This project aims at utilizing BAM-1020 PM2.5 values to 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.
The results from the project indicated that the relative humidity (RH) and PM2.5 concentration had a significant effect on the PM2.5 sensor readings, which increased with increasing RH. Besides, the original manufacture’s data consistently overestimated PM2.5 concentrations. Therefore, calibration of the sensors data was required before being used. In addition, this project found that the outer casing of the commercial PM2.5 sensors, including Vision, Honeywell, Plantower, and Sensirion, were not sealed completely, causing the air leakage. As a result, the sensors' response time (RT) was longer than two and a half minutes (varying from 150 to 180 seconds). The RT of PMS5003 sensor is smaller than one minute when the flowrate of the sensor is fixed (0.1 mL/min). However, the RT will increase significantly to 20 minutes if the flowrate of the sensor is not fixed. This result indicates that the sampling flowrate of sensor will affect the measurement of PM concentration in the environment. The sensor can response to the change of the concentration in the environment immediately without delay. The performance of the data of the sensor also becomes better.
This project also calibrated the Plantower PMS5003 PM2.5 sensor data (raw or manufacturer-calibrated data) using the BAM-1020 data. The results showed that the obtain R-squared (R^2) values of the linear regression (LR) were 0.78, 0.77, 0.79, and 0.85 for Keelung (KL), Zhongming (ZM), Tainan (TN), and Pingtung (PT), respectively. By using the non-linear regression (NLR) equation, the R2 values for KL, ZM, TN, and PT were increased to 0.81, 0.81, 0.84 and 0.89, respectively. Meanwhile, the mean normalized bias (MNB) values were decreased substantially to ±10% from +25.01~+43.90% and the mean normalized error (MNE) values were decreased to 29% from 45.91~61.03%. For the Sensirion SPS30 PM2.5 sensor data (raw or manufacturer-calibrated data), the R2 values of the LR were 0.83 and 0.80 for Penzhen (PZ) and Shilin (SL), respectively. In comparison, the R2 values of the NLR for PZ and SL were increased to 0.89 and 0.83, respectively, while the MNB and MNE values were decreased substantially to ±5% from -33.02~-44.57% and 23% from 40.00~47.02%, respectively. For the Honeywell HPMA115S0 PM2.5 sensor data (raw or manufacturer-calibrated data), the R^2 values of the LR were 0.81 and 0.80 for Banqiao (BQ) and Qianjin (QJ) stations, respectively. On the other hand, the R2 values of the NLR for BQ and QJ were increased to 0.88 and 0.84, respectively, while MNB and MNE values were decreased substantially to ±5% from+13.64~+20.18% and 22% from 31.31~36.46%, respectively.
In this study, the Sensirion SGP30 VOC sensor 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 when LR was used for calibration. When the MLR was used for 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%).
Another focus of this project is to audit the performance of PM2.5 sensors deployed in 17 counties or cities. Until now, the auditing of 792 sensors has been finished accounting for 100% of 600 deployed sensors. The quality index varied from 78.7 to 98.4%, which was higher than 80% in most counties and cities except Hinschu city, which quality index was only 74.6% due to negligence in data management. It was found that the sensor manufacturers used the BAM-1020 data at the air quality monitoring stations as the reference values for the deployed sensors, which resulted in under-measurement when these sensors were deployed at the field. It is recommended that further auditing could be conducted at some specific places or industrial zones to establish the auditing criteria and examination regulations for field auditing in the future.
