Evaluating Particulate Matter (PM2.5 and PM10) Impact on Human Health in Oman Based on a Hybrid Artificial Neural Network and Mathematical Models

Abstract

The statistics of the World Health Organization (WHO) indicate that outdoor air pollution in 2016 is a significant cause of premature mortality, with an average of 4.2 million death cases. This mortality is due to exposure to PM2.5 particulate matter, which causes many diseases such as respiratory, cardiovascular, and cancers. The concentration of particulate matter (PM) is the most popular air pollutant that affects short term and long term health. The paper aims to study and investigate the concentration dispersion of particulates (PM 2.5 and PM10) and its impact on human health in Oman. The study suggested a hybrid neural and mathematical approaches for analyzing the effect rate of particulate matter (PM2.5 and PM10). The paper implements a comparative study to analyze the proposed neural and mathematical models, which predict the future levels of pollutants in a fast, cheap, and safe way. The Linear regression models achieve fewer results of R², MSE, RMSE (0.7604, 0.0673, 0.2595), respectively. However, the non-linear regression polynomial prediction model obtained excellent results based on the coefficient of determination (R²) value of 0.9394 and mean square error (MSE) rate of 0.0209, and root mean square error (RMSE) value of 0.1447. Moreover, the Neural SOM model obtained the highest results in predicting the experimental data that achieved an MSE value of 0.0064, correlation rate (R) value of 0.994, NMSE value of 0.01392, and MAE value of 0.0467. All the results were correctly verified based on suitable mathematical methods.