Sentinel-5P Satellite Research Articles

Urban form and Urban-Agricultural Eco-Efficiency as an Indicator for Sustainable Urban Development in Huancayo Province, Peru

Aims: The urban form of each district was estimated; the entry indicators and exit indicators of urban eco-efficiency; as well as the environmental costs and economic outcomes of agricultural eco-efficiency in the 28 districts of the Huancayo Province.
 Study design: Descriptive-Correlational.
 Place and duration of study: The research project lasted 1 year, the data collection of agricultural-urban eco-efficiency was carried out from January to December 2020 by district, as well as the delimitation of productive and non-productive agricultural areas.
 Methodology: The World Business Council for Sustainable Development eco-efficiency model developed by Data Envelopment Analysis (DEA) was used. To estimate the Urban Form, the Landsat 8 OLI/TIRS satellite image was used. For the Ecoeficiencia Urban-Agricultural data was collected by interviewing field and respective households using probability sampling, to estimate the polluting gases by province, the Sentinel-5P satellite image was used. Pearson's r coefficient and bilateral Student's t-test were used for the statistical analysis [26; 2.05].
 Results: The Urban Form presents compactness ratio 0.27, shape ratio 0.21, elasticity ratio 5.47 and population density 753.09hab/km2. The Urban-Input indicators are urban water consumption 5889116.31ML/year electric energy consumption 2062019.23MW/year, food consumption 2664.56 ton/year. The Urban-Output indicators are emission of polluting gases 40335.11 ton/year, economic revenue 279.35PEN/year and wastewater discharge 90581.38ML/year. The Environmental Costs-Agricultural are water consumption for the agricultural sector 3348.34ML/year, fertilizer consumption 69.14 ton/year and phytosanitary consumption 46 ton/year. The Economic Outputs-Agricultural are agricultural production of 4779.79 kg/year, agricultural land rent 6390.3 PEN/year, gross value of production 4854.35 PEN/year. Agricultural Eco-efficiency [Eec-Ag]=0.89 and Urban Eco-efficiency [Eec-Ur]=0.98, ratio coefficient r=-0.13 and tc=0.64.
 Conclusion: The 28 districts of the province of Huancayo have an Eec-Ur [Urban Eco-efficiency] of 0.98 and Eec-Ag [Agricultural Eco-efficiency] of 0.89 where it maintains the added value while generating 98% and 89% of its environmental pressures. There is no statistically significant relationship between urban eco-efficiency and agricultural eco-efficiency.

DETECTION OF METHANE PLUMES IN HYPERSPECTRAL IMAGES FROM SENTINEL-5P BY COUPLING ANOMALY DETECTION AND PATTERN RECOGNITION

Abstract. Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks using hyperspectral data from the Sentinel-5P satellite. For that we exploit the fine spectral sampling of Sentinel-5P data to detect methane absorption features visible in the shortwave infrared wavelength range (SWIR). Our method involves three separate steps: i) background subtraction, ii) detection of local maxima in the negative logarithmic spectrum of each pixel and iii) anomaly detection in the background-free image. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections. In the last step we use an anomaly detector to isolate potential methane plumes and we intersect those potential plumes with what have been detected in the second step. This process strongly reduces the number of false alarms. We validate our method by comparing the detected plumes against a dataset of plumes manually annotated on the Sentinel-5P L2 methane product.

Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

A method for random uncertainties validation and probing the natural variability with application to TROPOMI on board Sentinel-5P total ozone measurements

Abstract. In this paper, we discuss the method for validation of random uncertainties in the remote sensing measurements based on evaluation of the structure function, i.e., root-mean-square differences as a function of increasing spatiotemporal separation of the measurements. The limit at the zero mismatch provides the experimental estimate of random noise in the data. At the same time, this method allows probing of the natural variability of the measured parameter. As an illustration, we applied this method to the clear-sky total ozone measurements by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite. We found that the random uncertainties reported by the TROPOMI inversion algorithm, which are in the range 1–2 DU, agree well with the experimental uncertainty estimates by the structure function. Our analysis of the structure function has shown the expected results on total ozone variability: it is significantly smaller in the tropics compared to mid-latitudes. At mid-latitudes, ozone variability is much larger in winter than in summer. The ozone structure function is anisotropic (being larger in the latitudinal direction) at horizontal scales larger than 10–20 km. The structure function rapidly grows with the separation distance. At mid-latitudes in winter, the ozone values can differ by 5 % at separations 300–500 km. The method discussed is a powerful tool in experimental estimates of the random noise in data and studies of natural variability, and it can be used in various applications.

Variation of tropospheric NO2 over Indo-Gangetic plain during COVID-19 outbreak in India

Nitrogen dioxide (NO2) is a gaseous air pollutant which primarily gets in the air when fossil fuels such as coal, oil, gas or diesel are burned at high temperatures through industries, power plant, automobiles and locomotive. Continuous exposure to elevated concentration of NO2 may impose development of asthma and potentially increase in susceptibility to respiratory infections in humans. The present study examined impact of lockdown due to COVID-19 in terms of spatial and temporal variation of tropospheric NO2 in Indo-Gangetic plain. The results showed steep reduction in tropospheric NO2 as a result of ceased industrial and economic activities. Analysis of Sentinel-5P satellite data was carried out for specific period during 2019–2020 at a regional scale and for major industrial cities. Tremendous positive impact of lockdown in atmospheric condition is observed, wherein NO2 concentration has gone down by 20–40% in all major cities except few exceptions were noticed. The concomitant impact of Covid-19 has ensued pragmatic constructive impact on environment and climate, due to drop in NO2 emissions.

Моніторинг якості атмосферного повітря над територією України з деталізацією для міст за даними супутника Sentinel-5P

The study describes methods for operative monitoring of atmospheric air quality over the territory of Ukraine using the Sentinel-5P satellite data. The methods provide possibility for data specification over the cities. The data processing is fully automatic and deals with the column data of nitrogen dioxide (NO2), carbon monoxide (CO), formaldehyde (HCHO), sulfur dioxide (SO2) and total ozone (O3). The system works every day and starts processing approximately 3 hours after the scanning of Ukrainian territory. The paper describes the procedure of files creation which represents the third level of data archiving. There are implemented the procedures of the adjusting to regular grids and the filtering of statistically unreliable data. The methods for data specification are developed which allow to analyze the content of chemical compounds over the cities. The paper discusses the main features for the interpretation of chemicals’ spatio-temporal distribution. It is emphasized the typical reasons for false interpretation and mistaken conclusions about atmospheric air quality while analyzing the satellite observations.

Air pollution in Ukraine: a view from the Sentinel-5P satellite

The study presents analysis of current air pollution state over Ukraine including remote regions and uncontrolled Ukrainian territories; features of NO2, SO2, and CO spatial distribution and seasonality under the influence of local anthropogenic emissions. The research is based on Sentinel-5P satellite data for the period of November 2018 – January 2020. Despite the increasing traffic emissions, the industrial emissions still greatly influence the air pollution in Ukraine. Sentinel-5P coverage allowed detecting a number of cities with huge anthropogenic NO2 and SO2 emissions, where ground-based measurements are absent. Uncontrolled territories on the east part of Ukraine still negatively affect air quality in the region due to the activity of coal-fired thermal power plants. The study indicates significant air quality changes during the heating season in winter and open burning in March – April. There were found differences in NO2 seasonal variability over clean remote regions and industrial zones. The paper analyzes features of shipping emissions during the tourist season for Ukrainian coastline of Black and Azov Seas, showing huge negative impact of chaotic movements of tourists boats near the Dzharylhach National Nature Park.

Nitrogen Dioxide (NO2) Level Changes during the Control of COVID-19 Pandemic in Thailand

The first case of COVID-19 infection was confirmed in Thailand on January 13, 2020; since then, work from home and lockdown measures have slowed the spread of COVID-19. A more stringent, curfew regulation was imposed on April 3, 2020. Under these measures, the activities of businesses, transportation, and industrial sectors were reduced or temporally closed. Therefore, this study aims to investigate the changes in the nitrogen dioxide (NO₂) level in the whole of Thailand and specifically in the Bangkok metropolitan area. The concentrations of tropospheric NO2 were extracted from the Sentinel-5P satellite data. The Google Earth Engine JavaScript API was used to retrieve and calculate the NO2 data. Python was also used to extract NO2 values from the output raster data. The results showed that the level of NO2 increased by 47.4% during the COVID-19 pandemic in the whole of Thailand; NO2 declined by 4.8% in the Bangkok metropolitan area, but there was no significant difference compared to before the COVID-19 pandemic. NO2 level during curfew regulation declined by 9.5% in the whole of Thailand and decreased by 20.1% in the Bangkok metropolitan area. After comparing the same time duration in 2019 and 2020 on a monthly basis, the results revealed that NO2 level in 2020 declined by 9.1% and 9.8% for the whole of Thailand and Bangkok metropolitan area, respectively. Our findings support that these measures can temporarily reduce NO2 levels. Once all sectors resume as normal, we concern that the NO2 emissions will rebound. Hence, the challenge after the COVID-19 pandemic is how to improve and protect our environment while restoring the economy, which is a crucial issue that policymakers should fully address.

Assessing nitrogen dioxide (NO2) impact on health pre- and post-COVID-19 pandemic using IoT in India

PurposeCorona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2; it affects the respiratory system of the human and sometimes leads to death. The COVID-19 mainly attacks the person with previous lung diseases; the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration. NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes. Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality. This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown. The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19.Design/methodology/approachSpatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things. Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship. Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data. Moreover, aerosol optical depth analysis has been carried out based on NASA’s Earth Observing System data.FindingsThe results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown. The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2.Originality/valueUsing the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities. The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body.

Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide

After the appearance of COVID-19 in China last December 2019, Italy was the first European country to be severely affected by the outbreak. The first diagnosis in Italy was on February 20, 2020, followed by the establishment of a light and a tight lockdown on February 23 and on March 8, 2020, respectively. The virus spread rapidly, particularly in the North of the country in the ‘Padan Plain’ area, known as one of the most polluted regions in Europe. Air pollution has been recently hypothesized to enhance the clinical severity of SARS-CoV-2 infection, acting through adverse effects on immunity, induction of respiratory and other chronic disease, upregulation of viral receptor ACE-2, and possible pathogen transportation as a virus carrier. We investigated the association between air pollution and subsequent COVID-19 mortality rates within two Italian regions (Veneto and Emilia-Romagna). We estimated ground-level nitrogen dioxide through its tropospheric levels using data available from the Sentinel-5P satellites of the European Space Agency Copernicus Earth Observation Programme before the lockdown. We then examined COVID-19 mortality rates in relation to the nitrogen dioxide levels at three 14-day lag points after the lockdown, namely March 8, 22 and April 5, 2020. Using a multivariable negative binomial regression model, we found an association between nitrogen dioxide and COVID-19 mortality. Although ecological data provide only weak evidence, these findings indicate an association between air pollution levels and COVID-19 severity.

Nitrogen Dioxide (NO2) Pollution Monitoring with Sentinel-5P Satellite Imagery over Europe during the Coronavirus Pandemic Outbreak

Nitrogen dioxide (NO2) is one of the main air quality pollutants of concern in many urban and industrial areas worldwide, and particularly in the European region, where in 2017 almost 20 countries exceeded the NO2 annual limit values imposed by the European Commission Directive 2008/50/EC (EEA, 2019). NO2 pollution monitoring and regulation is a necessary task to help decision makers to search for a sustainable solution for environmental quality and population health status improvement. In this study, we propose a comparative analysis of the tropospheric NO2 column spatial configuration over Europe between similar periods in 2019 and 2020, based on the ESA Copernicus Sentinel-5P products. The results highlight the NO2 pollution dynamics over the abrupt transition from a normal condition situation to the COVID-19 outbreak context, characterized by a short-time decrease of traffic intensities and industrial activities, revealing remarkable tropospheric NO2 column number density decreases even of 85% in some of the European big cities. The validation approach of the satellite-derived data, based on a cross-correlation analysis with independent data from ground-based observations, provided encouraging values of the correlation coefficients (R2), ranging between 0.5 and 0.75 in different locations. The remarkable decrease of NO2 pollution over Europe during the COVID-19 lockdown is highlighted by S-5P products and confirmed by the Industrial Production Index and air traffic volumes.

A correlation study between weather and atmosphere with COVID-19 pandemic in Islamabad, Pakistan

The present research aims to investigate the association amid weather and the most recent pandemic of COVID-19 in Islamabad, the capital of Pakistan. The source of COVID-19 surveillance data for the secondary data analysis was the Pakistan’s Ministry of National Health Services Regulations and Coordination. The weather data obtained from the Pakistan Metrological Department (PMD) was exercised in this research. The components of weather include wind speed (m/s), precipitation level (mm), normal, mean, maximum, and minimum temperature (°C). For data analysis, a non-parametric correlation test was used due to the reason that normality was not satisfied. Precipitation level (r = − 0.285; p =0 .022), normal temperature (r = 0.293; p = 0.019) as well as the maximum temperature (r = 0.347; p = 0.005) were very much associated with COVID-19 virus. Pollution data (showing the concentration of NO2) of the specific region comprising the study area extracted from the Sentinel-5P satellite was also compared for the two years (2019 and 2020). Since the country will be entering to a new weather season, the conclusions may well assist the strategy and decision-makers in the deterrence of COVID-19.

Impact of COVID-19 lockdown on NO2, O3, PM2.5 and PM10 concentrations and assessing air quality changes in Baghdad, Iraq

Covid-19 was first reported in Iraq on February 24, 2020. Since then, to prevent its propagation, the Iraqi government declared a state of health emergency. A set of rapid and strict countermeasures have taken, including locking down cities and limiting population's mobility. In this study, concentrations of four criteria pollutants, NO2, O3, PM2.5 and PM10 before the lockdown from January 16 to February 29, 2020, and during four periods of partial and total lockdown from March 1 to July 24, 2020, in Baghdad were analysed. Overall, 6, 8 and 15% decreases in NO2, PM2.5, and PM10 concentrations, respectively in Baghdad during the 1st partial and total lockdown from March 1 to April 21, compared to the period before the lockdown. While, there were 13% increase in O3 for same period. During the 2nd partial lockdown from June 14 to July 24, NO2 and PM2.5 decreases 20 and 2.5%, respectively. While, there were 525 and 56% increase in O3 and PM10, respectively for same period. The air quality index (AQI) improved by 13% in Baghdad during the 1st partial lockdown from March 1 to April 21, compared to its pre-lockdown. The results of NO2 tropospheric column extracted from the Sentinel-5P satellite shown the NO2 emissions reduced up to 35 to 40% across Iraq, due to lockdown measures, between January and July, 2020, especially across the major cities such as Baghdad, Basra and Erbil. The lockdown due to COVID-19 has drastic effects on social and economic aspects. However, the lockdown also has some positive effect on natural environment and air quality improvement.

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.

NO2 levels after the COVID-19 lockdown in Ecuador: A trade-off between environment and human health

The negative effects on human health, along with the fatalities caused by the new coronavirus, have led governments worldwide to take strict measures. However, a reduction in air pollution has been found in many regions on a global scale. This study is focused on how the COVID-19 pandemic is impacting on the air quality in Ecuador, one of the most alarming cases of COVID-19 contagion in Latin America, occupying the first place as regards deaths per capita. The spatio-temporal variations in NO2 concentrations in 12 highly populated cities were evaluated by comparing the NO2 tropospheric concentrations before (2019) and after (2020) the COVID-19 lockdown. The atmospheric data was collected from the TROPOMI on the Sentinel-5P satellite of the European Space Agency. A reduction in NO2 concentrations (−13%) was observed as a consequence of the COVID-19 lockdown in Ecuador. However, this reduction occurred to the greatest extent in the cases of Guayaquil (−23.4%) and Quito (−22.4%), the two most highly populated cities. Linking NO2 levels to confirmed cases/deaths of COVID-19, a strong correlation between air NO2 concentrations and the cases/mortality caused by coronavirus (r = 0.91; p<0.001) was observed. This work highlights the crucial role played by air quality as regards human health.

Impact of Environmental Pollution on COVID-19 pandemic in Pakistan

The global COVID-19 pandemic is not finished yet, so it’s taught us a lot of lessons already-the hard way. The current situation is dominated by studies on identifying the factors that impact, taking preventive steps and trying to find successful care at the top of the list. Countries around the globe have made some arrangement of lockdown in order to slow down their infection. However this lockdown makes some positive effect on air quality and substantial reduction in nitrogen dioxide (NO 2 ) air pollution worldwide. Luckily, NASA collected the images over Sentinel-5p satellite broadcasting using TROPOMI (TROPPspheric Monitoring Instrument) and issues the various images of major city of Pakistan before and after lockdown due to COVID-19 pandemic and its influence on the Pakistan air pollution.

Current state of atmospheric air pollution in Ukraine based on Sentinel-5P satellite data

Introduction. Industrial development and increasing traffic emissions cause air quality problems. The most accurate for air pollution analysis are ground-based data; however, it is very limited in space. Modelling could solve this problem, but huge amount of input information and limits of computational power make it difficult to analyze big territories with high resolution. Sentinel-5P satellite with TROPOMI instrument nowadays gives opportunities to monitor the air pollution with good spatial resolution. The purpose of the study is to analyse nitrogen dioxide, sulfur dioxide, carbon monoxide and formaldehyde spatial and temporal distribution over Ukraine and Black Sea and Azov Sea. Methods and raw data. There were selected Sentinel-5P data of NO2, SO2, CO and HCHO for the period from first data release in 2018 to June, 2019. Data processing implemented in Google Earth Engine using JavaScript programming in The Earth Engine Code Editor. Results and discussion. Joint analysis of NO2 tropospheric, stratospheric and total columns showed the prevailing of tropospheric NO2 content and therefore crucial role of anthropogenic emission sources. While background NO2 total column varies from 4·10-5 mol/m2 to 7·10-5 mol/m2, in the most polluted cities content exceeds 1·10-4 mol/m2. The highest values are observed in Kyiv and industrial cities in Donbas region. Some of them are situated in the area of Joint Forces Operation outside the demarcation line. Sentinel-5P data catch the large emissions from the local industries; however, no official confirmations about production volume are available. NO2 dispersion increase during winter with the values range from 1·10-5 mol/m2 to 3·10-5 mol/m2 over clean territories and 8·10-5 mol/m2 to 1·10-3 mol/m2 in industrial cities. Seasonal maximal observed during warm period in unpolluted regions and during winter in the cities. Cold seasonal NO2 content outbreaks in the mountains are the result of its transportation from industrial cities. Spatial distribution of CO total column over Ukraine is rather homogeneous because of CO lifetime in the atmosphere up to several months. It could be transported on the long distances, which makes identification of polluted regions difficult. However, several cities with maximal average values of more than 0.037mol/m2 prove the existence of extreme anthropogenic emissions. Overall background CO contents vary within 0.033–0.035 mol/m2. Coastal regions of the Black Sea and Azov Sea are more polluted by CO compared to the mainland, which could be the consequences of ships emissions. Temporal analysis of CO total column found 4 powerful emissions: three of them are anthropogenic in industrial cities and one is natural, connected with forest fires in Belarus and northern part of Rivne region in April, 2019. There is one location with huge SO2 emission in Ukraine, observed in Novyi Svit (Donetsk region) outside the demarcation line of Joint Forces Operation, which corresponds to Starobeshivska thermal power station. In general, higher SO2 contents are observed over Donetsk, Zaporizhia and Dnipro regions; also over the Black Sea and Azov Sea. For HCHO spatial distribution minimal values are typical for the Carpathians. It is well seen that SO2 content are higher in the Southern part of Ukraine than in the Western part. The research finds regularities in spatial distribution of pollutants over the sea area. During cold season higher concentrations could be observed over the main merchant vessels tracks in the Black Sea. In summer months, which coincide with the main tourist season, most polluted are shoreline area because of emissions from cabotage and fishing vessels. The study updates information about most polluted cities in Ukraine, especially in the regions with absence of ground-based measurements. Some of them are mentioned for the first time among other research. Air quality analysis in many industrial cities significantly changed over the last two years. It shows difficulties of long-term pollution forecast and scenarios based on historical data and observed trends. It is recommended to use ensemble modelling for this purpose with scenarios of emission reduction, increasing and temporal stability. Sentinel-5P data confirm the conclusions of previous research about dependence of NO2 annual cycle and seasonality phases shift from the level of anthropogenic load. Prospects for further research. Sentinel-5P spatial resolution expands the horizons for air pollution research. The most relevant are monitoring of short-term anthropogenic and natural emissions, pollutants’ seasonality changes in different macroclimatic conditions, research of ships emissions in Black Sea and Azov Sea, combination of satellite air pollution data with methods of “artificial intelligence” for individual emissions detection.