Silver linings in the dark clouds of COVID-19: Improvement of air quality over India and Delhi metropolitan area from measurements and WRF-CHIMERE model simulations

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

Background

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

Methodology

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

Results

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

Conclusion

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

A new hybrid models based on the neural network and discrete wavelet transform to identify the CHIMERE model limitation

A greater understanding of ozone damage to the environment and health led to an increased demand for accurate predictions. This study provides two new accurate hybrid models of ozone prediction. The first one (CHIMERE-NARX) is based on a NARX model as a post-processing of the CHIMERE model. In the second (CHIMERE-NARX-DWT), a discrete wavelet transform (DWT) has been added. Our models were built and validated using ozone measurements from the Mediouna station in Casablanca, Morocco, from February 1^st to March 27^th, 2021. The results highlighted the CHIMERE model limitations, such as wind speed overestimation and insufficient emission data. The first hybrid successfully increased the correlation coefficient from 88 to 93% and reduced RMSE from 23.99 μg/m³ to -3.54 μg/m³, overcoming CHIMERE limitations to some extent, especially during nighttime. A second hybrid addressed the first hybrid limitation, such as using ozone as a single input. This hybrid successfully balanced the weight of NARX at night against the day, increasing the correlation coefficient to 98% and decreasing RMSE to -0.02 μg/m³. This study presents a new generation of post-processing based on deterministic model processes, with the possibility of training them with minimum input data, which can be applied to other models using various pollutants.

The effect of COVID-19 lockdown on atmospheric total particle numbers, nanoparticle numbers and mass concentrations in the UK

The main aim of the COVID-19 lockdown was to curtail the person-to-person transmission of COVID-19. However, it also acted as an air quality intervention. The effect of the lockdown has been extensively analysed on NO₂, O₃, PM₁₀ and PM_2.5, however, little has been done on how total (TPN) and nanoparticle numbers (NPN) have been affected by the lockdown. This paper quantifies the effect of the lockdown on TPN and NPN in the UK, and compares how the effect varies between rural, urban background and traffic sites. Furthermore, the effect on particle numbers is compared with particle mass concentrations, mainly PM₁₀ and PM_2.5. Two approaches are used: (a) comparing measured levels of the pollutants in 2019 with 2020 during the lockdown periods; and (b) comparing the predictions of machine learning with measured concentrations using business as usual (BAU) scenario during the lockdown period. P₁₀₀ (particle size ≤100 nm) increased by 39% at Chilbolton Observatory (CHO) and decreased by 13% and 14% at London Honor Oak Park (LHO) and London Marylebone Road (LMR), respectively. Particles from 101 to 200 nm (P₂₀₀) showed a similar trend to P₁₀₀, however, average levels of particles 201-605 nm (P₆₀₅) decreased at all sites. TPN, PM₁₀ and PM_2.5 concentrations decreased at LMR and LHO sites. Estimated PM₁₀, PM_2.5 and TPN decreased at all three sites, however, the amount of change varied from site to site. Pollutant concentrations increased back the to pre-pandemic levels, suggesting more sustainable interventions for permanent air quality improvement.

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

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.

Effect of COVID-19-induced lockdown on NO2 pollution using TROPOMI and ground-based CPCB observations in Delhi NCR, India

The present study investigates the reduction in nitrogen dioxide (NO2) levels using satellite-based (Sentinel-5P TROPOMI) and ground-based (Central Pollution Control Board) observations of 2020. The lockdown duration, monthly, seasonal and annual changes in NO2 were assessed comparing the similar time period in 2019. The study also examines the role of atmospheric parameters like wind speed, air temperature, relative humidity, solar radiation and atmospheric pressure in altering the monthly and annual values of the pollutant. It was ascertained that there was a mean reduction of ~ 61% (~ 66.5%), ~ 58% (~ 51%) in daily mean NO2 pollution during lockdown phase 1 when compared with similar period of 2019 and pre-lockdown phase in 2020 from ground-based (satellite-based) measurements. April month with ~ 57% (~ 57%), summer season with ~ 48% (~ 32%) decline and an annual reduction of ~ 20% (~ 18%) in tropospheric NO2 values were observed (p < 0.001) compared to similar time periods of 2019. It was assessed that the meteorological parameters remained almost similar during various parts of the year in 2019 and 2020, indicating a negligent role in reducing the values of atmospheric pollution, particularly NO2 in the study area. It was concluded that the halt in anthropogenic activities and associated factors was mainly responsible for the reduced values in the Delhi conglomerate. Similar work can be proposed for other pollutants to holistically describe the pollution scenario as an aftermath of COVID-19-induced lockdown.

An ensemble LSTM-based AQI forecasting model with decomposition-reconstruction technique via CEEMDAN and fuzzy entropy

Air quality affects people’s daily life. Air quality index (AQI) is an essential indicator for controlling air pollution and ensuring public health, whose accurate forecasting can provide timely air pollution warnings and remind people to take protective measures against air pollution in advance. To address this issue, this paper developed a new ensemble learning model for AQI forecasting. In this study, (1) the signal decomposition technique complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is introduced to decompose the nonlinear and nonstationary AQI history data series into several more regular and more stable subseries firstly. (2) Fuzzy entropy (FE) is selected as the feature indicator to recombine the subseries with similar trends to avoid the problem of over-decomposition and reduce the computing time. (3) An ensemble long short-term memory (LSTM) neural network is established to forecast each reconstructed subseries, whose values are superimposed to predict the AQI value eventually. To validate the predicting performance of the proposed model, daily AQI data of Wuhan, China, dating from January 1, 2019, to February 28, 2022, is used as the experiment case. And comparative analysis is made between the proposed model and other common-used forecasting models. Benchmarking results of the numerical study demonstrate that the proposed model is superior to the other forecasting models with better AQI prediction accuracy.

Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India

The Community Multi-Scale Air Quality (CMAQ) model was applied to evaluate the air quality in the coastal city of Kannur, India, during the 2020 COVID-19 lockdown. From the Pre1 (March 1-24, 2020) period to the Lock (March 25-April 19, 2020) and Tri (April 20-May 9, 2020) periods, the Kerala state government gradually imposed a strict lockdown policy. Both the simulations and observations showed a decline in the PM_2.5 concentrations and an enhancement in the O₃ concentrations during the Lock and Tri periods compared with that in the Pre1 period. Integrated process rate (IPR) analysis was employed to isolate the contributions of the individual atmospheric processes. The results revealed that the vertical transport from the upper layers dominated the surface O₃ formation, comprising 89.4%, 83.1%, and 88.9% of the O₃ sources during the Pre1, Lock, and Tri periods, respectively. Photochemistry contributed negatively to the O₃ concentrations at the surface layer. Compared with the Pre1 period, the O₃ enhancement during the Lock period was primarily attributable to the lower negative contribution of photochemistry and the lower O₃ removal rate by horizontal transport. During the Tri period, a slower consumption of O₃ by gas-phase chemistry and a stronger vertical import from the upper layers to the surface accounted for the increase in O₃. Emission and aerosol processes constituted the major positive contributions to the net surface PM_2.5, accounting for a total of 48.7%, 38.4%, and 42.5% of PM_2.5 sources during the Pre1, Lock, and Tri periods, respectively. The decreases in the PM_2.5 concentrations during the Lock and Tri periods were primarily explained by the weaker PM_2.5 production from emission and aerosol processes. The increased vertical transport rate of PM_2.5 from the surface layer to the upper layers was also a reason for the decrease in the PM_2.5 during the Lock periods.

Increased Shamal winds and dust activity over the Arabian Peninsula during the COVID-19 lockdown period in 2020

While anthropogenic pollutants have decreased during the lockdown imposed as an effort to contain the spread of the Coronavirus disease 2019 (COVID-19), changes in particulate matter (PM) do not necessarily exhibit the same tendency. This is the case for the eastern Arabian Peninsula, where in March-June 2020, and with respect to the same period in 2016-2019, a 30 % increase in PM concentration is observed. A stronger than normal nocturnal low-level jet and subtropical jet over parts of Saudi Arabia, in response to anomalous convection over the tropical Indian Ocean, promoted enhanced and more frequent episodes of Shamal winds over the Arabian Peninsula. Increased surface winds associated with the downward mixing of momentum to the surface fostered, in turn, dust lifting and increased PM concentrations. The stronger low-level winds also favoured long-range transport of aerosols, changing the PM values downstream. The competing effects of reduced anthropogenic and increased dust concentrations leave a small positive signal (<5 W m^-2) in the net surface radiation flux (R_net), with the former dominating during daytime and the latter at night. However, in parts of the Arabian Gulf, Sea of Oman and Iran R_net increased by >20 W m^-2 with respect to the baseline period, owing to a clearer environment and weaker winds. It is concluded that a reduction in anthropogenic emissions due to the lockdown does not necessarily go hand in hand with lower particulate matter concentrations. Therefore, emissions reduction strategies need to account for feedback effects in order to reach the planned long-term outcomes.

Impact of COVID-19 Pandemic on Air Quality: A Systematic Review

With the emergence of the COVID-19 pandemic, several governments imposed severe restrictions on socio-economic activities, putting most of the world population into a general lockdown in March 2020. Although scattered, studies on this topic worldwide have rapidly emerged in the literature. Hence, this systematic review aimed to identify and discuss the scientifically validated literature that evaluated the impact of the COVID-19 pandemic and associated restrictions on air quality. Thus, a total of 114 studies that quantified the impact of the COVID-19 pandemic on air quality through monitoring were selected from three databases. The most evaluated countries were India and China; all the studies intended to evaluate the impact of the pandemic on air quality, mainly concerning PM₁₀, PM_2.5, NO₂, O₃, CO, and SO₂. Most of them focused on the 1st lockdown, comparing with the pre- and post-lockdown periods and usually in urban areas. Many studies conducted a descriptive analysis, while others complemented it with more advanced statistical analysis. Although using different methodologies, some studies reported a temporary air quality improvement during the lockdown. More studies are still needed, comparing different lockdown and lifting periods and, in other areas, for a definition of better-targeted policies to reduce air pollution.

Assessment of potential benefits of traffic and urban mobility reductions during COVID-19 lockdowns: dose-response calculations for material corrosions on built cultural heritage

Air pollution, particularly in urban areas, puts human health in danger and has adverse impacts on the built environment. It can accelerate the natural corrosion rate of cultural heritages and monuments, leading to premature aging and lowering their aesthetic value. Globally, at the beginning of 2020, to tackle the spread of novel COVID-19, the lockdown was enforced in the most hard-hit countries. Therefore, this study assesses, as a first time, the plausible benefits of traffic and urban mobility reductions on the natural process of deterioration of materials during COVID-19 lockdown in twenty-four major cities on five continents. The potential risk is estimated based on exceeding the tolerable degradation limits for each material. The notable impact of COVID-19 mobility restrictions on air quality was evidenced in 2020 compared to 2019. The introduced mobility restrictions in 2020 could decrease the surface recession rate of materials. Extremely randomized trees analysis showed that PM10 was the main influencing factor for corrosion of portland, copper, cast bronze, and carbon steel with a relative importance of 0.60, 0.32, 0.90, and 0.64, respectively, while SO2 and HNO3 were mainly responsible for corrosion of sandstone and zinc with a relative importance of 0.60 and 0.40, respectively. The globally adverse governed meteorological conditions in 2020 could not positively influence the movement restrictions around the world in air quality improvements. Our findings can highlight the need for additional policies and measures for reducing ambient pollution in cities and the proximity of sensitive cultural heritage to avoid further damage.

Pollutant Concentration Changes During the COVID-19 Lockdown in Barcelona and Surrounding Regions: Modification of Diurnal Cycles and Limited Role of Meteorological Conditions

One of the consequences of the COVID-19 lockdowns has been the modification of the air quality in many cities around the world. This study focuses on the variations in pollutant concentrations and how important meteorological conditions were for those variations in Barcelona and the surrounding area during the 2020 lockdown. Boundary-layer height, wind speed, and precipitation were compared between mid-March and April 2016-2019 (pre-lockdown) and the same period in 2020 (during lockdown). The results show the limited influence of meteorological factors on horizontal and vertical dispersion conditions. Compared with the pre-lockdown period, during lockdown the boundary-layer height slightly increased by between 5% and 9%, mean wind speed was very similar, and the fraction of days with rainfall increased only marginally, from 0.33 to 0.34, even though April 2020 was extremely wet in the study area. Variations in nitrogen dioxide ( NO 2 ), particulate matter with a diameter less than 10 μ m (PM10), and ozone ( O 3 ) concentrations over a 10-year period showed a 66% reduction in NO 2 , 37% reduction in PM10, and 27% increase in O 3 at a traffic station in Barcelona. The differences in the daily concentration cycle between weekends and weekdays were heavily smoothed for all pollutants considered. The afternoon NO 2 peak at the traffic station was suppressed compared with the average daily cycle. The analysis of ozone was extended to the regional scale, revealing lower concentrations at rural sites and higher ones in urban zones, especially in Barcelona and the surrounding area. The results presented not only complement previous air quality COVID-19 lockdown studies but also provide insights into the effects of road-traffic reduction.

Impact of COVID-19 induced lockdown on land surface temperature, aerosol, and urban heat in Europe and North America

The outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (-0.11 °C to -2.6 °C), USA (-0.70 °C) and Canada (-0.27 °C) in March-May of the pandemic year 2020 compared to the mean of 2015-2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. -0.46 °C to -0.96 °C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1-2 °C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapor content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation.

Comparing different approaches for assessing the impact of COVID-19 lockdown on urban air quality in Reading, UK

Many studies investigated the impact of COVID-19 lockdown on urban air quality, but their adopted approaches have varied and there is no consensus as to which approach should be used. In this paper we compare three of the main approaches and assess their performance using both estimated and measured data from several air quality monitoring stations (AQMS) in Reading, Berkshire UK. The approaches are: (1) Sequential approach - comparing pre-lockdown and lockdown periods 2020; (2) Parallel approach - comparing 2019 and 2020 for the equivalent time of the lockdown period; and (3) Machine learning modelling approach - predicting pollution levels for the lockdown period using business as usual (BAU) scenario and comparing with the observations. The parallel and machine learning approaches resulted in relative higher reductions and both showed strong correlation (0.97) and less error with each other. The sequential approach showed less reduction in NO and NOx, showed positive gain in PM₁₀ and NO₂ at most of the sites and demonstrated weak correlation with the other two approaches, and is not recommended for such analysis. Overall, the sequential approach showed -14, +4, -32, and + 56% change, the parallel approach showed -46, -43, -43 and + 7% change, and the machine learning approach showed -47, -44, -38 and + 5% change in NOx, NO₂, NO and PM₁₀ concentrations, respectively. The pollution roses demonstrated that the UK received easterly polluted winds from the central and eastern Europe, promoting secondary particulates and O₃ formation during the lockdown. Changes in pollutant concentrations vary both in space and time according to the approach used, environment type of the monitoring site and the data type (e.g., deweathered vs. raw data). Therefore, the reported results (here or elsewhere) should be viewed in light of these factors before making any conclusion.

Impacts of Aerosol Loading in the Hindu Kush Himalayan Region Based on MERRA-2 Reanalysis Data

The impacts of climate change have severely affected geosphere, biosphere and cryosphere ecosystems in the Hindu Kush Himalayan (HKH) region. The impact has been accelerating further during the last few decades due to rapid increase in anthropogenic activities such as modernization, industrialization and urbanization, along with energy demands. In view of this, the present work attempts to examine aerosol optical depth (AOD) over the HKH region using the long-term homogeneous MERRA-2 reanalysis data from January, 1980 to December, 2020. The AOD trends are examined statistically with student’s t-test (t). Due to a vast landmass, fragile topography and harsh climatic conditions, we categorized the HKH region into three sub-regions, namely, the northwestern and Karakoram (HKH1), the Central (HKH2) and the southeastern Himalaya and Tibetan Plateau (HKH3). Among the sub-regions, the significant enhancement of AOD is observed at several potential sites in the HKH2 region, namely, Pokhara, Nainital, Shimla and Dehradun by 55.75 × 10−4 ± 3.76 × 10−4, 53.15 × 10−4 ± 3.94 × 10−4, 51.53 × 10−4 ± 4.99 × 10−4 and 39.16 × 10−4 ± 4.08 × 10−4 AOD year−1 (550 nm), respectively, with correlation coefficients (Rs) of 0.86 to 0.93. However, at a sub-regional scale, HKH1, HKH2 and HKH3 exhibit 23.33 × 10−4 ± 2.28 × 10−4, 32.20 × 10−4 ± 2.58 × 10−4 and 9.48 × 10−4 ± 1.21 × 10−4 AOD year−1, respectively. The estimated trends are statistically significant (t > 7.0) with R from 0.81 to 0.91. Seasonally, the present study also shows strong positive AOD trends at several potential sites located in the HKH2 region, such as Pokhara, Nainital, Shimla and Dehradun, with minimum 19.81 × 10−4 ± 3.38 × 10−4 to maximum 72.95 × 10−4 ± 4.89 × 10−4 AOD year−1 with statistical significance. In addition, there are also increasing AOD trends at all the high-altitude background sites in all seasons.

Aerosol Loading and Radiation Budget Perturbations in Densely Populated and Highly Polluted Indo-Gangetic Plain by COVID-19: Influences on Cloud Properties and Air Temperature

Aerosols emitted in densely populated and industrialized Indo-Gangetic Plain, one of the most polluted regions in the world, modulate regional climate, monsoon, and Himalayan glacier retreat. Thus, this region is important for understanding aerosol perturbations and their resulting impacts on atmospheric changes during COVID-19 lockdown period, a natural experimental condition created by the pandemic. By analyzing 5 years (2016-2020) data of aerosols and performing a radiative transfer calculation, we found that columnar and near-surface aerosol loadings decreased, leading to reductions in radiative cooling at the surface and top of the atmosphere and atmospheric warming during lockdown period. Further, satellite data analyses showed increases in cloud optical thickness and cloud-particle effective radius and decrease in lower tropospheric air temperature during lockdown period. These results indicate critical influences of COVID-19 lockdown on regional climate and water cycle over Indo-Gangetic Plain, emphasizing need for further studies from modeling perspectives.

The Impact of Direct and Indirect COVID-19 Related Demand Shocks on Sectoral CO2 Emissions: Evidence from Major Asia Pacific Countries

COVID-19’s demand shocks have a significant impact on global CO2 emissions. However, few studies have estimated the impact of COVID-19’s direct and indirect demand shocks on sectoral CO2 emissions and linkages. This study’s goal is to estimate the impact of COVID-19’s direct and indirect demand shocks on the CO2 emissions of the Asia-Pacific countries of Bangladesh, China, India, Indonesia, and Pakistan (BCIIP). The study, based on the Asian Development Bank’s COVID-19 economic impact scenarios, estimated the impact of direct and indirect demand shocks on CO2 releases using input–output and hypothetical extraction methods. In the no COVID-19 scenario, China emitted the most CO2 (11 billion tons (Bt)), followed by India (2 Bt), Indonesia (0.5 Bt), Pakistan (0.2 Bt), and Bangladesh (0.08 Bt). For BCIIP nations, total demand shocks forced a 1–2% reduction in CO2 emissions under a worst-case scenario. Given BCIIP’s current economic recovery, a best or moderate scenario with a negative impact of less than 1% is more likely in coming years. Direct demand shocks, with a negative 85–63% share, caused most of the CO2 emissions decrease. The downstream indirect demand had only a 15–37% contribution to CO2 emissions reduction. Our study also discusses policy implications.

Short-term impacts of air pollutants in three megacities of India during COVID-19 lockdown

Lockdown was imposed by the Indian government in the month of March 2020 as an early precaution to the COVID-19 pandemic which obstructed the socio-economic growth globally. The main aim of this study was to analyse the impact of lockdown (imposed in March and continued in April 2020) on the existing air quality in three megacities of India (Delhi, Mumbai and Kolkata) by assessing the trends of PM10 and NO2 concentrations. A comparison of the percentage reduction in concentrations of lockdown period with respect to same period in year 2019 and pre-lockdown period (February 14-March 24) was made. It was observed from the study that an overall decrease of pollutant concentrations was in the ranges of 30-60% and 52-80% of PM10 and NO2, respectively, in the three cities during lockdown in comparison with previous year and pre-lockdown period. The overall decrease in concentrations of pollutants at urban sites was greater than the background sites. Highest decline in concentrations of PM10 were observed in Kolkata city, followed by Mumbai and Delhi, while decline in NO2 was highest in Mumbai. Results also highlighted that capital city Delhi had the worst air quality amongst three cities, with particulate matter (PM10) being the dominant pollutant. Although COVID-19 has significantly affected the human life considering the mortality and morbidity, lockdowns imposed to control the pandemic had significantly improved the air quality in the selected study locations, although for the short amount of period.

Assessment of the COVID-19 Lockdown Effects on Spectral Aerosol Scattering and Absorption Properties in Athens, Greece

COVID-19 is evolving into one of the worst pandemics in recent history, claiming a death toll of over 1.5 million as of December 2020. In an attempt to limit the expansion of the pandemic in its initial phase, nearly all countries imposed restriction measures, which resulted in an unprecedented reduction of air pollution. This study aims to assess the impact of the lockdown effects due to COVID-19 on in situ measured aerosol properties, namely spectral-scattering (bsca) and absorption (babs) coefficients, black carbon (BC) concentrations, single-scattering albedo (SSA), scattering and absorption Ångström exponents (SAE, AAE) in Athens, Greece. Moreover, a comparison is performed with the regional background site of Finokalia, Crete, for a better assessment of the urban impact on observed differences. The study examines pre-lockdown (1–22 March 2020), lockdown (23 March–3 May 2020) and post-lockdown (4–31 May 2020) periods, while the aerosol properties are also compared with a 3–4 year preceding period (2016/2017–2019). Comparison of meteorological parameters in Athens, between the lockdown period and respective days in previous years, showed only marginal variation, which is not deemed sufficient in order to justify the notable changes in aerosol concentrations and optical properties. The largest reduction during the lockdown period was observed for babs compared to the pre-lockdown (−39%) and to the same period in previous years (−36%). This was intensified during the morning traffic hours (−60%), reflecting the large decrease in vehicular emissions. Furthermore, AAE increased during the lockdown period due to reduced emissions from fossil-fuel combustion, while a smaller (−21%) decrease was observed for bsca along with slight increases (6%) in SAE and SSA values, indicating that scattering aerosol properties were less affected by the decrease in vehicular emissions, as they are more dependent on regional sources and atmospheric processing. Nighttime BC emissions related to residential wood-burning were slightly increased during the lockdown period, with respect to previous-year means. On the contrary, aerosol and pollution changes during the lockdown period at Finokalia were low and highly sensitive to natural sources and processes.

Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe

In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.