In the present era, due to the increasing growth of urban population, expansion of industrial activities an‎d development of urban infrastructure, the trend of urban air quality has undergone significant changes that have negative effects on human health; therefore, a review of urban planning an‎d environmental policies is necessary an‎d inevitable. In this study, air pollutants in Isfahan, one of the industrial an‎d sensitive metropolises of Iran, were investigated, analyzed an‎d predicted using data mining techniques. The main goal of this research is to explore the relationships between variables affecting the trend of air quality changes an‎d to use data from the past days to predict todayʹs weather conditions. The data under study are related to pollutants, meteorological parameters an‎d other factors, which have been collected over three years. After the cleaning process an‎d statistical analysis between the variables, the target variable was predicted, an‎d data mining algorithms were also used to analyze an‎d build prediction models. These models allowed for the prediction of different levels of pollutants with acceptable accuracy. The results indicate that meteorological factors such as temperature, humidity, pressure, along with human factors such as closures an‎d traffic, play a useful role in predicting pollution levels, an‎d data mining-based models are able to provide reliable predictions for more accurate management of air quality crises. The use of these models can contribute significantly to the development of effective policies for pollution control an‎d public health promotion in Isfahan. This study has been attempted to be a step forward in the implementation of smart urban policymaking an‎d better management of environmental resources, an‎d suggests that achieving environmental goals by utilizing modern tools such as artificial intelligence is more practical an‎d effective in macro-decision making.

رضا حجازي طاقانكي

مهدي علينقيان , صبا صارمي نيا