Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index

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

Deadline for manuscript submissions: 19 July 2024 | Viewed by 1579

Special Issue Editors


E-Mail Website
Guest Editor
Department of Theoretical and Industrial Electrical Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 04200 Kosice, Slovakia
Interests: measurement; sensors; particulate matter; air quality index; correlation; chaos; autonomous circuit; boundary surface

E-Mail Website
Guest Editor
Department of Theoretical and Industrial Electrical Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice, Slovakia
Interests: industrial electronic engineering; industrial application; IoT; simulation and modeling applications; automated measurement systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although the area of measuring particular matter (PM), modeling the development of PM, or the Air Quality Index (AQI), and measuring the individual components affecting AQI (PM2.5, PM10, CO, NO2, O3, SO2) is well established, it continues to lack a wider global range of measurements and modeling that expands knowledge in this field.

Therefore, we would like to invite you to contribute research articles reflecting your new measurements, proposed measurement chains, and novel findings related to PM/AQI developments, including new locations around the world, in the journal Atmosphere, and a Special Issue entitled “Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index”.

Dr. Milan Guzan
Dr. Tibor Vince
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

  • sensors
  • particulate matter
  • ultrafine particles
  • air quality index
  • correlation
  • typical particle size
  • number concentration
  • mass concentration

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 4104 KiB  
Article
Research on CC-SSBLS Model-Based Air Quality Index Prediction
by Lin Wang, Yibing Wang, Jian Chen, Shuangqing Zhang and Lanhong Zhang
Atmosphere 2024, 15(5), 613; https://doi.org/10.3390/atmos15050613 - 19 May 2024
Viewed by 279
Abstract
Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable [...] Read more.
Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results. Full article
Show Figures

Figure 1

0 pages, 7124 KiB  
Article
Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine
by Sergei Sabanov, Abdullah Rasheed Qureshi, Ruslana Korshunova and Gulim Kurmangazy
Atmosphere 2024, 15(2), 200; https://doi.org/10.3390/atmos15020200 - 5 Feb 2024
Viewed by 914
Abstract
Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate [...] Read more.
Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects. Full article
Show Figures

Figure 1

Back to TopTop