Virtual sensor-based proxy for black carbon estimation in IoT platforms

Black carbon (BC) has been under the spotlight of research during the last few years due to its non-regulation, its role in air pollution, and its hazardous effects. Given the high cost of the instrumentation needed to measure BC concentrations, data-driven techniques have been adopted to implement proxies that provide BC measurements from other sensor measurements. These sensors may present data quality issues due to maintenance actions, loss of data, or relocation, among others. In this paper, we propose a data-driven proxy model for BC estimation that is powered by a hybrid sensor array, including physical and virtual sensors created from machine learning techniques and governmental air quality monitoring networks. Therefore, the proposed method provides an accurate alternative to traditional data-driven BC proxies in scenarios where some physical sensors are unavailable. The results show how a BC proxy can be partially implemented using virtual sensors, obtaining only an increase in the estimation error of around 4%, allowing the estimation of BC levels even when some physical sensors are absent.