Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM), as inferred by the difference between PM10 and PM2.5. Coarse PM concentrations in 2015 averaged 52 µg m-3 in the North China Plain (NCP) and 23 µg m-3 in the Seoul Metropolitan Area (SMA), contributing nearly half of PM10. Strong daily correlations between coarse PM and carbon monoxide imply a dominant source from anthropogenic fugitive dust. Coarse PM concentrations in the NCP and the SMA decreased by 21 % from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. Anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate nitrate, a major contributor to PM2.5 pollution. GEOS-Chem model simulation of surface and aircraft observations from the Korea–United States Air Quality (KORUS-AQ) campaign over the SMA in May–June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of fine particulate nitrate. The effect is smaller in the NCP which has a larger excess of ammonia. Model sensitivity simulations for 2015–2019 show that decreasing anthropogenic coarse PM directly increases PM2.5 nitrate in summer, offsetting 80 % the effect of nitrogen oxide and ammonia emission controls, while in winter the presence of coarse PM increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Decreasing coarse PM helps to explain the lack of decrease in wintertime PM2.5 nitrate observed in the NCP and the SMA over the 2015–2021 period despite decreases in nitrogen oxide and ammonia emissions. Continuing decrease of fugitive dust pollution means that more stringent nitrogen oxide and ammonia emission controls will be required to successfully decrease PM2.5 nitrate.

Does environmental quality and weather induce COVID-19: Case study of Istanbul, Turkey

The coronavirus pandemic has infected more than 100 million people worldwide with COVID-19, with millions of deaths across the globe. In this research, we explored the effects of environmental and weather variables with daily COVID-19 cases and COVID-19 fatalities in Istanbul, Turkey. Turkey has the 8th highest number of COVID-19 cases globally, with the highest infections and deaths in Istanbul. This may be the first study to conduct a comprehensive investigation for environmental quality (air quality pollutants, e.g., PM2.5 and PM10, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, etc.), weather parameters (temperature, humidity) and COVID-19 in Turkey. The authors collected meteorological data from 11 March 2020 to 8 February 2021 and COVID-19 data from Istanbul and other regions. The results from empirical estimations, correlation analysis, and quantile on quantile techniques support that air quality and temperature significantly influence COVID-19 deaths in Istanbul. This research may help policymakers and health scientists to take specific measures to reduce the spread of coronavirus across different global cities.The effects of air quality on COVID-19 in Istanbul was investigated.The study applied correlation and quantile on quantile techniques over daily data.Temperature significantly induces the spread of COVID-19 in Istanbul at all quantiles.Air quality and Nitrogen are positively linked with COVID-19 new cases.

Updating and Evaluating Anthropogenic Emissions for NOAA's Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Significant effect of COVID-19 induced lockdown on air quality of the Indian metropolitan city Kolkata using air quality index and health air quality index

The lockdown, commencing in India from March 23, 2020 to control the escalation of Covid-19 cases, exhibited a positive impact on the air quality. The study attempts to assess the outcome of lockdown on the air quality of Kolkata, India followed by the comparison of six priority pollutants during pre-lockdown, lockdown, and unlock phases. Averaged concentrations of PM10 (72%), PM2.5 (73%), NOx (84%), SO2 (48%), and CO (61%) showed reduction throughout lockdown in comparison with pre-lockdown phase, although no significant reduction was observed in ground-level Ozone. Unlock Phases I and II showed similar concentrations of the pollutants as that in the lockdown period whereas, in unlock Phase-III, the air quality became comparable to that before lockdown. Statistical analysis confirmed that the reduction in air pollution is attributed to atmospheric factors. PCA analysis established significant positive correlation between particulate matters, CO, SO2, and NOx;however, no significant correlation was observed between NOx and O3. January and December showed the highest load of most of the pollutants. Health risk was evaluated by calculating the Relative risk and Health Air Quality Index, which showed maximum health risk during the pre-lockdown and minimum during lockdown and unlock Phase-II with the highest contributor being PM10. The study outcome manifests a reduction in environmental pollution as a result of controlled anthropogenic activities.

Links between the concentrations of gaseous pollutants measured in different regions of Estonia.

The factors that determine the concentrations of air pollutants (NO, NO2, SO2, O3), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO2 emission quota, differences between rural and urban stations, maritime-continental difference for NO2 and ozone, and specific industrial impact in case of SO2. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO2, and CO2, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R 2 = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R 2 = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO2 concentration measured in the Tahkuse station did appear a good predictor for ozone (R 2 = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO2 influence during transport) overrides the local effects of NO and/or NO2.

Lockdown as an Empirical Strategy to Curb Air Pollution: Evidence from a Quasi‐Natural Experiment

In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID‐19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21‐days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.

Joint impact of key air pollutants on COVID-19 severity: prediction based on toxicogenomic data analysis.

Considering that some researchers point to a possible influence of air pollution on COVID-19 transmission, severity, and death rate, the aim of our in silico study was to determine the relationship between the key air pollutants [sulphur dioxide (SO), carbon monoxide (CO), 2particulate matter (PMx), nitrogen dioxide (NO2), and ozone (O3)] and COVID-19 complications using the publicly available toxicogenomic analytical and prediction tools: (i) Comparative Toxicogenomic Database (CTD) to identify genes common to air pollutants and COVID-19 complications; (ii) GeneMANIA to construct a network of these common and related genes; (iii) ToppGene Suite to extract the most important biological processes and molecular pathways; and (iv) DisGeNET to search for the top gene-disease pairs. SO2, CO, PMx, NO2, and O3 interacted with 6, 6, 18, 9, and 12 COVID-19-related genes, respectively. Four of these are common for all pollutants (IL10, IL6, IL1B, and TNF) and participate in most (77.64 %) physical interactions. Further analysis pointed to cytokine binding and cytokine-mediated signalling pathway as the most important molecular function and biological process, respectively. Other molecular functions and biological processes are mostly related to cytokine activity and inflammation, which might be connected to the cytokine storm and resulting COVID-19 complications. The final step singled out the link between the CEBPA gene and acute myelocytic leukaemia and between TNFRSF1A and TNF receptor-associated periodic fever syndrome. This indicates possible complications in COVID-19 patients suffering from these diseases, especially those living in urban areas with poor air quality.

The effect of measures taken during the COVID-19 pandemic on air pollution: a East Mediterranean example from Turkey

Objective: Air pollution is both an environmental and a social problem, as it leads to a multitude of adverse effects on human health, ecosystems, the built environment and the climate. Air pollutants are emitted from anthropogenic and natural sources. This study aimed to examine the effects of the measures taken during the COVID-19 pandemic on air pollution in the province of Adana in Turkey.

Temporal variation analysis, impact of COVID-19 on air pollutant concentrations, and forecasting of air pollutants over the cities of Bangalore and Delhi in India

Indian cities are highly vulnerable to atmospheric pollution in recent years, due to exponential growth in urbanisation and industrialisation, and the increased pollution has been made to focus on the temporal variation analysis and forecasting of air pollutants over major Indian cities like Delhi and Bangalore. PM2.5 concentrations are nearly 60.5% less than the annual average value during monsoon season while 76.3% more during the winter months. Ozone concentrations increase during the summer months (~ 46.3% more than the annual average) in Delhi, whereas in Bangalore, ozone concentrations are more (~ 75% more than the annual average) during the winter months. Variations of carbon monoxide and nitrogen oxides are significantly less comparatively. COVID-19 lockdown has a substantial positive impact on air pollution. Air pollutant concentrations are reduced during phase I and phase II of the lockdown. Pollutants, especially NOx and PM2.5 concentrations, are drastically reduced compared to the previous years. NOx concentrations are reduced by ~ 20% in Bangalore, whereas ~ 50% in Delhi. PM2.5 concentrations are reduced by ~ 41% in Delhi and ~ 55% in Bangalore. Forecasting of pollutants will be helpful in providing the valuable information for the optimal air pollution control strategies. It has been observed that linear model gives better results compared to ARIMA and Exponential Smoothening models. By forecasting, the concentration of NO2 is 115.288 µg/m3, the ozone is 30.636 µg/m3, SO2 is 11.798 µg/m3, and CO is 2.758 mg/m3 over Delhi in 2021. All the pollutants during forecasting showed a rising trend except sulphur dioxide.

Impact of COVID-19 large scale restriction on environment and economy in Indonesia. (Special Issue: Covid-19.)

COVID-19 has a severe and widespread impact, especially in Indonesia. COVID-19 was first reported in Indonesia on March 03, 2020 then rapidly spread to all 34 provinces by April 09, 2020. Since then, COVID-19 is declared a state of national disaster and health emergency. This research analyzes the difference of CO, HCHO, NO2, and SO2 density in Jakarta, West Java, Central Java, and South Sulawesi before and during the pandemic. Also, this study assesses the effect of large scale restrictions on the economic growth during COVID-19 pandemic in Indonesia. In a nutshell, the results on Wilcoxon and Fisher test by significance level a=5% as well as odds ratio showed that there are significant differences of CO density in all regions with highest odds ratio in East Java (OR=9.07), significant differences of HCHO density in DKI Jakarta, East Java, and South Sulawesi. There are significant differences of NO2 density before and during public activities limitation in DKI Jakarta, West Java, East Java, and South Sulawesi. However, the results show that there are no significant differences of SO2 density in all regions. In addition, this research shows that there are significant differences of retail, grocery and pharmacy, and residental mobility before and during the COVID-19 pandemic in Indonesia. This research also shows that during the COVID-19 pandemic there are severe economic losses, industry, companies, and real disruptions are severe for all levels of life due to large scale restrictions.

The effect of COVID-19 lockdown on the air environment in India. (Special Issue: Covid-19.)

COVID-19 is a huge tragedy for the world community. Everything in the world is affected due to this pandemic right from economy to resources where the economy of major countries of the world are facing recession and resources are surplus with no takers at all. The measures to contain COVID-19 pandemic include lockdown, social distancing, isolation, and home quarantine. Lockdown adopted by the different governments which involve non-functioning of all the industry and manufacturing units. However, as a blessing in disguise, these measures have a positive effect on the environment in terms of reduction in toxic gasses like nitrogen dioxide, aerosols, atmosphere ozone, particulate matter, and improvement in air quality. In this paper, the effect on various environmental parameters like aerosol, ozone, particulate matter, nitrogen dioxide, sulfur dioxide, carbon monoxide, and temperature on India by lockdown due to COVID-19 as a preventive measure has been analyzed. The work involves the refining and preprocessing of raw data of this year and last year of various harmful pollutants present in the environment along with satellite images from National Aeronautics and Space Administration for comparison of different parameters. It has been observed that with the above adopted measures temperature has been reduced to near about 15 degree Celsius, there is also reduction in humidity i.e. it is reduced to 40%, particulate matter (PM2.5) reaches near about normal i.e. 40 g/m3 and carbon monoxide levels has also been reduced to 10 ppm. The main idea is to emphasize the fact that how the environment is self-healing during the lockdown. And this study will be beneficial to environmentalists and industry professionals to make the future strategy for improving the environment.

Appraisal of variation in particulate pollution loading with a change induced by anthropogenic cultural activity over a south Indian city-Visakhapatnam

The study focusses on the variation of air quality assessed from mass concentrations of air pollutants in the year 2020 (particulate matter (PM2.5 and PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3)) amidst COVID-19 restrictions on firework activity during Diwali festive period in Visakhapatnam city. The results are compared with 2018 and 2019. The results indicate that the firework activity affected ambient air quality. The effect is lower in 2020 than in 2018 and even in 2019. In 2019, the effect is lower compared to 2018 due to the washout of pollutants caused by unusual rains on those days.

The changes in the air quality of Wazirpur, Delhi due to the COVID-19 shutdown

The Corona Virus Disease (COVID)-19 pandemic led to the death of countless lives worldwide, which forced most countries and cities to impose a shutdown, bringing a halt to major human activities. While this shutdown caused a significant economic crisis, resulting in loss of livelihood to many people, it caused relief to the environment. Delhi in India is among the highest air-contaminated cities worldwide, and the COVID-19 shutdown helped improve air quality. This paper studied the variation in air quality for Wazirpur, Delhi, during shutdown in 2020 and a similar time-period in 2019. The data was acquired from the Central Pollution Control Board (CPCB) open-access portal for six air contaminants viz. Carbon-monoxide (CO), Nitrogen-dioxide (NO2), Ozone (O3), Particulate Matter (PM10 and PM2.5), and Sulphur-dioxide (SO2). Inferential statistical analysis was done to determine the trend in air quality variation during the shutdown compared to the previous year. Mean, standard deviation, percentage difference, linear regression and correlation analysis were made, and variable reduction in most air contaminants was noted. It was noted that for most of our observed time, the concentration of NO2, O3, PM10, PM2.5 and SO2 in 2020 is lower than in 2019, while the concentration of CO is greater in 2020 than the corresponding time in 2019. The maximum decline was observed for PM10 (70.5%) during phase-1, while the maximum increase was observed in CO (32.3%) during phase-1. As the shutdown restrictions were eased out, an increase in the air contaminants was also noted.

Review of Top-Down Method to Determine Atmospheric Emissions in Port. Case of Study: Port of Veracruz, Mexico

Indicators of environmental policies in force in Mexico, fossil fuels will continue to be used in industrial sectors, especially marine fuels, such as marine diesel oil, in port systems for some time. Considering this, we have evaluated several methods corresponding to a top-down system for determining fuel consumption and sulfur dioxide atmospheric emissions for the port of Veracruz in 2020 by type of ship on a daily resolution, considering a sulfur content of 0.5% mass by mass in marine fuel. After analyzing seven methods for determining sulfur dioxide atmospheric emission levels, Goldsworthy’s method was found to be the best option to characterize this port. The port system has two maritime zones, one of which is in expansion, which represented 55.66% of fuel consumption and 23.05% of atmospheric emissions according to the typology of vessels. We found that higher fuel consumption corresponded to container vessels, and tanker vessels represented higher atmospheric emission levels in the berthing position. The main differences that we found in the analysis of the seven methods of the top-down system corresponded to the load factor parameter, main and auxiliary engine power, and estimation of fuel consumption by type of vessel.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Assessment of air pollution status during COVID-19 lockdown (March-May 2020) over Bangalore City in India.

The coronavirus disease 2019 (COVID-19), which became a global pandemic by March 2020, forced almost all countries over the world to impose the lockdown as a measure of social distancing to control the spread of infection. India also strictly implemented a countrywide lockdown, starting from 24 March to 12 May 2020. This measure resulted in the reduction of the sources of air pollution in general: industrial, commercial, and vehicular pollution in particular, with visible improvement in ambient air quality. In this study, the impact of COVID-19 lockdown on the ambient concentration of air pollutants over the city of Bangalore (India) is assessed using Continuous Ambient Air Quality Measurement (CAAQM) data from 10 monitoring stations spread across the city. The data was obtained from Central Pollution Control Board (CPCB) and Karnataka State Pollution Control Board (KSPCB). The analysis of the relative changes in the ambient concentration of six major air pollutants (NO, NO2, NOX, PM2.5, O3, and SO2) has been carried out for two periods: March-May 2020 (COVID-19 lockdown) and the corresponding period of 2019 during when there was no lockdown. The analysis revealed significant reduction in the concentration of ambient air pollutants at both daily and monthly intervals. This can be attributed to the reduction in sources of emission; vehicular traffic, industrial, and other activities. The average reduction in the concentration of NO, NO2, NOX, PM2.5, and O3 between 01 March and 12 May 2020 was found to be 63%, 48%, 48%, 18%, and 23% respectively when compared to the same period in 2019. Similarly, the comparative analysis of pollutant concentrations between pre-lockdown (01-23 March 2020) and lockdown (24 March-12 May 2020) periods has shown a huge reduction in the ambient concentration of air pollutants, 47.3% (NO), 49% (NO2), 49% (NOX), 10% (SO2), 37.7% (PM2.5), and 15.6% (O3), resulting in improved air quality over Bangalore during the COVID-19 lockdown period. It is shown that the strict lockdown resulted in a significant reduction in the pollution levels. Such lockdowns may be useful as emergency intervention strategies to control air pollution in megacities when ambient air quality deteriorates dangerously.

Effect of COVID-19 pandemic on air quality: a study based on Air Quality Index.

The COVID-19 pandemic has significantly affected economic activities all around the world. Though it took a huge amount of human breathes as well as increases unemployment, it puts a positive impression on the environment. To stop the speedy extend of this disease, the maximum Government has imposed a strict lockdown on their citizens which creates a constructive impact on the atmosphere. Air pollutant concentration has been investigated in this study to analyze the impact of lockdown on the environment. Based on the air pollutant concentration, Air Quality Index (AQI) is deliberated. The Air Quality Index indicates the most and least polluted cities in the world. A higher value of AQI represents the higher polluted city and a lesser value of Air Quality Index represents a less polluted city. The impact of lockdown on air quality has been studied in this work and it is observed that the air pollutant concentration has reduced in every city of the world during the lockdown period. It has been also detected that the PM2.5 and PM10 are the most affecting air concentrator which controls the air quality of all the selected places during and after lockdown.

COVID-19's lockdown effect on air quality in Indian cities using air quality zonal modeling.

The complete lockdown due to COVID-19 pandemic has contributed to the improvement of air quality across the countries particularly in developing countries including India. This study aims to assess the air quality by monitoring major atmospheric pollutants such as AOD, CO, PM2.5, NO2, O3 and SO2 in 15 major cities of India using Air Quality Zonal Modeling. The study is based on two different data sources; (a) grid data (MODIS- Terra, MERRA-2, OMI and AIRS, Global Modeling and Assimilation Office, NASA) and (b) ground monitoring station data provided by Central Pollution Control Board (CPCB) / State Pollution Control Board (SPCB). The remotely sensed data demonstrated that the concentration of PM2.5 has declined by 14%, about 30% of NO2 in million-plus cities, 2.06% CO, SO2 within the range of 5 to 60%, whereas the concentration of O3 has increased by 1 to 3% in majority of cities compared with pre lockdown. On the other hand, CPCB/SPCB data showed more than 40% decrease in PM2.5 and 47% decrease in PM10 in north Indian cities, more than 35% decrease in NO2 in metropolitan cities, more than 85% decrease in SO2 in Chennai and Nagpur and more than 17% increase in O3 in five cities amid 43 days pandemic lockdown. The restrictions of anthropogenic activities have substantial effect on the emission of primary atmospheric pollutants.

Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India.

The present study investigates the air pollution pattern over India during the COVID-19 lockdown period (24 March-31 May 2020), pre-lockdown (1-23 March 2020) and the same periods from 2019 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra aerosol optical depth (AOD) with level 2 (10 km × 10 km) and level 3 (1° × 1° gridded) collection 6.1 Dark Target Deep Blue (DT-DB) aerosol product the Tropospheric Monitoring Instrument (TROPOMI) NO2 and SO2 data with a spatial resolution of 7 km × 3.5 km. We also use long-term average (2000-2017) of AOD for March-May to identify existing hotspot regions and to compare the variations observed in 2019 and 2020. The aim of the present work is to identify the pollution hotspot regions in India that existed during the lockdown and understanding the future projection scenarios reported by previous studies in light of the present findings. We have incorporated Menn-Kendall trend analysis to understand the AOD trends over India and percentage change in AOD, NO2 and SO2 to identify air pollution pattern changes during the lockdown. The results indicate higher air pollution levels over eastern India over the coal-fired power plants clusters. By considering the earlier projected studies, our results suggest that eastern India will have higher levels of air pollution, making it a new hotspot region for air pollution with highest magnitudes.

Significant change in air quality parameters during the year 2020 over 1st smart city of India: Bhubaneswar.

Prevention of Coronavirus results in lockdown in India from 24 March 2020 to 31 May 2020. Eastern India, which is having a dense cluster of coal-fired power plants and home to many mines, mineral industries, has not shutdown power plants and coal mines during this lockdown period, though other industrial and vehicular emissions were almost zero. The present study attempts to find the change in various atmospheric pollutants during this lockdown period over an eastern tropical Indian station-Bhubaneswar, which is the first smart city proposed in smart city mission of Government of India. The study analyses hourly concentrations of PM2.5, PM10, NO X , O3, and CO for March-May 2019 and 2020. The study shows a significant increase (rather than decrease) in PM2.5 and PM10, increase in O3 and a decrease in CO and NO X during the lockdown period. Results are advocating the impact of transported pollution over the study area for maintaining the PM2.5 and PM10 values even during the lockdown situation.