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Applied Sciences
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Short- and Long-Term Air Pollution Analysis, Modeling and Prediction
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Short- and Long-Term Air Pollution Analysis, Modeling and Prediction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 20 April 2024 | Viewed by 4580

Special Issue Editors

Prof. Dr. Hyo Choi

E-Mail Website
Guest Editor
1. Department of Atmospheric & Environmental Sciences, College of Natural Sciences, Gangneung-Wonju National University (GWNU), Jukheongil 7, Gangneung, Gangwondo 25457, Republic of Korea
2. Atmospheric & Oceanic Disaster Research Institute, Dalim Apt. 209ho, Namgang-chogyo 2gil 44, Gangneung, Gangwondo 25563, Republic of Korea
Interests: numerical modeling of air pollution; air pollutant measurement and assessment; coastal and oceanic atmospheric boundary layer modeling; physical oceanographic modeling (waves and typhoons); statistical modeling (artificial neuron network modeling, multiple regression modeling)
Special Issues, Collections and Topics in MDPI journals
Dr. Milton S. Speer

E-Mail Website
Guest Editor
1. School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, Australia
2. Climate Change Research Centre, Faculty of Science, The University of New South Wales, Sydney, Australia
Interests: severe weather; climate variability and change; synoptic and mesoscale meteorology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Selahattin Incecik

E-Mail Website
Guest Editor
Department of Meteorological Engineering, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey
Interests: air pollution; air quality; physics of global warming; numerical weather prediction; air pollution modeling; meteorology; climatology

Special Issue Information

Dear Colleagues,

In recent decades, with the industrialization of most countries in the world and the desertification of land through climate change due to the increase in carbon dioxide, severe air pollution has been generated.  Either our human activities in farm fields, industrial zones, and urban areas or natural phenomena have created important issues that are still underlying, both in advanced and in undeveloped countries. By investigating the characteristics of atmospheric pollutants, we attempt to suggest various effective ways of improving the air pollution condition. We would like to invite scientific papers on “Short- and Long-term Air Pollution Analysis, Modeling and Prediction”, related to air pollution mapping and monitoring using satellite and GIS, air pollution assessment and control strategy, the photochemical reaction of air pollutants and their phase changes, indoor air pollution, and the prediction of particulate matter and gas concentrations using artificial neural network modeling, multivariate statistical modeling and numerical modeling, the atmospheric boundary layer effect on severe air pollution, the meteorological effect on a high air pollution state, the emission and transport of atmospheric pollutants, air pollution modeling for its local, regional, and global scales, air pollution impact on human health and its control, etc. Finally, we would like to welcome any manuscripts which may be slightly outside the above topics.

Prof. Dr. Hyo Choi
Dr. Milton S. Speer
Prof. Dr. Selahattin Incecik
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 monitoring and analysis of particulate matters and gaseous
  • air pollution mapping and monitoring using satellite and geographical information system (GIS)
  • artificial neural network modeling, multivariate statistical modeling, and numerical modeling on air pollutant concentration and its dispersion
  • air quality variation affected by the atmospheric boundary layer and severe weather
  • renewable energy
  • desertification of land through climate change due to the increase in carbon dioxide
  • emission and transport of air pollutants
  • air pollution impact on human health
  • improvement of air pollution state

Published Papers (5 papers)

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Research

17 pages, 2447 KiB  
Article
Air Quality Research Based on B-Spline Functional Linear Model: A Case Study of Fujian Province, China
by Yihan Xu, Tiange You, Yuanyao Wen, Jing Ning, Yanglan Xiao and Huirou Shen
Appl. Sci. 2023, 13(20), 11206; https://doi.org/10.3390/app132011206 - 12 Oct 2023
Cited by 1 | Viewed by 779
Abstract
It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air [...] Read more.
It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) were fitted to functional curves using the B-spline basis function. Compared with discrete data, functional data can better express the dynamic characteristics of data and reduce information loss. Additionally, functional clustering based on the principal component coefficient was established to analyze the spatiotemporal dynamic characteristics of air quality, and a functional linear model was established to analyze the relationship between pollutants and anthropogenic factors. The results showed that air pollutants in Fujian Province were found to have certain temporal and spatial heterogeneity, among which the seasonal characteristics of NO2 and O3 (high in summer, low in winter) were opposite to those of the other pollutants considered. The spatial distribution of air pollution was low (high) pollution in inland (coastal) areas, and the primary air pollutants in Fujian Province were PM10 and PM2.5. The functional linear model indicated that anthropogenic factors (e.g., vehicle numbers and emissions of industrial NOX emissions) were found to have a notable impact on air pollutants. The findings of this study could act as a reference in support of air pollution control. Full article
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
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18 pages, 2882 KiB  
Article
Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs
by Minwook Kim, Myoungki Song, Sea-Ho Oh, Geun-Hye Yu, Seoyeong Choe, Hajeong Jeon, Jin-Ho Kim and Min-Suk Bae
Appl. Sci. 2023, 13(19), 10783; https://doi.org/10.3390/app131910783 - 28 Sep 2023
Viewed by 537
Abstract
In this study, the impact of long-range transport, one of the factors contributing to the presence of PM2.5, was examined, and an analysis of marker compounds associated with its long-range transport was conducted. Aerosol optical depth, wind field, CO/CO2 back-trajectory [...] Read more.
In this study, the impact of long-range transport, one of the factors contributing to the presence of PM2.5, was examined, and an analysis of marker compounds associated with its long-range transport was conducted. Aerosol optical depth, wind field, CO/CO2 back-trajectory analysis, and satellite observation results were performed to determine PM2.5, volatile organic compound (VOC), CO, CO2, SO2, O3, NO, NO2, and NH3 levels at an orchard located in Jeollabuk-do, Republic of Korea. The characteristic of long-range transport at the observation area was evaluated during the research period. The concentrations for long-range transport based on concentration changes in gaseous materials and composition changes in PM2.5 were analyzed. A back-trajectory analysis for the ratio of CO to CO2 with satellite observation results was used to identify long-range transport. Furthermore, the proportionality between the ratio of 1,2-dichloroethane to naphthalene in VOCs and the quantity of precursor compounds linked to long-range transport were observed. Full article
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
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27 pages, 8032 KiB  
Article
Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
by Soo-Min Choi, Hyo Choi and Woojin Paik
Appl. Sci. 2023, 13(19), 10556; https://doi.org/10.3390/app131910556 - 22 Sep 2023
Viewed by 677
Abstract
Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (Korea), associated with two-days-earlier data [...] Read more.
Multivariate regression models for real-time coastal air quality forecasting were suggested from 18 to 27 March 2015, with a total of 15 kinds of hourly input data (three-hours-earlier data of PM and gas with meteorological parameters from Kangnung (Korea), associated with two-days-earlier data of PM and gas from Beijing (China)). Multiple correlation coefficients between the predicted and measured PM10, PM2.5, NO2, SO2, CO and O3 concentrations were 0.957, 0.906, 0.886, 0.795, 0.864 and 0.932 before the yellow sand event at Kangnung, 0.936, 0.982, 0.866, 0.917, 0.887 and 0.916 during the event and 0.919, 0.945, 0.902, 0.857, 0.887 and 0.892 after the event. As the significance levels (p) from multi-regression analyses were less than 0.001, all correlation coefficients were very significant. Partial correlation coefficients presenting the contribution of 15 input variables to 6 output variables using the models were presented for the three periods in detail. Scatter plots and their hourly distributions between the predicted and measured values showed the quite good accuracy of the modeling performance for the current time forecasting of six output values and their high applicability. Full article
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
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16 pages, 1504 KiB  
Article
Determination of Vehicle Emission Rates for Ammonia and Organic Molecular Markers Using a Chassis Dynamometer
by Geun-Hye Yu, Myoung-Ki Song, Sea-Ho Oh, Seo-Yeong Choe, Min-Wook Kim and Min-Suk Bae
Appl. Sci. 2023, 13(16), 9366; https://doi.org/10.3390/app13169366 - 18 Aug 2023
Cited by 1 | Viewed by 1015
Abstract
Stringent regulations have been implemented to address vehicle exhaust emissions and mitigate air pollution. However, the introduction of exhaust gas reduction devices, such as Three-Way Catalytic converters, has raised concerns about the generation and release of additional pollutants such as NH3. [...] Read more.
Stringent regulations have been implemented to address vehicle exhaust emissions and mitigate air pollution. However, the introduction of exhaust gas reduction devices, such as Three-Way Catalytic converters, has raised concerns about the generation and release of additional pollutants such as NH3. This study utilized a chassis dynamometer to investigate the characteristics of exhaust pollutants, including carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM), ammonia (NH3), organic carbon (OC), and elemental carbon (EC). The emissions were examined across various vehicle fuel types, namely liquefied petroleum gas, gasoline, and diesel (EURO4, EURO6), to assess their individual contributions to exhaust emissions. The results revealed significant variations in the emission levels of regulated pollutants (CO, HC, NOx, and PM) during driving, depending on factors such as engine technology, emissions control strategies, fuel type, and test cycle. Notably, NH3 emissions analysis according to driving mode indicated that gasoline vehicles exhibited the highest NH3 emissions, while diesel vehicles emitted negligible amounts. This observation can be attributed to the production of NH3 as a byproduct of catalytic reduction processes implemented by exhaust gas reduction devices targeting CO, HC, and NOx. In addition, EURO4 vehicles demonstrated higher emission levels of OC and EC compared with other fuel types. Furthermore, the presence of diesel particulate filters (DPFs) in diesel vehicles effectively reduced PM emissions. Moreover, this study investigated the emission characteristics of organic molecular markers within the organic carbon fraction, revealing distinct emission profiles for each vehicle and fuel type. These findings contribute to the identification of emission sources by discerning the primary components emitted by specific fuel types. Full article
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
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29 pages, 11868 KiB  
Article
Air Quality Improvement in Urban Street Canyons: An Assessment of the Effects of Selected Traffic Management Strategies Using OSPM Model
by Robert Oleniacz, Marek Bogacki, Mateusz Rzeszutek and Paulina Bździuch
Appl. Sci. 2023, 13(11), 6431; https://doi.org/10.3390/app13116431 - 24 May 2023
Viewed by 950
Abstract
Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in [...] Read more.
Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent. Full article
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
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