Estimate the Factors Affecting Air Pollution in Iraq Using Fuzzy Regression Models

Abstract

Human existence depends on air, and the health of humans is positively correlated with air quality. Pollutants have recently entered the air, and there are several changes that have an impact on the air's purity. In order to determine the extent of air pollution in all regions of the earth, an index was built to identify air pollution based on several variables called the Air Impact Coefficient. In order to show the most influential variables on this coefficient, a regression model was built, and since the air effect coefficient is inaccurate, the fuzzy regression model was used in the parameters and the dependent variable, or the explanatory variables, so they were decisive. In our research, a fuzzy regression model was built for air pollution in Iraq, and the parameters of the model were estimated to determine the factors that have a greater impact on air pollution in Iraq, after collecting data from the Iraqi Ministry of Environment on the Air Quality Impact Coefficient (AQI), it is regarded as a fundamental criterion for determining whether air pollution is good, acceptable, or bad for sensitive people or dangerous through six factors representing the explanatory variables (CO2, O3, SO2, PM10.PM2.5, and NO2), whose data is clear. It is measured in the air using stations dispersed throughout all governorates. For a whole month, data was collected by taking three readings every day, every eight hours, for every governorate. The data was tested to confirm the multicollinearity issue and revealed that the issue exists. For this reason, the model parameters will be estimated using fuzzy ridge regression.