*3.2.1. Time only*

Since the transportation is the main source of pollution in Quito, and this human activity is relatively stereotypic all day long, the simplest approach is to build a predictive model of PM2.5 based on time parameters, only. In this case, the number of features is limited to three, which are Xminutes, Yminutes, and PM2.5.

The linear regression model obtained after running the algorithm is as follows:

 **PM2.5 = −2.2242 \* Xminutes + −1.7366 \* Yminutes + 13.8294**

The prediction accuracy of the model is evaluated as

$$\mathbf{r} \quad \mathbf{\qquad} \quad \mathbf{= 0.21}$$

$$\mathbf{RMSE} = \mathbf{8.76}$$

In the present model, the coefficients attributed to both features are negative. It means that the higher are the two temporal attributes, the lower are the concentrations of fine particulate matter. However, the performance of this first model is quite low (r ≈ 0.2). This is confirmed by the value of the RMSE, which is around nine out of an average level of PM2.5 = 13.8 μg/m3 for the studied period.
