EPA074109004

109

11,480 千元

數位建設-建構開放政府及智慧城鄉服務

2020/03/03

2020/12/31

蔡春進教授

監資處

蔡宜君

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

李書安教授、林冠宇教授

洪廣華

03-5712121#55548

ghhong.ev05g@nctu.edu.tw

本計畫的工作內容主要為利用BAM－1020 （現有）比對與校正PM2.5感測器，另外巡檢抽測已布建在16縣市的PM2.5感測器的性能，並分析及應用其監測數據，最後透過民眾說明會及教育訓練會議，宣導PM2.5感測器的應用範圍與影響其測值正確性之原因，希望對於感測器物聯網的推廣及正確使用有所助益。本研究發現RH及PM2.5濃度對PM2.5感測器的測值準確性影響很大，感測器測值都是隨著RH變高而增加，原廠數據也都呈現高估的現象，因此使用時需加以校正。另外本計畫也發現環境風速對感測器濃度有影響，風速增加及感測器內部的洩漏會使感測器進氣流量降低，導致測值降低。目前的PM2.5感測器流量太低，使得量測的數目濃度太低，計算求得的PM2.5質量濃度亦很低。以PMS5003為例，計算而得的PM2.5質量濃度僅為顯示濃度的1／2004。本計畫先利用BAM－1020比對Plantower PMS5003感測器的PM2.5小時平均濃度原始數據（未經布建廠商校正之數據），發現線性迴歸判定係數R2分別為0.77（基隆）、0.60（桃園）、0.77 （忠明）、0.76 （台南）及0.69 （屏東）。為了進一步提升數據品質，再經本計畫發展的非線性迴歸公式校正後，R2可提昇至0.80（基隆）、0.64（桃園）、0.81 （忠明）、0.81 （台南）及0.74（屏東），且平均正規化偏差（MNB）可大幅降低至±10％ （原為＋27.50~＋54.54％），MNE可大幅降至36％內（原為49.46~66.64％）。Sensirion SPS30及Honeywell HPMA115S0感測器在經非線性迴歸校正後，相較於未校正前PM2.5小時平均濃度的MNB降低至±10％ （原為－21.41~＋39.45％），MNE可大幅降至17％內（原為32.78~42.92％）。在PM2.5的24小時平均濃度方面，Plantower、Sensirion及Honeywell感測器的原廠數據經過非線性迴歸校正後，MNB與MNE分別在±13％與19％內，因此將來感測器可作為空品測站之補充監測之用。放置於空品測站上PMS5003感測器的PM2.5測值在24個月長時間放置下未有明顯老化及數據品質變差的現象。本研究發現目前市售感測器的風扇流量太低，PM10的測值幾乎都等於PM2.5的測值（即PM10測值被嚴重低估），因此將PMS5003感測器的流量提升至0.1 L／min，發現PM10濃度中粗微粒（＞ 2.5 μm）的質量占比可由原來的20.7％明顯上升至47.8％，經過理論計算及與測站PM10測值的比對校正後，發現此感測器可用於PM10的監測，可解決目前PM10無法以感測器測得的困境。本計畫另個重點為巡檢已布建在16縣市的PM2.5感測器。截至109年10月12日止，已完成巡檢抽測905 台感測器，佔全部已布建7,400台感測器的12.2％。由於PM2.5平均濃度低於15 μg／m3時感測性的準確性較差，以±5~±8 μg／m3差值作為查核的標準， PM2.5濃度≥15 μg／m3時，則以＜30％或＜50％的誤差百分比分別作為測站及現地的查核標準。查核結果顯示各縣市的品質滿意度（取最佳72小時）大都高於80％，僅有新竹縣低於80％。各縣市全時段（192小時）的品質滿意度較最佳72小時的品質滿意度差，其中Plantower與Sensirion的表現最為穩定；Honeywell感測器在環境PM2.5濃度大於30 μg／m3時容易超過查核標準；Amphenol感測器在環境PM2.5濃度10~30 μg／m3時容易超過查核標準；經昌電子感測器在環境PM2.5濃度大於20 μg／m3時容易低於查核標準，但因設置地點環境PM2.5濃度大部分都低於15 μg／m3，標準較寬鬆使得品質滿意度均高於90％，但PM2.5濃度較高時的品質表現尚待確認。截至109年11月1日止，已完成5場次宣導說明會，讓參與民眾、廠商、環保機關同仁及有興趣投入微型感測器的業者，充分瞭解PM2.5感測器布建情形及數據使用限制，希望有助於提昇感測器的性能表現，以增加國人對PM2.5感測器測值的信心。

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

20201221-期末定稿.pdf

12MB

2021/1/10 上午 12:00:00

This project aims at utilizing BAM－1020 PM2.5 values to calibrate the PM2.5 sensors. The calibration results were then applied to analyze the measured PM2.5 data of the randomly selected sensors deployed at sixteen counties or cities to evaluate their performance. Finally, the main goal of this project is to promote the application of the smart IoT sensor network through public education and training workshops to disseminate its applicable range and discuss the factors influencing the sensor reading.The results from the project indicated that the relative humidity （RH） and PM2.5 concentration have a significant effect on the PM2.5 sensor readings which increase with increasing RH. Besides, original manufacture’s data always overestimate PM2.5 concentrations. Therefore, calibration of the sensors data is needed before use. An increase in wind speed and internal leakage will decrease the measured values because of the decrease in sampling flowrate. Currently, the flowrate of PM2.5 sensor is too low so that the detected number concentrations are also very low which lead to very small calculated PM2.5 mass concentrations. Taking PMS5003 as an example, calculated PM2.5 mass concentration is only 1／2004 times indicated values.Hourly average PM2.5 sensor data of Plantower PMS5003 （manufacturer calibration） were first calibrated by those of BAM－1020 using linear regression and the obtained R－squared （R＾2） values were 0.77, 0.60, 0.77, 0.76 and 0.69 for Keelung, Taoyuan, Zhongming, Tainan and Pingtung, respectively. To improve the data quality further, non－linear regression based on PM2.5 concentrations and RH values were then used to calibrate the sensor data. R2 values for Keelung, Taoyuan, Zhongming, Tainan, and Pingtung were increased to 0.80, 0.64, 0.81, 0.81 and 0.74, respectively, while MNB values were decreased substantially to ±10％ from ＋27.50~＋54.54％ and MNE values were also decreased substantially to 36％ from 49.46~66.64％. The non－linear regression equations were also used to calibrate the Sensirion SPS30 and Honeywell HPMA115S0 data. After calibration, the MNB and MNE of hourly average PM2.5 concentrations were reduced to ＜±10％ and ＜17％ from－21.41~＋39.45％ and 32－78~42.92％, respectively. On the aspect of 24－hour average PM2.5 concentrations, MNB and MNE were reduced to ＜±13％ and ＜19％, respectively, for Plantower PMS5003 Sensirion SPS30 and Honeywell HPMA115S0 sensors after non－linear regression. Therefore, well calibrated sensors can be used for supplementary monitoring of air quality stations. It was found that the PM2.5 data of PMS5003 had no obvious aging and data quality degradation after 24－month deployment. It was also found that the Plantower PMS5003 flowrate is too low and PM10 data are almost same as PM2.5 data （PM10 data are severely underestimated）. In order to resolve this problem, sensor flowrate was increased to 0.1 L／min to sample more coarse particles （PM10－2.5）. As a result, PM10－2.5／PM10 ratio was increased to 47.8％ as compared to 20.7％ of the original sensor data. PM10 data were found to be more accurate and can be used for PM10 monitoring after theoretical calculation and calibration with the PM10 data of air quality station in Hsinchu city. This project audited the performance of PM2.5 sensors deployed at 16 counties or cities. Until now, the auditing of 905 sensors has been finished accounting for 12.2％ of 7,400 deployed sensors. Sensors are less accurate at low PM2.5 concentrations of ＜15 μg／m3 leading to low quality index. Auditing criteria is based on concentration difference of ±5 to ±8 μg／m3 when PM2.5 concentration less than 15 g／m3； or less than 30％ or 50％ error when PM2.5 concentrations ≥15 μg／m3 at station auditing and field auditing, respectively. The best 72－hour quality index is over 80％ in most counties and cities except Hsinchu county. The quality index based on the whole auditing period （168~192 hours） is worse than that of best 72－hour period. Among these sensors, the Plantower and Sensirion showed the best stable performance while the quality criteria was exceeded by Honeywell and Amphenol sensors when PM2.5 concentrations were ＞30 g／m3, ＞ 20 g／m3 , respectively. ITRI sensors were below the quality criteria in the range of 10－30 g／m3. However, the effect of PM2.5 concentration ranges needs to be studied further since measured concentrations are less than 15 g／m3 during the auditing period for ITRI sensors.There were over 250 people attending five public education and expert training workshops, which have improved the understanding of the deployment status of PM2.5 sensors and the limitation of the utilization of sensor data. It is hoped these workshops can enhance the performance of PM2.5 sensors and increase the confidence of the domestic people on sensor data.

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