Analysis and Forecasting of PM2.5, PM4, and PM10 Dust Concentrations, Based on In Situ Tests in Hard Coal Mines

Dominik Bałaga, Marek Kalita, Piotr Dobrzaniecki, Sebastian Jendrysik, Krzysztof Kaczmarczyk, Krzysztof Kotwica and Iwona Jonczy
Additional contact information
Dominik Bałaga: Division of Machines and Equipment, KOMAG Institute of Mining Technology, Pszczyńska 37 Street, 44-101 Gliwice, Poland
Marek Kalita: Division of Machines and Equipment, KOMAG Institute of Mining Technology, Pszczyńska 37 Street, 44-101 Gliwice, Poland
Piotr Dobrzaniecki: Division of Machines and Equipment, KOMAG Institute of Mining Technology, Pszczyńska 37 Street, 44-101 Gliwice, Poland
Sebastian Jendrysik: Division of Mechatronic Systems, KOMAG Institute of Mining Technology, Pszczyńska 37 Street, 44-101 Gliwice, Poland
Krzysztof Kaczmarczyk: Division of Machines and Equipment, KOMAG Institute of Mining Technology, Pszczyńska 37 Street, 44-101 Gliwice, Poland
Krzysztof Kotwica: Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Mickiewicza 30 Av., 30-059 Kraków, Poland
Iwona Jonczy: Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, Akademicka 2 Street, 44-100 Gliwice, Poland

Energies, 2021, vol. 14, issue 17, 1-17

Abstract: The method of analyzing the results of dust concentration measurements in mine workings that was conducted within the ROCD (Reducing risks from Occupational exposure to Coal Dust) European project using the developed dust prediction algorithm is presented. The analysis was based on the measurements of average dust concentration with the use of the CIP-10R gravimetric dust meters, for the respirable PM4 dust concentration, and IPSQ analyzer for instantaneous concentration measurements (including PM2.5 dust) and with the use of Pł-2 optical dust meters for instantaneous concentration measurements of PM10 dust. Based on the analyses of the measurement results, the characteristics of the distribution of PM10, PM4, and PM2.5 dust particles were developed for the tested dust sources. Then, functional models based on power functions were developed. The determined models (functions) allow predicting the dust distribution in such conditions (and places) for which we do not have empirical data. The developed models were implemented in a specially developed online tool, which enables predicting the concentration of PM10, PM4, and PM2.5 dust (on the basis of dust concentration of one source) at any distance from the dust source.

Keywords: dust; in situ tests; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/17/5527/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/17/5527/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:17:p:5527-:d:629037

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().