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Atmosphere
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Novel Insights into Air Pollution over East Asia (Second Edition)
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Novel Insights into Air Pollution over East Asia (Second Edition)

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

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 2303

Special Issue Editor

Dr. Jin-Seok Han

E-Mail Website
Guest Editor
Department of Environmental Energy Engineering, Anyang University, Anyang 14028, Republic of Korea
Interests: air pollution; air quality control; source apportionment; HAPs; atmospheric chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up to the first Special Issue entitled “Novel Insights into Air Pollution over East Asia” (https://www.mdpi.com/journal/atmosphere/special_issues/775R4356C3), published in Atmosphere in 2024, and will cover all aspects of strategies for air pollution over East Asia.

Several countries in East Asia have experienced rapid economic development. The growth and change in industrial structure have led to increased air pollution, which became a serious issue. Air pollution in East Asian countries varies widely over time and space, but vehicle emissions and industrial emissions are the most important pollutants in urban areas, while biomass burning is a very important emission source. East Asia is one of the most populous regions in the world, which means that many people are exposed to regional air pollution. Research into air pollution in East Asia is important; thus, it is the focus of this Special Issue.

The purpose of this Special Issue is to act as a platform for the exchange of research insights related to air pollution in East Asia. Areas of interest in this Special Issue include, but are not limited to, the following topics:

  • Air quality measurement technology;
  • Air pollution source apportionment;
  • Emission inventory and air quality modeling;
  • Hazardous air pollutants and health effects;
  • Air pollutant management and control;
  • Secondary air pollutant (O3 and PM2.5) formation mechanisms.

We look forward to receiving insightful contribution!

Dr. Jin-Seok Han
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

  • air pollution
  • atmospheric chemistry
  • source apportionment
  • pollutants control policy
  • health effects
  • secondary air pollutants

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Published Papers (2 papers)

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Research

13 pages, 934 KiB  
Article
Performance Evaluation of PM2.5 Forecasting Using SARIMAX and LSTM in the Korean Peninsula
by Chae-Yeon Lee, Ju-Yong Lee, Seung-Hee Han, Jin-Goo Kang, Jeong-Beom Lee and Dae-Ryun Choi
Atmosphere 2025, 16(5), 524; https://doi.org/10.3390/atmos16050524 (registering DOI) - 29 Apr 2025
Abstract
Air pollution, particularly fine particulate matter (PM2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM2.5 [...] Read more.
Air pollution, particularly fine particulate matter (PM2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM2.5 forecasting accuracy. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia (Second Edition))
18 pages, 2504 KiB  
Article
Characteristics and Source Profiles of Volatile Organic Compounds (VOCs) by Several Business Types in an Industrial Complex Using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS)
by Kyoung-Chan Kim, Byeong-Hun Oh, Jeong-Deok Baek, Chun-Sang Lee, Yong-Jae Lim, Hung-Soo Joo and Jin-Seok Han
Atmosphere 2024, 15(10), 1156; https://doi.org/10.3390/atmos15101156 - 27 Sep 2024
Cited by 2 | Viewed by 1996
Abstract
Volatile organic compounds (VOCs) are one of significant contributors to air pollution and have profound effects on human health and the environment. This study introduces a detailed analysis of VOC emissions from various industries within an industrial complex using a high-resolution measurement instrument. [...] Read more.
Volatile organic compounds (VOCs) are one of significant contributors to air pollution and have profound effects on human health and the environment. This study introduces a detailed analysis of VOC emissions from various industries within an industrial complex using a high-resolution measurement instrument. This study aimed to identify the VOC profiles and their concentrations across 12 industries. Sampling was conducted across 99 facilities in an industrial complex in South Korea, and VOC analysis was performed based on measurement data using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS). The results indicated that the emission of oxygenated VOCs (OVOCs) was dominant in most industries. Aromatic hydrocarbons were also dominant in most industries, except in screen printing (SP), lubricating oil and grease manufacturing (LOG), and industrial laundry services (ILS) industries. Chlorinated VOCs (Cl-VOCs) showed a relatively higher level in the metal plating (MP) industry than those in other industries and nitrogen-containing VOCs (N-VOCs) showed high levels in general paints and similar product manufacturing (PNT), MP, and ILS industries, respectively. The gravure printing industry was identified as the highest emitter of VOCs, with the highest daily emissions reaching 5934 mg day−1, primarily consisting of ethyl acetate, toluene, butyl acetate, and propene. The findings suggest that the VOC emissions from the gravure printing and plastic synthetic leather industries should be primarily reduced, and it would be the most cost-effective approach to improving air quality. This study can provide the fundamental data for developing effective reduction technologies and policies of VOC, ultimately contributing to enhanced atmospheric models and regulatory measures. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia (Second Edition))
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