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Atmosphere
Special Issues
Remote Sensing Techniques in Air Pollution Assessment
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Remote Sensing Techniques in Air Pollution Assessment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (21 April 2023) | Viewed by 4028

Special Issue Editor

Dr. Chao Zeng

E-Mail Website
Guest Editor
School of Resources and Environmental Sciences, Wuhan University, Wuhan 430072, Hubei, China
Interests: environmental monitoring; multisource information fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution is now considered to be the world's largest environmental health threat. It is also closely linked to the Earth’s climate and ecosystems globally. Various pieces of equipment have been developed to assess complex air pollution in recent decades. With the advantage of large area coverage and repetitive monitoring, remote sensing techniques have become more popular in the last few years. Remote sensing techniques have been used to assess different air pollutants, including particulate matter, ozone, carbon monoxide, sulfur dioxide, nitrogen dioxide, VOCs, and so on. With the development of satellite sensors and data science, more possibilities can be found today.

The purpose of this Special Issue, “Remote Sensing Techniques in Air Pollution Assessment”, is to discuss novel remote sensing techniques in air pollution assessment. This Special Issue accepts papers related to (but not limited to) remote-sensing-based retrieval methods, validation methods, visualization methods, and analytical methods for air pollution assessment.

Dr. Chao Zeng
Guest Editor

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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • atmospheric remote sensing
  • air pollution assessment
  • remote sensing retrievals
  • data fusion
  • deep learning

Published Papers (2 papers)

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Research

32 pages, 14470 KiB  
Article
Data-Driven Air Quality and Environmental Evaluation for Cattle Farms
by Jennifer Hu, Rushikesh Jagtap, Rishikumar Ravichandran, Chitra Priyaa Sathya Moorthy, Nataliya Sobol, Jane Wu and Jerry Gao
Atmosphere 2023, 14(5), 771; https://doi.org/10.3390/atmos14050771 - 23 Apr 2023
Cited by 1 | Viewed by 1986
Abstract
The expansion of agricultural practices and the raising of animals are key contributors to air pollution. Cattle farms contain hazardous gases, so we developed a cattle farm air pollution analyzer to count the number of cattle and provide comprehensive statistics on different air [...] Read more.
The expansion of agricultural practices and the raising of animals are key contributors to air pollution. Cattle farms contain hazardous gases, so we developed a cattle farm air pollution analyzer to count the number of cattle and provide comprehensive statistics on different air pollutant concentrations based on severity over various time periods. The modeling was performed in two parts: the first stage focused on object detection using satellite data of farm images to identify and count the number of cattle; the second stage predicted the next hour air pollutant concentration of the seven cattle farm air pollutants considered. The output from the second stage was then visualized based on severity, and analytics were performed on the historical data. The visualization illustrates the relationship between cattle count and air pollutants, an important factor for analyzing the pollutant concentration trend. We proposed the models Detectron2, YOLOv4, RetinaNet, and YOLOv5 for the first stage, and LSTM (single/multi lag), CNN-LSTM, and Bi-LSTM for the second stage. YOLOv5 performed best in stage one with an average precision of 0.916 and recall of 0.912, with the average precision and recall for all models being above 0.87. For stage two, CNN-LSTM performed well with an MAE of 3.511 and an MAPE of 0.016, while a stacked model had an MAE of 5.010 and an MAPE of 0.023. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Air Pollution Assessment)
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18 pages, 3655 KiB  
Article
Assessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes
by Christopher Sobecki, Alfred Garrett, Brian d’Entremont, Ryan Connal and Sebastian Aleman
Atmosphere 2023, 14(4), 754; https://doi.org/10.3390/atmos14040754 - 21 Apr 2023
Viewed by 1703
Abstract
Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were [...] Read more.
Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery. Full article
(This article belongs to the Special Issue Remote Sensing Techniques in Air Pollution Assessment)
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