# Development of an Automatic Low-Cost Air Quality Control System: A Radon Application
## 4 Conclusions  
This work's main objective was to create a fully autonomous low-cost air quality control system that would maintain the air quality at a certain level. A secondary objective was to define an air quality metric and use the system for testing several interpolation windows for the air quality prediction algorithm that uses the defined metric and compare the results to see which one would achieve the best air quality given the metric. We achieved the primary objective by creating a low-cost system based on open source technologies, consisting of monitoring devices able to collect radon concentration, temperature, humidity, barometric pressure, and TVOCs. To achieve the second objective, due to the health problems that high radon concentrations cause [10; 11; 12; 13], and the particular high radon concentration of Galicia (northwest of Spain), we used the radon concentration as the air quality metric. When the devices send data to the server, the prediction algorithm predicts the air quality using the defined metric. If the algorithm predicts a decrease in air quality, it sends a command to the control devices to turn on the airflow system they are attached to, located in the same room as the monitoring devices. We carried out several tests in a radon research room with naturally high radon concentration, using 10 min, 30 min, 1 h, and 2 h for the air quality prediction algorithm's interpolation window, and compared the results with the levels reached with the airflow system deactivated. The results show that using a two-hour interpolation window gives the best results regarding the total time it keeps the radon concentration below the limit and the mean and maximum radon concentration reached during the test. This work's objective was to test the system's fully autonomous nature and create an air quality metric to test it. In future works, we plan to create a multivariate, more complex air quality metric that would consider more of the variables the monitoring devices can sense. This more complex metric should be a weighted derived metric that uses the radon concentration, temperature, humidity, and TVOCs. The metric should give more weight to the radon concentration due to the health risk it poses. The weight the metric assigns to the other variables could be determined by testing to obtain values close to the ones considered optimal in the literature [28; 29; 30]. Although the prediction algorithm created in this work is simple, the results prove that it works appropriately with a simple air quality metric, such as the one used in this work. Supposing a more complex air quality metric does not work appropriately with the current prediction algorithm, we could use a machine learning-based predictor that used all the variables the devices are collecting. However, this kind of model also requires a more powerful device to run it, and the algorithm created in this work is intended to run on a compact edge device. One crucial challenge of using a machine learning predictor would be to create the dataset for training the machine learning model. The air quality monitoring and airflow control based on those values would be a dynamic system. The outputs of a prediction model (the commands to the airflow system) directly impact the system's input, the air quality. Creating a dataset would entail running the system and turning the airflow system on and off at as many different air quality states as possible to gather a fair distribution of data that maps the prediction model's inputs and outputs. We only tested the system with an airflow system that can be turned on or off, but we cannot regulate its speed. As a possible improvement, we could modify the prediction algorithm to output the percentage of the total speed the airflow system should work at and the heater's temperature, thus reducing the noise and power consumption further. This percentage could be based on the amount the prediction algorithm predicts the radon concentration will be above the threshold. Conceptualization, J.R.R.