Development and performance evaluation of a mixed-sensor system for fine particles and road traffic noise

In recent years, the Internet of Things and low-cost sensor technologies have been applied to establish low-cost sensor networks for monitoring single pollutants in the environment. Few studies have developed a mixed-sensor system for simultaneous measurements of particles and noise, but the influences of meteorology and other pollutants are not taken into account. This study aimed to develop a mixed-sensor system for fine particles and noise with low-cost sensor technologies for considering effects of temperature, relative humidity, and carbon dioxide (CO₂). This mixed-sensor system was evaluated in the laboratory and field by using regular direct-reading instruments, including a portable dust monitor for fine particles and a class 1 sound level meter for noise measurements. Linear regression models were used to establish the relationships between measured values in the direct-reading instruments and sensor values. The present study established a predictive model of PM_2.5 concentration with a high predictive capacity (R²=0.89) and good accuracy (bias and precision of 0.74±1.67μg/m³; accuracy of 1.82μg/m³ based on relative humidity, CO₂ levels, and PM_2.5 sensor values). A predictive model of noise levels was built with a high predictive capacity (R²=0.96) and moderate accuracy (bias and precision of 2.92 ± 2.96 dBA; accuracy of 4.16 dBA based on temperature, relative humidity, CO₂ levels, and noise sensor values). The developed predictive models with the high and moderate accuracy for a mixed-sensor system can be applied to monitor PM_2.5 and noise levels simultaneously for exposure assessment in exposure studies.