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Atmosphere
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Atmospheric Pollution in Highly Polluted Areas
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Atmospheric Pollution in Highly Polluted Areas

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5380

Special Issue Editors

Dr. Shenbo Wang

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Guest Editor
School of Ecology and Environment, Zhengzhou University. No.100 Science Avenue, Zhengzhou 450001, China
Interests: environmental analysis; air quality; reactive nitrogen compounds; secondary aerosol
Dr. Shasha Yin

E-Mail Website
Guest Editor
School of Ecology and Environment, Zhengzhou University. No.100 Science Avenue, Zhengzhou 450001, China
Interests: emission inventory; VOCs; ozone; greenhouse gases; ammonia
Dr. Xiao Li

E-Mail Website
Guest Editor
School of Ecology and Environment, Zhengzhou University. No.100 Science Avenue, Zhengzhou 450001, China
Interests: fine particulate matter; brown carbon; secondary organic aerosol; carbonaceous aerosols
Dr. Xiaohui Ma

E-Mail
Guest Editor
School of Environmental Engineering, Henan University of Technology, No. 100 Lianhua Street, High-Tech Industrial Development District, Zhengzhou 450001, China
Interests: atmospheric pollution; aerosol formation; reaction mechanism; gas-liquid interface; molecular dynamics

Special Issue Information

Dear Colleagues,

Air pollution remains the greatest environmental health threat worldwide, with only seven countries meeting the WHO annual PM2.5 guideline (annual average of 5 µg/m3 or less) in 2023. Moreover, in some countries and regions, the PM2.5 concentrations exceed the standard by more than five times, such as in Central Asia, South Asia, North Africa, Southern Europe, Latin America, China’s NCP region, and so on. Research regarding air pollution in these regions is often lagging and insufficient due to the economic development constraints. However, under conditions of unique emission characteristics, meteorological conditions, and geographical locations, the formation mechanisms of atmospheric pollution may vary, thus necessitating in-depth research. Therefore, we aim to promote the publication of papers focusing on air pollutants in highly polluted areas in this Special Issue. In particular, whether original research papers or review articles, the Special Issue invites studies including, but not limited to, the following topics:

  1. Investigating the characteristics of air pollutants in pollution progress;
  2. Exploring the sources and formation mechanisms of air pollutants in highly polluted areas;
  3. Assessing the impacts of air pollutants on human health, ecosystems, and climate systems;
  4. Discussing strategies and interventions for mitigating air pollution and improving air quality.

Dr. Wang Shenbo
Dr. Shasha Yin
Dr. Xiao Li
Dr. Xiaohui Ma
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 pollutant monitoring
  • emissions
  • chemical mechanisms
  • model simulations
  • haze
  • ozone
  • sand storm

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Published Papers (6 papers)

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Research

16 pages, 2935 KiB  
Article
Analysis of Fine Dust Impacts on Incheon and Busan Port Areas Resulting from Port Emission Reduction Measures
by Moon-Seok Kang, Jee-Ho Kim, Young Sunwoo and Ki-Ho Hong
Atmosphere 2025, 16(5), 521; https://doi.org/10.3390/atmos16050521 (registering DOI) - 29 Apr 2025
Abstract
PM2.5 concentrations in major port cities in the Republic of Korea, such as Incheon and Busan, are as serious as those in land-based metropolises, such as Seoul, and fine dust generated in port cities is mainly emitted from ships. To identify the [...] Read more.
PM2.5 concentrations in major port cities in the Republic of Korea, such as Incheon and Busan, are as serious as those in land-based metropolises, such as Seoul, and fine dust generated in port cities is mainly emitted from ships. To identify the specific substances influencing local air quality, the occurrence and effects of high concentrations of PM2.5 at the ports of Incheon and Busan were analyzed. To analyze the effects of improving air quality based on the Republic of Korea’s port and ship-related reduction measures, we calculated an emissions forecast for 2025 following the implementation/non-implementation of these measures and analyzed all impacts using the WRF-SMOKE-CMAQ modeling system. The ratio of ionic components constituting PM2.5 at the ports of Incheon and Busan was generally high in nitrate composition; however, the ratio of sulfate was high on high PM2.5 concentration days. When implementing measures to reduce emissions related to ports and ships, forecasted PM2.5 and SO2 emissions showed substantial decreases in port areas as well as nearby land and sea areas. The associated decrease in the PM2.5 concentration was highly influential in reducing the concentration of sulfate. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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22 pages, 4810 KiB  
Article
Assessing the Spatiotemporal Dynamics and Health Impacts of Surface Ozone Pollution in Beijing, China
by Fangxu Yin, Jiewen You and Lu Gao
Atmosphere 2025, 16(4), 397; https://doi.org/10.3390/atmos16040397 - 29 Mar 2025
Viewed by 342
Abstract
Surface ozone has emerged as a concerning pollutant in Beijing, China. This study assessed ozone pollution and its health impacts in Beijing using ground (35 stations) and satellite data (2014–2023). Temporal trends were analyzed across various temporal scales, while spatial variability was evaluated [...] Read more.
Surface ozone has emerged as a concerning pollutant in Beijing, China. This study assessed ozone pollution and its health impacts in Beijing using ground (35 stations) and satellite data (2014–2023). Temporal trends were analyzed across various temporal scales, while spatial variability was evaluated using integrated ground and satellite-derived continuous data. Health impacts were quantified via a log-linear concentration–response model. Results show that for ozone concentrations during the post-pandemic period (2019–2023, covering the onset of COVID-19 in 2019 and the period following), daytime concentrations decreased by 6.8 μg/m3, but nighttime concentrations increased by 5.4 μg/m3. Spatially, ozone concentrations were higher in urban areas than in suburban areas in summer, but the reverse occurred in other seasons. Satellite data revealed broader Grade II (160 μg/m3) exceedance variability (3.0–20.3%) compared to station estimates (15.3–18.7%). Health impact assessments indicated that achieving the Grade I standard (100 μg/m3) could prevent approximately 576 (95% CI: 317–827) all-cause deaths and 294 (95% CI: 111–467) cardiovascular deaths per year, which is 3.5 times more than the reductions from meeting the Grade II standard (160 μg/m3). These findings underscore the need for adaptive ozone controls and tiered mitigation strategies to reduce health risks in Beijing. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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17 pages, 2538 KiB  
Article
The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City
by Ronan Adler Tavella, Daniele Feijó das Neves, Gustavo de Oliveira Silveira, Gabriella Mello Gomes Vieira de Azevedo, Rodrigo de Lima Brum, Alicia da Silva Bonifácio, Ricardo Arend Machado, Letícia Willrich Brum, Romina Buffarini, Diana Francisca Adamatti and Flavio Manoel Rodrigues da Silva Júnior
Atmosphere 2025, 16(4), 363; https://doi.org/10.3390/atmos16040363 - 24 Mar 2025
Viewed by 302
Abstract
This study investigated the relationship between surface meteorological variables and the levels of surface air pollutants (O3, PM10, and PM2.5) in scenarios of simulated temperature increases in Rio Grande, a medium-sized Brazilian city with strong industrial influence. [...] Read more.
This study investigated the relationship between surface meteorological variables and the levels of surface air pollutants (O3, PM10, and PM2.5) in scenarios of simulated temperature increases in Rio Grande, a medium-sized Brazilian city with strong industrial influence. This study utilized five years of daily meteorological data (from 1 January 2019 to 31 December 2023) to model atmospheric conditions and two years of daily air pollutant data (from 21 December 2021 to 20 December 2023) to simulate how pollutant levels would respond to annual temperature increases of 1 °C and 2 °C, employing a Support Vector Machine, a supervised machine learning algorithm. Predictive models were developed for both annual averages and seasonal variations. The predictive analysis results indicated that, when considering annual averages, pollutant concentrations showed a decreasing trend as temperatures increased. This same pattern was observed in seasonal scenarios, except during summer, when O3 levels increased with the simulated temperature rise. The greatest seasonal reduction in O3 occurred in winter (decreasing by 10.33% and 12.32% under 1 °C and 2 °C warming scenarios, respectively), while for PM10 and PM2.5, the most significant reductions were observed in spring. The lack of a correlation between temperature and pollutant levels, along with their relationship with other meteorological variables, explains the observed pattern in Rio Grande. This research provides important contributions to the understanding of the interactions between climate change, air pollution, and meteorological factors in similar contexts. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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16 pages, 4518 KiB  
Article
Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm
by Baojiang Li, Gang Cheng, Chunlin Shang, Ruirui Si, Zhenping Shao, Pu Zhang, Wenyu Zhang and Lingbin Kong
Atmosphere 2025, 16(2), 114; https://doi.org/10.3390/atmos16020114 - 21 Jan 2025
Viewed by 854
Abstract
Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the [...] Read more.
Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m−3, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m−3, 7 μg·m−3, 8 μg·m−3, and 15 μg·m−3 for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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19 pages, 8388 KiB  
Article
Development of Machine Learning and Deep Learning Prediction Models for PM2.5 in Ho Chi Minh City, Vietnam
by Phuc Hieu Nguyen, Nguyen Khoi Dao and Ly Sy Phu Nguyen
Atmosphere 2024, 15(10), 1163; https://doi.org/10.3390/atmos15101163 - 29 Sep 2024
Cited by 4 | Viewed by 2063
Abstract
The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution [...] Read more.
The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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12 pages, 18779 KiB  
Article
Characteristics of Aerosol Water Content and Its Implication on Secondary Inorganic Aerosol Formation during Sandy Haze in an Inland City in China
by Shiting Zhai, Panru Kang, Shenbo Wang and Ruiqin Zhang
Atmosphere 2024, 15(7), 850; https://doi.org/10.3390/atmos15070850 - 19 Jul 2024
Viewed by 982
Abstract
Sand events continue to occur frequently and affect the North China region. Under unfavorable meteorological conditions, they can easily combine with haze pollution, forming sandy haze events that have a significant impact on human health. Aerosol water content (AWC) is known to have [...] Read more.
Sand events continue to occur frequently and affect the North China region. Under unfavorable meteorological conditions, they can easily combine with haze pollution, forming sandy haze events that have a significant impact on human health. Aerosol water content (AWC) is known to have a significant impact on PM2.5, but its effect is still unclear in sandy haze. In this work, sandy haze and haze periods were observed in Zhengzhou using a series of high-time-resolution instruments. The AWC calculated by the ISORROPIA-II model reached 11 ± 5 μg m−3, accounting for 10% of the PM2.5, in the sandy haze period. Sensitivity tests show that AWC was mainly relative humidity (RH)-dependent. Additionally, elevated SO42−, TNO3, and TNH4 were crucial in the increase in AWC. The increase in Ca2+ ions in the sandy haze led to lower AWC than that in the haze periods. Specifically, (NH4)2SO4 was the major contributor to the AWC when the RH was between 30 and 46% in the sandy haze period, and NH4NO3 gradually became the main contributor with the increase in RH. In turn, AWC could enhance the formation of sulfate and nitrate, even during the sandy haze period. Therefore, the emergency control of gaseous precursors should also be implemented before the sand events. Full article
(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)
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