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4.1
2.9
Journals
Atmosphere
Special Issues
Multiple Data Analyses of Air Quality Diagnosis
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Multiple Data Analyses of Air Quality Diagnosis

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (25 January 2022) | Viewed by 3001

Special Issue Editors

Dr. Ja-Ho Koo

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, Yonsei University, Seoul, Korea
Interests: atmospheric chemistry; air pollution; air quality; polar environment; meteorology
Special Issues, Collections and Topics in MDPI journals
Dr. Guanyu Huang

E-Mail Website
Guest Editor
Environmental and Health Sciences Program, Seplman College, GA 30314, USA
Interests: air quality; remote sensing; machine learning/AI; atmospheric chemistry; lidar; numerical model and environmental health

Special Issue Information

Dear Colleagues,

Nowaday, massive volumes  of data have been produced from multiple platforms: e.g., Eularian and Lagrangina style in-situ measurements, ground-based or satellite remote sensing, model simulations, reanalysis results from data assimilation. Analyses based on a single data set may not provide sufficient information for air quality diagnose.. Sophisticated analyses based on data from multiple resources are strongly necessary for reliable diagnosis of local, regional, and global air quality.  Meanwhile, novel methods of data analyses (e.g. machine learning, artificial intelligence, etc.) have been utilized widely to leverage these “big data” for air quality diagnosis.   

This special issues seeks studies on the use of “big data” from multiple resources and novel data analysis methods to multi-scale air quality diagnose. We also welcome studies on the use of machine learning, artificial intelligence, deep learning for air quality diagnosis.

Prof. Ja-Ho Koo
Dr. Guanyu Huang
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. 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

  • air quality
  • big data
  • machine learning
  • AI

Published Papers (1 paper)

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Research

13 pages, 2052 KiB  
Article
Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port
by Hyunsu Hong, Hyungjin Jeon, Cheong Youn and Hyeonsoo Kim
Atmosphere 2021, 12(9), 1172; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12091172 - 12 Sep 2021
Cited by 14 | Viewed by 2450
Abstract
Air pollution sources and the hazards of high particulate matter 2.5 (PM2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to [...] Read more.
Air pollution sources and the hazards of high particulate matter 2.5 (PM2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the PM2.5 concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 µg/m3 for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent PM2.5 air pollutant-induced damage in seaports. Full article
(This article belongs to the Special Issue Multiple Data Analyses of Air Quality Diagnosis)
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