This project aims at utilizing BAM-1020 PM2.5 dates to compare and calibrate the PM2.5 sensor data. The calibration results were then applied to analyze the measured PM2.5 data of the randomly selected sensors deployed at eighteen counties or cities to evaluate their performance. It is expected to promote the smart IoT sensor network's application by enhancing the performance of the PM2.5 and VOC sensors. During year 108 to 111, a total of 7 types of PM2.5 sensors were tested to be qualified and were set up for field monitoring. For the average quality index each year of different types of sensors, the average quality index of Plantower ranged from 83.5 to 95.1%. The data quality deteriorated year by year, and the quality index is lower than 85% after three years of usage. The average quality index of Honeywell ranged from 87.0 to 92.2%. Compared to year 109~110, the quality index of the year 111 dcreased by 5%. The average quality index of Honeywell ranged from 78.2 to 95.9%. The quality index of this sensor decreased significantly in year 111 since the manufacturer calibrated the sensors’ output data by using the data of BAM-1020 deployed at the nearest air quality monitoring station, resulting the underestimation and the poor quality of the monitoring data of on-site PM2.5 sensors. The other 4 PM2.5 sensors include Sharp, Amphenol, the first and second generation of Vision. The deploy time of the sensors are less than two years, making it difficult to evaluate their quality of long-term usage. Among them, the averge quality of Sharp ranged from 74.9 to 76.0% during year 108 ~ 109, reporting poor performance. Until now, 949 PM2.5 sensors have been field-verified. Except for Hsinchu County with a data quality rate of 85%, the data quality rates in other counties and cities are above 90%. It indicates that the calibration coefficients did not cover higher environmental PM2.5 concentrations, leading to significant errors. This project conducts comparative analysis of PM10 measurements using the Ensens SAQ-200 and SAQ-210 sensors at the Pingtung monitoring station (BAM-1020) and the fugitive dust monitoring station (E-BAM). It was found that using environmental wind speed and particle size distribution as calibration parameters can effectively improve the accuracy of sensor readings. After MLR-R calibration to the PM10 sensors at the Pingtung monitoring station and the fugitive dust monitoring station, the RMSE decreased from 10.1~13.4μg/m3 (manufacture) to 7.4~8.6μg/m3 (MLR-R), resulting in a significant improvement in data quality. After calibration, the Figaro TGS5141 electrochemical CO sensor exhibits excellent data quality, making it suitable for subsequent applications in mobile sources and industrial pollution emission monitoring. Furthermore, the Figaro FECS44-100 electrochemical ammonia gas sensor is significantly prone to overestimating measurements in various concentration ranges due to the influence of environmental temperature and humidity. The ammonia gas sensor was compared with the reference instrument, a semi-automated gas monitoring system (PPWD-IC), in the atmospheric environment. It was found that the R2 value was only 0.18, with an RMSE of 127.44 ppb, indicating poor correlation and significant overestimation of measurements. After MLR (T＆RH) calibration was conducted in this study, the R2 value improved to 0.65, and the RMSE decreased to 2.08 ppb, resulting in an improvement in data quality.

PM2.5 sensor, low cost air sensor, monitoring, Environmental internet of things, Multiple variable regression