Table 1.

Summary of recent studies on COVID-19 and air quality impacts.

Study area (city, country)	Key findings	Author (year)
India (Delhi, Mumbai, Kolkata and Bangalore)	•Assessed overall impact of social and travel lockdown in five megacities of India and evaluated spatiotemporal variations in five criteria pollutants over two time periods, i.e., March-April 2019 and March-April 2020 and 10th-20th March 2020 (before lockdown) and 25th March to 6th April 2020 (during lockdown). •Statistically significant reduction was found in all megacities for all pollutants except for O3, with concentration declines in PM2.5 (∼41%) PM10 (52%), NO2 (51%) and CO (28%) during the lockdown phase in Delhi when compared to before lockdown. Similar reductions were observed for other megacities.	Jain and Sharma (2020)
India (Delhi)	•Analysed PM10, PM2.5, SO2, NO2, CO, O3 and NH3 over 34 monitoring stations in Delhi during pre-lockdown periods and during the lockdown. •Air quality significantly improved during lockdown, with reductions of 60% (PM10), 39% (PM2.5), 53% (NO2) and 30% (CO) compared to 2019.	Mahato et al. (2020)
India (Kolkata)	•Measured atmospheric CO2 levels with a portable CO2 analyzer at 12 sites during April 2019 (pre-lockdown) and April 2020 (post-lockdown). •30–40% decrease in CO2 levels with significant temporal variation was observed (p < 0.01), but no statistically significant variation was observed between sites.	Mitra et al. (2020)
India (22 cities in different regions)	•Examined impact of lockdown measures on criteria pollutant (PM10, PM2.5, CO, NO2, O3 and SO2) concentration reductions and analysed data between 16 March to 14 April from 2017 to 2020. •Compared to previous years (2017−2019), during lockdown periods, reductions in concentrations were up to 43% (PM2.5), 31% (PM10), ∼52% (mean excessive PM risks), 10% (CO), and 18% (NO2), while an increase of 17% in O3 and negligible changes in SO2 were detected. Reductions in AQI were up to 44% (North), 33% (South), 29% (East), 15% (Central) and 32% (West) India.	Sharma et al. (2020)
India	•Based on data-driven estimation methods and curve fitting, a 30-day projection of the effectiveness of preventive measures (social isolation and lockdown) on the spread of COVID-19 in India was developed. •Authors highlighted that the proposed method well estimated and predicted the positive cases and number of recovered cases within a certain range and will be a beneficial tool for policymakers and health officials.	Tomar and Gupta (2020)
Brazil (São Paulo)	•Assessed impacts of partial lockdown in São Paulo on concentration levels of CO, NO, NO2, and O3. •CO, NO, NO2, and O3 concentrations reduced by 65, 77, 54 and 30%, respectively, during the lockdown period.	Nakada and Urban (2020)
China	•Data from the TROPOspheric Monitoring Instrument (TROPOMI) sensor on-board ESA’s Sentinel-5 satellite showed reductions in NO2 concentrations due to lockdown near Wuhan, China (∼30%) and worldwide. •CO2 also decreased by 25% in China and by 6% worldwide. Fatalities might have decreased due to reduced air pollution levels.	Dutheil, Baker, and Navel (2020)
China	•Daily mortality due to air pollution and COVID-19 between Dec 2019 and Mar 11th 2020 showed huge differences, indicating that lockdown likely saved more lives by preventing ambient air pollution than by preventing infection. •NASA satellite images showed reductions of up to 30% in NO2 levels and about 25% carbon emissions (≈100 Mt equivalent to 6% of the global emissions) over the same period in Feb 2020 due to quarantine.	Isaifan (2020)
China (330 cities) and USA (New York)	•Evaluated the significance of environmental (including air quality) impacts of the COVID-19 lockdown in 330 Chinese cities and NewYork (USA). •When compared with 2019 data, air quality in 2020 improved by 11% across 330 cities of China and 50% in New York (USA).	Saadat, Rawtani, and Hussain (2020)
China	•Investigated impact of reduced anthropogenic activities due to lockdown on air pollution using simulation with the community multi-scale air quality model between 01 Jan and 12 Feb 2020 and compared three air pollution scenarios. •Decreased PM2.5 in Beijing, Shanghai, Guangzhou, and Wuhan by 9.23, 6.37, 5.35, and 30.79μgm−3, respectively. However, reduction ratios of PM2.5 concentrations were smaller than those of precursor emissions, partially due to unfavorable meteorological conditions.	Wang, Chen, Zhu, Wang, and Zhang (2020)
China	•Assessed the dynamic environmental (including air quality) impacts of COVID-19 in China during the period of Jan-Mar 2020 compared to 2019. •Reduction in CO2 emissions by >25% ∼ 1M tonne of C or 6% of global emissions over two weeks (spring festival 2020 and 2019). Satellite data: decline in NO2 (>30% China; 50% Wuhan). Air-pollutant monitoring in 337major cities (Jan-Mar 2020): Decline in PM2.5 (14.8%), NO2 (25%), CO (6.2%), PM10 (20.5%), SO2 (21.4%); no change in O3. •Reduced economic activities decrease energy consumption and hence environmental pollution.	Wang and Su (2020)
China and Europe (France, Germany, Spain, and Italy)	•Studied positive and negative impacts of the COVID-19 lockdown on the environment in severely affected countries such as China, USA, Italy and Spain. •Quarantine led to reduced air pollutant concentrations in: (i) China, for NO2 (12.9–22.8μgm−3, Wuhan) and PM2.5 (18.9μgm−3 in 367 cities (Wuhan-1.4μgm−3)) ∼ 20−30% between the monthly average for February 2020 against monthly averages for last three years (February 2017−2019); and (ii) Europe (Rome, Madrid, and Paris), in NO2 and PM2.5 concentrations in February 2020 compared to previous three years (2017−2019).	Zambrano-Monserrate, Ruano, and Sanchez-Alcalde (2020)
China (120 cities)	•Using generalised additive models, the authors explored relationships between ambient air pollutant (PM2.5, PM10, SO2, CO, NO2 and O3) concentrations and COVID-19 infection, utilising associations between meteorological variables (temperature, wind speed, RH) and daily COVID-19 confirmed cases. •Significant positive correlations were found between pollutant concentrations (PM2.5, PM10, NO2 and O3) and newly COVID-19 confirmed cases. For example, a 10μgm−3 increase in PM2.5, PM10, NO2, and O3 was linked to a 2.24%, 1.76%, 6.94%, and 4.76% increase in daily counts of confirmed cases, respectively. Conversely, a 10 μg m-3 increase in SO2 was linked to a 7.79% decrease in COVID-19 confirmed cases.	Zhu et al. (2020)
New York, Los Angeles, Zaragoza, Rome, Dubai, Delhi, Mumbai, Beijing and Shanghai	•Dec 2019-Mar 2020 (COVID-19 outbreak period) compared with 2017−2019 for changes in PM2.5 concentration (data from USEPA). •Decline in PM2.5 concentration in March 2020 compared to March 2019in. Dubai (11%), Rome (no change), Delhi (35%), Mumbai (14%), Beijing (50%), Shanghai (50%), New York (32%), Los Angeles (4%). No change in Zaragoza.	Chauhan and Singh (2020)
China, Spain, France, Italy, USA	•Study compiled environmental data released by NASA and ESA (European Space Agency) before and after the pandemic (Jan-Mar, 2019 and 2020) and discussed its impact on environmental quality. •Found reductions in NO2 levels of up to 20−30% in Wuhan (China), Spain, France, Italy and the USA.	Muhammad, Long, and Salman (2020)
Global	•Studied the impact of weather variables and air pollution (CO2, NO2, PM) on the global infection and spreading rate of COVID-19. •Air pollution was linked to an increased risk of COVID-19 infection and, therefore, strict and early lockdown measures (particularly in India and China) led to significant reductions in concentrations of NO2 and CO2 and this was observed across many metropolitan cities globally.	Paital (2020)
Global (27 countries, China, India and Europe)	•Using satellite data and a network of more than 10,000 air quality stations, the authors investigated whether or not reduced air pollution levels during Feb-Mar 2020 were related to COVID-19 lockdown events. •7400 (340 to 14,600) premature deaths and 6600 (4900 to 7900) pediatric asthma cases were avoided over two weeks post-lockdown. PM2.5-related avoided premature mortality was estimated for China as 1400 (1100 to 1700) and for India as 5300 (1000 to 11,700). Globally, 0.78 (0.09–1.5) million premature deaths and 1.6 (0.8–2) million pediatric asthma cases could be avoided in 2020, assuming the lockdown-induced reduction in concentrations is maintained throughout the year.	Venter et al. (2020)
Iran (Tehran, Mazandaran, Alborz, Gilan, and Qom)	•Examined the influence of several parameters on COVID-19 spread. Parameters included weather variables (e.g. average temperature, average precipitation, humidity, wind speed, and average solar radiation), number of COVID-19 infected people, population density, intra-provincial movement, and infection days. •Population density and intra-provincial movement showed a direct correlation with the infection outbreak, while regions with comparatively low wind speed, humidity and solar radiation exposure showed higher rates of infection due to favourable conditions for virus survival.	Ahmadi, Sharifi, Dorosti, Ghoushchi, and Ghanbari (2020)
Iran	•Air samples from 2−5 m of patients’ beds were collected to measure airborne transmission of COVID-19. •All tests results were negative, with no positive readings within 2m distance of patients.	Faridi et al. (2020)
Italy (Brescia, Lodi, Monza, Alessandria, Milan, Turin, Padua, Bergamo and Cremona, Rovigo and Genoa, Lombardy region)	•Determined associations between infected people and environmental, demographic and geographical factors governing transmission dynamics of COVID-19. •Cities with more than 100 days of air pollution (i.e. surpassing PM10 or O3 limits) showed significantly higher average numbers of infected individuals (∼3600 infected individuals on 7 April 2020) than in cities with less than 100 days of air pollution (∼1000 infected individuals).	Coccia (2020)
Spain (National)	•Using generalised linear mixed models, the authors estimated the shape of the epidemic curve of accumulated cases and evaluated the effect of the intervention introduced by the Spanish government to mitigate the COVID-19 epidemic. •After one day of implementation of the measures, the variation rate of accumulated cases was reported to reduce daily on average from 3.1–5.1%. However, until 14 March 2020, the introduced measures to reduce the epidemic curve of COVID-19 have not reached the planned phase.	Saez, Tobias, Varga, and Barceló (2020)
Spain (Barcelona)	•Investigated changes in air pollution levels during the lockdown in terms of urban background and traffic air quality observed stations. •After two weeks of lockdown, the authors found a substantial reduction in BC (-45%) and NO2 (-51%), mostly related to traffic emissions. PM10 also decreased from -28 to -31%, whereas levels of O3 increased from +33 to +57%.	Tobías et al. (2020)
Turkey (Nine cities : Istanbul, Izmir, Ankara, Konya, Kocaeli, Sakarya, Isparta, Bursa and Adana, Turkey)	•Studied the impact of meteorological variables (temperature, dew point temperature, humidity, and wind speed) on the COVID-19 pandemic over four periods (1, 3, 7, and 14 days). •Population, wind speed 14 days ago, and temperature on the day showed the highest correlations, respectively.	Şahin (2020)
USA (New York)	•Investigated correlations between climate indicators (average temperature, minimum temperature, maximum temperature, rainfall, average humidity, wind speed, and air quality) and the COVID-19 pandemic. •Meteorological variables (average temperature, minimum temperature) and air quality showed strong correlation with the COVID-19 pandemic.	Bashir, Ma et al. (2020)
USA (Nationwide)	•Investigated associations between long-term average exposure to PM2.5 and increased risk of COVID-19 death in the United States. •Found that an increase of 1 μg m−3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% CI 2%–15%).	Wu, Nethery et al. (2020)
Malaysia and Southeast Asia	•Investigated air quality impact of lockdown. Decrease in AOD (Singapore, Brunei, Malaysia and the Philippines), tropospheric NO2 column density (27−34% in most countries except for Ho Chi Minh and Yangon cities) was noted. AODs remained very high (up to 2) in northern Southeast Asia due to extensive forest fires and agricultural burning. •In Malaysia (March-April 2020), decrease in AOD (urban area: 40−70%), PM10 (industrial: 28–39%, urban: 26−31%), PM2.5 (industrial: 20–42%, urban: 23−32%), NO2 (industrial: 33–46%, urban: 63−64%), SO2 (urban: 9−20%), and CO (urban: 25−31%) compared with 2018 and 2019 was noted.	Kanniah, Zaman, Kaskaoutis, and Latif (2020)
Southern European cities (Nice, Rome, Valencia and Turin) and Wuhan (China)	•Presented the challenge of reducing the formation of secondary pollutants such as O3 even with lockdown’s reduced emission. In comparison to 2017−19, O3 increased (24% in Nice, 14% in Rome, 27% in Turin, 2.4% in Valencia and 36% in Wuhan) due to reduced NOx and lower O3 titration by NO, while reductions were observed in NO2 (∼53% in Europe and 57% in Wuhan), NO (∼63% in Europe), and PM2.5 and PM10 (∼8% in Europe and ∼42% in Wuhan) at urban stations. NO2 and NO decreased by ∼65% and ∼78% respectively at traffic stations in Europe. •Last years’ weekend comparison showed that NOx was ∼ 49% lower in all cities, O3 was ∼10% higher in Southern Europe and 38% higher in Wuhan, PM was similar (∼6%) in Southern Europe.	Sicard et al. (2020)
Yangtze River Delta Region (China)	•The WRF-CAMx modelling system and monitoring data were applied to investigate the impact of lockdown on air quality and sources of residual pollution for future air pollution control. •Reductions in SO2 (16–26%), NOx (29–47%), PM2.5 (27–46%) and VOCs (37–57%) emissions were observed. Declines in PM2.5 (31.8%, 33.2%), NO2 (45.1%, 27.2%) and SO2 (20.4%, 7.6%) were observed during the two periods of lockdown compared to 2019, however ozone increased greatly. Though primary emissions reduced (15%–61%), PM2.5 varied little (15−79μgm−3), suggesting high background and residual pollution. •Source apportionment pointed to industry (32.2–61.1%), mobile (3.9–8.1%), dust (2.6–7.7%), and residential (2.1–28.5%) sources of PM2.5 and a 14.0–28.6% contribution of long-range transport from northern China.	Li, Li et al. (2020)
44 cities in northern China	•Estimated the effects of COVID-19 related travel restrictions on air pollution. •The AQI decreased by 7.80%, and SO2, PM2.5, PM10, NO2, and CO decreased by 6.76%, 5.93%, 13.66%, 24.67%, and 4.58% respectively. Human movements were reduced by 69.85%, partially causing reduction in the AQI, PM2.5, and CO, while completely mediating SO2, PM10, and NO2 reductions.	Bao and Zhang (2020)
Almaty (Kazakhstan)	•Analysed the effects of COVID-19 lockdown on air pollutants. Reductions in PM2.5 (21%, spatial variations: 6–34%), CO (49%) and NO2 (35%) were observed compared to 2018–2019, whereas O3 increased by 15% compared to 17 days before the lockdown. Benzene and toluene were 2–3 times higher than for 2015–2019. •Pointed towards non-traffic-related sources, such as coal-fired combined heat and power plants, household heating systems, garbage burning and bathhouses.	Kerimray et al. (2020)
Delhi (India)	•Assessed pollutant datasets and observed a significant improvement in ambient air quality due to lockdown. •NOx reduced by ∼14 times the peak value (342 to 24ppb from 12 January to 30 March 2020). Significant reduction in the PM10, PM2.5, NH3, SO2, NO, NO2, NOx and CO concentrations.	Kotnala, Mandal, Sharma, and Kotnala (2020)
Review (Global)	•Reviewed the evidence for SARS-CoV-2 transmission by particulate matter pollutants. •PM2.5 was suggested to transmit coronavirus via aerosols in Italy and Wuhan. PM2.5 may have direct correlation with virus transmission and related mortality.	Sharma and Balyan (2020)
Lucknow and New Delhi (India)	•Analysed primary air pollutant data before and after lockdown (21-days). Significant decline in PM2.5, NO2 and CO was seen in both cities, with less significant decline in SO2. •Perceptible air pollution mitigation was due to adoption short and periodic lockdowns.	Srivastava, Kumar, Bauddh, Gautam, and Kumar (2020)
Northern China	•Quantified surface PM2.5, NO2, CO, and SO2 reductions during the lockdown. •PM2.5 and NO2 decreased by 29±22% and 53±10%, respectively, but O3 increased by a factor 2.0±0.7. Similar reductions (PM2.5: 31±6%, NO2: 54±7%) and increase (O3: 2.2±0.2 fold) were noted in the urban area of Wuhan.	Shi and Brasseur (2020)
Rio de Janeiro (Brazil)	•Discussed the partial lockdown impact on city air quality, comparing 2019 and weeks prior to the virus outbreak. •CO, related to light-duty vehicular emissions, reduced to 30.3–48.5%. Due to industrial and diesel input, NO2 decreased to a lower extent and PM10 reduced only during the first week. O3 increased due to the decrease in nitrogen oxide levels in a VOC-controlled scenario. •In April, vehicular flux and people movement increased due to public disregard of lockdown. Compared to 2019, NO2 and CO median values were 24.1–32.9 and 37.0–43.6% lower. Meteorological interferences (e.g. transport of industrial pollutants) might have also impacted the results.	Dantas, Siciliano, França, da Silva, and Arbilla (2020)
Global	•Tested the hypothesis of improved environmental quality due to lockdown induced atmospheric pollutants reduction. •COVID-19 cases in the tropical regions were relatively lower than the European and American regions. Reductions in NO2 (Substantial: 0.00002mol m−2), CO (low: <0.03mol m−2) and AOD (low-to-moderate: ∼0.1–0.2) were observed in the major hotspots of COVID-19 outbreak during Feb–Mar 2020. High hazard was projected in major areas of the globe (absolute humidity: 4−9g m−3) during Apr–Jul 2020. The northern hemisphere may be more susceptible in May–Jul 2020 while tropical regions in Oct–Nov 2020. •Scope for restoring the global environment from the ill-effects of anthropogenic activities through temporary shutdown measures was suggested.	Lal et al. (2020)
California (USA)	•Employed Spearman and Kendall correlation tests to analyse the association of PM2.5, PM10, SO2, NO2, Pb, VOC, and CO with COVID-19 cases. •PM10, PM2.5, SO2, NO2, and CO had significant correlation with the COVID-19 epidemic and adoption of green environmental policies was promoted to shield human life.	Bashir, Bilal, and Komal (2020)
Northern China	•Evaluated AQI, PM2.5, PM10, CO, SO2, NO2, and O3 changes during the COVID-19 control period. The AQI decreased from 89.6–71.6. 322 out of 366 cities experienced AQI decline. All pollutants decreased except O3 because of less scavenging of HO2 due to lower fine particle loadings. Reductions in NO2, PM2.5, CO, and SO2 were linked to reduced activities of transportation, secondary industries and industrial sector respectively. •Importance of reactions between gaseous and particulate pollutants, and control of residential emissions were illustrated. Lowering both NOx and VOCs will be needed to control O3.	Wang, Yuan et al. (2020)
Milan (Italy)	•Assessed the effect of partial and total lockdown on air quality in meteorologically comparable periods. •A significant reduction of PM10, PM2.5, BC, benzene, CO and NOx was observed mainly due to reduced vehicular traffic. SO2 also dropped but remained unchanged in the adjacent areas. O3 increased due to the minor NO concentration and was more accentuated in the adjacent areas with reduced concentrations of benzene.	Collivignarelli et al. (2020)
Salé City (Morocco)	•Analysed air pollutants before and during the lockdown period. PM10, SO2 and NO2 concentrations were reduced respectively by 75%, 49% and 96%. •The three-dimensional air mass backward trajectories, using the HYSPLIT model, demonstrated that long-range transported aerosol contributions out-balanced the reductions in locally emitted PM10. Differences in the air mass back trajectories and the meteorology between these two periods were shown.	Otmani et al. (2020)
Dwarka river basin within Jharkhand and West Bengal (India)	•Explored the impact of forced lockdown on PM10, land surface temperature, river water quality and noise using image- and field-derived data. •PM10 concentration reduced from 189−278μgm−3 in the pre-lockdown period to 50−60μgm−3 after 18 days of lockdown in selected four stone crushing clusters.	Mandal and Pal (2020)