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Applied Sciences
Special Issues
Characteristics of Air Pollution Assessment, Modeling and Reduction
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Characteristics of Air Pollution Assessment, Modeling and Reduction

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 4883

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
Department of Mathematical and Physical Sciences, The University of Technology Sydney, Sydney, Australia
Interests: severe weather; climate variability and change; synoptic and mesoscale meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The air pollution problem produced by either our human activities in farm fields, industrial zones, and urban areas or natural phenomena has created issues that are still underlying, both in advanced and in undeveloped countries. Through investigating the characteristics of atmospheric pollutants, we try to suggest various and effective ways for the reduction of atmospheric pollutants. We would like to invite scientific papers on “Characteristics of Air Pollution Assessment, Modeling, and Reduction”, related to air pollution mapping and monitoring, air pollution assessment and control strategy, photochemical reaction of air pollutants, indoor air pollution and reduction, reduction of dusts (PM10, PM2.5, PM1) and its dispersion modeling, air quality of particulate matters and gases in the atmosphere and their phase changes, atmospheric boundary layer modeling on severe air pollution, the meteorological effect on a high air pollution state, 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 beyond the above topics, because our main purpose for this Special Issue is to improve the air pollution state using various prediction models, technologies, and reduction methods.

Prof. Dr. Hyo Choi
Dr. Milton S. Speer
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 and reduction
  • Air quality of particulate matter and gas
  • Emission and transport of air pollutants
  • Air pollution impact on human health

Published Papers (2 papers)

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Research

15 pages, 58855 KiB  
Article
Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods
by Soo-Min Choi and Hyo Choi
Appl. Sci. 2021, 11(24), 11958; https://doi.org/10.3390/app112411958 - 15 Dec 2021
Cited by 2 | Viewed by 1631
Abstract
Multiple statistical prediction modeling of PM10, PM2.5 and PM1 at Gangneung city, Korea, was performed in association with local meteorological parameters (air temperature, wind speed and relative humidity) and PM10 and PM2.5 concentrations of an upwind site [...] Read more.
Multiple statistical prediction modeling of PM10, PM2.5 and PM1 at Gangneung city, Korea, was performed in association with local meteorological parameters (air temperature, wind speed and relative humidity) and PM10 and PM2.5 concentrations of an upwind site in Beijing, China, in the transport route of Chinese yellow dusts which originated from the Gobi Desert and passed through Beijing to the city from 18 March to 27 March 2015. Before and after the dust periods, the PM10, PM2.5 and PM1 concentrations showed as being very high at 09:00 LST (the morning rush hour) by the increasing emitted pollutants from vehicles and flying dust from the road and their maxima occurred at 20:00 to 22:00 LST (the evening departure time) from the additional pollutants from resident heating boilers. During the dust period, these peak trends were not found due to the persistent accumulation of dust in the city from the Gobi Desert through Beijing, China, as shown in real-time COMS-AI satellite images. Multiple correlation coefficients among PM10, PM2.5 and PM1 at Gangneung were in the range of 0.916 to 0.998. Multiple statistical models were devised to predict each PM concentration, and the significant levels through multi-regression analyses were p < 0.001, showing all the coefficients to be significant. The observed and calculated PM concentrations were compared, and new linear regression models were sequentially suggested to reproduce the original observed PM values with improved correlation coefficients, to some extent. Full article
(This article belongs to the Special Issue Characteristics of Air Pollution Assessment, Modeling and Reduction)
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20 pages, 4423 KiB  
Article
Combination of Different Approaches to Infer Local or Regional Contributions to PM2.5 Burdens in Graz, Austria
by Bernadette Kirchsteiger, Magdalena Kistler, Thomas Steinkogler, Christopher Herzig, Andreas Limbeck, Christian Schmidt, Harald Rieder and Anne Kasper-Giebl
Appl. Sci. 2020, 10(12), 4222; https://doi.org/10.3390/app10124222 - 19 Jun 2020
Cited by 3 | Viewed by 2341
Abstract
In early 2017 high particulate matter (PM) levels were observed across mid-Europe, including Austria. Here we characterize PM pollution in the city of Graz during January to March 2017, a period with substantial exceedances (34 days) of the European Union (EU) PM10 [...] Read more.
In early 2017 high particulate matter (PM) levels were observed across mid-Europe, including Austria. Here we characterize PM pollution in the city of Graz during January to March 2017, a period with substantial exceedances (34 days) of the European Union (EU) PM10 short time limit value. This study evaluates whether the observed exceedances can be attributed to the accumulation of pollutants emitted by local sources or to a larger scale pollution episode including transport. The analyses are based on the ratios of PM10 concentrations determined at an urban and background site, and the analyses of chemical composition of PM2.5 samples (i.e., water soluble ions, organic and elemental carbon, anhydro-sugars, humic-like substances, aluminum, and polycyclic aromatic hydrocarbons). Source apportionment was realized using a macro-tracer model. Overall, the combination of different approaches (PM10 ratios, chemical composition, and macro-tracer derived source apportionment) enabled a conclusive identification of time periods characterized by the accumulation of emissions from local sources or regional pollution episodes. Full article
(This article belongs to the Special Issue Characteristics of Air Pollution Assessment, Modeling and Reduction)
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