| 英文摘要 |
This project aims at utilizing BAM-1020 PM2.5 values 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 seventeen 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 2019 to 2022, 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. This project audits the performance of PM2.5 sensors deployed in 17 counties or cities. Until now, 394 sensors have been audited (48.8%). The quality index of the audited counties and cities ranged from 53~99%, wherein the quality index of Keelung, New Taipei and Hsinchu County are lower than 90%, which are 68%, 53% and 74% respectively. It is found that most of the manufacturers calibrated the sensor data by using the data of BAM-1020 deployed at the nearest air quality monitoring station, leading to the underestimation of sensors’ monitoring data.
In this study, the Sensirion SGP30 VOC sensor (SMVS)’s output data (manufacturer calibration) was also calibrated using the NMHC data from the air quality station of EPA. The R2 was 0.52 for ZM station, 0.52 for HC station and 0.37 for TN station after LR calibration. After MLR calibration, the R2 of ZM, HC and TN station was increased to 0.78, 0.70 and 0.67 respectively. While MNB and MNE values of ZM station were decreased substantially to +10.70±44.94% from +406.32±280.12% and 32.70± 32.63% from 406.32±280.12%, respectively. Values of MNB and MNE of HC station were decreased substantially to -32.51±65.50% from 245.44±155.19% and 52.68±50.71% from 247.28±152.24%. Values of MNB and MNE of TN station were decreased substantially to -25.36±58.71% from 185.04±169.59% and 42.14±48.10% from 187.37±167.01%. The limit of detection (LOD) of the VOC sensors is calculated to be 138.19 ppbv based on the measured values of the long-term VOC sensors deployed on ZM and TN station. The results of MNB and MNE in different VOC concentration intervals shows that, the MNB and MNE values of ZM station were decreased to -1.80±29.68% from 261.33±150.55% and 22.92±18.95% from 261.33±150.55% respectively. in the interval which VOC concentration is larger than 100 ppbv. For HC station, the MNB and MNE values were decreased to -19.95±27.02% from 150.39±97.68% and 27.72±18.98% from 151.03±96.68% respectively. For TN station, the MNB and MNE values were decreased to -15.63±29.57% from 127.34±119.03% and 27.69±18.77% from 128.80±117.45% respectively. The result has met the standard of hot spot tracking application recommended by the US EPA (MNE<30%).In this study, the BTEX species monitored at the PAMS were used as a reference for field comparison with VOC sensor data. The field comparison was calibrated with data from the Zhongmin PAMS over a long period (>1 year). The conversion formulas were developed in this study to convert the data of SMVS to the concentration of BTEX at Zhongmin station. The results show that the MNB (MNE) of converted SMVS data to benzene is ＋27.63% (27.63%), the MNB (MNE) of converted data to ethylbenzene is +19.48% (46.13%), the MNB (MNE) of converted data to toluene is +35.95% (63.81%), and the MNB (MNE) of converted data to xylene is +18.35% (49.90%). It can be found that the hourly benzene data of the converted SMVS can meet the standard of hot spot tracking application and evaluation of personal exposure (MNE<30%), while the hourly ethylbenzene and xylene data of the coverted SMVS can meet the standard of education and information (MNE<50%). In addition, the 24-hour average of the converted SMVS data of BTEX is studied to identify the traffic or industrial pollution sources by leveraging X/E and X/B ratios.
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