Regression methods in the calibration of low-cost sensors for ambient particulate matter measurements

Abstract

The article presents comparison of regression methods used to obtain calibration formulas for low-cost optical particulate matter sensors. Data for analysis were taken from 1-year collocation study of PMS7003 sensors (Plantower) with research-grade instrument TEOM 1400a. The PM2.5 fraction was considered in this study. The results of measurements showed that PMS7003 was characterized by high reproducibility between units (coefficient of variation was lower than 10%), but the raw sensor outputs significantly overestimated PM2.5 concentrations. Data analysis revealed that simple univariate models were sufficient to obtain a good fitting quality to TEOM data; however, the best results were achieved for raw PM1 outputs (R2 ≈ 0.81). The fitting quality was improved when multi-variable equations were examined (R2 ≈ 0.84). The addition of temperature and relative humidity in the models was also beneficial (R2 ≈ 0.87). Stepwise selection algorithm was used to choose the best subset of variables in the model. The results of that method were compared with “all possible regression” approach, demonstrating the convenience of stepwise regression. Data from Plantower sensor were also used for training of artificial neural network. That algorithm proved to be very effective for fitting data from one sensor (R2 ≈ 0.9), but it was susceptible to deviations in the data from the other units. In general, regression analysis proved to be useful for sensor systems for ambient particulate matter measurements.