In this work, we study  the short-term dynamics of the Surface  Air Temperature (SAT) using data obtained  from a  meteorological station in Bogotá from 2009 to 2019  and using  time series.  The data that we used correspond to the  monthly mean of the historical registers of SAT and three  pollutants. A descriptive analysis of  the data follows. Then, some predictions are obtained from two different approaches:  (i) a univariate analysis of SAT through a  SARIMA model, which shows a good fit; and  (ii) a multivariate analysis of SAT and  pollutants  using a SVAR model. Suitable transformations were first applied on the original dataset to work with stationary time series. Subsequently, A SARIMA model and a VAR(2) with its associated SVAR model are estimated. Furthermore, we obtain one-year forecasts for the logarithm of SAT in both models. Our forecasts simulate the natural fluctuation of SAT, presenting peaks and valleys in months when SAT is high and low, respectively. The SVAR model allows us to identify certain shocks that affect the instant relationships among variables. These relations were studied by the impulse-response function and the VAR model variance decomposition. Although the statistical methods used in this study are classical, they continue being widely used in the environmental field, presenting god fits, and the results obtained in this study are consistent with  environmental theories.