Estimating monthly surface ozone using multi-source satellite products in China based on Deep Forest model

Surface ozone (O3), a well-recognized air pollutant, exists in the atmosphere, which has a detrimental effect on public health and the ecological environment. It is reported that surface O3 has seen a significant increase in many cities from 2019 to 2021 (COVID-19 pandemic). In this study, we applied an innovative machine learning model (Deep Forest) coupled with satellites, the Troposphere Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI), and meteorological datasets to estimate monthly surface O3 of 1 km spatial resolution across China during this pandemic period. Our model achieved an overall R2 of 0.974, 0.963, and root mean square error (RMSE) of 6.016 μg/m3, 7.214 μg/m3 on TROPOMI-based datasets and OMI-based datasets, respectively. Also, we found the higher ozone concentration regions were in Eastern China. Simultaneously, the surface O3 concentration was high in summer(average = 110.57 ± 15.01 μg/m3). And the ozone concentration in summer 2020 (average = 107.78 ± 13.90 μg/m3) declined unprecedently than in summer 2019 (average = 110.54 ± 16.58 μg/m3). Our results indicated that TROPOMI data could provide robust data support for surface ozone concentration estimation. Furthermore, this study could enhance our comprehension of the formation mechanisms of surface O3 in China and assist air environment management decision-making.

Assessment of NO2 concentrations over industrial state Jharkhand, at the time frame of pre, concurrent, and post-COVID-19 lockdown along with the meteorological behaviour: an overview from satellite and ground approaches.

Burning of fossil fuels in the form of coal or gasoline in thermal power plants, industries, and automobiles is a prime source of nitrogen dioxide (NO2), a major air pollutant causing health problems. In this paper, spatio-temporal unevenness of NO2 concentrations via both spaceborne Sentinel-5P and ground-based in situ data have been studied for the period of 2017-2021. Annual and seasonal distribution of TROPOMI-NO2 depict consistency over the Jharkhand region, highlighting six hotspot regions. As compared to 2019, a notable dip of 11% in the spatial annual average TROPOMI-NO2 was achieved in 2020, which were elevated again by 22% in 2021 as the lockdown gradually goes out of the picture. Among eight ground-monitoring stations, Tata and Golmuri stations always displayed a higher level of TROPOMI-NO2 ranges up to 15.2 ×1015molecules.cm-2 and 16.9 ×1015molecules.cm-2 respectively, as being located in the highly industrialised district of Jamshedpur. A big percentage reduction of up to 30% in TROPOMI-NO2 has been reported in Jharia and Bastacola stations in Dhanbad in the lockdown phase of 2020 compared to 2019. Good agreement between TROPOMI-NO2 and surface-NO2 has been achieved with R = 0.8 and R = 0.71 during winter and post-monsoon respectively. Among four meteorological parameters, TROPOMI-NO2 was majorly found to be influenced by precipitation, having R = 0.6-0.8 for almost all stations. More advanced satellite algorithms and ground-based data may be used to estimate NO2 in places where monitoring facilities are limited and thus can help in air pollution control policy.

Integral Assessment of Atmospheric Air Quality in the Largest Cities of Russia Based on TROPOMI (Sentinel-5P) Data for 2019–2020

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm.

Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO2), ozone (O3), PM2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM10 (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM2.5, PM10, and NO2 was offset by amplified O3 pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O3 concentration. The amplified O3 pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NOx) under a VOC-limited regime and high background O3 concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM2.5, PM10, and NO2 more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future. © 2023 by the authors.

Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak

COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.

Year-round changes in tropospheric nitrogen dioxide caused by COVID-19 in China using satellite observation.

The lockdown policy deals a severe blow to the economy and greatly reduces the nitrogen oxides (NOx) emission in China when the coronavirus 2019 spreads widely in early 2020. Here we use satellite observations from Tropospheric Monitoring Instrument to study the year-round variation of the nitrogen dioxide (NO2) tropospheric vertical column density (TVCD) in 2020. The NO2 TVCD reveals a sharp drop, followed by small fluctuations and then a strong rebound when compared to 2019. By the end of 2020, the annual average NO2 TVCD declines by only 3.4% in China mainland, much less than the reduction of 24.1% in the lockdown period. On the basis of quantitative analysis, we find the rebound of NO2 TVCD is mainly caused by the rapid recovery of economy especially in the fourth quarter, when contribution of industry and power plant on NO2 TVCD continues to rise. This revenge bounce of NO2 indicates the emission reduction of NOx in lockdown period is basically offset by the recovery of economy, revealing the fact that China's economic development and NOx emissions are still not decoupled. More efforts are still required to stimulate low-pollution development.

Identification of volatile organic compound emissions from anthropogenic and biogenic sources based on satellite observation of formaldehyde and glyoxal.

Anthropogenic volatile organic compounds (VOCs) are serious pollutants in the atmosphere because of their toxicity and as precursors of secondary organic aerosols and ozone pollution. Although in-situ measurements provide accurate information on VOCs, their spatial coverage is limited and insufficient. In this study, we provide a global perspective for identifying anthropogenic VOC emission sources through the ratio of glyoxal to formaldehyde (RGF) based on satellite observations. We assessed typical cities and polluted areas in the mid latitudes and found that some Asian cities had higher anthropogenic VOC emissions than cities in Europe and America. For heavily polluted areas, such as the Yangtze River Delta (YRD), the areas dominated by anthropogenic VOCs accounted for 23 % of the total study areas. During the COVID-19 pandemic, a significant decline in RGF values was observed in the YRD and western United States, corresponding to a reduction in anthropogenic VOC emissions. Furthermore, developing countries appeared to have higher anthropogenic VOC emissions than developed countries. These observations could contribute to optimising industrial structures and setting stricter pollution standards to reduce anthropogenic VOCs in developing countries.

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.

Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic

The study evaluates the impacts of India's COVID-19 lockdown and unlocking periods on the country's ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and satellite data of NO2 emissions for several Indian cities to assess the impacts of the lockdowns in India. Additionally, we analyzed NO2 data acquired from the Sentinel-5P TROPOMI sensor over a few districts of the Punjab state, as well as the National Capital Region. The comparisons between the in situ and satellite NO2 emissions were performed for the years 2019, 2020 and up to July 2021. Further analysis was conducted on the satellite data to map the NO2 emissions over India during March to July for the years of 2019, 2020 and 2021. Based on the in situ and satellite observations, we observed that the NO2 emissions significantly decreased by 45–55% in the first wave and 30% in the second wave, especially over the Northern Indian cities during the lockdown periods. The improved air quality over India is indicative of reduced pollution in the atmosphere due to the lockdown process, which slowed down the industrial and commercial activities, including the migration of humans from one place to another. Overall, the present study contributes to the understanding of the trends of the ambient air quality over large geographical areas using the Sentinel-5P satellite data and provides valuable information for regulatory bodies to design a better decision support system to improve air quality. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Impact of COVID-19 lockdown on the ambient air-pollutants over the Arabian Peninsula

Lockdowns imposed across the world to combat the spread of the COVID-19 pandemic also reduced the anthropogenic emissions. This study investigates the changes in the anthropogenic and natural pollution levels during the lockdown over the Arabian Peninsula (AP), a region where natural pollutants (mineral dust) dominate. In-situ and satellite observations, reanalysis products, and Weather Research and Forecasting model (WRF) coupled with Chemistry module (WRF-Chem) simulations were analyzed to investigate the influence of COVID-19 lockdown on the aerosols (PM2.5, PM10, and AOD) and trace gases (NO2 and SO2). WRF-Chem reasonably reproduced the satellite and in-situ measurements during the study period, with correlation coefficients varying between 0.6-0.8 (0.3-0.8) for PM10 (NO2 and SO2) at 95% confidence levels. During the lockdown, WRF-Chem simulations indicate a significant reduction (50-60%) in the trace gas concentrations over the entire AP compared to the pre-lockdown period. This is shown to be mostly due to a significant reduction in the emissions and an increase in the boundary layer height. An increase in the aerosol concentrations over the central and northern parts of the AP, and a decrease over the north-west AP, Red Sea, and Gulf of Aden regions are noticeable during the lockdown. WRF-Chem simulations suggest that the increase in particulate concentrations over the central and northern AP during the lockdown is mainly due to an increase in dust concentrations, manifested by the stronger convergence and upliftment of winds and warmer surface temperatures (15-25%) over the desert regions. The restricted anthropogenic activities drastically reduced the trace gas concentrations, however, the reduction in particulate concentration levels is offset by the increase in the natural processes (dust emissions).

<span contenteditable="false" data-cke-magic-line="1" style="width: 695px;height: 0px;padding: 0px;margin: 0px;display: block;z-index: 9999;color: rgb(255,255,255);font-size: 0px;line-height: 0px;position: absolute;border-top: 1px dashed rgb(255,0,0);user-select: none;left:-2px;top: 64.2px;"><span style="color:#ffffff"> </span><span style="color:#ffffff"> </span><span style="color:#ffffff">?</span></span>Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R-2 of 0.81 and RMSE of 3.45 mu g/m(3) in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R-2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

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

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

Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

NO2 pollution over selected cities in the Po valley in 2018-2021 and its possible effects on boosting COVID-19 deaths.

This work analyzes nitrogen dioxide (NO2) pollution over a set of cities in the Po Valley in northern Italy, using satellite and in situ observations. The cities include Milan, Bergamo, and Brescia, the first area of the COVID-19 outbreak and diffusion in Italy, with a higher mortality rate than in other parts of Italy and Europe. The analysis was performed for three years, from May 2018 to April 2021, including the period of first-wave diffusion of COVID-19 over the Po Valley, that is, January 2020-April 2020. The study aimed at giving a more general picture of the NO2 temporal and spatial variation, possibly due to the lockdown adopted for the pandemic crisis containment and other factors, such as the meteorological conditions and the seasonal cycle. We have mainly investigated two effects: first, the correlation of NO2 pollution with atmospheric parameters such as air and dew point temperature, and second the possible correlation between air quality and COVID-19 deaths, which could explain the high mortality rate. We have found a good relationship between air quality and temperature. In light of this relationship, we can conclude that the air quality improvement in March 2020 was primarily because of the lockdown adopted to prevent and limit virus diffusion. We also report a good correlation between NO2 pollution and COVID-19 deaths, which is not seen when considering a reference city in the South of Italy. The critical factor in explaining the difference is the persistence of air pollution in the Po Valley in wintertime. We found that NO2 pollution shows a seasonal cycle, yielding a non-causal correlation with the COVID-19 deaths. However, causality comes in once we read the correlation in the context of current and recent epidemiological evidence and leads us to conclude that air pollution may have acted as a significant risk factor in boosting COVID-19 fatalities.

Disentangling the Impact of the COVID-19 Lockdowns on Urban NO2 From Natural Variability.

TROPOMI satellite data show substantial drops in nitrogen dioxide (NO2) during COVID-19 physical distancing. To attribute NO2 changes to NO x emissions changes over short timescales, one must account for meteorology. We find that meteorological patterns were especially favorable for low NO2 in much of the United States in spring 2020, complicating comparisons with spring 2019. Meteorological variations between years can cause column NO2 differences of ~15% over monthly timescales. After accounting for solar angle and meteorological considerations, we calculate that NO2 drops ranged between 9.2% and 43.4% among 20 cities in North America, with a median of 21.6%. Of the studied cities, largest NO2 drops (>30%) were in San Jose, Los Angeles, and Toronto, and smallest drops (<12%) were in Miami, Minneapolis, and Dallas. These normalized NO2 changes can be used to highlight locations with greater activity changes and better understand the sources contributing to adverse air quality in each city.

Studying The Trend of Carbon Monoxide for Lockdown Period Over India

In the present study, the atmospheric concentrations of Carbon Monoxide (CO) over India during COVID-19 (2020) were studied by comparing it with 2019 and 2021. COVID-19 has created an undesirable impact all over the world. However, as a blessing in disguise, these measures have a positive effect on the environment due to closing the mass gathering places. The work has undergone using the TROPOMI instrument, on-board Sentinel-5 Precursor. The results, evidence that human activities like transportation in Delhi, Industrial activities near Indo-Gangetic Plain have sharply fallen during the lockdown phase. On Contrary, there is a sharp increment in the area of Thermal power plants being coal-based. On the whole, the mean concentration of CO over India has minimal change due to long lifetime (1~2 months), indicating the duration of the (68 days) lockdown did not capture prompt and short-term atmospheric change. © 2021 IEEE.

NO2 monthly product for air quality in South America

Air quality is assessed by determining criteria pollutant levels in the atmosphere. While the most significant measurements are ground based, satellite remote sensing is rising as a complementary technique to reveal spatial distribution of pollutants in the integrated tropospheric column. In this work we present a new CONAE's value-added monthly product of nitrogen dioxide (NO2) for South America, derived from the tropospheric NO2 column density estimated by TROPOMI/Sentinel5p (ESA) data. Dataset generation of monthly mean, median, standard deviation and quantity of data used per pixel, along with distribution formats of downloading and visualizing data, are explained in order to provide to different users their characteristics and access. In addition, a spatial and temporal analysis is made for the Buenos Aires, Santiago and Sao Paulo cities along with ground measurements, for the august 2018 to may 2021 period and on a monthly basis. For this matter, higher values of nitrogen dioxide were observed in wintertime for the three cities, due to a greater quantity of stagnation episodes. While satellite derived data follows the temporal profile of ground-based concentrations, Santiago was the city of higher levels and bigger contrast to the summer levels. COVID-19 pandemic restrictions to traffic circulation is also noticed in the diminishing of NO2 in the two datasets, as it was also reported in previous studies. The publication of this new dataset holds the objective of supporting air quality monitoring in South America, helping non specialized users to freely access to interoperational data.

Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels

In this article, we aim to show the capabilities, benefits, as well as restrictions, of three different air quality-related information sources, namely the Sentinel-5Precursor TROPOspheric Monitoring Instrument (TROPOMI) space-born observations, the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based measurements and the LOng Term Ozone Simulation-EURopean Operational Smog (LOTOS-EUROS) chemical transport modelling system simulations. The tropospheric NO2 concentrations between 2018 and 2021 are discussed as air quality indicators for the Greek cities of Thessaloniki and Ioannina. Each dataset was analysed in an autonomous manner and, without disregarding their differences, the common air quality picture that they provide is revealed. All three systems report a clear seasonal pattern, with high NO2 levels during wintertime and lower NO2 levels during summertime, reflecting the importance of photochemistry in the abatement of this air pollutant. The spatial patterns of the NO2 load, obtained by both space-born observations and model simulations, show the undeniable variability of the NO2 load within the urban agglomerations. Furthermore, a clear diurnal variability is clearly identified by the ground-based measurements, as well as a Sunday minimum NO2 load effect, alongside the rest of the sources of air quality information. Within their individual strengths and limitations, the space-borne observations, the ground-based measurements, and the chemical transport modelling simulations demonstrate unequivocally their ability to report on the air quality situation in urban locations.

USING COPERNICUS SENTINEL MEASUREMENTS TO MONITOR COVID-19 IMPACT ON THE ENVIRONMENT

Since the onset of the COVID-19 pandemic in early 2020, many countries worldwide implemented a series of social distancing and containment measures as an attempt to limit its spread. Those measures have led to a significant slowing down of economic activities, drastic drops in road and air traffic, and strong reductions of industrial activities in nonessential sectors, which in turn affected atmospheric emissions and air quality worldwide. Concentrations of short-lived pollutants, such as nitrogen dioxide, are indicators of changes in economic slowdowns and are comparable to changes in emissions. Nitrogen oxides are mainly produced by human activity and the combustion of (fossil) fuels, such as road traffic, ships, power plants and other industrial facilities. Nitrogen Dioxide can have a significant impact on human health, both directly and indirectly through the formation of ozone and small particles. The Copernicus Sentinel-5P satellite nitrogen dioxide concentrations measurements have been used to investigate COVID-19 impact on air quality from space. Global maps of Copernicus Sentinel-5P tropospheric Nitrogen Dioxide measurements have been included – together with other Sentinel measurements – into an on-line tool (dashboard) to provide investigations/results about changes to the Earth environment caused by the COVID-19 pandemic to the public: race.esa.int. © 2021 IEEE.

Unexpected Impact of COVID-19 Lockdown on the Air Quality in the Metro Atlanta, USA Using Ground-based and Satellite Observations

We studied the impact of COVID-19 (coronavirus disease 2019) lockdown on the air quality over the Atlanta area using satellite and ground-based observations, meteorological reanalysis data and traffic information. Unlike other cities, we found the air quality has improved slightly over the Atlanta area during the 2020 COVID-19 lockdown period (March 14–April 30, 2020), compared to the analogous period of 2019 (March 14-April 30, 2019). Ground NO2 concentrations have decreased slightly 10.8% and 8.2% over the near-road (NR) and urban ambient (UA) stations, respectively. Tropospheric NO2 columns have reduced 13%-49% over the Atlanta area from space-borne observations of TROPOspheric Monitoring Instrument (TROPOMI). Ground ozone and PM2.5 have decreased 15.7% an ~5%, respectively. This slight air quality improvement is primarily caused by the reduced human activities, as COVID-19 lockdowns have reduced ~50% human activities, measured by traffic volume. Higher wind speed and precipitations also make the meteorological conditions favorable to this slight air quality improvement. We have not found a significant improvement in Atlanta amid the lockdown when human activities have reduced ~50%. Further studies are needed to understand the impacts of reduced human activities on atmospheric chemistry. We also found TROPOMI and ground measurements have disagreements on NO2 reductions, as collocated TROPOMI observations revealed ~23% and ~21% reductions of tropospheric NO2 columns over NR and UA stations, respectively. Several factors may explain this disagreement: First, tropospheric NO2 columns and ground NO2 concentrations are not necessarily the same, although they are highly correlated in the afternoon;Second, meteorological conditions may have different impacts on TROPMI and ground measurements. Third, TROPOMI may underestimate tropospheric NO2 due to uncertainties from air mass factors. Fourth, the uncertainties of chemiluminescence NO2 measurements used by ground stations. Consequently, studies using space-borne tropospheric NO2 column and ground NO2 measurements should take these factors into account.