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

International Journal on Informatics Visualization: JOIV

article

Mustafa Hamid Hassan¹  Salama A. Mostafa²  Zirawani Baharum³  Aida Mustapha²  Mohd Zainuri Saringat²  Rita Afyenni⁴ 

[1] Imam Ja;Universiti Tun Hussein Onn Malaysia;Universiti Kuala Lumpur;Politeknik Negeri Padang

关键词: Air pollution;    dynamic risk;    Monte Carlo simulation;    nested Monte Carlo Simulation.;   

DOI  :  10.30630/joiv.6.4.1228

来源: Politeknik Negeri Padang

【 摘 要 】

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.

【 授权许可】

Unknown