A novel low-cost sensors system for real-time multipollutant indoor air quality monitoring - Development and performance

Indoor air pollution poses a significant health risk, thus demanding effective indoor air quality (IAQ) monitoring strategies. Low-cost sensors (LCS) have gained attraction for IAQ monitoring, but their data accuracy and robustness remain key challenges. This study addresses gaps in the literature by developing and evaluating a novel low-cost monitor prototype for IAQ monitoring. A detailed design methodology based on requirements analysis was used to create two identical low-cost monitors (LCM) prototypes for multipollutant monitoring, including PM2.5, CO2, CO, O-3, NO2, temperature and relative humidity. The LCM were deployed in an in-field monitoring campaign alongside research-grade instruments. The uncorrected sensor signals showed linear response compared to research-grade instruments with high Pearson Correlation Coefficients for 1-min mean: PM2.5 (0.97), CO2 (0.81-0.89), CO (0.95-0.98), and O-3 (0.80-0.85). The concentration range of pollutants was observed to play a crucial role in the response and accuracy of the LCS. Kalman filter was implemented with optimised parameters to reduce noise from the signal of ozone sensors. Univariate and multivariate linear models were used to field calibrate the sensors. Linear regression models, with high coefficients of determination (>0.8) and low error values, imply that the developed LCM prototypes can be reliably used for indicative monitoring. In most of the cases, univariate linear models using only the sensor response of LCS as explanatory variables showed significant improvement. Future work will involve longer monitoring campaigns in different microenvironments, evaluation of calibration drift and to assess failure episodes in long-term use.