Modeling Sulphur Dioxide (SO2) Quality Levels of Jeddah City Using Machine Learning Approaches with Meteorological and Chemical Factors

Abstract

Modeling air quality in city centers is essential due to environmental and health-related issues. In this study, machine learning (ML) approaches were used to approximate the impact of air pollutants and metrological parameters on SO2 quality levels. The parameters, NO, NO2, O3, PM10, RH, HyC, T, and P are significant factors affecting air pollution in Jeddah city. These factors were considered as the input parameters of the ANNs, MARS, SVR, and Hybrid model to determine the effect of those factors on the SO2 quality level. Hence, ANN was employed to approximate the nonlinear relation between SO2 and input parameters. The MARS approach has successful applications in air pollution predictions as an ML tool, employed in this study. The SVR approach was used as a nonlinear modeling tool to predict the SO2 quality level. Furthermore, the MARS and SVR approaches were integrated to develop a novel hybrid modeling scheme for providing a nonlinear approximation of SO2 concentration. The main innovation of this hybrid approach applied for predicting the SO2 quality levels is to develop an efficient approach and reduce the time-consuming calibration processes. Four comparative statistical considerations, MAE, RMSE, NSE, and d, were applied to measure the accuracy and tendency. The hybrid SVR model outperforms the other models with the lowest RMSE and MAE, and the highest d and NSE in testing and training processes.

The Non-Linear Relationship between Air Pollution, Labor Insurance and Productivity: Multivariate Adaptive Regression Splines Approach

“…Zhang and Goh [41] applied MARS to predict pile drivability, and their study showed that MARS is more efficient than BPNN. In addition to engineering, MARS has also been applied in air pollution modeling [13], geotechnical engineering [42], and solar and renewable energy [14].…”

“…Furthermore, the relationship between air pollution, labor insurance, and their combined impact on productivity has received insufficient attention, despite labor insurance playing a vital role in enhancing productivity [9][10][11]. To bridge these knowledge gaps, this study employs state-of-the-art machine learning techniques renowned for their exceptional predictive capabilities and capacity to model nonlinear relationships [12][13][14], particularly among air pollution, labor insurance, other economic factors, and labor productivity.…”