contribution of predictive statistics in the evaluation of correlations between air pollutants and traffic intensity in the city of kénitra

In recent years, the problem of air pollution has become increasingly important in the field of the environment. that is why our research focuses on the air quality of this coastal city. it seems essential to carry out a diagnosis for this city. we have rigorously chosen eight sites based on their diverse conditions. the selected and targeted parameters are the following: total suspended particulate (tsp), lead (pb), cadmium (cd), nitrogen dioxide (no2), sulfur dioxide (so2), and traffic intensity, which represent the explanatory variables and the explained variable respectively. in addition to the evaluation of the concentration of each pollutant in the study area, we analyzed the correlations between the exogenous variables and the endogenous variable. the results obtained suggest that, according to the coefficient table, the tsp and heavy metals such as pb and cd do not seem to play a decisive role in explaining the intensity of traffic because their significant values exceed 5%. on the other hand, nitrogen dioxide and sulfur dioxide showed values significantly below the significance level of 5%, i.e., 0.005 and 0.018, respectively. these factors could provide an explanation for the intensity of traffic. however, the standard error results of these two variables have changed the meaning of their correlation, indicating that only nitrogen dioxide is positively evolving in the same direction as traffic intensity. nitrogen dioxide exhibits a strong correlation with traffic intensity. no2 could therefore be considered an indicator of traffic-related urban air pollution. the advantage of this analysis and interpretation methodology lies in its ability to provide a predictive and preventive tool to identify specific measures to reduce air pollution.