the aim of the study was to investigate the levels of air pollutants (O3, NO2, SO2, PM2.5, PM10) and PM2.5/PM10 ratio before and after the COVID-19 pandemic in four cities in the state of Rio Grande do Sul, Brazil, evaluating the weekend effect at these two scenarios and also identifying how the restriction measures applied locally had an impact on this effect. For this, daily data from two years of monitoring of air pollutants were collected and the weekend effect was calculated based on the levels from Monday to Friday (weekday) and Saturday and Sunday (weekend). There was a positive weekend effect for almost all criteria air pollutants in the four cities, and an intrinsic relation between the weekend effect and the restriction measures adopted. A negative weekend effect was observed in the scenario characterized by less restrictive and more permissive policies for daily and occupational activities. Conversely, when more stringent measures were implemented, this trend was reversed and higher intensities of positive weekend effect were observed as restrictions increased. In conclusion, the COVID-19 perturbation to air quality changed as regions tighten and loosen restrictions on human mobility. These insights that can guide responsible authorities about future strategies and policies for air quality control.

Impact on Air Quality Index of India Due to Lockdown

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40-45%), compared before the lockdown period, followed by NO2 (27-35%), Sulphur Dioxide (SO2) (2-10%), PM2.5 (35-40%), but the Ozone (O3) rises (12-25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality.

Effects of COVID-19 era on a subtropical river basin in Bangladesh: Heavy metal(loid)s distribution, sources and probable human health risks

The COVID-19 era has profoundly affected everyday human life, the environment, and freshwater ecosystems worldwide. Despite the numerous influences, a strict COVID-19 lockdown might improve the surface water quality and thus provide an unprecedented opportunity to restore the degraded freshwater resource. Therefore, we intend to investigate the spatiotemporal water quality, sources, and preliminary health risks of heavy metal(loid)s in the Karatoya River basin (KRB), a tropical urban river in Bangladesh. Seventy water samples were collected from 35 stations in KRB in 2019 and 2022 during the dry season. The results showed that the concentrations of Ni, Cu, Zn, Pb, Cd, and Cr were significantly reduced by 89.3-99.7 % during the post-lockdown period (p < 0.05). However, pH, Fe, Mn, and As concentrations increased due to the rise of urban waste and the usage of disinfectants during the post-lockdown phase. In the post-lockdown phase, the heavy metal pollution index, heavy metal evaluation index, and Nemerow's pollution index values lessened by 8.58 %, 42.86 %, and 22.86 %, respectively. Besides, the irrigation water quality indices also improved by 59 %-62 %. The total hazard index values increased by 24 % (children) and 22 % (adults) due to the rise in Mn and As concentrations during the lockdown. In comparison, total carcinogenic risk values were reduced by 54 % (children) and 53 % (adults) in the post-lockdown. We found no significant changes in river flow, rainfall, or land cover near the river from the pre to post-lockdown phase. The results of semivariogram models have demonstrated that most attributes have weak spatial dependence, indicating restricted industrial and agricultural effluents during the lockdown, significantly improving river water quality. Our study confirms that the lockdown provides a unique opportunity for the remarkable improvement of degraded freshwater resources. Long-term management policies and regular monitoring should reduce river pollution and clean surface water.

Substantial changes in Gaseous pollutants and health effects during COVID-19 in Delhi, India

Background

Coronavirus disease has affected the entire population worldwide in terms of physical and environmental consequences. Therefore, the current study demonstrates the changes in the concentration of gaseous pollutants and their health effects during the COVID-19 pandemic in Delhi, the national capital city of India.

Methodology

In the present study, secondary data on gaseous pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and ozone (O3) were collected from the Central Pollution Control Board (CPCB) on a daily basis. Data were collected from January 1, 2020, to September 30, 2020, to determine the relative changes (%) in gaseous pollutants for pre-lockdown, lockdown, and unlockdown stages of COVID-19.

Results

The current findings for gaseous pollutants reveal that concentration declined in the range of 51%-83% (NO), 40%-69% (NOx), 31%-60% (NO2), and 25%-40% (NH3) during the lockdown compared to pre-lockdown period, respectively. The drastic decrease in gaseous pollutants was observed due to restricted measures during lockdown periods. The level of ozone was observed to be higher during the lockdown periods as compared to the pre-lockdown period. These gaseous pollutants are linked between the health risk assessment and hazard identification for non-carcinogenic. However, in infants (0-1 yr), Health Quotient (HQ) for daily and annual groups was found to be higher than the rest of the exposed group (toddlers, children, and adults) in all the periods.

Conclusion

The air quality values for pre-lockdown were calculated to be "poor category to "very poor" category in all zones of Delhi, whereas, during the lockdown period, the air quality levels for all zones were calculated as "satisfactory," except for Northeast Delhi, which displayed the "moderate" category. The computed HQ for daily chronic exposure for each pollutant across the child and adult groups was more than 1 (HQ > 1), which indicated a high probability to induce adverse health outcomes.

Quantifying COVID-19's silver lining: Avoided deaths from air quality improvements in Bogotá

In cities around the world, COVID-19 lockdowns have significantly improved outdoor air quality. Even if only temporary, these improvements could have longer-lasting effects by making chronic air pollution more salient and boosting political pressure for change. To that end, it is important to develop objective estimates of both the air quality improvements associated with lockdowns and the benefits they generate. We use panel data econometric models to estimate the effect of Bogotá's 16-month lockdown on PM_2.5 and NO₂ pollution, epidemiological models to simulate the effect of reductions in these pollutants on long- and short-term mortality, and benefit transfer methods to value the avoided mortality. We find that on average, Bogotá's lockdown cut PM_2.5 pollution by 15% and NO₂ pollution by 21%. However, the magnitude of these effects varied considerably over time and across the city's neighborhoods. Equivalent permanent reductions in these pollutants would reduce long-term premature deaths from air pollution by 23% each year, a benefit valued at $1 billion annually. Finally, we estimate that if they occurred ceteris paribus, the temporary reductions in pollutant concentrations in 2020-2021 due to Bogotá's lockdown would have cut short-term deaths from air pollution by 19%, a benefit valued at $244 million.