The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China

Urban Air Quality Shifts in China: Application of Additive Model and Transfer Learning to Major Cities

The impact of reduced human activity on air quality in seven major Chinese cities was investigated by utilizing datasets of air pollutants and meteorological conditions from 2016 to 2021. A Generalized Additive Model (GAM) was developed to predict air quality during reduced-activity periods and rigorously validated against ground station measurements, achieving an R2 of 0.85-0.93. Predictions were compared to the observed pollutant reductions (e.g., NO2 declined by 34% in 2020 vs. 2019), confirming model reliability. Transfer learning further refined the accuracy, reducing RMSE by 32-44% across pollutants when benchmarked against real-world data. Notable NO2 declines were observed in Beijing (42%), Changchun (38%), and Wuhan (36%), primarily due to decreased vehicular traffic and industrial activity. Despite occasional anomalies caused by localized events such as fireworks (Beijing, February 2020) and agricultural burning (Changchun, April 2020), our findings highlight the strong influence of human activity reductions on urban air quality. These results offer valuable insights for designing long-term pollution mitigation strategies and urban air quality policies.

Impact of Reduced Anthropogenic Emissions Associated With COVID-19 Lockdown on PM2.5 Concentration and Canopy Urban Heat Island in Canada

Extensive lockdowns during the COVID-19 pandemic caused a remarkable decline in human activities that have influenced urban climate, especially air quality and urban heat islands. However, the impact of such changes on local climate based on long term ground-level observations has hitherto not been investigated. Using air pollution measurements for the four major Canadian metropolitan areas (Toronto, Montreal, Vancouver, and Calgary), we find that PM2.5 markedly decreased during and after lockdowns with peak reduction ranging between 42% and 53% relative to the 2000-2019 reference period. Moreover, we show a substantial decline in canopy urban heat island intensity during lockdown and in the post lockdowns periods with peak reduction ranging between 0.7°C and 1.6°C in comparison with the 20-year preceding period. The results of this study may provide insights for local policymakers to define the regulation strategies to facilitate air quality improvement in urban areas.

A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO2 was evaluated. NO2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants.

Characterization of air pollution and associated health risks in Gansu Province, China from 2015 to 2022

Air pollution poses a major threat to both the environment and public health. The air quality index (AQI), aggregate AQI, new health risk-based air quality index (NHAQI), and NHAQI-WHO were employed to quantitatively evaluate the characterization of air pollution and the associated health risk in Gansu Province before (P-I) and after (P-II) COVID-19 pandemic. The results indicated that AQI system undervalued the comprehensive health risk impact of the six criteria pollutants compared with the other three indices. The stringent lockdown measures contributed to a considerable reduction in SO2, CO, PM2.5, NO2 and PM10; these concentrations were 43.4%, 34.6%, 21.4%, 17.4%, and 14.2% lower in P-II than P-I, respectively. But the concentration of O3 had no obvious improvement. The higher sandstorm frequency in P-II led to no significant decrease in the ERtotal and even resulted in an increase in the average ERtotal in cities located in northwestern Gansu from 0.78% in P-I to 1.0% in P-II. The cumulative distribution of NHAQI-based population-weighted exposure revealed that 24% of the total population was still exposed to light pollution in spring during P-II, while the air quality in other three seasons had significant improvements and all people were under healthy air quality level.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Source-specific light absorption and radiative effects decreases and indications due to the lockdown

The 'extreme' emission abatement during the lockdown (from the end of 2019 to the early 2020) provided an experimental period to investigate the corresponding source-specific effects of aerosol. In this study, the variations of source-specific light absorption (babs) and direct radiative effect (DRE) were obtained during and after the lockdown period by using the artificial neural network (ANN) and source apportionment environmental receptor model. The results showed that the babs decreased for all sources during the two periods. The most reductions were observed with ∼90% for traffic-related emissions (during the lockdown) and ∼85% for coal combustion (after the lockdown), respectively. Heightened babs (370 nm) values were obtained for coal and biomass burning during the lockdown, which was attributed to the enhanced atmospheric oxidization capacity. Nevertheless, the variations of babs (880 nm) after the lockdown was mainly due to the weakening of oxidation and reduced emissions of secondary precursors. The present study indicated that the large-scale emission reduction can promote both reductions of babs (370 nm) and DRE (34-68%) during the lockdown. The primary emissions decrease (e.g., Traffic emission) may enhance atmosphere oxidation, increase the ultraviolet wavelength light absorption and DRE efficiencies. The source-specific emission reduction may be contributed to various radiation effects, which is beneficial for the adopting of control strategies.

The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China

Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m-3 in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m-3 in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM2.5 levels in 2020-2021. Despite a 22~62% decline in NOx levels in 2020-2021, the increased O3 caused a similar NO3- concentration (20.69~23.00 μg m-3) in 2020-2021 to that (22.93 μg m-3) in 2019, hindering PM2.5 reduction in Zhengzhou. Six PM2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019-2021. Compared to 2019, the reduction in PM2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O3 and low precursor gases to mitigate air pollution in the future.

Impact of vertical stratification on the 2020 spring bloom in the Yellow Sea

The Yellow Sea is one of the world's most abundant marine resources, providing food and economic benefits to the Korean and Chinese populations. In spring 2020, a decrease in the intensity of phytoplankton bloom was observed. While one study attributed this decline to a decrease in nutrient associated with the COVID-19 pandemic, our previous research proposed weakened thermal stratification accompanied by a surface cooling anomaly as the cause. However, the relationship between the marine environment and ecosystem has not been fully elucidated. Using observations and marine physical-biogeochemical model data, we identified the weakened stratification as a critical factor for suppressing the 2020 spring bloom. Intense vertical mixing hindered the accumulation of nutrient and chlorophyll-a concentrations within the euphotic zone, resulting in a diminished phytoplankton bloom. In contrast, reduced nitrate and phosphate concentrations in 2020 were insignificant compared to those in 2017-2019, despite the notable decline in PM2.5 in March 2020 due to COVID-19. In April 2020, nutrient levels fell within the range of interannual variability based on long-term observations, reflecting a negligible effect on the spring phytoplankton bloom. Our findings provide insight into the importance of marine physical factors on the phytoplankton biomass in the Yellow Sea.

Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits

This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (n = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM10, PM2.5, O3, NO2, CO, and SO2. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO2 also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O3 demonstrated mixed results. Both PM10 and PM2.5 showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.

Air quality changes in NE Romania during the first Covid 19 pandemic wave

This study analyzes for the first time uniformly and causally the level of pollution and air quality for the NE-Romania Region, one of the poorest region in the European Union. Knowing the level of pollution and air quality in this region, which can be taken as a benchmark due to its positional and economic-geographical attributes, responds to current scientific and practical needs. The study uses an hourly database (for five pollutants and five climate elements), from 2009 to 2020, from 19 air quality monitoring stations in northeastern Romania. Pollutant levels were statistically and graphically/cartographically modeled for the entire 2009-2020 interval on the distributive-spatial and regime, temporal component. Inter-station differences and similarities were analyzed causally. Taking advantage of the emergency measures between March 16 and May 14, 2020, we observed the impact of the event on the regional air quality in northeastern Romania. During the emergency period, the metropolitan area of Suceava (with over 100,000 inhabitants) was quarantined, which allowed us to analyze the impact of the quarantine period on the local air quality. We found that, in this region, air quality falls into class I (for NO2, SO2 and CO), II for O3 and III for PM10. During the lockdown periods NO2 and SO2 decreased for the entire region by 8.6 and 14.3%, respectively, and in Suceava by 13.9 and 40.1%, respectively. The causes of the reduction were anthropogenic in nature.

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO₂ column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO₂ and particulate material (PM; coarse: PM₁₀ and fine: PM_2.5)]. For this purpose, tropospheric NO₂ obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM₁₀, most stations showed correlations lower than 0.2, which were not significant. The results for PM_2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO₂ proved to be a good predictor for NO₂ concentrations at ground level. Considering all stations with NO₂ measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO₂ throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO₂) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO₂). Our results demonstrate that Tropospheric NO₂ column densities can serve as good predictors of NO₂ concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Research of the impact of economic decline on air quality in Wuhan under COVID-19 epidemic

A novel economic impact model is proposed by this paper to analyze the impact of economic downturn on the air quality in Wuhan during the epidemic period, and to explore the effective solutions to improve the urban air pollution. The Space Optimal Aggregation Model (SOAM) is used to evaluate the air quality of Wuhan from January to April in 2019 and 2020. The analysis results show that the air quality of Wuhan from January to April 2020 is better than that of the same period in 2019, and it shows a gradually better trend. This shows that although the measures of household isolation, shutdown and production stoppage adopted during the epidemic period in Wuhan caused economic downturn, it objectively improved the air quality of the city. In addition, the impact of economic factors on PM2.5, SO2 and NO2 is 19%, 12% and 49% respectively calculated by the SOMA. This shows that industrial adjustment and technology upgrading for enterprises that emit a large amount of NO2 can greatly improve the air pollution situation in Wuhan. The SOMA can be extended to any city to analyze the impact of the economy on the composition of air pollutants, and it has extremely important application value at the level of industrial adjustment and transformation policy formulation.

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO₂, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O₃ concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO2, O3, CO, and SO2 in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO2 concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O3 increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO2 showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.

Green recovery or pollution rebound? Evidence from air pollution of China in the post-COVID-19 era

Under the strict control measures, China has achieved phased victory in combating with the COVID-19, production activities have gradually returned to normal. This paper examined whether air pollution was rebounded or realized green recovery in the post-COVID-19 era with a dataset of weather normalized pollutant concentrations using difference-in-differences models. Results showed that air pollution experienced a significant decline due to the wide range of control measures. With entering the post-epidemic period, air pollution raised due to the orderly production resumption. Specifically, production resumption increased the PM_2.5 concentrations of lockdown cities and non-lockdown cities by 43.2% (22.3 μg/m³) and 35.9% (17.3 μg/m³) compared with that in the period of COVID-19 breakout. Although the economic activities of China have been gradually recovered, PM_2.5 concentrations were 8.8-11.2 μg/m³ lower than the level of pre-epidemic period. In addition, the environmental effects varied across cities. With the process of production resumption, the PM_2.5 concentrations of cities with higher GDP, higher secondary industry output, more private cars and higher export volume rebounded less. Most developed cities realized green recovery by economy growth and air quality improvement, such as Beijing and Shanghai. While cities with heavy industry reflected pollution rebound with slow economy recovery, such as Shenyang and Harbin. Understanding the environmental effects of control measure and production resumption can provide crucial information for developing epidemic recovery policies and dealing with pollution issues for both China and other countries.

Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations

To understand the temporal variation, spatial distribution and factors influencing the public's sensitivity to air pollution in China, this study collected air pollution data from 2210 air pollution monitoring sites from around China and used keyword-based filtering to identify individual messages related to air pollution and health on Sina Weibo during 2017-2021. By analyzing correlations between concentrations of air pollutants (PM2.5, PM10, CO, NO2, O3 and SO2) and related microblogs (air-pollution-related and health-related), it was found that the public is most sensitive to changes in PM2.5 concentration from the perspectives of both China as a whole and individual provinces. Correlations between air pollution and related microblogs were also stronger when and where air quality was worse, and they were also affected by socioeconomic factors such as population, economic conditions and education. Based on the results of these correlation analyses, scientists can survey public concern about air pollution and related health outcomes on social media in real time across the country and the government can formulate air quality management measures that are aligned to public sensitivities.

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

The COVID-19 pandemic that has hit the whole world has caused losses in various aspects. Several countries have implemented lockdowns to curb the spread of the SARS-CoV-2 virus that caused death. However, for developing countries such as Indonesia, it is not suitable for lockdown because it considers the economic recession. Instead, the Large-scale Social Restrictions (LSSR) regulation is applied, the same as the partial lockdown. Thus, it is hypothesized that implementing LSSR that limits anthropogenic activities can reduce heat emissions and air pollution. Utilization of remote sensing data such as Terra-MODIS LST and Sentinel-5P images to investigate short-term trends (i.e., comparison between baseline year and COVID-19 year) in surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO₂, CO, and O₃ in Malang City and Surabaya City, East Java Province. Spatial downscaling of LST using the Random Forest Regression technique was also carried out to transform the spatial resolution of the Terra-MODIS LST image to make it feasible on a city scale. Raster re-gridding was also implemented to refine the Sentinel-5P spatial resolution. The accuracy of LST spatial downscaling results is quite satisfactory in both cities. Surface temperatures in both cities slightly decreased (below 1 °C) during LSSR was applied (P < 0.05). SUHII in both cities experienced a slight increase in both cities during LSSR. NO₂ gas was reduced significantly (P < 0.05) in Malang City (∼38%) and Surabaya City (∼28%) during LSSR phase due to reduced vehicle traffic and restrictions on anthropogenic activities. However, CO and O₃ gases did not indicate anomaly during LSSR. Moreover, this study provides insight into the correlation between SUHII change and the distribution of air pollution in both cities during the pandemic year. Air temperature and wind speed are also added as meteorological factors to examine their effect on air pollution. The proposed models of spatial downscaling LST and re-gridding satellite-based air pollution can help decision-makers control local air quality in the long and short term in the future. In addition, this model can also be applied to other ecological research, especially the input variables for ecological spatial modeling.

Haze caused by NOx oxidation under restricted residential and industrial activities in a mega city in the south of North China Plain

The formation of secondary aerosol species, including nitrate and sulfate, induces severe haze in the North China Plain. However, despite substantial reductions in anthropogenic pollutants due to severe restriction of residential and industrial activities in 2020 to stop the spread of COVID-19, haze still formed in Zhengzhou. We compared ionic compositions of PM_2.5 during the period of the restriction with that immediately before the restriction and in the comparison period in 2019 to investigate the processes that caused the haze. The average concentration of PM_2.5 was 83.9 μg m^-3 in the restriction period, 241.8 μg m^-3 before the restriction, and 94.0 μg m^-3 in 2019. Nitrate was the largest contributor to the PM_2.5 in all periods, with an average mass fraction of 24%-30%. The average molar concentration of total nitrogen compounds (NO_x + nitrate) was 0.89 μmol m^-3 in the restriction period, which was much lower than that in the non-restriction periods (1.85-2.74 μmol m^-3). In contrast, the concentration of sulfur compounds (SO₂ + sulfate) was 0.34-0.39 μmol m^-3 in all periods. The conversion rate of NO_x to nitrate (NOR) was 0.35 in the restriction period, significantly higher than that before the restriction (0.26) and in 2019 (0.25). NOR was higher with relative humidity in 40-80% in the restriction period than in the other two periods, whereas the conversion rate of SO₂ to sulfate did not, indicating nitrate formation was more efficient during the restriction. When O₃ occupied more than half of the oxidants (O_x = O₃ + NO₂), NOR increased rapidly with the ratio of O₃ to O_x and was much higher in the daytime than nighttime. Therefore, haze in the restriction period was caused by increased NO_x-to-nitrate conversion driven by photochemical reactions.

Changes in air quality over different land covers associated with COVID-19 in Turkey aided by GEE

With the increased urbanization, the rise of the manufacturing industry, and the use of fossil fuels, poor air quality is one of the most serious and pressing problems worldwide. The COVID-19 outbreak prompted absolute lockdowns in the majority of countries throughout the world, posing new research questions. The study's goals were to analyze air and temperature parameters in Turkey across various land cover classes and to investigate the correlation between air and temperature. For that purpose, remote sensing data from MODIS and Sentinel-5P TROPOMI were used from 2019 to 2021 over Turkey. A large amount of data was processed and analyzed in Google Earth Engine (GEE). Results showed a significant decrease in NO₂ in urban areas. The findings can be used in long-term strategies for lowering global air pollution. Future research should look at similar investigations in various study sites and evaluate changes in air metrics over additional classes.

A review of the environmental implications of the COVID-19 pandemic in the United Arab Emirates

This paper reviews the environmental implications associated with the COVID-19 pandemic at the individual and community levels in the UAE. The positive effects emanating from the pandemic include improved air quality and reduced contamination of public spaces with pollutants. On the other hand, far-reaching negative effects include poor disposal of medical plastic waste and facemasks and the rise in unhygienic health practices amongst residents of UAE. The long-term ecological implications of the pandemic are still not well understood. The findings shed the light on the importance of addressing the consequences of the COVID-19 pandemic through preventative policies and strategies for better environmental health and readiness for future crises. Future research could assess the long-term environmental conse-quences of the pandemic on the UAE.

Aerosol Characteristics during the COVID-19 Lockdown in China: Optical Properties, Vertical Distribution, and Potential Source

The concentration changes of aerosols have attracted wide-ranging attention during the COVID-19 lockdown (CLD) period, but the studies involving aerosol optical properties (AOPs) are relatively insufficient, mainly AOD (fine-mode AOD (AODf) and coarse-mode AOD (AODc)), aerosol absorption optical depth (AAOD), and aerosol extinction coefficient (AEC). Here, the remote-sensing observations, Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products, backward-trajectory, and potential-source-contribution models are used to assess the impact of AOPs, vertical distribution, and possible sources on the atmosphere environment in North China Plain (NCP), Central China (CC), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB) during the CLD period. The results demonstrate that both AOD (MODIS) and near-surface AEC (CALIPSO, <2 km) decreased in most areas of China. Compared with previous years (average 2017–2019), the AOD (AEC) of NCP, CC, YRD, PRD, and SB reduced by 3.33% (10.76%), 14.36% (32.48%), 10.80% (29.64%), 31.44% (22.68%), and 15.50% (8.44%), respectively. In addition, MODIS (AODc) and MERRA-2 (AODc) decreased in the five study areas compared with previous years, so the reduction in dust activities also contributed to improving regional air quality during the epidemic. Despite the reduction of anthropogenic emissions (AODf) in most areas of China during the CLD periods, severe haze events (AODf > 0.6) still occurred in some areas. Compared to previous years, there were increases in BC, OC (MERRA-2), and national raw coal consumption during CLD. Therefore, emissions from some key sectors (raw coal heating, thermal power generation, and residential coal) did not decrease, and this may have increased AODf during the CLD. Based on backward -rajectory and potential source contribution models, the study area was mainly influenced by local anthropogenic emissions, but some areas were also influenced by northwestern dust, Southeast Asian biomass burning, and marine aerosol transport. This paper underscores the importance of emissions from the residential sector and thermal power plants for atmospheric pollution in China and suggests that these sources must be taken into account in developing pollution-mitigation plans.

Consequences of pre and post confinement on the atmospheric air pollutants during spread of COVID-19 in India

COVID-19, a severe respiratory syndrome, was diagnosed in Wuhan, China, and in the last week of January 2020, it was reported in India. The drastic speed of spreading of COVID-19 imposed a total lockdown in India for the first time in four stages. This leads to restrictions on transport, industries, coal-based power plants, etc. During these stages of lockdown, a detailed analysis was done to study the effect of confinement on various air pollutants, PM10, PM2.5, SO2, CO, NH3, and NOx (NO, NO2) over the thirteen different stations situated at different states in India. The data were compared with pre-confinement duration at different locations in India. During confinement, the air pollutants showed less value when compared with the pre-confinement stage alarming everyone and also the Indian government to bring up rules and regulations for better air quality index so that such pandemics should be reduced.

Temporal characteristics and spatial heterogeneity of air quality changes due to the COVID-19 lockdown in China

Previous studies have evaluated the impact of lockdown measures on air quality during the COVID-19 pandemic in China, but few have focused on the temporal characteristics and spatial heterogeneity of the impact across all 337 prefecture cities. In this study, we estimated the impact of the lockdown measures on air quality in each of 337 cities using the Regression Discontinuity in Time method. There was a short-term influence from January 24th to March 31th in 2020. The 337 cities could be divided into six categories showing different response and resilience patterns to the epidemic. Fine particulate matter (PM2.5) in 89.5% of the cities was sensitive to the lockdown measures. The change of air pollutants showed high spatial heterogeneity. The provinces with a greater than 20% reduction in PM2.5 and PM10 and greater than 40% reduction in NO2 during the impact period were mainly concentrated southeast of the "Hu Line". Compared to the no-pandemic scenario, the national annual average concentration of PM2.5, NO2, PM10, SO2, and CO in 2020 were decreased by 6.3%, 10.6%, 7.4%, 9.0%, and 12.5%, respectively, while that of O3 increased by 1.1%.This result indicates that 2020 can still be used as a baseline for setting and allocating air improvement targets for the next five years.

Restarting MSMEs and start-ups post COVID-19: a grounded theory approach to identify success factors to tackle changed business landscape

Purpose

Small businesses and start-ups have started to reopen post coronavirus disease 2019 (COVID-19) lockdowns but are facing numerous challenges mainly due to changed customer preferences and the need to fine-tune the business models. This research aims to identify the important aspects that start-ups need to focus on, as they weather the COVID-19 pandemic storm.

Design/methodology/approach

Research uses constructivist grounded theory methodology to analyse data collected through in-depth semi-structured interviews with entrepreneurs and senior employees at start-ups. A conceptual model based on nine categories impacting a start-up’s performance is investigated. Interview memos are thematically analysed to identify repeated ideas, concepts or elements that become apparent.

Findings

Study reveals that employees’ and customers’ safety, prudent cost management and online presence/doorstep services are key for start-ups to succeed today's changed business landscape due to COVID-19.

Practical implications

Findings act as a practical guide for start-ups in setting mechanisms, optimizing operations and fine-tuning strategy to address COVID-19 challenges. Start-ups are advised to evaluate the implications of the three findings on their respective businesses to successfully tackle the challenges posed by COVID-19.

Originality/value

This research, being cognizant of a start-up’s unique characteristics and nuances, takes a fresh approach to identify key aspects that start-ups need to focus on and fine-tune in the wake of COVID-19. The paper enriches scientific research of understanding impact of COVID-19 on organizations by specifically surfacing how start-ups can learn and adapt by knowing how other start-ups are surviving today.

Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants: A Case Study of Chinese Cities in Winter of 2020

Fine particulate matter (PM2.5) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM2.5 distributions in Chinese cities, thus helping form better policies for future PM2.5 mitigation. Based on panel data of 332 cities, we analyzed the function of natural and anthropogenic factors to PM2.5 pollution by applying the geographically and temporally weighted regression (GTWR) model. We found that the PM2.5 concentration of 84.3% of cities decreased after lockdown. Spatially, in the winter of 2020, cities with high PM2.5 concentrations were mainly distributed in Northeast China, the North China Plain and the Tarim Basin. Higher temperature, wind speed and relative humidity were easier to promote haze pollution in northwest of the country, where enhanced surface pressure decreased PM2.5 concentrations. Furthermore, the intensity of trip activities (ITAs) had a significant positive effect on PM2.5 pollution in Northwest and Central China. The number of daily pollutant operating vents of key polluting enterprises in the industrial sector (VOI) in northern cities was positively correlated with the PM2.5 concentration; inversely, the number of daily pollutant operating vents of key polluting enterprises in the power sector (VOP) imposed a negative effect on the PM2.5 concentration in these regions. This work provides some implications for regional air quality improvement policies of Chinese cities in wintertime.

Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations

In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.

Air Quality Analysis in Lima, Peru Using the NO2 Levels during the COVID-19 Pandemic Lockdown

The emergence of the new COVID-19 virus in Peru forced the Peruvian government to take swift measures to stop its proliferation. Consequently, a state of emergency was declared, which included mandatory social isolation and quarantine. This action meant that people would transit only in emergency cases. In this context, this study’s objective is to analyze the air quality changes in terms of the capital city’s NO2 levels due to these government decisions using satellite imagery data obtained from the Sentinel-5P satellite. One critical problem is the lack of spatially distributed air quality data. The Peruvian Meteorological Service only monitors air quality in Lima, the capital city. In addition, the air quality ground stations are not always functioning. Thus, there is a need to find new reliable methods to complement the official data obtained. One method of doing so is the use of remote sensing products, although the accuracy and applicability are yet to be determined; therefore, this is the article’s focus. A temporal and spatial analysis was developed quantitatively and qualitatively to measure the levels of NO2 in eighteen regions of Lima to contrast the quarantine’s effect on polluting gas emission levels. The measurements are also compared with the official Peruvian data from ground sensors using Pearson correlation coefficients, thus, showing that Sentinel-5P data can be used for changes in the mean daily concentration of NO2. We also developed the first version of an open platform that converts the satellite data into a friendly format for visualization. The results show NO2 ambient concentration reductions compared to 2019 of between 60% and 40% in the first two weeks and between 50% and 25% in the following two weeks of the COVID-19 lockdown. However, this effect could not be observed two months after the start of the lockdown.

Spatiotemporal Variations of Aerosols in China during the COVID-19 Pandemic Lockdown

The widespread nature of the coronavirus disease 2019 (COVID-19) pandemic is gradually changing people’s lives and impacting economic development worldwide. Owing to the curtailment of daily activities during the lockdown period, anthropogenic emissions of air pollutants have greatly reduced, and this influence is expected to continue in the foreseeable future. Spatiotemporal variations in aerosol optical depth (AOD) can be used to analyze this influence. In this study, we comprehensively analyzed AOD and NO2 data obtained from satellite remote sensing data inversion. First, data were corrected using Eidetic three-dimensional-long short-term memory to eliminate errors related to sensors and algorithms. Second, taking Hubei Province in China as the experimental area, spatiotemporal variations in AOD and NO2 concentration during the pandemic were analyzed. Finally, based on the results obtained, the impact of the COVID-19 pandemic on human life has been summarized. This work will be of great significance to the formulation of regional epidemic prevention and control policies and the analysis of spatiotemporal changes in aerosols.

COVID-19 pandemic: What can we learn for better air quality and human health?

The COVID-19 lockdown resulted in improved air quality in many cities across the world. With the objective of what could be the new learning from the COVID-19 pandemic and subsequent lockdowns for better air quality and human health, a critical synthesis of the available evidence concerning air pollution reduction, the population at risk and natural versus anthropogenic emissions was conducted. Can the new societal norms adopted during pandemics, such as the use of face cover, awareness regarding respiratory hand hygiene, and physical distancing, help in reducing disease burden in the future? The use of masks will be more socially acceptable during the high air pollution episodes in lower and middle-income countries, which could help to reduce air pollution exposure. Although post-pandemic, some air pollution reduction strategies may be affected, such as car-pooling and the use of mass transit systems for commuting to avoid exposure to airborne infections like coronavirus. However, promoting non-motorized modes of transportation such as cycling and walking within cities as currently being enabled in Europe and other countries could overshadow such losses. This demand focus on increasing walkability in a town for all ages and populations, including for a differently-abled community. The study highlighted that for better health and sustainability there. is also a need to promote other measures such as work-from-home, technological infrastructure, the extension of smart cities, and the use of information technology.

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

Atmospheric pollution has recently drawn significant attention due to its proven adverse effects on public health and the environment. This concern has been aggravated specifically in Southeast Asia due to increasing vehicular use, industrial activity, and agricultural burning practices. Consequently, elevated PM2.5 concentrations have become a matter of intervention for national authorities who have addressed the needs of monitoring air pollution by operating ground stations. However, their spatial coverage is limited and the installation and maintenance are costly. Therefore, alternative approaches are necessary at national and regional scales. In the current paper, we investigated interpolation models to fuse PM2.5 measurements from ground stations and satellite data in an attempt to produce spatially continuous maps of PM2.5 nationwide over Thailand. Four approaches are compared, namely the inverse distance weighted (IDW), ordinary kriging (OK), random forest (RF), and random forest combined with OK (RFK) leveraging on the NO2, SO2, CO, HCHO, AI, and O3 products from the Sentinel-5P satellite, regulatory-grade ground PM2.5 measurements, and topographic parameters. The results suggest that RFK is the most robust, especially when the pollution levels are moderate or extreme, achieving an RMSE value of 7.11 μg/m3 and an R2 value of 0.77 during a 10-day long period in February, and an RMSE of 10.77 μg/m3 and R2 and 0.91 during the entire month of March. The proposed approach can be adopted operationally and expanded by leveraging regulatory-grade stations, low-cost sensors, as well as upcoming satellite missions such as the GEMS and the Sentinel-5.

Identification of NOx hotspots from oversampled TROPOMI NO2 column based on image segmentation method

Satellite-based measures of NO₂ have become increasingly available for resolving the limitation on insufficient spatial and temporal coverage of ground-level monitoring networks. Oversampled NO₂ column density can obtain more detailed features of NO₂ column with a spatial resolution as high as 2 km × 2 km, while it is still challenging to identify hotspots of NO_x pollution plume in city-scale due to background interference. In this study, we proposed a method for detecting the NO_x hotspot grids from oversampled satellite NO₂ column based on the image segmentation method, and identifying major source types using Term frequency-inverse document frequency (TF-IDF). A fractal model was used to evaluate and eliminate the background portion of the NO₂ column and an adaptive threshold method was adopted to identify the region of interest (ROI) of local hotspot NO₂ column. Hot-grid index, counting the frequency of NO₂ hotspot ROI in each grid, was conducted to identify the hotspot grids. TF-IDF was used to semantically analyze the major source types of NO₂ hotspot grids. Taking Central and Eastern China as the studied domain, the hotspot grids of NO₂ and the relevant major source types were identified based on the proposed method. The major non-road mobile sources (such as Beijing Capital International Airport), industrial areas (such as Caofeidian Industrial Park) and urban areas were clearly distinguished. The power plant, Coke and Iron and Steel were identified as major source types in the whole year in the corresponding NO_x hotspot grids. Notably, the identification of hotspot grids indicated a higher probability of a local high-intensity NO_x pollution plume rather than a quantitative NO_x emission; the key source types were the semantic keywords in hotspot grids, which does not mean there were no other exiting emission sources. This proposed method has strong implications on rapidly identifying the NO_x hotspot grids based on oversampled TROPOMI NO₂ column and the list of industrial enterprises.

Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO2 and total SO2 products against ground-based MAX-DOAS at a single site (Xianghe) and OMI products over a seriously polluted region (North China Plain, NCP) in China. The results show that both NO2 and SO2 data from three datasets exhibit a similar tendency and seasonality. In addition, TROPOMI tropospheric NO2 columns are generally underestimated compared with collocated MAX-DOAS and OMI data by about 30–60%. In contrast to NO2, the monthly average SO2 retrieved from TROPOMI is larger than MAX-DOAS and OMI, with a mean bias of 2.41 (153.8%) and 2.17 × 1016 molec cm−2 (120.7%), respectively. All the results demonstrated that the TROPOMI NO2 as well as the SO2 algorithms need to be further improved. Thus, to ensure reliable analysis in NCP area, a correction method has been proposed and applied to TROPOMI Level 3 data. The revised datasets agree reasonably well with OMI observations (R > 0.95 for NO2, and R > 0.85 for SO2) over the NCP region and have smaller mean biases with MAX-DOAS. In the application during COVID-19 pandemic, it showed that the NO2 column in January-April 2020 decreased by almost 25–45% compared to the same period in 2019 due to the lockdown for COVID-19, and there was an apparent rebound of nearly 15–50% during 2021. In contrast, a marginal change of the corresponding SO2 is revealed in the NCP region. It signifies that short-term control measures are expected to have more effects on NO2 reduction than SO2; conversely, we need to recognize that although the COVID-19 lockdown measures improved air quality in the short term, the pollution status will rebound to its previous level once industrial and human activities return to normal.

Influence of background dynamics on the vertical distribution of trace gases (CO/WV/O3) in the UTLS region during COVID-19 lockdown over India

The COVID-19 pandemic lockdown has led to the significant reductions in the pollutant levels across the globe. Several studies have been carried out for examining and quantifying the improvement in the air quality due to the reduction of the pollution at the surface. Unlike most of the studies carried out earlier on COVID-19 lockdown, this study investigates the role of the dynamics on the vertical distribution of the trace gases (Carbonmonoxide (CO), Water Vapor (WV) and Ozone (O₃)) over India in the Boundary Layer (BL), Middle Troposphere (MT) and Upper Troposphere (UT) during COVID-19 lockdown using satellite observations and re-analysis data products obtained during 2010-2020. Substantial differences in the time series and variability have been observed over different zones of India in different atmospheric layers. The changes observed in these species are large over Central India compared to South India and Indo-Gangetic plain regions. An enhancement in CO (~25-40%) and WV (50-60%) has been noticed over Central India in the UT at 147 hPa and 215 hPa, respectively, during lockdown. The strong updrafts before the lockdown and the extended weak zonal wind aloft over this region are found responsible for the observed enhancement in these trace gases in the UT. In spite of the non-availability of the anthropogenic pollution during the lockdown, this study highlights the transport of pollutants through long-range transport (always present even before lockdown) dominance over the Indian region not only near the surface but also aloft due to associated atmospheric dynamics.

Air Quality during the COVID–19 Lockdown and Unlock Periods in India Analyzed Using Satellite and Ground-based Measurements

Abstract

A nationwide lockdown was imposed in India from 24 March 2020 to 31 May 2020 to contain the spread of COVID-19. The lockdown has changed the atmospheric pollution across the continents. Here, we analyze the changes in two most important air quality related trace gases, nitrogen dioxide (NO₂) and tropospheric ozone (O₃) from satellite and surface observations, during the lockdown (April–May 2020) and unlock periods (June–September 2020) in India, to examine the baseline emissions when anthropogenic sources were significantly reduced. We use the Bayesian statistics to find the changes in these trace gas concentrations in different time periods. There is a strong reduction in NO₂ during the lockdown as public transport and industries were shut during that period. The largest changes are found in IGP (Indo-Gangetic Plain), and industrial and mining areas in Eastern India. The changes are small in the hilly regions, where the concentrations of these trace gases are also very small (0–1 × 10¹⁵ molec./cm²). In addition, a corresponding increase in the concentrations of tropospheric O₃ is observed during the period. The analyses over cities show that there is a large decrease in NO₂ in Delhi (36%), Bangalore (21%) and Ahmedabad (21%). As the lockdown restrictions were eased during the unlock period, the concentrations of NO₂ gradually increased and ozone deceased in most regions. Therefore, this study suggests that pollution control measures should be prioritized, ensuring strict regulations to control the source of anthropogenic pollutants, particularly from the transport and industrial sectors.

Highlights

• Most cities show a reduction up to 15% of NO₂ during the lockdown

• The unlock periods show again an increase of about 40–50% in NO₂

• An increase in tropospheric O₃ is observed together with the decrease in NO₂

Supplementary Information

The online version contains supplementary material available at 10.1007/s40710-022-00585-9.

Impacts of short-term lockdown during COVID-19 on air quality in Egypt

COVID-19 is a pandemic disease that is actively spread over the globe in a few months. Most of the Nations took the appropriate measures including lockdown to reduce the risk of spreading and safe human health and life. Egypt took the measures of partial and complete lockdown from 15th March till 30th June 2020. Such short-term lockdown has had a significant impact on the reduction of emissions from transportation, industrial and human activities. This research used multi-data sensors from space to map the changes of air quality over Egypt in the first 6 months from January to June 2020 due to the lockdown and compare with previous years of 2018 and 2019. It is clearly observed that the air quality over the whole country is improved as a result of reducing pollutants emissions, with NO₂ reduced by 45.5%, CO emissions reduced by 46.23%, Ozone concentration decreased by about 61.1%, and AOD reduced by 68.5% compared to the previous 2 years. It is found that the lockdown is an effective mitigation measure against air pollution to improve air quality and reduce the air pollution that creates pressure on the human health and health system. It might be difficult to implement long lockdown, as a mitigation measure, due to its direct impact on social and economic needs. However, we recommend a complete lockdown for 2–3 days (long weekend) every at least 2-3 months, on national and/or global level, which will significantly enhance our air quality and improve the health environment of the planet.

Contrasting Iran's air quality improvement during COVID-19 with other global cities

BACKGROUND AND PURPOSE

In late 2019, a novel infectious disease (COVID-19) was identified in Wuhan China, which turned into a global pandemic. Countries all over the world have implemented some sort of lockdown to slow down its infection and mitigate it. This study investigated the impact of the COVID-19 pandemic on air quality during 1st January to 30th April 2020 compared to the same period in 2016-2019 in ten Iranian cities and four major cities in the world.

METHODS

In this study, the required data were collected from reliable sites. Then, using SPSS and Excel software, the data were analyzed in two intervals before and after the corona pandemic outbreak. The results are provided within tables and charts.

RESULTS

The current study showed the COVID-19 lockdown positively affected Iran's air quality. During the COVID-19 pandemic, the four-month mean air quality index (AQI) values in Tehran, Wuhan, Paris, and Rome were 76, 125, 55, and 60, respectively, which are 8 %, 22 %, 21 %, and 2 % lower than those during the corresponding period (83, 160, 70, and 61) from 2016 to 2019.

CONCLUSIONS

Although the outbreak of coronavirus has imposed devastating impacts on economy and health, it can have positive effects on air quality, according to the results.

Modelling the effect of local and regional emissions on PM2.5 concentrations in Wuhan, China during the COVID-19 lockdown

PM_2.5 concentrations in Wuhan, China decreased by 36.0% between the period prior to the COVID-19 pandemic (1–23 January, 2020) and the COVID-lockdown period (24 January to 29 February, 2020). However, decreases in PM_2.5 concentration due to regional PM_2.5 transport driven by meteorological changes, and the relationship between the PM_2.5 source and receptor, are poorly understood. Therefore, this study assessed how changes in meteorology, local emissions, and regional transport from external source emissions contributed to the decrease in Wuhan's PM_2.5 concentration, using FLEXPART-WRF and WRF-Chem modelling experiments. The results showed that meteorological changes in central China explain up to 22.2% of the total decrease in PM_2.5 concentrations in Wuhan, while the remaining 77.8% was due to air pollutant emissions reduction. Reduction in air pollutant emissions depended on both local and external sources, which contributed alomst equally to the reduction in PM_2.5 concentrations (38.7% and 39.1% of the total reduction, respectively). The key emissions source areas affecting PM_2.5 in Wuhan during the COVID-lockdown were identified by the FLEXPART-WRF modeling, revealing that regional-joint control measures in key areas accounted for 89.3% of the decrease in PM_2.5 concentrations in Wuhan. The results show that regional-joint control can be enhanced by identifying key areas of emissions reduction from the source–receptor relationship of regional PM_2.5 transport driven by meteorology under the background of East Asian monsoon climate change.

How is the Asian economy recovering from COVID-19? Evidence from the emissions of air pollutants

This study examines how economic and social activities in Asia were affected by the COVID-19 pandemic, using the emissions of various air pollutants as representative measures of those activities. Our review of emissions data suggests that the amount of air pollutants emitted decreased in most subnational regions from 2019 to 2020. We also determined that economic and social activities have restarted in some regions in many countries. Moreover, we conduct regression analyses to identify the types of regions that restarted earlier. Regional characteristics are distinguished by employing a remotely sensed land cover dataset and OpenStreetMap. Results reveal that in the case of the Association of Southeast Asian Nations (ASEAN) forerunners, economic and social activities in cropland, industrial estates, accommodations, restaurants, education, and public services have not yet returned to previous levels.

PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017-2019, and also investigated the differences in PM variations between the 'curfew' and 'non curfew' hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 μg/m3) compared to the corresponding previous three years (2017-2019); however, the PM10 concentration (122.5 μg/m3) increased relative to 2017 (116.6 μg/m3), and 2019 (92.8 μg/m3). After removing the 'dust effects', both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.

COVID-19 prevention, air pollution and transportation patterns in the absence of a lockdown

Recent studies demonstrate that air quality improved during the coronavirus pandemic due to the imposition of social lockdowns. We investigate the impact of COVID-19 on air pollution in the two largest cities in Taiwan, which were not subject to economic or mobility restrictions. Using a difference-in-differences approach and real-time data on air quality and transportation, we estimate that anthropogenic air pollution from local sources increased during working days and decreased during non-working days during the COVID-19 pandemic. This led to a 3-7 percent increase in CO, O₃, SO₂, PM₁₀ and PM_2.5. We demonstrate that the increase in air pollution resulted from a shift in preferred mode of travel away from public transportation and towards personal motor vehicles during working days. In particular, metro and shared bicycle usage decreased between 8 and 18 percent, on average, while automobile and scooter use increased between 11 and 21 percent during working days. Similar COVID-19 prevention behaviors in regions or countries emerging from lockdowns could likewise result in an increase in air pollution. Taking action to reduce the transmissibility of COVID-19 on metro cars, trains and buses could help policymakers limit the substitution of personal motor vehicles for public transit, and mitigate increases in air pollution when lifting mobility restrictions.

Untangling the contributions of meteorological conditions and human mobility to tropospheric NO2 in Chinese mainland during the COVID-19 pandemic in early 2020

In early 2020, unprecedented lockdowns and travel bans were implemented in Chinese mainland to fight COVID-19, which led to a large reduction in anthropogenic emissions. This provided a unique opportunity to isolate the effects from emission and meteorology on tropospheric nitrogen dioxide (NO2). Comparing the atmospheric NO2 in 2020 with that in 2017, we found the changes of emission have led to a 49.3 ± 23.5% reduction, which was ∼12% more than satellite-observed reduction of 37.8 ± 16.3%. The discrepancy was mainly a result of changes of meteorology, which have contributed to an 8.1 ± 14.2% increase of NO2. We also revealed that the emission-induced reduction of NO2 has significantly negative correlations to human mobility, particularly that inside the city. The intra-city migration index derived from Baidu Location-Based-Service can explain 40.4% ± 17.7% variance of the emission-induced reduction of NO2 in 29 megacities, each of which has a population of over 8 million in Chinese mainland.

Spatial Disparity of Meteorological Impacts on Carbon Monoxide Pollution in China during the COVID-19 Lockdown Period

Lockdown due to the novel coronavirus disease 2019 (COVID-19) pandemic offers a unique opportunity to study the factors governing the variation in air pollution. A number of studies have investigated the cause underlying the occurrence of heavy haze pollution around the world during the lockdown period. However, information about spatiotemporal variations in gaseous pollutants and detailed quantifications of potential meteorological (METRO) impacts are limited. Ground measurements show that carbon monoxide (CO) pollution deteriorated in northern China despite strict control of human and industrial activities during the lockdown period in early 2020. In this study, a four-dimensional decomposition model was used to quantitatively extract the METRO impacts on the CO pollution over China. The results show that weakened winds elevated CO concentrations near Beijing and in northeastern China. Increased temperatures slightly elevated CO concentrations in northern and eastern China but reduced CO concentrations in northwestern China. Remarkable amounts of CO increases in northern China (e.g., by 0.21 mg/m3 within Beijing) were explained by anomalously high humidity, which could be associated with an enhanced interaction between aerosol and the boundary layer. After excluding the METRO impacts, the CO concentrations drastically declined across China (e.g., by 0.22 mg/m3 within Beijing), indicating that the lockdown indeed greatly lessened CO concentrations. However, the adverse METRO conditions counteracted the beneficial outcomes of emission reductions, leading to a deterioration of the CO pollution in northern China. These results indicate that the METRO factors can play a critical role in worsening air pollution despite a strict control of anthropogenic emissions.

Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing

Air pollution is one of the vital problems for the sustainability of cities and public health. The lockdown caused by the COVID-19 outbreak has become a natural laboratory, enabling to investigate the impact of human/industrial activities on the air pollution. In this study, we investigated the spatio-temporal density of TROPOMI-based nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) products, and MODIS-derived Aerosol Optical Depth (AOD) from January 2019 to September 2020 (also covering the first wave of the COVID-19) over Turkey using Google Earth Engine (GEE). The results showed a significant decrease in NO₂ and AOD, while SO₂ unchanged and had slightly higher concentrations in some regions during the lockdown compared to 2019. The relationship between air pollutants and meteorological parameters during the lockdown showed that air temperature and pressure were highly correlated with air pollutants, unlike precipitation and wind speed. Moreover, Purchasing Managers' Index (PMI) data, indicator of economic/industrial activities, also provided poor correlation with air pollutants. TROPOMI-based NO₂ and SO₂ were compared with station-based pollutants for three sites (suburban, urban, and urban-traffic classes) in Istanbul, revealing 0.83, 0.70 and 0.65 correlation coefficients for NO₂, respectively, while SO₂ showed no significant correlation. Besides, AOD data were validated using two AERONET sites providing 0.86 and 0.82 correlation coefficients. Overall, the satellite-based data provided significant outcomes for the spatio-temporal evaluation of air quality, especially during the first wave of the COVID-19 lockdown.

The impact of improved air-quality due to COVID-19 lockdown on surface meteorological parameters and planetary boundary layer over Gadanki, a tropical rural site in India

The nation-wide lockdowns imposed in India during March--May 2020 (in four phases) to curb the spread of the novel Corona virus, greatly enhanced the near-surface air-quality due to lowering of industrial, transport and human activities. The present study focuses on the changes in the vertical structure of aerosol concentration and how those changes impacted radiation balance, the planetary boundary layer (PBL) height and surface meteorological parameters. Instrumented tower and Ceilometer measurements made at Gadanki (13.45°N, 79.18°E), located in a rural environment, coupled with satellite-derived Aerosol Optical Depth (AOD) data have been used to understand the changes in lockdown period. Significant reduction in backscatter density during the lockdown compared to 2019 indicates that aerosol reduction during the lockdown is not only limited to the surface, rather observed in the entire PBL. Except for the fourth phase of lockdown during which several relaxations have been given for vehicular movement and other anthropogenic activities, the reduction in backscatter density is seen in all phases of lockdown. However, the reduction is prominently seen in the second and third phases. The AOD also reduced by 40% around Gadanki, comparable to that of in urban regions. Due to the reduction in aerosols during the lockdown period, the insolation increases by 60 Wm^-2, which is expected to increase the temperature. However, the increased loss of long-wave radiation (due to reduction in trapping gases) and more rain events during the lockdown period decreased the temperature by ~1 °C. Measurements also suggest that the most of net radiation is partitioned into the latent heat flux increasing the humidity and lowering the PBL height (due to reduced strength of thermals and sensible heat flux).

Measurement report of the change of PM2.5 composition during the COVID-19 lockdown in urban Xi'an: Enhanced secondary formation and oxidation

Enhanced secondary aerosol formation was observed during the COVID-19 lockdown in Xi'an, especially for polluted episodes. More oxidized‑oxygenated organic aerosol (MO-OOA) and sulfate showed the dominant enhancements, especially in large particle-mode. Meanwhile, relative humidity (RH) showed a positive promotion on the formation of sulfate and MO-OOA during the lockdown, but had no obvious correlation with less oxidized‑oxygenated organic aerosol (LO-OOA) or nitrate. Organosulfurs (OS) displayed a higher contribution (~58%) than inorganic sulfate to total sulfate enhancement in the polluted episode during the lockdown. Although the total nitrate (TN) decreased during the lockdown ascribing to a larger reduction of inorganic nitrate, organic nitrate (ON) showed an obvious increase from pre-lockdown (0.5 ± 0.6 μg m^-3 and 1 ± 2% of TN) to lockdown (5.3 ± 3.1 μg m^-3 and 17 ± 9% of TN) in the polluted case (P < 0.05). In addition, RH also displayed a positive promotion on the formation of ON and OS, and the increases of both OS and ON were much efficient in the nighttime than in the daytime. These results suggest that higher RH and stagnant meteorology might facilitate the sulfate and MO-OOA enhancement, especially in the nighttime, which dominated the secondary aerosol enhancement in haze pollution during the lockdown.

Spatial-Temporal Patterns of Air Pollutant Emissions From Landing and Take-Off Cycles in the Yangtze River Delta of China During the COVID-19 Outbreak

The global aviation industry has been experiencing catastrophic disruption since the beginning of 2020 due to the unprecedented impact of the COVID-19 pandemic on air traffic. Although the decline in regular commercial air travel has caused tremendous economic loss to aviation stakeholders, it has also led to the reduction in the amount of recorded air pollutants. Most of the aircraft emissions are released during the cruise phase of flight, however they have relatively small impact on humans due to the fact that those emissions are released directly into the upper troposphere and lower stratosphere. Therefore, the scope of this study is to investigate the ground-level aircraft emissions from landing and take-off (LTO) cycles, as they have a greater influence on the ambient environment of the airports in a specific region. In this paper, we study the variation of typical air pollutant concentrations (i.e., HC, CO, and NO_x) from the LTO cycles during the outbreak of COVID-19 pandemic in both temporal and spatial scales. These ground-level emissions are estimated for the 22 airports in the Yangtze River Delta, China. The results indicate that the variation pattern of the three air pollutants were significantly influenced by the dramatic onset of the COVID-19 pandemic, as well as the pertinent policies to suppress the spread of the virus. The results also reveal non-uniform distribution of the emission quantified at different airports. It is noticeable that the emission quantity generally declined from the east coast to the central and western part of the research region. Furthermore, discrepancies in the target markets also create disparities in the variation pattern of the emissions at different airports under the context of COVID-19.

Air Quality over China

The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project “Air quality over China” were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling.

Global assessment of tropospheric and ground air pollutants and its correlation with COVID-19

The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.

Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of ∼ 30-50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01046-2.

Substantial changes of chemical composition and sources of fine particles during the period of COVID-19 pandemic in Taiyuan, Northern China

To better understand the effects of COVID-19 on air quality in Taiyuan, hourly in situ measurements of PM_2.5(particulate matter with an aerodynamic diameter less than 2.5 mm) and chemical components (water-soluble ions, organic carbon (OC), elemental carbon (EC), and trace elements) were conducted before (P1: 1 January-23 January 2020) and during (P2: 24 January-15 February 2020) the coronavirus disease 2019 (COVID-19) outbreak. The average concentrations of PM_2.5 dropped from 122.0 μg/m³ during P1 to 83.3 μg/m³ during P2. Compared with P1, except for fireworks burning-related chemical components (K⁺, Mg²⁺, K, Cu, Ba), the concentrations of other chemical components of PM_2.5 decreased by14.9-69.8%. Although the large decrease of some emission sources, fireworks burning still resulted in the occurrence of pollution events during P2. The analysis results of positive matrix factorization model suggested that six PM_2.5 sources changed significantly before and during the outbreak of the epidemic. The contributions of vehicle emission, industrial process, and dust to PM_2.5 decreased from 23.1%, 3.5%, and 4.0% during P1 to 7.7%, 3.4%, and 2.3% during P2, respectively, whereas the contributions of secondary inorganic aerosol, fireworks burning, and coal combustion to PM_2.5 increased from 62.0%, 1.8%, and 5.5% to 71.5%, 9.0%, and 6.2%, respectively. The source apportionment results were also affected by air mass transport. The largest reductions of vehicle emission, industrial process, and dust source were distinctly seen for the air masses from northwest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01082-y.