110A295

110

4,770 千元

數位建設-建構開放政府及智慧城鄉服務

2022/02/23

2022/12/31

蔡春進 講座教授

監資處

鐘偉瑜

台灣PM2.5監測與控制產業發展協會

環境監測及檢測

PM2.5感測器、低成本空氣感測器、空氣品質監測、環境物聯網、多變數迴歸

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

洪廣華 博士

洪廣華

03-5712121#55524

gh.houng@gmail.com

本計畫的工作內容主要為利用BAM-1020比對與校正PM2.5感測器，另外巡檢抽查已布建在17縣市的PM2.5感測器性能，並分析與應用監測的數據，希望提升我國目前已布建的PM2.5及VOC感測器數據品質，對於感測器物聯網的推動及使用有所助益。 本計畫統計108至111年間，共計7款PM2.5感測器通過型式驗證並設置在實場監測，以各廠牌為基準不分縣市在同一年度的平均數據品質滿意度，結果顯示Plantower的平均品質滿意度介於83.5~95.1%，數據品質滿意度呈現逐年遞減趨勢，使用3年後的品質滿意度已低於85%。Honeywell的平均品質滿意度介於87.0~92.2%，相較109-110年度的品質滿意度，在111年度略降5%左右。Sensirion的平均品質滿意度介於78.2~95.9%，該品牌在111年度品質滿意度明顯降低，主因為廠商以鄰近空氣品質測站的BAM-1020為參考標準作校正，導致現場PM2.5感測器測值明顯偏低，而數據品質變差。另4款PM2.5感測器包含Sharp、Amphenol、經昌1代及經昌2代，設置時間都少於2年，較難評估長期使用的品質滿意度，其中Sharp在108-109年度兩次的查核，平均品質滿意度僅74.9~76.0間，性能表現較差。本計畫巡檢已布建在17縣市的PM2.5感測器，已完成巡檢抽測394台感測器(佔48.8%)，發現各縣市的數據品質滿意度介於53~99%，其中宜蘭縣、基隆市、新北市及新竹縣平均滿意度低於90%，分別為85、68、53及74%。本研究發現感測器廠商多以鄰近空氣品質測站的BAM-1020作參考標準，導致現場感測器測值偏低。 本研究利用空品測站的NMHC比對Sensirion SGP30 VOC感測器 (SMVS) 的原始數據(未經布建廠商校正之數據)，發現LR判定係數R2為0.52 (忠明)、0.52 (新竹) 與0.37 (臺南)，經過MLR校正後，R2可大幅提昇至0.78 (忠明) 、0.70 (新竹) 與0.67 (臺南)。忠明測站MNB由406.32±280.12% 降至10.70±44.94%，MNE由406.32±280.12% 降至10.70±44.94%，新竹測站MNB由245.44±155.19% 降至 -32.51±65.50%，MNE由247.28±152.24% 降至 52.68±50.71%，臺南測站MNB由185.04±169.59% 降至 -25.36±58.71%，MNE由187.37±167.01% 降至 42.14±48.10%。以忠明及台南等測站上長期佈掛的VOC感測器測值求得VOC感測器的LOD為138.19 ppb。MLR校正後在不同的濃度區間(>100ppbv)結果顯示，忠明測站MNB由261.33±150.55% 降至 -1.80±29.68%，MNE由261.33±150.55% 降至 22.92±18.95%、新竹測站MNB由150.39±97.68% 降至 -19.95±27.02%，MNE由151.03±96.68% 降至 27.72±18.98%、臺南測站MNB由127.34±119.03% 降至 -15.63±29.57%，MNE由128.80±117.45% 降至 27.69±18.77%，已達美國環保署規範建議的熱區追蹤應用等級(MNE<30%)。本研究利用環保署光化測站監測的BTEX物種作為參考標準，與VOC感測器的數據進行實場比對，並以忠明光化測站長時間(>1年)的數據實場比對校正。本研究開發轉換公式將忠明測站的SMVS測值轉換為BETX濃度，結果顯示苯轉換後的MNB (MNE)為＋27.63% (27.63%) ，乙苯轉換後的MNB (MNE)為+19.48% (46.13%)，甲苯轉換後的MNB (MNE)為+35.95% (63.81%)。二甲苯乙苯轉換後的MNB (MNE)為+18.35% (49.90%)。發現轉換後的小時苯測值可符合等級II 和 IV的熱區追蹤和個人暴露評估(MNE<30%)，另乙苯和二甲苯則可達等級Ⅰ的教育與資訊使用等級(MNE<50%)，其中SMVS的甲苯測值無法符合任一性能應用目標的建議。

PM2.5感測器、低成本空氣感測器、空氣品質監測、環境物聯網、多變數迴歸

2022-air sensor final report 定稿-v3.pdf

9MB

2023/1/13 上午 12:00:00

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.

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