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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 32009

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Prof. Dr. Maria João Costa

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Guest Editor

Institute of Earth Sciences (ICT), Institute of Research and Advanced Training, University of Évora, 7000-671 Évora, Portugal
Interests: aerosol and cloud physics; solar radiation; atmospheric radiative transfer; atmospheric remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Daniele Bortoli

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Guest Editor

Department of Physics, Institute of Earth Sciences, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal
Interests: atmospheric sciences; air pollution control; differential optical absorption spectroscopy (DOAS); ozone hole; optoelectronic remote sensing instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution is a worldwide environmental hazard with serious consequences not only for health and climate, but also for agriculture, ecosystems, and cultural heritage, among others. According to the WHO, there are 8 million premature deaths every year resulting from exposure to ambient air pollution. In addition, more than 90% of the world’s population lives in places where air quality is poor, exceeding the recommended limits, most of them in low- or middle-income countries. On the other hand, air pollution and climate influence each other through complex physicochemical interactions in the atmosphere, altering the Earth’s energy balance, with implications in climate change and air quality.

It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze the different scenarios aiming to assess air pollution levels and develop early warning and forecast systems as means to improve air quality and assure public health, in favor of a reduction in air pollution casualties and mitigation of climate change phenomena. This Special Issue invites contributions dealing with remote sensing of air quality, including combination with in situ data, modeling approaches, and synergy of different instrumentations and techniques.

Prof. Dr. Maria João Costa
Prof. Dr. Daniele Bortoli
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing
Air quality
Aerosols
Trace gases
Air pollution
Climate

Published Papers (12 papers)

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Editorial

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3 pages, 201 KiB

Open AccessEditorial

Editorial for the Special Issue “Air Quality Research Using Remote Sensing”

by Maria João Costa and Daniele Bortoli

Remote Sens. 2022, 14(21), 5566; https://doi.org/10.3390/rs14215566 - 04 Nov 2022

Viewed by 992

Abstract

Air pollution is a worldwide environmental hazard with serious consequences for health and climate as well as for agriculture, ecosystems, and cultural heritage, among others [...] Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

Research

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13 pages, 3962 KiB

Open AccessCommunication

Machine Learning-Based Approach Using Open Data to Estimate PM_2.5 over Europe

by Saleem Ibrahim, Martin Landa, Ondřej Pešek, Lukáš Brodský and Lena Halounová

Remote Sens. 2022, 14(14), 3392; https://doi.org/10.3390/rs14143392 - 14 Jul 2022

Cited by 7 | Viewed by 2321

Abstract

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM_2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM_2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM_2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R² of 0.69, RMSE of 5 μg/m³, and MAE of 3.3 μg/m³. The outcome of this study is a daily full coverage PM_2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM_2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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16 pages, 1104 KiB

Open AccessArticle

Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM_2.5 in China—Implications for PM_2.5 Remote Sensing

by Ling Qi, Haotian Zheng, Dian Ding, Dechao Ye and Shuxiao Wang

Remote Sens. 2022, 14(12), 2762; https://doi.org/10.3390/rs14122762 - 08 Jun 2022

Cited by 9 | Viewed by 1631

Abstract

PM_2.5 retrieval from satellite-observed aerosol optical depth (AOD) is still challenging due to the strong impact of meteorology. We investigate influences of meteorology changes on the inter-annual variations of AOD and surface PM_2.5 in China between 2006 and 2017 using a nested 3D chemical transport model, GEOS-Chem, by fixing emissions at the 2006 level. We then identify major meteorological elements controlling the inter-annual variations of AOD and surface PM_2.5 using multiple linear regression. We find larger influences of meteorology changes on trends of AOD than that of surface PM_2.5. On the seasonal scale, meteorology changes are beneficial to AOD and surface PM_2.5 reduction in spring (1–50%) but show an adverse effect on aerosol reduction in summer. In addition, major meteorological elements influencing variations of AOD and PM_2.5 are similar between spring and fall. In winter, meteorology changes are favorable to AOD reduction (−0.007 yr⁻¹, −1.2% yr⁻¹; p < 0.05) but enhanced surface PM_2.5 between 2006 and 2017. The difference in winter is mainly attributed to the stable boundary layer that isolates surface PM_2.5 from aloft. The significant decrease in AOD over the years is related to the increase in meridional wind speed at 850 hPa in NCP (p < 0.05). The increase of surface PM_2.5 in NCP in winter is possibly related to the increased temperature inversion and more stable stratification in the boundary layer. This suggests that previous estimates of wintertime surface PM_2.5 using satellite measurements of AOD corrected by meteorological elements should be used with caution. Our findings provide potential meteorological elements that might improve the retrieval of surface PM_2.5 from satellite-observed AOD on the seasonal scale. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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19 pages, 2134 KiB

Open AccessArticle

Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations

by Peidong Wang, Tracey Holloway, Matilyn Bindl, Monica Harkey and Isabelle De Smedt

Remote Sens. 2022, 14(9), 2191; https://doi.org/10.3390/rs14092191 - 04 May 2022

Cited by 8 | Viewed by 2542

Abstract

This study evaluates formaldehyde (HCHO) over the U.S. from 2006 to 2015 by comparing ground monitor data from the Air Quality System (AQS) and a satellite retrieval from the Ozone Monitoring Instrument (OMI). Our comparison focuses on the utility of satellite data to inform patterns, trends, and processes of ground-based HCHO across the U.S. We find that cities with higher levels of biogenic volatile organic compound (BVOC) emissions, including primary HCHO, exhibit larger HCHO diurnal amplitudes in surface observations. These differences in hour-to-hour variability in surface HCHO suggests that satellite agreement with ground-based data may depend on the distribution of emission sources. On a seasonal basis, OMI exhibits the highest correlation with AQS in summer and the lowest correlation in winter. The ratios of HCHO in summer versus other seasons show pronounced seasonal variability in OMI, likely due to seasonal changes in the vertical HCHO distribution. The seasonal variability in HCHO from satellite is more pronounced than at the surface, with seasonal variability 20–100% larger in satellite than surface observations. The seasonal variability also has a latitude dependency, with more variability in higher latitude regions. OMI agrees with AQS on the interannual variability in certain periods, whereas AQS and OMI do not show a consistent decadal trend. This is possibly due to a rather large interannual variability in HCHO, which makes the small decadal drift less significant. Temperature also explains part of the interannual variabilities. Small temperature variations in the western U.S. are reflected with more quiescent HCHO interannual variability in that region. The decrease in summertime HCHO in the southeast U.S. could also be partially explained by a small and negative trend in local temperatures. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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19 pages, 3959 KiB

Open AccessArticle

The Effect of Urban Form on PM_2.5 Concentration: Evidence from China’s 340 Prefecture-Level Cities

by Ying Liu, Lijie He, Wenmin Qin, Aiwen Lin and Yanzhao Yang

Remote Sens. 2022, 14(1), 7; https://doi.org/10.3390/rs14010007 - 21 Dec 2021

Cited by 11 | Viewed by 2999

Abstract

Exploring how urban form affects the Particulate Matter 2.5 (PM_2.5) concentration could help to find environmentally friendly urbanization. According to the definition of geography, this paper constructs a comprehensive urban form evaluation index system applicable to many aspects. Four urban form metrics, as well as road density and five control variables are selected. Based on 2015 data on China’s 340 prefecture-level cities, the spatial regression model and geographically weighted regression model were used to explore the relationship between the urban form evaluation index system and PM_2.5 pollution. The main results show that the spatial distribution of PM_2.5 in China follows an increasing trend from northwest to southeast. Urban form indicators such as AI, LPI, PLAND, LSI and road density were all significantly related to PM_2.5 concentrations. More compact urban construction, lower fragmentation of urban land, and lower density of the road network are conducive factors for improving air quality conditions. In addition, affected by seasonal changes, the correlation between urban form and PM_2.5 concentration in spring and winter is higher than that in summer and winter. This study confirmed that a reasonable urban planning strategies are very important for improving air quality. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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13 pages, 2647 KiB

Open AccessArticle

Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe

by Saleem Ibrahim, Martin Landa, Ondřej Pešek, Karel Pavelka and Lena Halounova

Remote Sens. 2021, 13(15), 3027; https://doi.org/10.3390/rs13153027 - 02 Aug 2021

Cited by 11 | Viewed by 2728

Abstract

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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15 pages, 6082 KiB

Open AccessArticle

Change of CO Concentration Due to the COVID-19 Lockdown in China Observed by Surface and Satellite Observations

by Minqiang Zhou, Jingyi Jiang, Bavo Langerock, Bart Dils, Mahesh Kumar Sha and Martine De Mazière

Remote Sens. 2021, 13(6), 1129; https://doi.org/10.3390/rs13061129 - 16 Mar 2021

Cited by 15 | Viewed by 2450

Abstract

The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Infrared Atmospheric Sounding Interferometer (IASI)). It is found that the average CO surface concentration in 2020 was close to that in 2019 before the lockdown, and became 18.7% lower as compared to 2019 during the lockdown. The spatial variation of the change in the CO surface concentration is high, with an 8–27% reduction observed for Beijing, Shanghai, Chengdu, Zhengzhou, and Guangzhou, and almost no change in Wuhan. The TROPOMI and IASI satellite observations show that the CO columns decreased by 2–13% during the lockdown in most regions in China. However in South China, there was an 8.8% increase in the CO columns observed by TROPOMI and a 36.7% increase observed by IASI, which is contrary to the 23% decrease in the surface CO concentration. The enhancement of the CO column in South China is strongly affected by the fire emissions transported from Southeast Asia. This study provides an insight into the impact of COVID-19 on CO concentrations both at the surface and in the columns in China, and it can be extended to evaluate other areas using the same approach. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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15 pages, 6336 KiB

Open AccessArticle

A Satellite-Based Land Use Regression Model of Ambient NO₂ with High Spatial Resolution in a Chinese City

by Lina Zhang, Changyuan Yang, Qingyang Xiao, Guannan Geng, Jing Cai, Renjie Chen, Xia Meng and Haidong Kan

Remote Sens. 2021, 13(3), 397; https://doi.org/10.3390/rs13030397 - 24 Jan 2021

Cited by 6 | Viewed by 2536

Abstract

Previous studies have reported that intra-urban variability of NO₂ concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO₂ concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO₂ concentrations at a city scale by including satellite-retrieved NO₂ tropospheric column density, population density, traffic indicators, and NO_x emission data. The R² of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO₂ variations at a 100 m × 100 m resolution and demonstrated that NO₂ pollution in winter is 1.46 times higher than that in summer. NO₂ concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO₂ concentrations exceeding the annual-mean standard at 40 μg/m³ in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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18 pages, 3915 KiB

Open AccessArticle

Global-Scale Patterns and Trends in Tropospheric NO₂ Concentrations, 2005–2018

by Sadegh Jamali, Daniel Klingmyr and Torbern Tagesson

Remote Sens. 2020, 12(21), 3526; https://doi.org/10.3390/rs12213526 - 28 Oct 2020

Cited by 26 | Viewed by 3066

Abstract

Nitrogen dioxide (NO₂) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO₂ concentrations globally using data from the satellite-based Ozone Monitoring Instrument (OMI). Additional aims are to compare the satellite data with ground-based observations, and to find the timing and magnitude of greatest breakpoints in tropospheric NO₂ concentrations for the time period 2005–2018. The OMI NO₂ concentrations showed strong relationships with the ground-based observations, and inter-annual patterns were especially well reproduced. Eastern USA, Western Europe, India, China and Japan were identified as hotspot areas with high concentrations of NO₂. The global average trend indicated slightly increasing NO₂ concentrations (0.004 × 10¹⁵ molecules cm⁻² y⁻¹) in 2005–2018. The contribution of different regions to this global trend showed substantial regional differences. Negative trends were observed for most of Eastern USA, Western Europe, Japan and for parts of China, whereas strong, positive trends were seen in India, parts of China and in the Middle East. The years 2005 and 2007 had the highest occurrence of negative breakpoints, but the trends thereafter in general reversed, and the highest tropospheric NO₂ concentrations were observed for the years 2017–2018. This indicates that the anthropogenic contribution to air pollution is still a major issue and that further actions are necessary to reduce this contribution, having a substantial impact on human and environmental health. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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Other

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15 pages, 5987 KiB

Open AccessTechnical Note

The Cross-Border Transport of PM_2.5 from the Southeast Asian Biomass Burning Emissions and Its Impact on Air Pollution in Yunnan Plateau, Southwest China

by Qingjian Yang, Tianliang Zhao, Zhijie Tian, Kanike Raghavendra Kumar, Jiacheng Chang, Weiyang Hu, Zhuozhi Shu and Jun Hu

Remote Sens. 2022, 14(8), 1886; https://doi.org/10.3390/rs14081886 - 14 Apr 2022

Cited by 8 | Viewed by 2028

Abstract

Southeast Asia is one of the largest biomass burning (BB) regions in the world, and the air pollutants generated by this BB have an important impact on air pollution in southern China. However, the mechanism of the cross-border transport of BB pollutants to neighboring regions is yet to be understood. Based on the MODIS remote sensing products and conventional observation data of meteorology and the environment, the WRF-Chem and FLEXPART-WRF models were used to simulate a typical PM_2.5 pollution episode that occurred during 24–26 March 2017 to analyze the mechanism of cross-border transport of BB pollutants over Yunnan Plateau (YP) in southwest China. During this air pollution episode, in conjunction with the flourishing BB activities over the neighboring Indo-China Peninsula (ICP) regions in Southeast Asia, and driven by the southwesterly winds prevailing from the ICP to YP, the cross-border transport of pollutants was observed along the transport pathway with the lifting plateau topography in YP. Based on the proximity to the BB sources in ICP, YP was divided into a source region (SR) and a receptor region (RR) for the cross-border transport, and the negative and positive correlation coefficients (R) between PM_2.5 concentrations and wind speeds, respectively, were presented, indicating the different impacts of BB emissions on the two regions. XSBN and Kunming, the representative SR and RR sites in the border and hinterland of YP, respectively, have distinct mechanisms that enhance PM_2.5 concentrations of air pollution. The SR site is mainly affected by the ICP BB emissions with local accumulation in the stagnant meteorological conditions, whereas the RR site is dominated by the regional transport of PM_2.5 with strong winds and vertical mixing. It was revealed that the large PM_2.5 contributions of ICP BB emissions lift from the lower altitudes in SR to the higher altitudes in RR for the regional transport of PM_2.5. Moreover, the contributions of regional transport of PM_2.5 decrease with the increase in transport distance, reflecting an important role of transport distance between the source–receptor areas in air pollution change. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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12 pages, 3652 KiB

Open AccessTechnical Note

Aerial Mapping of Odorous Gases in a Wastewater Treatment Plant Using a Small Drone

by Javier Burgués, María Deseada Esclapez, Silvia Doñate, Laura Pastor and Santiago Marco

Remote Sens. 2021, 13(9), 1757; https://doi.org/10.3390/rs13091757 - 30 Apr 2021

Cited by 20 | Viewed by 3963

Abstract

Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. Current monitoring methodologies based on fixed gas detectors and sporadic olfactometric measurements (human panels) do not allow for an accurate spatial representation of such emissions. In this paper we use a small drone equipped with an array of electrochemical and metal oxide (MOX) sensors for mapping odorous gases in a mid-sized WWTP. An innovative sampling system based on two (10 m long) flexible tubes hanging from the drone allowed near-source sampling from a safe distance with negligible influence from the downwash of the drone’s propellers. The proposed platform is very convenient for monitoring hard-to-reach emission sources, such as the plant’s deodorization chimney, which turned out to be responsible for the strongest odor emissions. The geo-localized measurements visualized in the form of a two-dimensional (2D) gas concentration map revealed the main emission hotspots where abatement solutions were needed. A principal component analysis (PCA) of the multivariate sensor signals suggests that the proposed system can also be used to trace which emission source is responsible for a certain measurement. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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13 pages, 6039 KiB

Open AccessLetter

Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada

by Debora Griffin, Chris Anthony McLinden, Jacinthe Racine, Michael David Moran, Vitali Fioletov, Radenko Pavlovic, Rabab Mashayekhi, Xiaoyi Zhao and Henk Eskes

Remote Sens. 2020, 12(24), 4112; https://doi.org/10.3390/rs12244112 - 16 Dec 2020

Cited by 17 | Viewed by 2704

Abstract

A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO

_{2}

) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much [...] Read more.

A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO

_{2}

) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and CHemistry), was used together with TROPOMI to determine expected NO

_{2}

columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO

_{2}

emissions due to the lockdown were seen, with an average

40 %

(roughly 10 kt[NO

_{2}

]/yr) in Toronto and Mississauga and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20–30%, which demonstrates the importance of taking meteorology into account. A model run with reduced emissions (from 65 kt[NO

_{2}

]/yr to 40 kt[NO

_{2}

]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO

_{2}

columns. Full article

(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)

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