The great challenge of low-cost sensors in the world has been to generate reliable data to be considered in making public policy decisions regarding air quality. In this project, three low-cost Clarity sensors were evaluated, co-located in reference stations of the Bogotá monitoring network (Usaquén, Ferias and Kennedy). The measurements of four and two months of the sensors and the reference equipment for PM2.5, in time-time resolution, were compared to evaluate aspects such as response linearity, dependence on relative humidity and temperature and improvement of the sensor's accuracy (calibration), with a multiple linear regression model. Two scenarios were proposed: 1. Calibration with two-month data and application of the calibration equation to the following two months. 2. Calibration with data from four months and application of the calibration equation to the same period. For the calibration period with two-month data, the R2 values before and after calibration were very low: 0.23, 0.32 at Kennedy; 0.41, 0.44 in Fairs and 0.40, 0.44 in Usaquén. ii) For the four-month calibration period, the results showed improvements as follows: 0.33, 0.41 in Kennedy; 0.54, 0.57 in Fairs and 0.55 to 0.59 in Usaquén. These correlations are lower than those found in similar published comparisons. Inexpensive sensors can be used to make indicative assessments of air quality, but they do not produce highly accurate results. Also, to install community information networks. Since the sensors depend on a Wi-Fi connection to upload the records to the Clarity server, information loss occurs when the connection is lost due to power outages or energy-saving practices in the institutions or communities where the sensors are installed. Therefore, it is recommended to use systems that allow on-site storage of records during interruptions of the Wi-Fi connection or internet service.