Predicting Air Quality from Low-Cost Sensor Measurements

Urban air pollution poses a significant global health risk, but due to the high expense of measuring air quality, the amount of available data on pollutant exposure has generally been wanting. In recent years this has motivated the development of several cheap, portable air quality monitoring instruments. However, these instruments also tend to be unreliable, and thus the raw measurements require preprocessing to make accurate predictions of actual air quality conditions, making them an apt target for machine learning techniques. In this paper we use a dataset of measurements from a low cost air-quality instrument—the ODIN-SD—to examine which techniques are most appropriate, and the limitations of such an approach. From theoretical and experimental considerations, we conclude that a robust linear regression over measurements of air quality metrics, as well as relative humidity and temperature measurements produces the model with greatest accuracy. We also discuss issues of concept drift which occur in this context, and quantify how much training data is required to strike the right balance between predictive accuracy and efficient data collection.