Development and Performance Evaluation of a Mixed-Sensor System for Fine Particles and Road Traffic Noise
In recent years, the Internet of Things (IoT) 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. This mixed-sensor system was validated 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 measurement. 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 PM2.5 concentration with a high predictive capacity (R2=0.89) and good accuracy (bias and precision of 0.74±1.67 μg/m3; accuracy of 1.82 μg/m3 based on relative humidity, CO2 levels, and PM2.5 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 PM2.5 and noise levels simultaneously for exposure assessment in exposure studies
Year of publication: |
[2022]
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Authors: | Wu, Chia-Chi ; Tsai, Cheng-Yu ; Chuang, Hsiao-Chi ; Chang, Ta-Yuan |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
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