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Air pollution is one of the major causes of health risks as it leads to widespread disease and death each year. Countries have invested heavily in fighting air pollution, arguably without convincing results. The outbreak of the highly infectious disease COVID-19 in December 2019 has been declared a pandemic and a worldwide health crisis by World Health Organization (WHO). Countries resorted to city lockdowns that sternly curtailed personal mobility and economic activities to control the spread of this deadly coronavirus disease. This paper examines the impact of Covid-19 city lockdowns on air quality. The researchers adopted a comprehensive interpretative document analysis for this study, which guided the careful but rigorous examination of air quality and coronavirus data. This method affirmed the authenticity of the information examined and interpreted in the US, Italy and China, the study areas. The study found that Covid-19 city lockdowns have contributed to a significant improvement in air quality within the first four months of the outbreak of Covid-19. National Aeronautics and Space Administration (NASA) had reported that NO2 concentrations in the study areas had reduced significantly using evidence from their Sentinel-5P instrument. Air quality in Covid-19 cities' lockdowns also improved because of the enforcement of other types of measures enacted to battle the virus. WHO still believes that the amount of NO2 concentration in the atmosphere is still high per their standards and regulations. Based on this, the researchers recommend that governments and other stakeholders put in much effort in terms of legislation to "win the war” against air pollution.
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With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2. © 2023 by the authors.
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This paper aims to study the major pollutants of the four metro cities of India before and after covid 19 first wave. The cities considered for the study are Bangalore, Delhi, Mumbai, and Kolkata. The major pollutants considered for the study are PM2.5, PM10, NO, NO2, NOx, SO2, CO, and Ozone. The basic aim of the study is to find the effect of lockdown and covid restrictions on the level of pollutants across the four major cities of India. We used both parametric and non-parametric tests for the analysis using SPSS. From the study, it is clear that there is a significant decrease in all the major pollutants across India's major cities.6. © 2023, University of Wollongong. All rights reserved.
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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.
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The study purpose was investigated the effect of aerator masks on the oxygen saturation (SO2) of mentally retarded athletes compared to medical masks. The researcher used a comparative study. Parametric test was used to test the difference in SO2 percent and evaluate the questionnaire in the two groups. The results showed after under-going exercise, SO2 percent in participants was 94.60+/-0.55 for aerator masks and 96.60+/-0.55 for medical masks. The study also showed that there was no significant difference in SO2 percent of participants wearing aerator masks and medical masks. However, the decrease in oxygen saturation percent of participants wearing aerator masks experienced a lower decrease, 1.60+/-0.55 compared to medical masks of 2.00+/-1.00. On the other hand, wearing an aerator mask is better because it does not prevent maximum performance, better comfort, the mask material feels better on the skin, does not experience difficulties breathable, and suitable for exercise. An aerator mask is a mask with an adequate supply of oxygen so that every breath is maintained even when used during sports activities. Copyright © the Author(s),2022 Licensee PAGEPress, Italy.
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In populated and developing countries, governments consider the regulation and protection of the environment as a major task and should take into consideration the concept of smart environment monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, datasets and other devices connected across the globe through a network. This system can basically help in monitoring air quality which is necessary in the field of meteorological studies and movement factors. Also, these factors contribute a lot in air pollution. So, forecasting air quality index using an intelligent environment system, which includes a machine learning model in order to predict air quality index for National Capital Region (NCR) was proposed. The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3 were used. In recent years, machine learning in most emerging technology is used for prediction with 99.99% of accuracy by using historical data.
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In response to the COVID-19 outbreak, severe steps have been taken to control its rapid spread by countries globally. A nationwide lockdown was executed at the end of January 2020 in China, which resulted in a significant change and an improvement in air quality patterns. In this study, the objectives were to assess the spatiotemporal impact of the COVID-19 lockdown on air quality in Nanjing, China. The present study researched the six air pollutant parameters, namely, PM10, PM2.5, SO2, NO2, CO, and O-3. The data were divided into six periods, P1-P3: pre-lockdown, during lockdown, and after lockdown periods, P4-P6: 2017-19 (same dates of lockdown). The results reveal that during the COVID-19 control period, a significant drop and an improvement in air quality were observed. According to our findings, the PM10, PM2.5, SO2, NO2, and CO concentrations were reduced by -33.03%, -35.41%, -21.26%, -39.79%, and -20.65%, respectively, while the concentration of O-3 significantly increased by an average of 104.85% in Nanjing. From the previous 3 years to lockdown variations, PM10 (-40.60%), PM2.5 (-40.02%), SO2 (-54.19%), NO2 (-33.60%), and CO (23.16%) were also reduced, while O-3 increased (10.83%). Moreover, compared with those in the COVID-19 period, the levels of PM10, SO2, NO2, CO, and O-3 increased by 2.84%, 28.55%, 4.68%, 16.44%, and 37.36%, respectively, while PM2.5 reduced by up to -14.34% after the lockdown in Nanjing. The outcomes of our study provide a roadmap for the scientific community and local administration to make policies to control air pollution.
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Various methods used by different countries' governments to control the spread of coronavirus disease 2019 (COVID-19), the cause of pandemic in 2020, affected air quality. The aim of this study was to evaluate the effects of lockdown in Armenia on the content of the main air pollutants-dust, SO2 and NO2. This was a cross-sectional study. We analyzed data on the concentrations of SO2, NO2 and dust from March to June, 2019 and the same period in 2020 as well as data on positive COVID-19 cases from Yerevan, Vanadzor and Hrazdan. In 2020, dust was found to be lower in Yerevan and in Hrazdan and higher in Vanadzor than in the same period in 2019. The same pattern was present for SO2 concentrations: in Yerevan and Hrazdan there was a decrease, and there was an increase in Vanadzor. The concentrations of NO2 increased in Yerevan and Hrazdan, with a slight decrease in Vanadzor. New cases of COVID-19 had a negative correlation with dust and a positive correlation with SO2. The strict quarantine measures were effective in containing the spread of COVID-19.
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Air pollution may be involved in spreading dengue fever (DF) besides rainfalls and warmer temperatures. While particulate matter (PM), especially those with diameter of 10 µm (PM10) or 2.5 µm or less (PM25), and NO2 increase the risk of coronavirus 2 infection, their roles in triggering DF remain unclear. We explored if air pollution factors predict DF incidence in addition to the classic climate factors. Public databases and DF records of two southern cities in Taiwan were used in regression analyses. Month order, PM10 minimum, PM2.5 minimum, and precipitation days were retained in the enter mode model, and SO2 minimum, O3 maximum, and CO minimum were retained in the stepwise forward mode model in addition to month order, PM10 minimum, PM2.5 minimum, and precipitation days. While PM2.5 minimum showed a negative contribution to the monthly DF incidence, other variables showed the opposite effects. The sustain of month order, PM10 minimum, PM2.5 minimum, and precipitation days in both regression models confirms the role of classic climate factors and illustrates a potential biological role of the air pollutants in the life cycle of mosquito vectors and dengue virus and possibly human immune status. Future DF prevention should concern the contribution of air pollution besides the classic climate factors.
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The purpose of this study was to investigate and assess how restrictive COVID-19 precautions affect air quality in Zonguldak, as well as to determine the relationship between air quality and meteorological variables under these conditions. Daily PM2.5, PM10, SO2, and NOx concentrations and meteorological data, from 1 March to 31 May 2018, 2019, and 2020 were collected for this research. During the 2020 restrictive COVID-19 precautions, it was determined that concentrations of air pollutants were different and low based on the 95% confidence interval by using paired t-test samples. Meteorological variables were found to be similar to previous years, and the correlation between them and air pollutants was found to be significant (P < 0.01) but low according to Pearson correlations. As a result, meteorological variables were determined to have no direct effect on the low concentrations of air quality emissions during the 2020 pandemic. The overall findings revealed that anthropogenic impact has a negative impact on air quality and the air quality had improved during the COVID pandemic. Furthermore, the restriction on the region's coalmines during the COVID-19 pandemic has significant impact on the improvement of air quality.
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Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.
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Due to COVID-19 countermeasures, air quality temporarily improved in several countries around the world, especially in urban areas. This study investigates predominantly the changes in concentration levels of air pollutants (PM10 and PM2.5) in the Republic of Slovenia during the year 2020, which was marked by COVID-19 lockdowns. In this study, the data for the year 2020 were divided into four periods, i.e., the period before COVID-19 (1 January-11 March 2020), the first lockdown (12 March-31 May 2020), after the first lockdown (1 June-17 October 2020), and the second lockdown (18 October-31 December 2020). The data were obtained from 25 ground-based nationwide stations, subdivided further by traffic and background sites. For comparison, data from 2018 and 2019 were evaluated as well. Our findings indicate that COVID-19 restrictions had a moderate and indirect impact on PM10 and PMM2.5 concentrations, which were more evident at PM10 monitoring sites near traffic locations. The results were additionally supported by providing t- and F- statistical tests. The impact of meteorological conditions (wind speed, temperature, relative humidity, and precipitation) on the concentration levels was also studied. The results show that, the most significant decrease of PM concentration in 2020 compared to 2018 and 2019 was found in the period after the first lockdown, while precipitation was not significantly different between the years during this time.
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Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020-15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021-1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020-1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.
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The present study aims to provide evidence on the effects of pandemic curtailment measures on public health, targeting the changes in breathable air quality, within urban areas. The analyzed period covers the full impact of lockdowns in Europe in 2020. We used everyday data for each analyzed pollutant, NO2, SO2, CO, PM2.5 and PM10, from urban monitoring stations that provided real-time concentrations (provided by Copernicus Atmosphere Monitoring Service, Environmental Protection Agency repository and European Environment Agency map services) and satellite data (provided by NASA Orbiting Carbon Observatory 2). In the present study, the urban air quality was computed using a composite index that was further analyzed in comparison with pandemic restrictions. Descriptive statistics, charts and maps were used to visualize the data that covered the analyzed countries. Our results show that air pollution was reduced by 12% after lockdowns in European urban areas, with a 0.76 correlation between air pollution and pandemic restrictions. All air pollutants registered significant drops.
Subject(s)
Air Pollutants , Air Pollution , Air Pollutants/analysis , Air Pollution/analysis , Environmental Monitoring/methods , Europe , Pandemics/prevention & control , Particulate Matter/analysis , Public HealthABSTRACT
The highly polluted Indian cities are retained their normal state during COVID-19 pandemic lockdown. Thus, the study aims to analysis the lockdown impact on NO2 and SO2 in the selected cities. So, the lockdown relative changes (C) are calculated based on baseline and pre-lockdown, for which the ground (Central Pollution Control Board data) and satellite (Sentinel 5P data) measured NO2 and SO2 data were utilised. The study reveals that the average pollutants concentration of NO2 and SO2 is declined to below-average level and indicates that the average dropdown level of NO2 and SO2 are -45.4% and -30.2% for ground pollutants;-45.1% and -43.4% for satellite pollutants respectively. The pandemic lockdown has restricted industrial, vehicular emission, and other anthropogenic activities which resulted in a positive effect on air quality. © 2021 IEEE.
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Air Pollution is a serious threat in the enlightened globe that impacts atmospheric change and human health. Air pollutants like Particulate Matter PM2.5, Particulate Matter PM10, ground-level ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO) lead to air pollution either in the form of gases or particles suspended in the air. The contemporary study aimed to analyze the source of air pollution and change in air quality during lockdown periods due to 1st and 2nd wave of COVID-19 pandemic within six different states namely, Uttar Pradesh, Madhya Pradesh, Delhi, Haryana, Rajasthan and Punjab. The selected states were analyzed in four different phases (i) Before First Covid Wave Lockdown (January 1, 2020 to March 24, 2020), (ii) During First Covid Wave Lockdown (March 25, 2020 to May 31, 2020), (iii) After First Covid Wave Lockdown (June 1, 2020 to April 19, 2021), (iv) During Second Covid Wave Lockdown (April 20, 2021 to May 26, 2021) from 45 air quality monitoring stations. This study also explains the limitations in stationary Ambient Air Monitoring Stations and the scope of IoT technology to improve the standards of monitoring air quality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Exposure to air pollution can exacerbate the severe COVID-19 conditions, subsequently causing an increase in the death rate. In this study, we investigated the association between long-term exposure to air pollution and risks of COVID-19 hospitalization and mortality in Arak, Iran. Air pollution data was obtained from air quality monitoring stations located in Arak, including particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3) and carbon monoxide (CO). Daily numbers of Covid-19 cases including hospital admissions (hospitalization) and deaths (mortality) were obtained from a national data registry recorded by Arak University of Medical Sciences. A Poisson regression model with natural spline functions was applied to set the effects of air pollution on COVID-19 hospitalization and mortality. The percent change of COVID-19 hospitalization per 10 µg/m3 increase in PM2.5 and PM10 were 8.5% (95% CI 7.6 to 11.5) and 4.8% (95% CI 3 to 6.5), respectively. An increase of 10 µg/m3 in PM2.5 resulting in 5.6% (95% CI: 3.1-8.3%) increase in COVID-19 mortality. The percent change of hospitalization (7.7%, 95% CI 2.2 to 13.3) and mortality (4.5%, 95% CI 0.3 to 9.5) were positively significant per one ppb increment in SO2, while NO2, O3 and CO were inversely associated with hospitalization and mortality. Our findings strongly suggesting that a small increase in long-term exposure to PM2.5, PM10 and SO2 elevating risks of hospitalization and mortality related to COVID-19.
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During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO2, sulfur dioxide-SO2, carbon monoxide-CO) and radon - 222Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Radon , Air Pollutants/analysis , COVID-19/epidemiology , Humans , Pandemics , Particulate Matter/analysis , Radon/analysis , Romania/epidemiology , SARS-CoV-2 , Time FactorsABSTRACT
The pandemic of COVID-19 currently shadows the world; the whole earth has been on an unprecedented lockdown. Social distancing among people interrupted domestic and international air traffic, suspended industrial productions and economic activities, and had various far-reaching and undetermined implications on air quality. Improvement in air quality has been reported in many cities during the lockdown. On March 22, 2020, the Turkish government enforced strict lockdown measures to reduce coronavirus disease transmission. This lockdown had a significant impact on the movement of people within the country, which resulted in a major drop in worldwide commercial activities. During this period, university campuses were emptied due to the transition to distance education. In this study, various air pollutants sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), total bacteria, and total fungi were measured in different indoor environments at Eskisehir Technical University Campus in Eskisehir, Turkey during COVID-19 lock down period. Also, to calculate the indoor and outdoor ratios (I/O) of the pollutants, simultaneous outdoor measurements were also carried out. The average indoor SO2, NO2, O3, and PM2.5 concentrations in different indoor environments ranged between 2.10 and 54.58, 1.36-30.89, 12.01-39.05, and 21-94 µg/m3, respectively. The total number of bacteria and fungi ranged between 21.83-514.15 and 13.10-83.36 CFU/m3, respectively. Our study intends to give a glimpse to quantify the impact of a pandemic on air quality in different indoor environments in a university campus in Eskisehir, Turkey and calls for follow-up studies. Indoor concentrations were evaluated together with outdoor concentrations. In general, it can be said that the calculated I/O ratios for SO2, NO2, O3, bacteria, and fungi were less than 1 in most indoor environments.
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The present paper deals with the studies on the change in concentration of three standard pollutants namely, respiratory suspended particulate matter (RSPM or PM10), Sulphur dioxide (SO2) and Nitrogen dioxide (NO2) because of lockdown in India to prevent the spread of COVID-19 pandemic in 2020. The monthly average concentrations of the above pollutants observed at four monitoring stations in and around Nagpur city during January to December 2020 were analyzed and compared. Due to COVID-19 pandemic, there was a complete lockdown from 25th March to 31st May 2020 and phased reopening of areas outside containment zones from June 1st onwards. It is found that the average concentration of all the three pollutants at all four stations was reduced by about 50 % to 75 % due to lockdown. During lockdown, the minimum concentration of PM10, SO2 and NO2 amongst all stations were found to be 40, 5 and 11 µg/m3, respectively, whereas the maximum concentrations were found to be 159, 20, and 50 µg/m3, respectively. The concentrations during lockdown were below the standards prescribed by CPCB, which were found to increase due to reopening. The Air quality index (AQI) at all four stations during lockdown was less than 50 (i.e. SATISFACTORY), whereas it increased above 100 (i.e. MODERATE) after reopening. As a result, the annual average concentration of pollutants was reduced in 2020 compared to previous years.