Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023)

Modelling of Air Pollution Dispersion in the Utilization of Used Oil as a Fuel

Authors
Arif Susanto1, *, Wiliam Engelbert Yochu2, Alifah Ainun Hasari3, Rizky Mahlisa4, Edi Karyono Putro5, Anthony Andorful Manuel6
1Department of Occupational Health & Safety, Universitas Jenderal Achmad Yani, West Java, Indonesia
2Department of Health Safety Environmental, Concentrating Division, PT Freeport Indonesia, Papua, Indonesia
3Department of Health Safety Environmental, Concentrating Division, PT Freeport Indonesia, Papua, Indonesia
4Department of Health Safety Environmental, Concentrating Division, PT Freeport Indonesia, Papua, Indonesia
5Department of Health Safety Environmental, Concentrating Division, PT Freeport Indonesia, Papua, Indonesia
6Departemet of Technical Service & Engineering, Concentrating Division, PT Freeport Indonesia, Papua, Indonesia
*Corresponding author. Email: asusanto2@fmi.com
Corresponding Author
Arif Susanto
Available Online 23 December 2023.
DOI
10.2991/978-94-6463-338-2_16How to use a DOI?
Keywords
AERMOD; dispersion modelling; quicklime burning; used oil
ABSTRACT

The need for quicklime has led to increased utilization of used oil as a substitute for fuel in its combustion process. Mahaka Lime Factory produces SO2 and NOx, which are emitted through the chimney and proportional to lime burning. Therefore, it is necessary to monitor the distribution of the emission concentration in the ambient air. One of the methods used to facilitate the monitoring process to ensure it is kept below the quality standard is dispersion modeling. This is an analytical model processed with the help of AERMOD software and the Gaussian equation. The SO2 concentrations of 0.001 mg/m3 and 0.001 mg/m3, as well as NOx of 0.027 mg/m3 and 0.044 mg/m3 obtained from the calculation, were below the applicable quality standard. Furthermore, emission concentrations directly observed were more significant than the Gaussian modeling results due to the presence of other pollutants around the factory. In conclusion, the dispersion modeling using AERMOD software showed that the largest and lowest distribution of emission concentrations are in the cliff area around the emission source and on the ground surface close to the chimney.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023)
Series
Advances in Intelligent Systems Research
Publication Date
23 December 2023
ISBN
10.2991/978-94-6463-338-2_16
ISSN
1951-6851
DOI
10.2991/978-94-6463-338-2_16How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Arif Susanto
AU  - Wiliam Engelbert Yochu
AU  - Alifah Ainun Hasari
AU  - Rizky Mahlisa
AU  - Edi Karyono Putro
AU  - Anthony Andorful Manuel
PY  - 2023
DA  - 2023/12/23
TI  - Modelling of Air Pollution Dispersion in the Utilization of Used Oil as a Fuel
BT  - Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023)
PB  - Atlantis Press
SP  - 104
EP  - 110
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-338-2_16
DO  - 10.2991/978-94-6463-338-2_16
ID  - Susanto2023
ER  -