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Remote Sensing
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Satellite and In Situ Observations of Air Pollution
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Satellite and In Situ Observations of Air Pollution

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 8275

Special Issue Editors

Dr. Cristiana Bassani

E-Mail Website
Guest Editor
Institute of Atmospheric Pollution Research of the Italian National Research Council (CNR-IIA), Research Area of Rome 1, Provincial Road 35d, Montelibretti, 9-00010 Rome, Italy
Interests: remote sensing; earth observation; air pollution; aerosol and trace gases; atmospheric radiative modelling
Prof. Dr. Jason Blake Cohen

E-Mail Website
Guest Editor
School of Environment and Spatial Informatics, China Univesity of Mining and Technology, Xuzhou 221116, China
Interests: integration of data across multiple satellites; remote sensing and modeling of aerosols; inverse modeling of atmospheric composition and emissions sources; remote sensing of air quality extremes; remote sensing of short lived climate forcers
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Bilal

E-Mail Website1 Website2
Guest Editor
Nelson Institute Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, WI 53726, USA
Interests: atmospheric remote sensing; air quality; aerosols; air quality and human health; aerosol classification; aerosol retrievals; remote sensing of land and atmospheric parameters; atmospheric correction of remote sensing data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Outdoor air pollution is a well-known environmental risk to human health, ecosystems, agriculture, agri-food production and the climate system. While air pollution strongly affects urban areas and nearby rural environments, it can also sometimes be observed in remote areas due to forest fires or long-range atmospheric transport. Due to its high spatial and temporal variability and complex set of optical properties, atmospheric pollution has been a challenge for remote sensing research to establish a system suitable for comprehensively characterizing and monitoring air pollution over wide areas. Over the last three decades, systematic remote sensing observations of aerosols and trace gases have been used to provide ever more insight into air pollution data, via both space-based and ground-based platforms. This ever-improving amount and quality of data have increased the accuracy and precision of air quality monitoring. However, a combination of large gaps in surface networks around the world and high degrees of uncertainty in many remotely sensed platforms lead to continuing uncertainties and imperfections.

With the new generation of high-precision satellites coming online (e.g., TROPOMI, GEMS, FY, etc.), daily and higher temporal scale maps of specific atmospheric pollutants are provided with unprecedented temporal and spatial resolution. In this regard, space-based remote sensing observations provide significant contributions that can extend the knowledge and precision currently provided by air-quality stations, which provide accurate, detailed and reliable data but are located in a non-uniform spatial distribution, if they exist at all. Consequently, the remote sensing observations support air pollution control policies and strategies applied to urban and peri-urban areas, air quality forecasting and the overall distribution of air pollutants during haze events, as well as the potential to constrain even remote or presently hard to monitor regions.

Articles regarding original methods and analysis and the results of studies conducted on aerosols and trace gases, which are remotely monitored from space-based or surface-based platforms (e.g., NO2, SO2, CO, O3, HCHO, CH4, N2O, NH3, BC), and incorporating further information from in situ observations are welcome. Specific topics of interest for this Special Issue include, but are not limited to:

  • Applications of satellite and in situ data in air pollution modeling;
  • Characterization of air pollution at the local and/or regional scales;
  • Space-ground integrated system for air pollution monitoring;
  • Impact of air pollution on urban, peri-urban and rural sites;
  • Air pollution characterization by multivariate time series of remote sensing data;
  • Synergistic ground–satellite products to analyze air pollutant emissions;
  • Extension of remote sensing to new species, which have in situ measurements but are not currently available via existing remotely sensed products;
  • Using remote sensing and in situ measurements together to constrain multiple species or impacts in tandem.

Dr. Cristiana Bassani
Prof. Dr. Jason Blake Cohen
Prof. Dr. Muhammad Bilal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • air pollution
  • earth observation
  • trace gases
  • aerosol

Published Papers (4 papers)

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Research

21 pages, 4680 KiB  
Article
Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations
by Wenhao Chu, Chunxiao Zhang and Heng Li
Remote Sens. 2023, 15(20), 4973; https://doi.org/10.3390/rs15204973 - 15 Oct 2023
Viewed by 1059
Abstract
Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. [...] Read more.
Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM2.5, achieving the Pearson coefficient (r), the coefficient determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m3, and 12.17 µg/m3, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R2: 0.85–0.87, RMSE: 18.13 µg/m3–20.18 µg/m3, and MAE: 11.21 µg/m3–12.52 µg/m3) and temporal-based (r: 0.91–0.98, R2: 0.82–0.96, RMSE: 3.8 µg/m3–21.89 µg/m3, and MAE: 2.71 µg/m3–14.00 µg/m3) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m3, 54.17 µg/m3, 56.24 µg/m3, and 55.00 µg/m3 in Beijing, Tianjin, Hebei, and the BTH region, respectively. Full article
(This article belongs to the Special Issue Satellite and In Situ Observations of Air Pollution)
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29 pages, 17297 KiB  
Article
Characterization of Nitrogen Dioxide Variability Using Ground-Based and Satellite Remote Sensing and In Situ Measurements in the Tiber Valley (Lazio, Italy)
by Cristiana Bassani, Francesca Vichi, Giulio Esposito, Serena Falasca, Annalisa Di Bernardino, Francesca Battistelli, Stefano Casadio, Anna Maria Iannarelli and Antonietta Ianniello
Remote Sens. 2023, 15(15), 3703; https://doi.org/10.3390/rs15153703 - 25 Jul 2023
Cited by 2 | Viewed by 1927
Abstract
The spatial–temporal distributions of nitrogen dioxide (NO2) in a rural area of Tiber valley were evaluated over one year (March 2022–February 2023) using remote sensing and in situ measurements. Surface concentration monitoring was conducted using a Pandora-2s spectrometer and a chemiluminescence [...] Read more.
The spatial–temporal distributions of nitrogen dioxide (NO2) in a rural area of Tiber valley were evaluated over one year (March 2022–February 2023) using remote sensing and in situ measurements. Surface concentration monitoring was conducted using a Pandora-2s spectrometer and a chemiluminescence analyzer operated at the Liberti Observatory (CNR-IIA). In spring, when the growing season and the agricultural activities increase, NO2 peaks were detectable by the Pandora but not by the in situ analyzer. The tropospheric Pandora and TROPOMI VCD products showed similar temporal patterns as those of the analyzer at the Observatory. High TROPOMI VCD levels in spring were detected at the Observatory and at six sites selected as representative of rural, residential, and industrial environments. WRF simulations found that high pollution events, observed by the Pandora and analyzer, occurred in calm wind conditions, favouring the accumulation of NO2 locally emitted. The complementary dataset provided by remote sensing and in situ techniques efficiently captured the spatial–temporal NO2 variability in a rural site exposed to low emission sources, thus supporting future decisional policies and actions. Full article
(This article belongs to the Special Issue Satellite and In Situ Observations of Air Pollution)
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28 pages, 20530 KiB  
Article
Simulation of Compact Spaceborne Lidar with High-Repetition-Rate Laser for Cloud and Aerosol Detection under Different Atmospheric Conditions
by Jie Ji, Chenbo Xie, Kunming Xing, Bangxin Wang, Jianfeng Chen, Liangliang Cheng and Xu Deng
Remote Sens. 2023, 15(12), 3046; https://doi.org/10.3390/rs15123046 - 10 Jun 2023
Cited by 1 | Viewed by 1613
Abstract
To provide references for the design of the lab’s upcoming prototype of the compact spaceborne lidar with a high-repetition-rate laser (CSLHRL), in this paper, the detection signal of spaceborne lidar was simulated by the measured signal of ground-based lidar, and then, the detection [...] Read more.
To provide references for the design of the lab’s upcoming prototype of the compact spaceborne lidar with a high-repetition-rate laser (CSLHRL), in this paper, the detection signal of spaceborne lidar was simulated by the measured signal of ground-based lidar, and then, the detection capability of spaceborne lidar under different atmospheric conditions was evaluated by means of the signal-to-noise ratio (SNR), volume depolarization ratio (VDR) and attenuated color ratio (ACR). Firstly, the Fernald method was used to invert the optical parameters of cloud and aerosol with the measured signal of ground-based lidar. Secondly, the effective signal of the spaceborne lidar was simulated according to the known atmospheric optical parameters and the parameters of the spaceborne lidar system. Finally, by changing the cumulative laser pulse number and atmospheric conditions, a simulation was carried out to further evaluate the detection performance of the spaceborne lidar, and some suggestions for the development of the system are given. The experimental results showed that the cloud layer and aerosol layer with an extinction coefficient above 0.3 km−1 could be easily obtained when the laser cumulative pulse number was 1000 and the vertical resolution was 15 m at night; the identification of moderate pollution aerosols and thick clouds could be easily identified in the daytime when the laser cumulative pulse number was 10,000 and the vertical resolution was 120 m. Full article
(This article belongs to the Special Issue Satellite and In Situ Observations of Air Pollution)
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23 pages, 6360 KiB  
Article
Quantitatively Disentangling the Geographical Impacts of Topography on PM2.5 Pollution in China
by Youyue Wen, Jianneng Xiao, Jian Yang, Saoman Cai, Minxuan Liang and Peng Zhou
Remote Sens. 2022, 14(24), 6309; https://doi.org/10.3390/rs14246309 - 13 Dec 2022
Cited by 5 | Viewed by 2580
Abstract
Fine particulate matter (PM2.5) pollution’s passive effects on public health have attracted a great deal of attention. Extensive studies have tried to uncover the impacts of external drivers on PM2.5 pollution variation; however, the topography’s effects on PM2.5 pollution [...] Read more.
Fine particulate matter (PM2.5) pollution’s passive effects on public health have attracted a great deal of attention. Extensive studies have tried to uncover the impacts of external drivers on PM2.5 pollution variation; however, the topography’s effects on PM2.5 pollution remain poorly understood. Using annual high-resolution concentration estimates for PM2.5, this paper quantitatively disentangled the geographical impacts of topography on the PM2.5 pollution in China and quantified the mountain blocking effects on the PM2.5 pollution dispersion on a macro scale. The results show that, in China, the plains and surrounding platforms and hills tend to suffer from long-term severe PM2.5 pollution. The most polluted topography type is the plains. In comparison, regions such as high-altitude mountains and plateaus are less affected by PM2.5 pollution. Mountains have significant and evident blocking effects on the cross-regional spread of PM2.5 pollution. Generally, Level I mountains (high elevation, density and coverage mountains) provide better blocking effects than Level II (middle elevation, density and coverage mountains) mountains and Level III mountains (low elevation, density and coverage mountains). The mountains’ blocking effects begin to play an efficient role when their altitudes reach a certain value; however, the exact altitude values vary by different mountains with a value of 163 m for all typical mountains with absolute PM2.5 concentration differences between their two sides greater than 10 μg/m3. In heavily polluted areas, PM2.5 pollution may overflow where the surrounding mountains are not high enough or the mountains’ stretch breaks. This study can provide key theoretical support for air pollution modelling and regional air pollution joint prevention and control. Full article
(This article belongs to the Special Issue Satellite and In Situ Observations of Air Pollution)
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