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Remote Sensing of Aerosols and Gases in Cities

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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 September 2021) | Viewed by 31201

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Dr. Hanlim Lee

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Department of Spatial Information Engineering, Pukyong National University, Busan 608737, Korea
Interests: atmosphere; remote sensing; atmospheric physics; atmospheric chemistry; air pollution; air quality; aerosols; trace gases; greenhouse gases; atmospheric radiation
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Special Issue Information

Dear Colleagues,

About 55% of the global population lives in urban areas. There are many kinds of facilities (e.g., power plants, transportations, industrial complexes, restaurants, etc.) that exist to support human activities in cities. Due to the emissions of these facilities and vast amounts of transport, various air pollutants and greenhouse gases (GHGs) are inevitably highly concentrated in cities. Some of the gases (e.g., NOx, SO₂, HCHO, CO, and BTEX) and aerosols (e.g., heavy metals, organic carbons, etc.) are known to have adverse health effects, and GHGs and aerosols play complicating roles in atmospheric radiation in urban areas and their surroundings. Thus, it is necessary to monitor the spatiotemporal characteristics of aerosol, trace gas, and GHG to understand their sources and physicochemical behavior. Remote sensing is an effective approach to provide spatial distribution information of atmospheric constituents. In recent years, atmospheric remote-sensing technologies have been rapidly improved. Various remote-sensing techniques from ground-based or airborne platforms to satellite can be effectively applied to aerosol and gas measurements over cities and nearby areas. The scope of this Special Issue, entitled “Remote Sensing of Aerosols and Gases in Cities“, is as follows:

Techniques: Passive and active techniques at various platforms, such as satellite measurements, MAX-DOAS, Zenith-DOAS, LP-DOAS, direct-sun DOAS, Pandora, LIDAR, DIAL, Raman LIDAR, FTIR, gas camera, correlation spectrometer, etc.;
Target species: Aerosol properties, trace gases, and greenhouse gases;
Measurement sites: Areas which may include an urban site;
Research scopes: Applications of the pre-existing remote-sensing techniques to measurements of urban aerosols and gases. Improvement in retrieval algorithms or optical devices. Development of new remote-sensing techniques. Simulation studies for feasibility or uncertainty assessment. Urban atmospheric chemistry and radiative transfer using remote-sensing data. Comparisons between the quantities retrieved from various platforms. Validation studies for space-borne measurements over cities.

Dr. Hanlim Lee
Guest Editor

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Keywords

satellite remote sensing
DOAS
LIDAR
FTIR
remote sensing
aerosol
trace gas
greenhouse gas
urban air pollution

Published Papers (11 papers)

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23 pages, 11687 KiB

Open AccessArticle

The Characterization of Haze and Dust Processes Using MAX-DOAS in Beijing, China

by Hongmei Ren, Ang Li, Pinhua Xie, Zhaokun Hu, Jin Xu, Yeyuan Huang, Xiaomei Li, Hongyan Zhong, Hairong Zhang, Xin Tian, Bo Ren, Shuai Wang, Wenxuan Chai and Chuanyao Du

Remote Sens. 2021, 13(24), 5133; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245133 - 17 Dec 2021

Cited by 4 | Viewed by 2105

Abstract

Haze and dust pollution have a significant impact on human production, life, and health. In order to understand the pollution process, the study of these two pollution characteristics is important. In this study, a one-year observation was carried out at the Beijing Southern Suburb Observatory using the MAX-DOAS instrument, and the pollution characteristics of the typical haze and dust events were analyzed. First, the distribution of aerosol extinction (AE) and H₂O concentrations in the two typical pollution events were studied. The results showed that the correlation coefficient (r) between H₂O and AE at different heights decreased during dust processes and the correlation slope (|k|) increased, whereas r increased and |k| decreased during haze periods. The correlation slope increased during the dust episode due to low moisture content and increased O₄ absorption caused by abundant suspended dry crustal particles, but decreased during the haze episode due to a significant increase of H₂O absorption. Secondly, the gas vertical column density (VCD) indicated that aerosol optical depth (AOD) increased during dust pollution events in the afternoon, while the H₂O VCD decreased; in haze pollution processes, both H₂O VCD and AOD increased. There were significant differences in meteorological conditions during haze (wind speed (WD) was <2 m/s, and relative humidity (RH) was >60%) and dust pollution (WD was >4 m/s, and RH was <60%). Next, the vertical distribution characteristics of gases during the pollution periods were studied. The AE profile showed that haze pollution lasted for a long time and changed slowly, whereas the opposite was true for dust pollution. The pollutants (aerosols, NO₂, SO₂, and HCHO) and H₂O were concentrated below 1 km during both these typical pollution processes, and haze pollution was associated with a strong temperature inversion around 1.0 km. Lastly, the water vapor transport fluxes showed that the water vapor transport from the eastern air mass had an auxiliary effect on haze pollution at the observation location. Our results are of significance for exploring the pollution process of tropospheric trace gases and the transport of water vapor in Beijing, and provide a basis for satellite and model verification. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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28 pages, 2036 KiB

Open AccessArticle

Automated Low-Cost LED-Based Sun Photometer for City Scale Distributed Measurements

by Cristobal Garrido, Felipe Toledo, Marcos Diaz and Roberto Rondanelli

Remote Sens. 2021, 13(22), 4585; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224585 - 15 Nov 2021

Cited by 1 | Viewed by 2192

Abstract

We propose a monochromatic low-cost automatic sun photometer (LoCo-ASP) to perform distributed aerosol optical depth (AOD) measurements at the city scale. This kind of network could fill the gap between current automatic ground instruments—with good temporal resolution and accuracy, but few devices per city and satellite products—with global coverage, but lower temporal resolution and accuracy-. As a first approach, we consider a single equivalent wavelength around 408 nm. The cost of materials for the instrument is around 220 dollars. Moreover, we propose a calibration transfer for a pattern instrument, and estimate the uncertainties for several units and due to the internal differences and the calibration process. We achieve a max MAE of 0.026 for 38 sensors at 408 nm compared with AERONET Cimel; a mean standard deviation of 0.0062 among our entire sensor for measurement and a calibration uncertainty of 0.01. Finally, we perform city-scale measurements to show the dynamics of AOD. Our instrument can measure unsupervised, with an expected error for AOD between 0.02 and 0.03. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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17 pages, 7644 KiB

Open AccessArticle

Study on Collaborative Emission Reduction in Green-House and Pollutant Gas Due to COVID-19 Lockdown in China

by Haowei Zhang, Xin Ma, Ge Han, Hao Xu, Tianqi Shi, Wanqin Zhong and Wei Gong

Remote Sens. 2021, 13(17), 3492; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173492 - 02 Sep 2021

Cited by 4 | Viewed by 2099

Abstract

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO₂, O₃, and CO₂ column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO₂ decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO₂, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO₂ and O₃ concentrations on changes in XCO₂. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO₂ concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O₃ concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO₂ concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O₃ concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO₂ concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O₃ concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO₂. Moreover, the results show that the effect of NO₂ concentration on XCO₂ is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO₂ and the O₃ concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO₂ fell by about 1.12 ppm in February. At last, the government should consider reducing XCO₂ and NO₂ concentration at the same time to make a synergistic reduction. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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22 pages, 4006 KiB

Open AccessArticle

Variabilities in PM2.5 and Black Carbon Surface Concentrations Reproduced by Aerosol Optical Properties Estimated by In-Situ Data, Ground Based Remote Sensing and Modeling

by Alessandro Damiani, Hitoshi Irie, Kodai Yamaguchi, Hossain Mohammed Syedul Hoque, Tomoki Nakayama, Yutaka Matsumi, Yutaka Kondo and Arlindo Da Silva

Remote Sens. 2021, 13(16), 3163; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163163 - 10 Aug 2021

Cited by 4 | Viewed by 2517

Abstract

Because of the increased temporal and spatial resolutions of the sensors onboard recently launched satellites, satellite-based surface aerosol concentration, which is usually estimated from the aerosol optical depth (AOD), is expected to become a strategic tool for air quality studies in the future. By further exploring the relationships of aerosol concentrations and their optical properties using ground observations, the accuracies of these products can be improved. Here, we analyzed collocated observations of surface mass concentrations of fine particulate matter (PM2.5) and black carbon (BC), as well as columnar aerosol optical properties from a sky radiometer and aerosol extinction profiles obtained by multi-axis differential optical absorption spectroscopy (MAX-DOAS), during the 2019–2020 period. We focused the analyses on a daily scale, emphasizing the role of the ultraviolet (UV) spectral region. Generally, the correlation between the AOD of the fine fraction (i.e., fAOD) and the PM2.5 surface concentration was moderately strong, regardless of considerations of boundary layer humidity and altitude. In contrast, the fAOD of the partial column below 1 km, which was obtained by combining sky radiometer and MAX-DOAS retrievals, better reproduced the variability of the PM2.5 and resulted in a linear relationship. In the same manner, we demonstrated that the absorption AOD of the fine fraction (fAAOD) of the partial column was related to the variability of the BC concentration. Analogous analyses based on aerosol products from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) confirmed these findings and highlighted the importance of the shape of the aerosol profile. Overall, our results indicated a remarkable consistency among the retrieved datasets, and between the datasets and MERRA-2 products. These results confirmed the well-known sensitivity to aerosol absorption in the UV spectral region; they also highlighted the efficacy of combined MAX-DOAS and sky radiometer observations. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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20 pages, 7084 KiB

Open AccessArticle

Superior PM_2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models

by Zhou Zang, Dan Li, Yushan Guo, Wenzhong Shi and Xing Yan

Remote Sens. 2021, 13(14), 2779; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142779 - 15 Jul 2021

Cited by 19 | Viewed by 3740

Abstract

Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM_2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM_2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM_2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM_2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM_2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM_2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMF&AOT-PM_2.5) data were tested as input to a deep learning model and four classical machine learning models. The results showed that FMF&AOT-PM_2.5 performed better than AOT (AOT-PM_2.5) in modelling PM_2.5 estimations. The FMF was then applied in satellite-based PM_2.5 retrieval over China during 2020, and FMF&AOT-PM_2.5 was found to have a better agreement with ground-level PM_2.5 than AOT-PM_2.5 on dust and haze days. The better linear correlation between PM_2.5 and fAOT on both haze and dust days (dust days: R = 0.82; haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM_2.5. This study demonstrates the importance of including the FMF to improve PM_2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM_2.5 retrieval. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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13 pages, 2035 KiB

Open AccessCommunication

Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

by Wonei Choi, Hyeongwoo Kang, Dongho Shin and Hanlim Lee

Remote Sens. 2021, 13(13), 2464; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132464 - 24 Jun 2021

Cited by 5 | Viewed by 2759

Abstract

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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22 pages, 34544 KiB

Open AccessArticle

Quantify the Contribution of Dust and Anthropogenic Sources to Aerosols in North China by Lidar and Validated with CALIPSO

by Zhuang Wang, Cheng Liu, Qihou Hu, Yunsheng Dong, Haoran Liu, Chengzhi Xing and Wei Tan

Remote Sens. 2021, 13(9), 1811; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091811 - 06 May 2021

Cited by 14 | Viewed by 2498

Abstract

Persistent heavy haze episodes have repeatedly shrouded North China in recent years. Besides anthropogenic emissions, natural dust also contributes to the aerosols in this region. Through continuous observation by a dual-wavelength Raman lidar, the primary aerosol types and their contributions to air pollution in North China were determined. The following three aerosol types can be classified: natural dust, anthropogenic aerosols, and the mixture of anthropogenic aerosols and dust (polluted dust). The classification results are basically consistent with the classification results from the cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) satellite measurements. The relative bias of the lidar ratio between the Raman lidar and CALIPSO is less than 25% over 90% of the cases, indicating that the CALIPSO lidar ratio selection algorithm is reasonable. The classification results show that approximately 45% of aerosols below 1.8 km are contributed by polluted dust during our one year observations. The contribution of dust increased with height, from 6% at 500 m to 28% at 1,800 m, while the contribution of anthropogenic aerosols decreased from 49% to 25%. In addition, polluted dust is the major aerosol subtype below 1.0 km in spring (over 60%) and autumn (over 70%). Anthropogenic aerosols contribute more than 75% of air pollution in summer. In winter, anthropogenic aerosols prevailed (over 80%) in the lower layer, while polluted dust (around 60%) dominated the upper layer. Our results identified the primarily aerosol types to assess the contributions of anthropogenic and natural sources to air pollution in North China, and highlight that natural dust plays a crucial role in lower-layer air pollution in spring and autumn, while controlling anthropogenic aerosols will significantly improve air quality in winter. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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19 pages, 3899 KiB

Open AccessArticle

Evaluation of OMI NO₂ Vertical Columns Using MAX-DOAS Observations over Mexico City

by Zuleica Ojeda Lerma, Claudia Rivera Cardenas, Martina M. Friedrich, Wolfgang Stremme, Alejandro Bezanilla, Edgar J. Arellano and Michel Grutter

Remote Sens. 2021, 13(4), 761; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040761 - 19 Feb 2021

Cited by 2 | Viewed by 3129

Abstract

Nitrogen dioxide (NO

_{2}

Nitrogen dioxide (NO

_{2}

) is a gas pollutant that can be measured from space and several operational products are now available from instruments on-board of satellite-based platforms. There are still, however, many unknowns about the accuracy of these products under different viewing and surface conditions since ground-based observations are generally scarce. This is particularly the case of high-altitude sub-tropical megacities such as the Mexico City Metropolitan Area (MCMA). In this study, we use more than five years of data from four ground-based MAX-DOAS instruments distributed within the MCMA in order to evaluate the DOMINO product from the Ozone Monitoring Instrument (OMI) on board the Aura satellite. We compare OMI against each MAX-DOAS site independently using the vertical column densities (VCDs) reported by each instrument. The VCDs are also compared after smoothing the MAX-DOAS profiles with the a priori and the Averaging Kernels of the satellite product. We obtain an overall correlation coefficient (R) of 0.6 that does not improve significantly after the smoothing is applied. However, the slopes in the linear regressions for the individual sites improve when applying the smoothing from 0.36 to 0.62 at UNAM, from 0.26 to 0.49 at Acatlán, from 0.78 to 1.23 at Vallejo, and from 0.50 to 0.97 at the Cuautitlán station. The large differences observed between the OMI and MAX-DOAS VCDs are attributed to a reduced sensitivity of the satellite product near the surface and the large aerosol loading typically present within the mixed layer of the MCMA. This may also contribute to a slight overestimation of the VCDs from the MAX-DOAS measurements that presents a total error (random + systematic) of about 20%. As a result of this comparison, we find that OMI retrievals are on average 56% lower than the MAX-DOAS without any correction. The near-surface concentrations are estimated from the lowest layers of the MAX-DOAS retrievals and these compare well with surface measurements from in situ analyzers operated at the co-located air quality monitoring stations. The diurnal variability for each station is analyzed and discussed in relation to their location within the city. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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17 pages, 1584 KiB

Open AccessArticle

Long-Term Variation of Black Carbon Absorption Aerosol Optical Depth from AERONET Data over East Asia

by Naghmeh Dehkhoda, Youngmin Noh and Sohee Joo

Remote Sens. 2020, 12(21), 3551; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213551 - 30 Oct 2020

Cited by 7 | Viewed by 2826

Abstract

Absorption aerosol optical depth induced by black carbon (AAOD_BC) was retrieved using the depolarization ratio and single scattering albedo provided by the Aerosol Robotic Network (AERONET) inversion products over East Asia. Our analysis considered AERONET data from six sites in East Asia that are mostly affected by anthropogenic pollution, black carbon (BC) emissions, and natural mineral dust, during the period 2001–2018. We identified a rapid reduction in total aerosol optical depth (AOD_T) of −0.0106 yr⁻¹ over Beijing, whereas no considerable trend was observed at the Korean and Japanese sites. The long-term data for AAOD_BC showed decreasing trends at all sites. We conclude that successful emission control policies were the major underlying driver of AOD_T and AAOD_BC reductions over East Asia, particularly in China, during the study period. Values of the AAOD_BC/AOD_T ratio revealed that, although these policies were successful, the Chinese government needs to undertake stricter measures toward reducing BC emissions. We found that AAOD_BC follows seasonal trends, peaking in the colder months. This suggests that in East Asia, particularly in China, domestic coal burning is still of concern. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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18 pages, 3488 KiB

Open AccessArticle

Validation of Water Vapor Vertical Distributions Retrieved from MAX-DOAS over Beijing, China

by Hua Lin, Cheng Liu, Chengzhi Xing, Qihou Hu, Qianqian Hong, Haoran Liu, Qihua Li, Wei Tan, Xiangguang Ji, Zhuang Wang and Jianguo Liu

Remote Sens. 2020, 12(19), 3193; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193193 - 29 Sep 2020

Cited by 9 | Viewed by 2918

Abstract

Water vapor vertical profiles are important in numerical weather prediction, moisture transport, and vertical flux calculation. This study presents the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) retrieval algorithm for water vapor vertical profiles and the retrieved results are validated with corresponding independent datasets under clear sky. The retrieved Vertical Column Densities (VCDs) and surface concentrations are validated with the Aerosol Robotic Network (AERONET) and National Climatic Data Centre (NCDC) datasets, achieving good correlation coefficients (R) of 0.922 and 0.876, respectively. The retrieved vertical profiles agree well with weekly balloon-borne radiosonde measurements. Furthermore, the retrieved water vapor concentrations at different altitudes (100–2000 m) are validated with the corresponding European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim datasets, achieving a correlation coefficient (R) varying from 0.695 to 0.857. The total error budgets for the surface concentrations and VCDs are 31% and 38%, respectively. Finally, the retrieval performance of the MAX-DOAS algorithm under different aerosol loads is evaluated. High aerosol loads obstruct the retrieval of surface concentrations and VCDs, with surface concentrations more liable to severe interference from such aerosol loads. To summarize, the feasibility of detecting water vapor profiles using MAX-DOAS under clear sky is confirmed in this work. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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16 pages, 4659 KiB

Open AccessTechnical Note

Assessment of Tropospheric Concentrations of NO₂ from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO₂ over South Korea

by Ukkyo Jeong and Hyunkee Hong

Remote Sens. 2021, 13(10), 1877; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101877 - 11 May 2021

Cited by 18 | Viewed by 3181

Abstract

Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO₂ column concentrations (C_TROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO₂ mixing ratios. In general, the instrument effectively detected major and moderate sources of NO₂ over South Korea with a clear weekday–weekend distinction. We compared the C_TROPOMI with surface NO₂ mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (S_KME; more than 570 sites), for 2019. Spatiotemporally collocated C_TROPOMI and S_KME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The C_TROPOMI and S_KME were well correlated around the Seoul metropolitan area, where significant amounts of NO₂ prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO₂ from TROPOMI to the surface mixing ratio (S_TROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the S_KME. The estimated S_TROPOMI generally underestimated the in-situ value obtained, S_KME (slope = 0.64), as reported in previous studies. Full article

(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities)

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