Prediction of NOx Emissions with A Novel ANN Model in Adana

NOx exmissions are one of the typical air pollutants that has drawn worldwide attention. NO emissions from air cause detrimental effects on the environment and human health such as lung cancer, asthma, allergic rhinitis, and mental diseases. Therefore, real-time NOx monitoring has been very popular research topics in atmospheric and environmental science. However, the spatial coverage of monitoring stations within Adana is limited and thus often insufficient for exposure. Moreover, NOx monitoring stations are also lacking to reveal the influences of meteorological and air pollutant effects. In this study, artificial neural network (ANN), which is a biological mimicked computer algorithm that simulates the functions of neurons using artificial neurons, has been used to present a quantitative determination of the NOx emission in Adana through the influences of temperature (°C), wind rate (km/h), and SO2 (µg/m³) on NOx emissions. The high R2 values in testing dataset lead to the conclusion that the artificial neural network model provides predictions. The developed model in study is a useful tool for the design and planning of air pollution control policies as well as reducing economic cost. The developed model in study is a useful tool for the design and planning of air pollution control policies as well as reducing economic cost.