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The box plot showing the concentrations of PM2.5 at various cities of Indian states like a Andhra Pradesh, b Maharashtra, c Madhya Pradesh, d Punjab, and e Rajasthan on pre, during, and post days of Christmas and New Year Celebration in three consecutive years (2019, 2020 and 2021)
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Urban air quality and COVID-19 have been considered significant issues worldwide in the last few years. The current study highlighted the variation in air pollutants (i.e., PM2.5, PM10, NO2, and SO2) profile between Christmas and new year celebrations in 2019, 2020, and 2021. It can be seen that the concentration of selected air pollutants shows a...
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Citations
... Diwali, the most important religious festival in India, which dates back 2500 years [52][53][54], is celebrated annually in October or November over approximately 5 to 6 days [55][56][57][58]. Known for its fireworks displays [59,60], it was estimated that in 2017, around 50,000 tons of fireworks were used [61,62]. Consequently, Diwali has the highest number of publications related to this research area, due to the massive burning of fireworks and the scientific community's concern over noise pollution, particularly in densely populated countries. ...
... WHO assessments underscore the connection between air pollution and a host of ailments including cardiovascular diseases, heart disease, stroke, chronic obstructive pulmonary disease, and lung cancer. Global trends in industrialization, transportation, and other anthropogenic activities have fueled air quality deterioration, disproportionately impacting vulnerable communities (Ban et al., 2023;Chen et al., 2023;Kumar et al., 2022;Patz et al., 2007). With escalating vehicle numbers, metropolitan areas are witnessing escalating air pollution, warranting comprehensive exploration and intervention strategies (Zhou et al., 2022;Gautam & Hens, 2022;Gautam et al., 2021a, b, c). ...
This comprehensive study delves into the complex issue of air pollution in Delhi, with a specific focus on the levels of PM2.5, PM10, NO2, and O3 during 2019 and 2020 across all four seasons. By analyzing primary data and employing advanced GIS techniques, the research not only quantifies pollution levels before and during the COVID-19 pandemic but also identifies high-risk areas and establishes a clear link between pollution and public health. The study reveals that 2019 witnessed more severe pollution levels compared to 2020, with PM2.5 and PM10 consistently exceeding WHO guidelines. Notably, PM10 levels breached Air Quality Index (AQI) standards, particularly during the winter season when it peaked at 67.99 µg/m³ and increased post-monsoon due to crop burning. Surprisingly, summer 2019 exhibited PM2.5 levels surpassing those of winter, underscoring the impact of reduced vehicle emissions during the summer months, while winter pollution levels remained relatively stable. The COVID-19 lockdowns in 2020 led to a substantial reduction in summer AQI by up to 58.00%, emphasizing the role of human activities in air quality. However, the study also indicates that monsoon AQI varied across different areas, with some experiencing higher emissions. Winter and post-monsoon AQI fluctuated by up to 24%, reinforcing the importance of continuous monitoring and source control measures. This research highlights the crucial role of Geographic Information Systems (GIS) in data analysis and informed decision-making for mitigating air pollution in Delhi. Its findings provide valuable insights for policymakers, offering guidance on promoting sustainability, public health, and a cleaner environment. In summary, the integration of GIS-driven pollution mapping aids in understanding and addressing the complex issue of air quality, ultimately contributing to a healthier and more environmentally friendly Delhi.
... On the other hand, these closures led to significant advantages, improving air quality and decreasing the environmental pollution levels . From an environmental standpoint, there are advantages, such as a notable improvement in air quality due to the entire cessation of industrial activity and transportation, which are well-known causes of air pollution, there has been a sudden drop in the concentration profile of air pollutants (Praveen Kumar et al., 2022). Furthermore, to evaluate the substantial changes in air quality, the National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) published air pollution data for Asian and European nations (Gautam, 2020). ...
Iraq and other nations have had fatalities as a result of the COVID-19 pandemic, which started from China. In Iraq, the 2020 coronavirus pandemic spread started on February 24–2020, in the Najaf province. Other cases of COVID-19 were detected in other governorates, where the overall number of confirmed infections in Iraq reached 2,114,313 including 24,267 deaths, as of January 16–2022. This study aims to identify infection recognition patterns using data mining applications by Remote Sensing RS and Geographical Information Systems GIS techniques to prepare coronavirus spread mapping based on spatial-temporal distribution and GIS-based spreading pattern processes in Iraq. In addition, to evaluate the air quality in the period of virus breakout and lockdown. The assessed data included the period from the beginning of the spread of the corona until its end. To present the mode of spread at the beginning of pandemic, we relied on statistical and remotely sensed data from February 24 to June 25–2020. Along with the results of the GIS spatial distribution maps, we provided a visual view of infection queries and presented the results as a spreading pattern map of COVID-19 in Iraq. Thus, GIS and remote sensing technologies are indispensable to overcoming contagious diseases by monitoring their geographical distribution.
... As a result, primary pollutants were greatly reduced, which led to a similar initial atmospheric condition in different regions (Bherwani et al., 2021;Gautam, 2020;Gautam et al., 2021). Kumar et al. (2022) compared the air pollutant levels during the Christmas and New Year celebration in 2019, 2020 and 2021 and found lower total concentration of air pollutants than the previous year when there was no pandemic situation in India. Such confinement served as a natural experiment for scholars to accurately evaluate the impacts on the formation of regional pollution. ...
The air pollution in China currently is characterized by high fine particulate matter (PM2.5) and ozone (O3) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM2.5 and O3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM2.5 and O3. Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM2.5 and O3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM2.5 decreased while O3 increased in most cities due to the effect of COVID-19, and the increase in O3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM2.5-O3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM2.5-O3 VM-DCCA exponents αVM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha_{VM}$$\end{document} in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its αVM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha_{VM}$$\end{document} is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM2.5 and O3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM2.5-O3 DHP coordinated control strategies.
... Atmospheric aerosols vary in size ranging from 0.001 to about 100 µm, with a broader classification of fine-and coarse-mode aerosols. The size of aerosols depends on their production mechanism [1]. Anthropogenic activities such as incomplete combustion of biofuels, biomass, and fossil fuels are the significant source of fine-mode aerosols, and naturally produced aerosols by a mechanical process, such as wind lifting of dust, wavebreaking, etc., are significant sources of coarse-mode particles [2][3][4]. ...
Impact of Lockdown on Column and Surface Aerosol Content over Ahmedabad and a Comparison with the Indo-Gangetic Plain
Nisha Vaghmaria, James ME , Alok Sagar Gautam, Sneha Gautam
Earth 2023, 4(2), 278-295; https://doi.org/10.3390/earth4020015
Abstract: Changes in vertical column concentration, size distribution, and surface concentration of aerosol associated with the lockdown imposed by the COVID-19 pandemic in 2020 over the Ahmedabad region in Gujarat State, India, were analyzed. The results are compared with changes over selected Indo-Gangetic Plain (IGP) regions. On 25 March 2020, the prime minister of India declared a complete lockdown throughout the country and later lifted restrictions in a phased manner. Aerosol optical depth (AOD) over the Ahmedabad region on 29 March dropped to as low as 0.11, and in the first two weeks of lockdown, the weekly average AOD was only 0.18. On almost all days of the lockdown period, AOD over the Ahmedabad region was lower than the decadal mean. It was found that the Ahmedabad region responded differently to lockdown conditions compared to the IGP regions. During the first lockdown phase, AOD decreased by about 29% compared to the pre-lockdown period over the Ahmedabad region. However, the average reduction over the IGP was much more, about 50%. The average Angstrom exponent (AE) of 0.96 during the pre-lockdown period over the Ahmedabad region increased phase-wise to 1.36 during the L3 lockdown phase, indicating dominance of fine-mode particles during the lockdown period. It suggests a reduction in anthropogenically produced coarse-mode particles, typically dust produced by vehicular movement, construction, and industrial activities. However, on the other hand, over the IGP region, the high dominance of fine-mode particles during the pre-lockdown period had changed to a high dominance of coarse-mode particles, especially over the Delhi region. This indicates a reduction in anthropogenically produced fine-mode particles, which are mainly generated by fossil and biofuels/biomass combustion, over the IGP region by lockdown conditions. Within a few days of lockdown, PM2.5 was reduced by 64% and 76% over the Ahmedabad and Delhi regions, respectively. The lockdown imposed by the pandemic provided an excellent opportunity to ascertain background aerosol conditions in the atmosphere.
Keywords: COVID-19; aerosol optical depth; Angstrom exponent; PM2.5; PM10; Indo-Gangetic Plain
... In addition, in recent years, the COVID-19 (coronavirus disease 2019) pandemic occurred and has had a decreasing effect on air pollution in some countries (Ambade et al., 2021a;Kumar et al., 2022b;Srivastava et al., 2020;Varotsos et al., 2021). Adjacent to China, Vietnam was one of the first countries affected by the epidemic. ...
Air pollution, especially in urban regions, is receiving increasing attention in Vietnam. Consequently, this work aimed to study and analyze the air quality in several provinces and cities in the country focusing on PM2.5. Moreover, the impacts of COVID-19 social distancing on the PM2.5 level were investigated. For this purpose, descriptive statistic, Box and Whisker plot, correlation matrix, temporal variation, and trend analysis were conducted. R-based program and the R package “openair” were employed for the calculations. Hourly PM2.5 data were obtained from 8 national air quality monitoring sites. The study results indicated that provinces and cities in the North experienced more PM2.5 pollution compared to the Central and South. PM2.5 concentrations at each monitoring site varied significantly. Among monitoring sites, the northern sites showed high PM2.5 correlations with each other than the other sites. Seasonal variation was observed with high PM2.5 concentration in the dry season and low PM2.5 concentration in the wet season. PM2.5 concentration variation during the week was not so different. Diurnal variation showed that PM2.5 concentration rose at peak traffic hours and dropped in the afternoon. There was mainly a decreasing trend in PM2.5 concentration over the studied period. The COVID-19 pandemic has contributed to PM2.5 reduction. In the months implemented social distancing for preventing the epidemic, PM2.5 concentration declined but it would mostly increase in the following months. This study provided updated and valuable assessments of recent PM2.5 air quality in Vietnam.
... The pollutants PM 2.5 , PM 10 , SO 2 , NO 2 , and Stochastic Environmental Research and Risk Assessment CO decreased significantly from February 12 to March 21 during the first wave of the study period compared with the same period in 2017-2019 and 2021, and the increase in O 3 was slightly reduced. Similar conclusions were reached by Ravina et al. (2021), who suggested that brief and strict lockdowns would help improve air quality (Das et al. 2021;Kumar et al. 2022). The concentrations of each pollutant also decreased during the second wave of the study period compared to the control period, with significant decreases in PM 10 and NO 2 . ...
As a key node city of the “Silk Road Economic Belt” Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. This study analyzed the air quality before and during the COVID-19 (Coronavirus disease 2019) pandemic and its potential health effects based on the data of PM2.5, PM10, SO2, NO2, CO, and O3_8h levels from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021. As per the results, the concentrations of the air pollutants PM2.5, PM10, SO2, NO2, CO, and O3_8h in Urumqi from 2017 to 2021 showed a cyclical trend, and the implementation of COVID-19 prevention and control measures could effectively reduce the concentration(ρ) of air pollutants. The mean value of ρ(PM2.5) decreased from 2017 to 2021, whereas ρ(O3_8h) showed a waveform change trend (increased in 2017–2018, decreased in 2018–2020, and increased after 2020). Meanwhile, the maximum annual average values of ρ(PM2.5) and ρ(O3_8h) for the six monitoring stations during 2017–2021 occurred at sites S2 (74.37 µg m⁻³) and S6 (91.80 µg m⁻³), respectively; rapid industrialization had a greater impact on PM2.5 and O3_8h concentrations compared to commercial and residential areas. In addition, the air quality index data series can characterize the fluctuation trend of PM2.5. The high pollution levels (Class IV and V) of the air pollutants PM2.5 and O3_8h in Urumqi have been decreasing annually, and good days can account for 80–95% of the total number of days in the year, indicating that the number of days with a potential threat to residents' health is gradually decreasing. Therefore, more attention should be paid in controlling and managing air pollution in Urumqi.
... Previous studies in several countries observed the interrelation of PM, CO, and O 3 with the COVID-19 confirmed cases and deaths (Thapliyal et al. 2022;Naqvi et al. 2022;Kumar et al. 2022;Chelani and Gautam 2022;Bherwani et al. 2021). A recent review also concluded that the there was a strong correlation between chronic exposure to outdoor air pollutants with the likelihood of developing COVID-19 instances as well as its severity and fatality during the second wave (Marquès and Domingo 2022). ...
... In summary, concentrations of PM 2.5 , PM 10, and CO followed the trend: lockdown \ pre-lockdown, and O 3 concentrations followed the trend: lockdown [ pre-lockdown period. The lower concentrations of PM 2.5 , PM 10 , and CO during the lockdown period compared to the pre-lockdown period in our study were similar to several earlier studies which were conducted during the first wave (Kumar et al. 2022;Kolluru et al. 2021;Mahato et al. 2020;Sharma et al. 2020) and second wave of COVID-19 (Shukla et al. 2021). During the lockdown, several restrictions were imposed on personal travel, economic and outdoor activities. ...
The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM 2.5 : 67 lg m-3 (lockdown) versus 81 lg m-3 (pre-lockdown); PM 10 : 171 lg m-3 versus 235 lg m-3 ; CO: 0.9 mg m-3 versus 1.1 mg m-3) except ozone which increased during the lockdown period (57 lg m-3 versus 39 lg m-3). The variation in pollutant concentrations revealed that PM 2.5 , PM 10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.
... Previous studies in several countries observed the interrelation of PM, CO, and O 3 with the COVID-19 confirmed cases and deaths (Thapliyal et al. 2022;Naqvi et al. 2022;Kumar et al. 2022;Chelani and Gautam 2022;Bherwani et al. 2021). A recent review also concluded that the there was a strong correlation between chronic exposure to outdoor air pollutants with the likelihood of developing COVID-19 instances as well as its severity and fatality during the second wave (Marquès and Domingo 2022). ...
... In summary, concentrations of PM 2.5 , PM 10, and CO followed the trend: lockdown \ pre-lockdown, and O 3 concentrations followed the trend: lockdown [ pre-lockdown period. The lower concentrations of PM 2.5 , PM 10 , and CO during the lockdown period compared to the pre-lockdown period in our study were similar to several earlier studies which were conducted during the first wave (Kumar et al. 2022;Kolluru et al. 2021;Mahato et al. 2020;Sharma et al. 2020) and second wave of COVID-19 (Shukla et al. 2021). During the lockdown, several restrictions were imposed on personal travel, economic and outdoor activities. ...
The onset of the second wave of COVID-19 devastated many countries worldwide. Compared with the first wave, the second wave was more aggressive regarding infections and deaths. Numerous studies were conducted on the association of air pollutants and meteorological parameters during the first wave of COVID-19. However, little is known about their associations during the severe second wave of COVID-19. The present study is based on the air quality in Delhi during the second wave. Pollutant concentrations decreased during the lockdown period compared to pre-lockdown period (PM2.5: 67 µg m⁻³ (lockdown) versus 81 µg m⁻³ (pre-lockdown); PM10: 171 µg m⁻³ versus 235 µg m⁻³; CO: 0.9 mg m⁻³ versus 1.1 mg m⁻³) except ozone which increased during the lockdown period (57 µg m⁻³ versus 39 µg m⁻³). The variation in pollutant concentrations revealed that PM2.5, PM10 and CO were higher during the pre-COVID-19 period, followed by the second wave lockdown and the lowest in the first wave lockdown. These variations are corroborated by the spatiotemporal variability of the pollutants mapped using ArcGIS. During the lockdown period, the pollutants and meteorological variables explained 85% and 52% variability in COVID-19 confirmed cases and deaths (determined by General Linear Model). The results suggests that air pollution combined with meteorology acted as a driving force for the phenomenal growth of COVID-19 during the second wave. In addition to developing new drugs and vaccines, governments should focus on prediction models to better understand the effect of air pollution levels on COVID-19 cases. Policy and decision-makers can use the results from this study to implement the necessary guidelines for reducing air pollution. Also, the information presented here can help the public make informed decisions to improve the environment and human health significantly.
The objective of the study was to examine the spatial and temporal variation of the tropospheric NO2 column compared with the economic growth during the COVID-19 pandemic. Therefore, the satellite based tropospheric NO2 was acquired from the Aura Satellite during 2019-2021 over the South Sumatra region of Indonesia. The GIS analysis was conducted to produce the quarterly tropospheric NO2 map over the study area. In this study, the gross regional domestic product (GRDP) was used as a benchmark for economic growth. The GRDP would relate to the air pollution to identify possible anthropogenically induced NO2 pollution. The result indicated the GRDP growth significantly decreased when the tropospheric NO2 concentration experienced a great reduction during 2020 (Quarters III-IV). The economic growth reduced from 5.79 to-1.58 during 2019-2020. It was noted that during the decline in GRDP, the variation of tropospheric NO2 was decreased by about 33%.
