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A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment

The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The exi...

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Main Authors: Hamid Hassan, Mustafa, A. Mostafa, Salama, Baharum, Zirawani, Mustapha, Aida, Saringat, Mohd Zainuri, Afyenni, Rita
Format: Article
Language:English
Published: Joiv 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9313/1/J15751_471cd52599a047cc97a48c215f50359a.pdf
http://eprints.uthm.edu.my/9313/
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spelling my.uthm.eprints.93132023-07-17T07:49:28Z http://eprints.uthm.edu.my/9313/ A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment Hamid Hassan, Mustafa A. Mostafa, Salama Baharum, Zirawani Mustapha, Aida Saringat, Mohd Zainuri Afyenni, Rita T Technology (General) The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The existing air pollution risk assessment employs different qualitative and quantitative methods. This study aims to develop an AQ-risk model based on the Nested Monte Carlo Simulation (NMCS) and concentrations of several air pollutant parameters for forecasting daily AQ in the atmosphere. The main idea of NMCS lies in two main parts, which are the Outer and Inner parts. The Outer part interacts with the data sources and extracts a proper sampling from vast data. It then generates a scenario based on the data samples. On the other hand, the Inner part handles the assessment of the processed risk from each scenario and estimates future risk. The AQ-risk model is tested and evaluated using real data sources representing crucial pollution. The data is collected from an Italian city over a period of one year. The performance of the proposed model is evaluated based on statistical indices, coefficient of determination (R2), and mean square error (MSE). R2 measures the prediction ability in the testing stage for both parameters, resulting in 0.9462 and 0.9073 prediction accuracy. Meanwhile, MSE produced average results of 9.7 and 10.3, denoting that the AQ-risk model provides a considerably high prediction accuracy. Joiv 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9313/1/J15751_471cd52599a047cc97a48c215f50359a.pdf Hamid Hassan, Mustafa and A. Mostafa, Salama and Baharum, Zirawani and Mustapha, Aida and Saringat, Mohd Zainuri and Afyenni, Rita (2023) A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment. INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, 6 (4). pp. 876-882.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Hamid Hassan, Mustafa
A. Mostafa, Salama
Baharum, Zirawani
Mustapha, Aida
Saringat, Mohd Zainuri
Afyenni, Rita
A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
description The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The existing air pollution risk assessment employs different qualitative and quantitative methods. This study aims to develop an AQ-risk model based on the Nested Monte Carlo Simulation (NMCS) and concentrations of several air pollutant parameters for forecasting daily AQ in the atmosphere. The main idea of NMCS lies in two main parts, which are the Outer and Inner parts. The Outer part interacts with the data sources and extracts a proper sampling from vast data. It then generates a scenario based on the data samples. On the other hand, the Inner part handles the assessment of the processed risk from each scenario and estimates future risk. The AQ-risk model is tested and evaluated using real data sources representing crucial pollution. The data is collected from an Italian city over a period of one year. The performance of the proposed model is evaluated based on statistical indices, coefficient of determination (R2), and mean square error (MSE). R2 measures the prediction ability in the testing stage for both parameters, resulting in 0.9462 and 0.9073 prediction accuracy. Meanwhile, MSE produced average results of 9.7 and 10.3, denoting that the AQ-risk model provides a considerably high prediction accuracy.
format Article
author Hamid Hassan, Mustafa
A. Mostafa, Salama
Baharum, Zirawani
Mustapha, Aida
Saringat, Mohd Zainuri
Afyenni, Rita
author_facet Hamid Hassan, Mustafa
A. Mostafa, Salama
Baharum, Zirawani
Mustapha, Aida
Saringat, Mohd Zainuri
Afyenni, Rita
author_sort Hamid Hassan, Mustafa
title A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
title_short A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
title_full A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
title_fullStr A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
title_full_unstemmed A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
title_sort nested monte carlo simulation model for enhancing dynamic air pollution risk assessment
publisher Joiv
publishDate 2023
url http://eprints.uthm.edu.my/9313/1/J15751_471cd52599a047cc97a48c215f50359a.pdf
http://eprints.uthm.edu.my/9313/
_version_ 1772813827519283200
score 13.211869

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