Black carbon proxy sensor model for air quality IoT monitoring networks

Most Internet of Things (IoT) air quality monitoring networks measure and report regulated pollutants such as O3, NO, NO2, SO2 or CO and PM2.5, PM10 particulates. However, there are pollutants such as black carbon that are not regulated by the authorities and are rarely measured, and if they are measured, the instrumentation is very expensive. One way to obtain measurements with cheaper equipment is to use the proxy concept, where from indirect measurements of other pollutants a virtual sensor is constructed using machine learning techniques. In this work, we design a machine learning-based proxy for black carbon based on low-cost sensor (LCS) nodes. We compare three techniques to build the proxy: support vector regression, random forest and a neural network. The LCSs have to be pre-calibrated also using machine learning techniques, linear or nonlinear. The results show, using data from a real deployment of IoT air quality sensor nodes, that the results obtained by the proxy with LCSs (R2=0.72) using support vector regression are a good approximation in terms of performance to those obtained by a proxy using high-cost reference instrumentation (R2=0.76).