Analyzing meteorological factors for forecasting PM10 and PM2.5 levels: a comparison between MLR and MLP models

Abstract

Over the past twenty years, the Middle East has experienced a surge in air pollution and dust, resulting in a range of issues affecting both people and the environment. Monitoring particulate matter (PM₁₀ and PM_2.5) has long been essential in assessing air quality. Thus, creating precise and proficient predictive models to estimate particulate matter concentrations is imperative for effectively managing and reducing air pollution. The estimation of seasonal and intra-annual PM concentrations was conducted in this study through the use of MLR and MLP models. A diverse range of meteorological parameters, including evaporation, temperature, wind speed, visibility, precipitation, and humidity, were employed along with aerosol optical depth (AOD). During autumn, the MLR and MLP models exhibited impressive performances. For PM10, the R values were 0.7 and 0.79, whereas for PM_2.5, they were 0.7 and 0.81, respectively. The MLP’s superior correlation between the observed and estimated seasonal and intra-annual PM concentrations was noteworthy, as it consistently favored PM2.5 and highlighted the superiority of the ANN-MLP model over MLR. The predictive data underscored a correlation between PM concentration and the four seasons, emphasizing the seasonal impact on PM levels. Sensitivity analysis revealed that relative humidity (RH) was the primary factor influencing the intra-annual levels of both PM₁₀ and PM_2.5. This study offers valuable insights into comprehending the formation process, implementing effective control measures, and establishing predictive models for PM, all aimed at proficiently managing air quality.