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Remote Sensing of Aerosols and Gases in Cities II
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Remote Sensing of Aerosols and Gases in Cities II

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

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 13147

Special Issue Editor

Dr. Hanlim Lee

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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 previous Special Issue ‘Remote Sensing of Aerosols and Gases in Cities’  was a great success. In this sense, the ‘II‘ in the title refers both to this Special Issue being the second volume on the topic, and also the next-generation requirements of methodology and applications related to the topic. The scope 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 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

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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

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

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Published Papers (5 papers)

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Research

18 pages, 4405 KiB  
Article
Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product
by Horațiu Ioan Ștefănie, Andrei Radovici, Alexandru Mereuță, Viorel Arghiuș, Horia Cămărășan, Dan Costin, Camelia Botezan, Camelia Gînscă and Nicolae Ajtai
Remote Sens. 2023, 15(12), 3072; https://doi.org/10.3390/rs15123072 - 12 Jun 2023
Cited by 3 | Viewed by 1643
Abstract
Aerosols play an important role in Earth’s climate system, and thus long-time ground- based measurements of aerosol optical properties are useful in understanding this role. Ten years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the [...] Read more.
Aerosols play an important role in Earth’s climate system, and thus long-time ground- based measurements of aerosol optical properties are useful in understanding this role. Ten years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the aerosol optical depth (AOD), single scattering albedo (SSA) and angstrom exponent obtained by the CIMEL sun photometer, part of the aerosol robotic network (AERONET), to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. The angstrom exponent has the lowest values in April and May, and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental-dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over a 10-year period. Full article
(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities II)
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24 pages, 5567 KiB  
Article
Measurements and Modelling of Total Ozone Columns near St. Petersburg, Russia
by Georgy Nerobelov, Yuri Timofeyev, Yana Virolainen, Alexander Polyakov, Anna Solomatnikova, Anatoly Poberovskii, Oliver Kirner, Omar Al-Subari, Sergei Smyshlyaev and Eugene Rozanov
Remote Sens. 2022, 14(16), 3944; https://doi.org/10.3390/rs14163944 - 14 Aug 2022
Cited by 15 | Viewed by 2251
Abstract
The observed ozone layer depletion is influenced by continuous anthropogenic activity. This fact enforced the regular ozone monitoring globally. Information on spatial-temporal variations in total ozone columns (TOCs) derived by various observational methods and models can differ significantly due to measurement and modelling [...] Read more.
The observed ozone layer depletion is influenced by continuous anthropogenic activity. This fact enforced the regular ozone monitoring globally. Information on spatial-temporal variations in total ozone columns (TOCs) derived by various observational methods and models can differ significantly due to measurement and modelling errors, differences in ozone retrieval algorithms, etc. Therefore, TOC data derived by different means should be validated regularly. In the current study, we compare TOC variations observed by ground-based (Bruker IFS 125 HR, Dobson, and M-124) and satellite (OMI, TROPOMI, and IKFS-2) instruments and simulated by models (ERA5 and EAC4 re-analysis, EMAC and INM RAS—RSHU models) near St. Petersburg (Russia) between 2009 and 2020. We demonstrate that TOC variations near St. Petersburg measured by different methods are in good agreement (with correlation coefficients of 0.95–0.99). Mean differences (MDs) and standard deviations of differences (SDDs) with respect to Dobson measurements constitute 0.0–3.9% and 2.3–3.7%, respectively, which is close to the actual requirements of the quality of TOC measurements. The largest bias is observed for Bruker 125 HR (3.9%) and IKFS-2 (−2.4%) measurements, whereas M-124 filter ozonometer shows no bias. The largest SDDs are observed for satellite measurements (3.3–3.7%), the smallest—for ground-based data (2.3–2.8%). The differences between simulated and Dobson data vary significantly. ERA5 and EAC4 re-analysis data show slight negative bias (0.1–0.2%) with SDDs of 3.7–3.9%. EMAC model overestimates Dobson TOCs by 4.5% with 4.5% SDDs, whereas INM RAS-RSHU model underestimates Dobson by 1.4% with 8.6% SDDs. All datasets demonstrate the pronounced TOC seasonal cycle with the maximum in spring and minimum in autumn. Finally, for 2004–2021 period, we derived a significant positive TOC trend near St. Petersburg (~0.4 ± 0.1 DU per year) from all datasets considered. Full article
(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities II)
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19 pages, 4607 KiB  
Article
Seasonal Investigation of MAX-DOAS and In Situ Measurements of Aerosols and Trace Gases over Suburban Site of Megacity Shanghai, China
by Aimon Tanvir, Muhammad Bilal, Sanbao Zhang, Osama Sandhu, Ruibin Xue, Md. Arfan Ali, Jian Zhu, Zhongfeng Qiu, Shanshan Wang and Bin Zhou
Remote Sens. 2022, 14(15), 3676; https://doi.org/10.3390/rs14153676 - 1 Aug 2022
Cited by 4 | Viewed by 3248
Abstract
Shanghai has gained much attention in terms of air quality research owing to its importance to economic capital and its huge population. This study utilizes ground-based remote sensing instrument observations, namely by Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), and in situ measurements [...] Read more.
Shanghai has gained much attention in terms of air quality research owing to its importance to economic capital and its huge population. This study utilizes ground-based remote sensing instrument observations, namely by Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), and in situ measurements from the national air quality monitoring platform for various atmospheric trace gases including Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), and Particulate Matter (PM; PM10: diameter ≤ 10 µm, and PM2.5: diameter ≤ 2.5 µm) over Shanghai from June 2020 to May 2021. The results depict definite diurnal patterns and strong seasonality in HCHO, NO2, and SO2 concentrations with maximum concentrations during winter for NO2 and SO2 and in summer for HCHO. The impact of meteorology and biogenic emissions on pollutant concentrations was also studied. HCHO emissions are positively correlated with temperature, relative humidity, and the enhanced vegetation index (EVI), while both NO2 and SO2 depicted a negative correlation to all these parameters. The results from diurnal to seasonal cycles consistently suggest the mainly anthropogenic origin of NO2 and SO2, while the secondary formation from the photo-oxidation of volatile organic compounds (VOCs) and substantial contribution of biogenic emissions for HCHO. Further, the sensitivity of O3 formation to its precursor species (NOx and VOCs) was also determined by employing HCHO and NO2 as tracers. The sensitivity analysis depicted that O3 formation in Shanghai is predominantly VOC-limited except for summer, where a significant percentage of O3 formation lies in the transition regime. It is worth mentioning that seasonal variation of O3 is also categorized by maxima in summer. The interdependence of criteria pollutants (O3, SO2, NO2, and PM) was studied by employing the Pearson’s correlation coefficient, and the results suggested complex interdependence among the pollutant species in different seasons. Lastly, potential source contribution function (PSCF) analysis was performed to have an understanding of the contribution of different source areas towards atmospheric pollution. PSCF analysis indicated a strong contribution of local sources on Shanghai’s air quality compared to regional sources. This study will help policymakers and stakeholders understand the complex interactions among the atmospheric pollutants and provide a baseline for designing effective control strategies to combat air pollution in Shanghai. Full article
(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities II)
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18 pages, 7360 KiB  
Article
Characterizing a Heavy Dust Storm Event in 2021: Transport, Optical Properties and Impact, Using Multi-Sensor Data Observed in Jinan, China
by Aiqin Tu, Zhenzhu Wang, Zhifei Wang, Wenjuan Zhang, Chang Liu, Xuanhao Zhu, Ji Li, Yujie Zhang, Dong Liu and Ningquan Weng
Remote Sens. 2022, 14(15), 3593; https://doi.org/10.3390/rs14153593 - 27 Jul 2022
Viewed by 2328
Abstract
On 15 March 2021, the strongest sandstorm of the last 10 years occurred in China. The MODIS, MPL lidar, EDM 180, ADI 2080 and Meteorological observation instruments were used to observe the dust in Jinan, China, while the HYSPLIT model was also employed [...] Read more.
On 15 March 2021, the strongest sandstorm of the last 10 years occurred in China. The MODIS, MPL lidar, EDM 180, ADI 2080 and Meteorological observation instruments were used to observe the dust in Jinan, China, while the HYSPLIT model was also employed to find the source. It was found that the dust originated from Mongolia and the Gobi desert and was transported to Jinan at night on 14th March, lasting until the 18th. Multi-layer dust was observed, of which the dust below the height of 1 km was strongest with the VDR about 0.2 and the maximum extinction coefficient up to 3 km−1. The values of AOD and AE were greater than 2 and less than 0.25, respectively. The mass concentrations of PM10 and PM2.5 increased rapidly, and were up to 573 µg/m3 and 3406 µg/m3, respectively. Additionally, the mass concentration ratio decreased rapidly, with a minimum of 17%. The particle size of the dust was mainly distributed between 0.58–6.50 micros due to larger particles increasing dramatically; simultaneously, both the proportion and the value for calcium ions in PM2.5 went up. The dust had an obvious impact on the vertical structure of the air temperature, resulting in occurrence of a strong inversion layer. Full article
(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities II)
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21 pages, 13120 KiB  
Article
PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD
by Xiaohe Yu, David J. Lary and Christopher S. Simmons
Remote Sens. 2021, 13(23), 4788; https://doi.org/10.3390/rs13234788 - 26 Nov 2021
Cited by 4 | Viewed by 2811
Abstract
In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium [...] Read more.
In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations. Full article
(This article belongs to the Special Issue Remote Sensing of Aerosols and Gases in Cities II)
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