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Atmosphere
Special Issues
Novel Insights into Air Pollution over East Asia
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Novel Insights into Air Pollution over East Asia

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

Deadline for manuscript submissions: 14 August 2024 | Viewed by 3739

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 Issue Information

Dear Colleagues,

Several countries in East Asia have experienced rapid economic development. The growth and change in industrial structure 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 your 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

Published Papers (5 papers)

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Research

13 pages, 3986 KiB  
Article
Characteristics of Atmospheric Pollutants in Paddy and Dry Field Regions: Analyzing the Oxidative Potential of Biomass Burning
by Myoungki Song, Minwook Kim, Sea-Ho Oh, Geun-Hye Yu, Seoyeong Choe, Hajeong Jeon, Dong-Hoon Ko, Chaehyeong Park and Min-Suk Bae
Atmosphere 2024, 15(4), 493; https://doi.org/10.3390/atmos15040493 - 17 Apr 2024
Viewed by 474
Abstract
This study aimed to identify the characteristics of atmospheric pollutants emitted by agricultural activities and to evaluate factors that may cause harm to human health. For the research, atmospheric pollutants were measured over the course of a year in representative rice farming and [...] Read more.
This study aimed to identify the characteristics of atmospheric pollutants emitted by agricultural activities and to evaluate factors that may cause harm to human health. For the research, atmospheric pollutants were measured over the course of a year in representative rice farming and field crop farming areas in South Korea. The results confirmed that the characteristics of atmospheric pollutants in agricultural areas are influenced by the nature of agricultural activities. Specifically, when comparing rice paddies and field crop areas, during summer, the correlation between oxidative potential and levoglucosan—a marker for biomass burning—weakens due to less burning activity in the rice-growing season, leading to lower oxidative potential despite different PM2.5 across areas. The study also finds that methyl sulfonic acid, indicating marine influence, plays a big role in keeping oxidative potential low in summer. This suggests that the main causes of PM2.5-related health risks in the area are from biomass burning and external sources, with burning being a significant factor in increasing oxidative potential. Based on these results, it is hoped that measures can be taken in the future to reduce atmospheric pollutants in agricultural areas. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
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27 pages, 4182 KiB  
Article
Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism
by Yumeng Wang, Ke Liu, Yuejun He, Pengfei Wang, Yuxin Chen, Hang Xue, Caiyi Huang and Lin Li
Atmosphere 2024, 15(4), 418; https://doi.org/10.3390/atmos15040418 - 27 Mar 2024
Viewed by 609
Abstract
Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for [...] Read more.
Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
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16 pages, 4392 KiB  
Article
The Impact of Vertical Eddy Diffusivity Changes in the CMAQ Model on PM2.5 Concentration Variations in Northeast Asia: Focusing on the Seoul Metropolitan Area
by Dong-Ju Kim, Tae-Hee Kim, Jin-Young Choi, Jae-bum Lee, Rhok-Ho Kim, Jung-Seok Son and Daegyun Lee
Atmosphere 2024, 15(3), 376; https://doi.org/10.3390/atmos15030376 - 19 Mar 2024
Viewed by 725
Abstract
The vertical eddy diffusion process plays a crucial role in PM2.5 prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM2.5 prediction tendencies [...] Read more.
The vertical eddy diffusion process plays a crucial role in PM2.5 prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM2.5 prediction tendencies vary according to the floor value of this parameter (Kzmin). This study aims to examine prediction characteristics according to Kzmin values, targeting days exceeding the Korean air quality standards, and to derive appropriate Kzmin values for predicting PM2.5 concentrations in the DJFM Seoul Metropolitan Area (SMA). Kzmin values of 0.01, 0.5, 1.0, and 2.0, based on the model version and land cover, were applied as single values. Initially focusing on December 4th to 12th, 2020, the prediction characteristics were examined during periods of local and inflow influence. Results showed that in both periods, as Kzmin increased, surface concentrations over land decreased while those in the upper atmosphere increased, whereas over the sea, concentrations increased in both layers due to the influence of advection and diffusion without emissions. During the inflow period, the increase in vertically diffused pollutants led to increased inflow concentrations and affected contribution assessments. Long-term evaluations from December 2020 to March 2021 indicated that the prediction performance was superior when Kzmin was set to 0.01, but it was not significant for the upwind region (China). To improve trans-boundary effects, optimal values were applied differentially by region (0.01 for Korea, 1.0 for China, and 0.01 for other regions), resulting in significantly improved prediction performance with an R of 0.78, IOA of 0.88, and NMB of 0.7%. These findings highlight the significant influence of Kzmin values on winter season PM2.5 prediction tendencies in the SMA and underscore the need for considering differential application of optimal values by region when interpreting research and making policy decisions. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
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11 pages, 1922 KiB  
Article
A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea
by Seung-Myung Park, Jong Sung Park, In-Ho Song, Jeonghwan Kim, Hyun Woong Kim, Jaeyun Lee, Jung Min Park, Jeong-ho Kim, Yongjoo Choi, Hye Jung Shin, Joon Young Ahn, Yu Woon Jang, Taehyoung Lee and Gangwoong Lee
Atmosphere 2024, 15(3), 320; https://doi.org/10.3390/atmos15030320 - 04 Mar 2024
Viewed by 685
Abstract
We performed continuous long-term measurements of PM2.5 mass, comprehensive chemical composition, and optical properties, including scattering and absorption coefficients, from March 2011 to December 2020 at the Metropolitan Air Quality Research Center in Seoul, South Korea. PM2.5 peaked at 38 μg/m [...] Read more.
We performed continuous long-term measurements of PM2.5 mass, comprehensive chemical composition, and optical properties, including scattering and absorption coefficients, from March 2011 to December 2020 at the Metropolitan Air Quality Research Center in Seoul, South Korea. PM2.5 peaked at 38 μg/m3 in 2013 and has been declining steadily since then, reaching 22 μg/m3 in 2020. The extinction coefficients also decreased with the decline in PM2.5, but the correlation between the two factors was not as pronounced. This deviation was mainly attributed to the rapid changes in the chemical composition of PM2.5 over the same period. The mass contribution of sulphate to PM2.5 decreased from 33.9 to 24.1%, but the fraction of nitrate and organic carbon increased from 23.4 and 20.0 to 34.1 and 32.2%, respectively, indicating that sulphate has been replaced by nitrate and organic carbon over the past decade. To assess the effect of changing aerosol chemical compositions on light extinction, we compared the measured extinction coefficients with those estimated via the various existing light extinction approaches, including the revised IMPROVE algorithm. We found that the simplified linear regression model provided the best fit to our data, with a slope of 1.03 and R2 of 0.87, and that all non-linear methods, such as the IMPROVE algorithms, overestimated the observed long-term light extinction by 23 to 48%. This suggests that the simple linear regression scheme may be more appropriate for reflecting the varying aerosol conditions over long periods of time, especially for urban air. However, for conditions where the chemical composition does not change much, non-linear methods such as the IMPROVE scheme are likely to be more appropriate for reproducing light extinction. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
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13 pages, 4718 KiB  
Article
Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes
by Yong-koo Lee, Ji-hwan Lee, Nam-gwon Beak, Kyoung-chan Kim and Jin-seok Han
Atmosphere 2024, 15(1), 30; https://doi.org/10.3390/atmos15010030 - 27 Dec 2023
Cited by 1 | Viewed by 671
Abstract
Particulate and gaseous polycyclic aromatic hydrocarbon (PAHs) samples (n = 108) were measured every six days from January to December 2022 at a representative point in the Korean Banwol National Industrial Complex. The measurement results revealed that the concentration of particulate Σ18 [...] Read more.
Particulate and gaseous polycyclic aromatic hydrocarbon (PAHs) samples (n = 108) were measured every six days from January to December 2022 at a representative point in the Korean Banwol National Industrial Complex. The measurement results revealed that the concentration of particulate Σ18 PAHs was 7.92 ± 4.04 ng/Sm3 in winter, 1.83 ± 1.99 ng/Sm3 in spring, 1.43 ± 0.95 ng/Sm3 in summer, and 2.58 ± 2.14 ng/Sm3 in autumn. The concentration of gaseous Σ18 PAHs was 3.32 ± 3.72 ng/Sm3 in winter, 6.34 ± 5.95 ng/Sm3 in spring, 8.33 ± 8.13 ng/Sm3 in summer, and 3.88 ± 1.71 ng/Sm3 in autumn. The results of the correlation analysis showed that particulate PAHs have positive relationships with PM10 and PM2.5 and negative relationships with temperature and O3. The diagnostic ratio and PAHs component slope showed that the emission source characteristics of the Banwol National Industrial Complex were dominated by biomass coal combustion over four seasons; however, the influence of petroleum combustion (automobile emissions) was not negligible. As for coal combustion, bituminous coal was the most influential, and lignite was relevant in summer and autumn. Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)
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