Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Tracking air quality trends and vehicle traffic dynamics at urban scale using satellite and ground data before and after the COVID-19 outbreak

The implications of the COVID-19 outbreak are subjected to an increasing number of studies. So far, air quality trends related to the lockdown due to the pandemic have been analysed in large cities or entire regions. In this work, the region studied is the metropolitan area of Cagliari, which is the main city on the island of Sardinia (Italy) and can be representative of a coastal city that includes industrial settlements. The purpose of the study is to evaluate the effect of restrictions related to the COVID-19 outbreak on air quality levels and the traffic dynamics in this type of urban area. Nitrogen Dioxide (NO₂) levels before, during and after COVID-19 lockdown have been investigated using data acquired from the Sentinel-5P/TROPOMI satellite combined with on-site measurements. Both TROPOMI detected and ground-based data have revealed higher levels of NO₂ before and after the lockdown, compared to those during the period of COVID-related restrictions, in particular in the urban area of Cagliari. On the other hand, NO2 registered in the oil refinery area did not show significant differences associated with lockdown. The correlation of TROPOMI NO₂ tropospheric column with ground data (surface NO2) on a monthly mean basis showed different values based on the background and the highest Pearson's coefficient was of about 0.78 near to the city centre, where traffic can be considered a significant source of emission. In addition, a comparison of the air pollution level with the dynamics of vehicle traffic was investigated. The study highlighted a remarkable correlation between the reduction of the number of vehicles and the corresponding tropospheric NO₂ values that decreased on a weekly mean basis.

Developing PM2.5 and PM10 prediction models on a national and regional scale using open-source remote sensing data

Clean air is the precursor to a healthy life. Air quality is an issue that has been getting under its well-deserved spotlight in the last few years. From a remote sensing point of view, the first Copernicus mission with the main purpose of monitoring the atmosphere and tracking air pollutants, the Sentinel-5P TROPOMI mission, has been widely used worldwide. Particulate matter of a diameter smaller than 2.5 and 10 μm (PM_2.5 and PM₁₀) significantly determines air quality. Still, there are no available satellite sensors that allow us to track them remotely with high accuracy, but only using ground stations. This research aims to estimate PM_2.5 and PM₁₀ using Sentinel-5P and other open-source remote sensing data available on the Google Earth Engine (GEE) platform for heating (December 2021, January, and February 2022) and non-heating seasons (June, July, and August 2021) on the territory of the Republic of Croatia. Ground stations of the National Network for Continuous Air Quality Monitoring were used as a starting point and as ground truth data. Raw hourly data were matched to remote sensing data, and seasonal models were trained at the national and regional scale using machine learning. The proposed approach uses a random forest algorithm with a percentage split of 70% and gives moderate to high accuracy regarding the temporal frame of the data. The mapping gives us visual insight between the ground and remote sensing data and shows the seasonal variations of PM_2.5 and PM₁₀. The results showed that the proposed approach and models could efficiently estimate air quality.

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.

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.

Impact of COVID-19 restrictions on air quality and surface urban heat island effect within the main urban area of Urumqi, China

The outbreak of coronavirus in 2019 (COVID-19) posed a serious global threat. However, the reduction in man-made pollutants during COVID-19 restrictions did improve the ecological environment of cities. Using multi-source remote sensing data, this study explored the spatiotemporal variations in air pollutant concentrations during the epidemic prevention and control period in Urumqi and quantitatively analyzed the impact of different air pollutants on the surface urban heat island intensity (SUHII) within the study area. Urumqi, located in the hinterland of the Eurasian continent, northwest of China, in the central and northern part of Xinjiang was selected as the study area. The results showed that during COVID-19 restrictions, concentrations of air pollutants decreased in the main urban area of Urumqi, and air quality improved. The most evident decrease in NO2 concentration, by 77 ± 1.05% and 15 ± 0.98%, occurred in the middle of the first (January 25 to March 20, 2020) and second (July 21 to September 1, 2020) COVID-19 restriction periods, respectively, compared with the corresponding period in 2019. Air pollutant concentrations and the SUHIIs were significantly and positively correlated, and NO2 exhibited the strongest correlation with the SUHIIs. We revealed that variations in the air quality characteristics and thermal environment were observed in the study area during the COVID-19 restrictions, and their quantitative relationship provides a theoretical basis and reference value for improving the air and ecological environment quality within the study area.

Changes in temporal pattern and spatial distribution of environmental pollutants in 8 Asian countries owing to COVID-19 pandemic

This study investigated the changes in air pollutant's concentration, spatio-temporal distribution and sensitivity of changes in air pollutant's concentration during pre and post COVID-19 outbreak. We employed Google Earth Engine Platform to access remote sensing datasets of air pollutants across Asian continent. Air pollution and cumulative confirmed-COVID cases data of Asian countries (Afghanistan, Bangladesh, China, India, Iran, Iraq, Pakistan, and Saudi Arabia) have been collected and analyzed for 2019 and 2020. The results indicate that aerosol index (AI) and nitrogen dioxide (NO₂) is significantly reduced during COVID outbreak i.e. in year 2020. In addition, we found significantly positive (P < 0.05, 95% confidence interval, two-tailed) correlation between changes in AI and NO₂ concentration for net active-COVID case increment in almost each country. For other atmospheric gases i.e. carbon monoxide (CO), formaldehyde (HCHO), ozone (O₃), and Sulfur dioxide (SO₂), insignificant and/or significant negative correlation is also observed. These results suggest that the atmospheric concentration of AI and NO₂ are good indicators of human activities. Furthermore, the changes in O₃ shows significantly negative correlation for net active-COVID case increment. In conclusion, we observed significant positive environmental impact of COVID-19 restrictions in Asia. This study would help and assist environmentalist and policy makers in restraining air pollution by implementing efficient restrictions on human activities with minimal economic loss.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining

This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO₂), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO₂ concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO₂ concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO₂ concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO₂ concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO₂ concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

In this paper, we present the total column water vapour (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the visible blue spectral band. The TROPOMI TCWV algorithm is being optimized and validated in the framework of the Sentinel 5 Precursor Product Algorithm Laboratory (S5P-PAL) project from the European Space Agency (ESA). The retrieval was first developed to retrieve TCWV from the Global Ozone Monitoring Experiment 2 (GOME-2). We have optimized the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV algorithm follows the typical two step approach, using spectral fit retrieval of slant columns, and conversion of the slant columns to vertical columns using air mass factors (AMFs). An iterative optimization algorithm is developed to dynamically find the optimal a priori water vapour profile for the AMF calculation. Further optimizations on the spectral retrieval, air mass factor calculations as well as a new surface albedo retrieval approach are implemented. The TCWV retrieval algorithm is applied to TROPOMI observations from May 2018 to May 2021. The results are validated by comparing them to ERA5 reanalysis data, GOME-2, MODerate resolution Imaging Spectroradiometer (MODIS) and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.96 to 0.99. The mean bias between TROPOMI and ERA5 data is -1.24 kg m^-2 for measurements over land and 0.73 kg m^-2 for measurements over water. The comparison to MODIS observations show similar results with small dry bias of 1.51,kg m^-2 for measurements over land and a small wet bias of 1.25 kg m^-2 for measurements over water. Slightly larger dry bias of 1.98 kg m^-2 for measurements over land and 1.74 kg m^-2 for measurements over water are found when compared to GOME-2 obserations. Compared to SSMIS data over water, TROPOMI observations are bias low by 3.25 kg m^-2. The small discrepancies found between TROPOMI and reference data sets are related to the differences in measurement technique, measurement time, surface albedo issue, as well as cloud and aerosol contamination. This study demonstrates that the algorithm can provide stable and consistent results on a global scale and can be applied to generate operational TCWV products from TROPOMI and the forthcoming Copernicus missions Sentinel-4 and Sentinel-5. We have also demonstrated the capability of retrieving fine scale water vapour structures in a case study over the Amazon. This indicates that the TROPOMI data set is also useful for local and regional climate studies.

Spatiotemporal analysis of COVID-19, air pollution, climate, and meteorological conditions in a metropolitan region of Iran

The COVID-19 pandemic has a close relationship with local environmental conditions. This study explores the effects of climate characteristics and air pollution on COVID-19 in Isfahan province, Iran. A number of COVID-19 positive cases, main air pollutants, air quality index (AQI), and climatic variables were received from March 1, 2020, to January 19, 2021. Moreover, CO, NO₂, and O₃ tropospheric levels were collected using Sentinel-5P satellite data. The spatial distribution of variables was estimated by the ordinary Kriging and inverse weighted distance (IDW) models. A generalized linear model (GLM) was used to analyze the relationship between environmental variables and COVID-19. The seasonal trend of nitrogen dioxide (NO₂), wind speed, solar energy, and rainfall like COVID-19 was upward in spring and summer. The high and low temperatures increased from April to August. All variables had a spatial autocorrelation and clustered pattern except AQI. Furthermore, COVID-19 showed a significant association with month, climate, solar energy, and NO₂. Suitable policy implications are recommended to be performed for improving people's healthcare and control of the COVID-19 pandemic. This study could survey the local spread of COVID-19, with consideration of the effect of environmental variables, and provides helpful information to health ministry decisions for mitigating harmful effects of environmental change. By means of the proposed approach, probably the COVID-19 spread can be recognized by knowing the regional climate in major cities. The present study also finds that COVID-19 may have an effect on climatic condition and air pollutants.

Association between satellite-detected tropospheric nitrogen dioxide and acute respiratory infections in children under age five in Senegal: spatio-temporal analysis

BACKGROUND

There is growing evidence to suggest that exposure to a high concentration of nitrogen dioxide (NO₂) can lead to a higher incidence of Acute Respiratory Infections (ARIs) in children; however, such an association remains understudied in Sub-Saharan Africa due to the limited availability of exposure data. This study explored this association by using the satellite-detected tropospheric NO₂ concentrations measured by Sentinel-5 Precursor and ARI symptoms in children under age five collected in the Demographic and Health Survey (DHS) in Senegal.

METHODS

We matched the daily tropospheric NO₂ exposure with the individual ARI symptoms according to the DHS survey clusters spatially and temporally and conducted a logistic regression analysis to estimate the association of exposure to NO₂ with ARI symptoms in two preceding weeks.

RESULTS

We observed a positive association between exposure to continuous levels of NO₂ and ARI symptoms after adjusting for confounders (OR 1.27 per 10 mol/m², 95% CI: 1.06 - 1.52). When the association was further examined by quartile exposure categories, the 4th quartile category was positively associated with symptoms of ARI after adjusting for confounders (OR 1.71, 95% CI: 1.08-2.69). This suggests that exposure to certain high levels of NO₂ is associated with the increased risk of children having symptoms of ARI in Senegal.

CONCLUSIONS

This study highlights the need for increased research on the effects of ambient NO₂ exposure in Africa as well as the need for more robust, ground-based air monitoring in the region. For a country like Senegal, where more than 90% of the population lives in areas that do not meet the national air quality standards, it is urgently required to implement air pollution prevention efforts to protect children from the health hazards of air pollution.

Full-coverage spatiotemporal mapping of ambient PM2.5 and PM10 over China from Sentinel-5P and assimilated datasets: Considering the precursors and chemical compositions

Ambient concentrations of particulate matters (PM_2.5 and PM₁₀) are significant indicators for monitoring the air quality relevant to living conditions. At present, most remote sensing based approaches for the estimation of PM_2.5 and PM₁₀ employed Aerosol Optical Depth (AOD) products as the main variate. Nevertheless, the coverage of missing data is generally large in AOD products, which can cause deviations in practical applications of estimated PM_2.5 and PM₁₀ (e.g., air quality monitoring and exposure evaluation). To efficiently address this issue, our study explores a novel approach using the datasets of the precursors & chemical compositions for PM_2.5 and PM₁₀ instead of AOD products. Specifically, the daily full-coverage ambient concentrations of PM_2.5 and PM₁₀ are estimated at 5-km (0.05°) spatial girds across China based on Sentinel-5P and assimilated datasets (GEOS-FP). The estimation models are acquired via an advanced ensemble learning method named Light Gradient Boosting Machine in this paper. For comparison, the Deep Blue AOD product from VIIRS is adopted in a similar framework as a baseline (AOD-based). Validation results show that the ambient concentrations are well estimated through the proposed approach, with the space-based Cross-Validation R²s and RMSEs of 0.88 (0.83) and 11.549 (22.9) μg/m³ for PM_2.5 (PM₁₀), respectively. Meanwhile, the proposed approach achieves better performance than the AOD-based in different cases (e.g., overall and seasonal). Compared to the related previous works over China, the estimation accuracy of our method is also satisfactory. Regarding the mapping, the estimated results through the proposed approach display consecutive spatial distribution and can exactly express the seasonal variations of PM_2.5 and PM₁₀. The proposed approach could efficiently present daily full-coverage results at 5-km spatial grids. It has a large potential to be extended for providing global accurate ambient concentrations of PM_2.5 and PM₁₀ at multiple temporal scales (e.g., daily and annual).

Effect of Anti-COVID-19 Measures on Atmospheric Pollutants Correlated with the Economies of Medium-sized Cities in 10 Urban Areas of Grand Est Region, France

Using Sentinel-5P data, this study investigated the magnitude of change in the concentration of air pollutants (NO₂, HCHO, SO₂, O₃, CO, and aerosol index) in the air of ten cities and urban areas of the French region of Grand Est as a result of the first lockdown imposed between March 17, 2020 and May 11, 2020. The results showed that the air quality in the urban environments of Grand Est improved significantly compared to the same period in 2019 without lockdown. NO₂, O₃, aerosol index and CO were the pollutants that exhibited maximum reductions by an average of -33.98%, -5.94%, -26.82% and -0.66%, respectively (the observed maximum decreases were -54.7%, -7.7%, -13.1%, and -5.3%, respectively). The largest decrease occurred in the Public Establishments of Inter-municipal Cooperation (EPCI, in French: Établissement public de coopération intercommunale) areas of Eurométropole de Strasbourg, CA Colmar, and CA Mulhouse Alsace. The maximum decrease in air pollution first occurred in land cover classes close to cities, followed by built-up urban areas. In this study, a global depollution index known as the atmospheric clearance index (ACI) was developed, which involved several air pollution parameters, and quantitatively analyzed the decrease in contamination levels of the atmosphere in this region. In addition, the correlation between the novel ACI and other population and economic development indices was studied. The results indicated that there was a negative and statistically significant correlation between ACI and population density, gross domestic product, gross value added (GVA) at basic prices, number of employees, and active enterprises.

Exploiting Sentinel-5P TROPOMI and Ground Sensor Data for the Detection of Volcanic SO2 Plumes and Activity in 2018-2021 at Stromboli, Italy

Sulfur dioxide (SO₂) degassing at Strombolian volcanoes is directly associated with magmatic activity, thus its monitoring can inform about the style and intensity of eruptions. The Stromboli volcano in southern Italy is used as a test case to demonstrate that the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (Sentinel-5P) satellite has the suitable spatial resolution and sensitivity to carry out local-scale SO₂ monitoring of relatively small-size, nearly point-wise volcanic sources, and distinguish periods of different activity intensity. The entire dataset consisting of TROPOMI Level 2 SO₂ geophysical products from UV sensor data collected over Stromboli from 6 May 2018 to 31 May 2021 is processed with purposely adapted Python scripts. A methodological workflow is developed to encompass the extraction of total SO₂ Vertical Column Density (VCD) at given coordinates (including conditional VCD for three different hypothetical peaks at 0-1, 7 and 15 km), as well as filtering by quality in compliance with the Sentinel-5P Validation Team's recommendations. The comparison of total SO₂ VCD time series for the main crater and across different averaging windows (3 × 3, 5 × 5 and 4 × 2) proves the correctness of the adopted spatial sampling criterion, and practical recommendations are proposed for further implementation in similar volcanic environments. An approach for detecting SO₂ VCD peaks at the volcano is trialed, and the detections are compared with the level of SO₂ flux measured at ground-based instrumentation. SO₂ time series analysis is complemented with information provided by contextual Sentinel-2 multispectral (in the visible, near and short-wave infrared) and Suomi NPP VIIRS observations. The aim is to correctly interpret SO₂ total VCD peaks when they either (i) coincide with medium to very high SO₂ emissions as measured in situ and known from volcanological observatory bulletins, or (ii) occur outside periods of significant emissions despite signs of activity visible in Sentinel-2 data. Finally, SO₂ VCD peaks in the time series are further investigated through daily time lapses during the paroxysms in July-August 2019, major explosions in August 2020 and a more recent period of activity in May 2021. Hourly wind records from ECMWF Reanalysis v5 (ERA5) data are used to identify local wind direction and SO₂ plume drift during the time lapses. The proposed analysis approach is successful in showing the SO₂ degassing associated with these events, and warning whenever the SO₂ VCD at Stromboli may be overestimated due to clustering with the plume of the Mount Etna volcano.

Spatial sensitivity analysis of COVID-19 infections concerning the satellite-based four air pollutants levels

The novel coronavirus (COVID-19), first reported in late December 2019, has affected the lives of many people throughout the world. Significant studies have been conducted on this pandemic, some of which have addressed understanding the relationship between different air pollutants and confirmed cases. In this study, the effects of four air pollutants (carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide) were assessed from February 19 to March 22, 2020 to explore how they can affect COVID-19 contagion in Iran. The mean concentrations of air pollutants were extracted from Sentinel 5P data. The COVID-19 confirmed case densities of two provinces, Semnan and Qom, were more than all other provinces. The effect of pollutants on the confirmed case densities was analyzed using multiple linear regression in order to estimate the impact coefficients for individual provinces. The impact coefficients determine the level of each pollutant's contribution to the density of total confirmed cases. Carbon monoxide, nitrogen dioxide, sulfur dioxide, and ozone had both considerable negative and positive correlations with the density of confirmed COVID-19 cases, although sulfur dioxide was correlated more negatively than positively. In Semnan, a high hot spot province, nitrogen dioxide had the most significant effect on the density of confirmed cases among all pollutants, while the effect of carbon monoxide was greater in Qom. The results indicated that even short-term exposure to higher concentrations of the pollutants could lead to an increased risk of COVID-19 outbreaks, which should be considered in adopting adequate and appropriate control policies to manage the disease.

Socioeconomic restrictions slowdown COVID-19 far more effectively than favorable weather-evidence from the satellite

We model the impact of restricting socioeconomic activities (SA) on the transmission of COVID-19 globally. Countries initiate public health measures to slow virus transmission, ranging from stringent quarantines including city lockdown to simpler social distancing recommendations. We use satellite readings of NO2, a pollutant emitted from socioeconomic activities, as a proxy for the level of social-economic restrictions, and discuss the implications under the influences of weather. We found that restricting SA has a leading contribution to lowering the reproductive number of COVID-19 by 18.3% ± 3.5%, while air temperature, the highest contributor among all weather-related variables only contributes 8.0% ± 2.6%. The reduction effects by restricting SA becomes more pronounced (23% ± 3.0%) when we limited the data to China and developed countries where the indoor climate is mostly controlled. We computed the spared infectees by restricting SA until mid-April. Among all polities, China spared 40,964 (95% CI 31,463-51,470) infectees with 37,727 (95% CI, 28,925-47,488) in the Hubei Province, the epicenter of the outbreak. Europe spared 174,494 (95% CI 139,202-210,841) infectees, and the United States (US) spared 180,336 (95% CI 142,860-219,445) with 79,813 (95% CI 62,887-97,653) in New York State. In the same period, many regions except for China, Australia, and South Korea see a steep upward trend of spared infectees due to restricting SA with the US and Europe far steeper, signaling a greater risk of reopening the economy too soon. Latin America and Africa show less reduction of transmissivity through the region-by-time fixed effects than other regions, indicating a higher chance of becoming an epicenter soon.

Air quality development during the COVID-19 pandemic over a medium-sized urban area in Thailand

The COVID-19 pandemic has triggered an industrial and financial slowdown due to unprecedented regulations imposed with the purpose to contain the spread of the virus. Consequently, the positive effect on the environment has been witnessed. One of the most prominent evidences has been the drastic air quality improvement, as a direct consequence of lower emissions from reduced industrial activity. While several studies have demonstrated the validity of this hypothesis in mega-cities worldwide, it is still an unsubstantiated fact whether the same holds true for cities with a smaller urban extent and population. In the present study we investigate the temporal development of atmospheric constituent concentrations as retrieved concurrently from the Sentinel-5P satellite and a ground meteorological station. We focus on the period before and during the COVID-19 pandemic over the city of Hat Yai, Thailand and present the effect of the lockdown on the atmospheric quality over this average populated city (156,000 inhabitants). NO2, PM2.5 and PM10 concentrations decreased by 33.7%, 21.8% and 22.9% respectively in the first 3 weeks of the lockdown compared to the respective pre-lockdown period; O3 also decreased by 12.5% and contrary to similar studies. Monthly averages of NO2, CO and PM2.5 for the month April exhibit in 2020 the lowest values in the last decade. Sentinel-5P retrieved NO2 tropospheric concentrations, both locally over the ground station and the spatial average over the urban extent of the city, are in agreement with the reduction observed from the ground station. Numerous studies have already presented evidence of the bettering of the air quality over large metropolitan areas during the COVID-19 pandemic. In the current study we demonstrate that this holds true for Hat Yai, Thailand; we propound that the environmental benefits documented in major urban agglomerations during the lockdown may extend to medium-sized urban areas as well.

Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy

Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China, Italy was the first European country to be severely affected, with the first local case diagnosed on 20 February 2020. The virus spread quickly, particularly in the North of Italy, with three regions (Lombardy, Veneto and Emilia-Romagna) being the most severely affected. These three regions accounted for >80% of SARS-CoV-2 positive cases when the tight lockdown was established (March 8). These regions include one of Europe's areas of heaviest air pollution, the Po valley. Air pollution has been recently proposed as a possible risk factor of SARS-CoV-2 infection, due to its adverse effect on immunity and to the possibility that polluted air may even carry the virus. We investigated the association between air pollution and subsequent spread of the SARS-CoV-2 infection within these regions. We collected NO2 tropospheric levels using satellite data available at the European Space Agency before the lockdown. Using a multivariable restricted cubic spline regression model, we compared NO2 levels with SARS-CoV-2 infection prevalence rate at different time points after the lockdown, namely March 8, 22 and April 5, in the 28 provinces of Lombardy, Veneto and Emilia-Romagna. We found little association of NO2 levels with SARS-CoV-2 prevalence up to about 130 μmol/m2, while a positive association was evident at higher levels at each time point. Notwithstanding the limitations of the use of aggregated data, these findings lend some support to the hypothesis that high levels of air pollution may favor the spread of the SARS-CoV-2 infection.

Investigating the Significance of Aerosols in Determining the Coronavirus Fatality Rate Among Three European Countries

The coronavirus pandemic has not only gripped the scientific community in the search for a vaccine or a cure but also in attempts using statistics and association analysis-to identify environmental factors that increase its potency. A study by Ogen (Sci Total Environ 726:138605, 2020a) explored the possible correlation between coronavirus fatality and high nitrogen dioxide exposure in four European countries-France, Germany, Italy and Spain. Meanwhile, another study showed the importance of nitrogen dioxide along with population density in determining the coronavirus pandemic rate in England. In this follow-up study, Aerosol Optical Depth (AOD) was introduced in conjunction with other variables like nitrogen dioxide and population density for further analysis in fifty-four administrative regions of Germany, Italy and Spain. The AOD values were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites while the nitrogen dioxide data were extracted from TROPOMI (TROPOspheric Monitoring Instrument) sensor onboard the Sentinel-5 Precursor satellite. Regression models, as well as multiple statistical tests were used to evaluate the predictive skill and significance of each variable to the fatality rate. The study was conducted for two periods: (1) pre-exposure period (Dec 1, 2019-Feb 29, 2020); (2) complete exposure period (Dec 1, 2019-Jul 1, 2020). Some of the results pointed towards AOD potentially being a factor in estimating the coronavirus fatality rate. The models performed better using the data collected during the complete exposure period, which showed higher AOD values contributed to an increased significance of AOD in the models. Meanwhile, some uncertainties of the analytical results could be attributed to data quality and the absence of other important factors that determine the coronavirus fatality rate.

Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality

Nitrogen dioxide (NO₂) is an ambient trace-gas result of both natural and anthropogenic processes. Long-term exposure to NO₂ may cause a wide spectrum of severe health problems such as hypertension, diabetes, heart and cardiovascular diseases and even death. The objective of this study is to examine the relationship between long-term exposure to NO₂ and coronavirus fatality. The Sentinel-5P is used for mapping the tropospheric NO₂ distribution and the NCEP/NCAR reanalysis for evaluating the atmospheric capability to disperse the pollution. The spatial analysis has been conducted on a regional scale and combined with the number of death cases taken from 66 administrative regions in Italy, Spain, France and Germany. Results show that out of the 4443 fatality cases, 3487 (78%) were in five regions located in north Italy and central Spain. Additionally, the same five regions show the highest NO₂ concentrations combined with downwards airflow which prevent an efficient dispersion of air pollution. These results indicate that the long-term exposure to this pollutant may be one of the most important contributors to fatality caused by the COVID-19 virus in these regions and maybe across the whole world.