A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants. (15th August 2022) - Wojcikowski, Marek; Pankiewicz, Bogdan; Bekasiewicz, Adrian; Cao, Tuan-Vu; Lepioufle, Jean-Marie; Vallejo, Islen; Odegard, Rune; Phuong Ha, Hoai
Highlights: Air pollution is important environmental and economic problem. Mobile devices for air monitoring can be used to prevent/reduce pollution exposure. Modular mobile platform for particulate matter (PM) monitoring has been proposed. PM data from low-cost sensor is corrected using a dedicated bi-stage algorithm. The approach offers high PM measurement accuracy at a low post-processing cost. Abstract: Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature.