The pollution of the air constitutes an environmental risk to health, crops, animals, forests and water. There are several policies for reducing air pollution regarding industry, energy, transportation, and agriculture. Unfortunately, there is limited monitoring of the air quality in cities and rural areas for supervising the accomplishment of these policies. Reliable monitoring of air pollutants is, typically, based on expensive fixed stations, which constitutes a barrier to tackle. This research presents the design, implementation and evaluation of a small, low-cost, station for monitoring atmospheric pollution. The prototype registers ozone (O 3) and carbon monoxide (CO) using inexpensive sensors. To assure high reliability of the measurements obtained by the sensors installed in this station, it is proposed a calibration procedure based on the selection of the best performance analysis of the following machine learning techniques: multiple linear regression, artificial neural networks, and random forest. Additionally, a decision rule is implemented to select an optimal combination of sensors for the estimation models, while the sample timestamp is considered as a temporal heuristic at the input of the system, assuming similarities in the daily environmental dynamics. In order to test the station in a realistic scenario, the calibration and evaluation sets were taken in two different time frames of one and two months, respectively. The overall process was implemented with reference data coming from a certified air quality fixed station in the city of Cuenca-Ecuador. Experimental results showed that the real-time reports of ozone provided by the prototype are quite similar to the fixed station during the evaluation period, with a resulting correlation of up to r = 0.92 and r = 0.91 in the calibration and evaluation set, respectively. However, signal drift and aging in CO x sensors diminished the accuracy of carbon monoxide calibration models, resulting in lower correlation (r ≤ 0.76) with the evaluation set.