modeling of pm10 particulate matter in ahvaz city using remote sensing and meteorological parameters

Introduction: in recent years, remote sensing (rs) products have emerged as effective tools for monitoring air pollution. this study aims to predict the concentrations of particulate matter with a diameter smaller than 10μm (pm10) using a multivariate linear regression (mlr) model, incorporating both aerosol optical depth (aod) products and meteorological parameters.material and methods: in this study, data on pm10 concentrations, aerosol optical depth (aod), and meteorological parameters (wind speed, temperature, humidity, and horizontal visibility) were used. the study focused on the time 15:00 each day, as this time was identified as having significant data relevance. the methodology section also consisted of three steps: 1) pairwise correlation analysis: the relationship between meteorological parameters, aod, and pm10 was assessed using the pairwise correlation method. 2) model development: a mlr model was developed to predict pm10 concentrations. 3) validation: the model was validated using a separate dataset, ensuring that 70% of the data was used for training, and 30% for testing and validation.results: the pairwise correlation analysis revealed a strong correlation (0.86) between aod remote sensing index and pm10. the highest correlation (0.9) was observed during the spring season. the five developed equations to estimate the pm10 index yielded correlation coefficients ranging from 0.86 to 0.90. notably, the highest correlation was achieved when aod data and all the meteorological parameters were utilized simultaneously. these results highlighted the utility of remote sensing products and meteorological data in air quality monitoring and prediction.conclusion: this study demonstrates that a mlr model incorporating aod and meteorological parameters can effectively predict pm10 concentrations in ahvaz city, particularly during dust storms in hot seasons. these findings can aid policymakers and public health officials in developing strategies to mitigate the adverse effects of dust storms on air quality and public health.