Modeling air pollution levels in volcanic geological regional properties and microclimatic conditions

Air pollution was predicted in this study by using multiple linear regression and 42 different artificial neural network models in Iğdır/Turkey. Daily air quality data for the years 2016–2018 have been used in the modeling. In the prediction of the particulate matter which has 10 µm or less in diameter (PM10) concentration, sulfur dioxide, nitrogen oxides, nitrogen monoxide, ozone, nitrogen dioxide, relative humidity, air pressure, wind direction and wind speed data were used as input parameters. In the artificial neural network structures, two different learning functions, three different transfer functions and seven different neuron numbers were examined in the MATLAB software. According to results, multiple linear regression did not predict the PM10 concentration in the atmosphere. The R2 value was determined as 0.543 for the multiple linear regression. In this model, the RMSE, MAE and R2 were determined as 0.0488, 0.0248 and 0.9826, respectively. Since the R2 value in this model was quite high, it was concluded that the model is suitable for the prediction of PM10 concentration. © 2020, Islamic Azad University (IAU)