|Titolo||Assessment of the Performance of a Low-Cost Air Quality Monitor in an Indoor Environment through Different Calibration Models|
|Tipo di pubblicazione||Articolo su Rivista peer-reviewed|
|Anno di Pubblicazione||2022|
|Autori||Suriano, Domenico, and Penza Michele|
|Parole chiave||Air Pollutants, Air quality, Apartment houses, Artificial Neural Network, atmospheric pollution, Calibration, Calibration model, Costs, Decision trees, emission, Environmental monitoring, Gas detectors, indoor air, indoor air pollution, Indoor air quality, Indoor environment, Low-cost gas sensor, Low-costs, Multivariate linear regressions, Neural networks, Nitrogen oxides, Ozone, performance, pollutant, public health, Random forests, Regression analysis, sensor, support vector machine, Support vector machines, Support vectors machine|
Air pollution significantly affects public health in many countries. In particular, indoor air quality can be equally, if not more, concerning than outdoor emissions of pollutant gases. However, monitoring the air quality in homes and apartments using chemical analyzers may be not affordable for households due to their high costs and logistical issues. Therefore, a new alternative is represented by low-cost air quality monitors (AQMs) based on low-cost gas sensors (LCSs), but scientific literature reports some limitations and issues concerning the quality of the measurements performed by these devices. It is proven that AQM performance is significantly affected by the calibration model used for calibrating LCSs in outdoor environments, but similar investigations in homes or apartments are quite rare. In this work, the assessment of an AQM based on electrochemical sensors for CO, NO2, and O3 has been performed through an experiment carried out in an apartment occupied by a family of four during their everyday life. The state-of-the-art of the LCS calibration is featured by the use of multivariate linear regression (MLR), random forest regression (RF), support vector machines (SVM), and artificial neural networks (ANN). In this study, we have conducted a comparison of these calibration models by using different sets of predictors through reference measurements to investigate possible differences in AQM performance. We have found a good agreement between measurements performed by AQM and data reported by the reference in the case of CO and NO2 calibrated using MLR (R2 = 0.918 for CO, and R2 = 0.890 for NO2), RF (R2 = 0.912 for CO, and R2 = 0.697 for NO2), and ANN (R2 = 0.924 for CO, and R2 = 0.809 for NO2). © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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