A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment
Abstract—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-make...
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my.uthm.eprints.87762023-06-12T07:06:24Z http://eprints.uthm.edu.my/8776/ A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment Hamid Hassan, Mustafa Mostafa, Salama A. Baharum, Zirawani Mustapha, Aida Saringat, Mohd Zainuri Afyenn, Rita TD Environmental technology. Sanitary engineering Abstract—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 : 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/8776/1/J15751_471cd52599a047cc97a48c215f50359a.pdf Hamid Hassan, Mustafa and Mostafa, Salama A. and Baharum, Zirawani and Mustapha, Aida and Saringat, Mohd Zainuri and Afyenn, Rita (2022) A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment. INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, 6 (4). pp. 876-882. |
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TD Environmental technology. Sanitary engineering Hamid Hassan, Mustafa Mostafa, Salama A. Baharum, Zirawani Mustapha, Aida Saringat, Mohd Zainuri Afyenn, Rita A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment |
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Abstract—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 Mostafa, Salama A. Baharum, Zirawani Mustapha, Aida Saringat, Mohd Zainuri Afyenn, Rita |
author_facet |
Hamid Hassan, Mustafa Mostafa, Salama A. Baharum, Zirawani Mustapha, Aida Saringat, Mohd Zainuri Afyenn, 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 |
2022 |
url |
http://eprints.uthm.edu.my/8776/1/J15751_471cd52599a047cc97a48c215f50359a.pdf http://eprints.uthm.edu.my/8776/ |
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1769845092977213440 |
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13.160551 |