環保專案成果報告查詢系統

專案計畫編號
經費年度	108
計畫經費	7,560 千元
預算科目	數位建設-建構開放政府及智慧城鄉服務
專案開始日期	2019/04/02
專案結束日期	2020/04/01
專案主持人	蔡春進
主辦單位	監資處
承辦人	蔡宜君
執行單位	台灣PM2.5監測與控制產業發展協會
專案分類	環境監測及檢測
中文關鍵字	PM2.5感測器、低成本空氣感測器、空氣品質監測、環境物聯網、人工神經網絡校正、多變數迴歸
英文關鍵字	PM2.5 sensor, low cost air sensor, Air quality monitoring, Environ-mental internet of things, ANN, Multiple variable regression
協同主持人	黃政雄，李書安，王琳麒，王國英
共同主持人
計畫聯絡人	洪廣華
計畫聯絡電話	03-5712121#55524
計畫聯絡信箱	ghhong.ev05g@nctu.edu.tw

中文摘要	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，另外對巡檢抽測已布建在14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、教育訓練及專家諮詢會議，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。研究發現RH對PM2.5感測器的測值影響很大，以環保署5個空品測站的BAM-1020 (現有)小時平均PM2.5濃度為基準，發現感測器測值都是隨著RH變高而增加，需加以校正。故本計畫利用BAM-1020比對PM2.5感測器的原始數據(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0.64 (桃園)、0.73 (忠明)、0.75 (台南)、0.74 (基隆)及0.68 (屏東)。經過濃度及濕度的多變數迴歸公式校正後，R2可提昇至0.79 (桃園)、0.85 (忠明)、0.85(台南)、0.84 (基隆)及0.81 (屏東)，且平均正規化偏差(MNB)可大幅降低至±10% (原為+37.8~+57.7%)，MNE可大幅降至30%內(原為46.47~69.8%)。另也發現環境風速對感測器濃度有影響，感測器的進口流量會隨風速變大而降低，使實際進入感測器的微粒顆數變少，造成感測器PM2.5濃度低估的現象。隨著感測器使用時間越長，在超過18個月後感測器有老化現象使數據品質有下降的趨勢，但此問題要再進一步研究。以上這些因素(感測器PM2.5濃度，環境相對濕度、風速，採樣流量及使用時間)均會影響測值，將來在應用感測器數據時需列入考量。本計畫巡檢已布建在14縣市的PM2.5感測器，分為暖季及冷季兩季。共已完成巡檢抽測538台感測器，佔全部以布建3,300台感測器的16.3%。台中市的布建時間已進入第二年，誤差標準為30%，其餘縣市布建僅一年誤差標準為50%。巡查發現暖季品質滿意度除台中市外皆介於77.6%~96.4%之間，只有新竹縣與高雄市低於65%。冷季巡查品質滿意度介於86.3%~100%之間，僅有苗栗縣品質滿意度低於65%。台中市在暖季時濃度低品質滿意度僅有50 %，在冷季時採用本研究的校正公式後滿意度大幅提升40%以上，顯見使用正確校正公式的重要性。本研究提出離群值分析方法用於判斷測值異常的感測器，結果顯示大園與觀音工業區各有5及8台極為可能異常的感測器，分別占總布建數目37及137台的13%及6%。另外使用ANN校正忠明測站的測值時，考慮感測器濃度、溫度及RH後，R2可由0.73上升至0.81，但仍低於本研究的校正結果，其R2可達0.85。智慧城鄉感測器推廣及專業訓練會議參與者達1,000人以上，已讓參與民眾、廠商及有興趣投入微型感測器研發者充分瞭解PM2.5感測器布建情形及數據使用限制，希望能提昇感測器的數據品質，以增加國人對PM2.5感測器的信心。
中文關鍵字	PM2.5感測器、低成本空氣感測器、空氣品質監測、環境物聯網、人工神經網絡校正、多變數迴歸

中文摘要

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，另外對巡檢抽測已布建在14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、教育訓練及專家諮詢會議，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。研究發現RH對PM2.5感測器的測值影響很大，以環保署5個空品測站的BAM-1020 (現有)小時平均PM2.5濃度為基準，發現感測器測值都是隨著RH變高而增加，需加以校正。故本計畫利用BAM-1020比對PM2.5感測器的原始數據(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0.64 (桃園)、0.73 (忠明)、0.75 (台南)、0.74 (基隆)及0.68 (屏東)。經過濃度及濕度的多變數迴歸公式校正後，R2可提昇至0.79 (桃園)、0.85 (忠明)、0.85(台南)、0.84 (基隆)及0.81 (屏東)，且平均正規化偏差(MNB)可大幅降低至±10% (原為+37.8~+57.7%)，MNE可大幅降至30%內(原為46.47~69.8%)。另也發現環境風速對感測器濃度有影響，感測器的進口流量會隨風速變大而降低，使實際進入感測器的微粒顆數變少，造成感測器PM2.5濃度低估的現象。隨著感測器使用時間越長，在超過18個月後感測器有老化現象使數據品質有下降的趨勢，但此問題要再進一步研究。以上這些因素(感測器PM2.5濃度，環境相對濕度、風速，採樣流量及使用時間)均會影響測值，將來在應用感測器數據時需列入考量。本計畫巡檢已布建在14縣市的PM2.5感測器，分為暖季及冷季兩季。共已完成巡檢抽測538台感測器，佔全部以布建3,300台感測器的16.3%。台中市的布建時間已進入第二年，誤差標準為30%，其餘縣市布建僅一年誤差標準為50%。巡查發現暖季品質滿意度除台中市外皆介於77.6%~96.4%之間，只有新竹縣與高雄市低於65%。冷季巡查品質滿意度介於86.3%~100%之間，僅有苗栗縣品質滿意度低於65%。台中市在暖季時濃度低品質滿意度僅有50 %，在冷季時採用本研究的校正公式後滿意度大幅提升40%以上，顯見使用正確校正公式的重要性。本研究提出離群值分析方法用於判斷測值異常的感測器，結果顯示大園與觀音工業區各有5及8台極為可能異常的感測器，分別占總布建數目37及137台的13%及6%。另外使用ANN校正忠明測站的測值時，考慮感測器濃度、溫度及RH後，R2可由0.73上升至0.81，但仍低於本研究的校正結果，其R2可達0.85。智慧城鄉感測器推廣及專業訓練會議參與者達1,000人以上，已讓參與民眾、廠商及有興趣投入微型感測器研發者充分瞭解PM2.5感測器布建情形及數據使用限制，希望能提昇感測器的數據品質，以增加國人對PM2.5感測器的信心。

中文關鍵字

PM2.5感測器、低成本空氣感測器、空氣品質監測、環境物聯網、人工神經網絡校正、多變數迴歸

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	EPA074108018.pdf	12MB		2020/5/10 上午 12:00:00

摘要	This project aims to compare and calibrate PM2.5 sensors using the exist-ing BAM-1020 monitors, audit the randomly selected sensors deployed at 14 counties or cities, analyze and apply the sensor data. Final goal is to pro-mote the smart IoT sensor network and the proper application of its data through public education, training workshops and expert advisory meetings to disseminate the applicable range and the factors influencing the readings of the sensors. It was found that there was a great influence of the relative humidity (RH) on PM2.5sensor readings. As RH increased, the sensor readings also in-creased, which justifies the need of calibration. Using BAM-1020 data, PM2.5 sensor data (manufacturer calibration) were calibrated by linear regression with R2 of 0.64(Taoyuan), 0.73(Zhongming), 0.75(Tainan), 0.74(Keelung) and 0.68(Pingtung), respectively. In this research, after multi-variable regression based on PM2.5 concentration and relative humidity, R2 was increased to 0.79(Taoyuan), 0.85(Zhongming) 0.85(Tainan), 0.84(Keelung) and 0.81 (Pingtung), respectively. MNB was decreased substantially to ±10% from +37.8~+57.7% and MNE was decreased to 30% from 46.47~69.8%. An increase in wind speed would decrease the measured value because of the decrease in inlet sampling flowrate and the number of sampled particles. When the sen-sors are used for more than eighteen months, it was found the data quality starts to degrade. But this aging problem needs to be investigated further. To sum up, factors such as PM2.5 concentrations, relative humidity, wind speed, sampling flow and ser-vice duration affect the measured values which should be considered when using sensor data in the future. This project audited the performance of PM2.5 sensors setup in 14 coun-ties or cities in two seasons. Until now, the inspection has been finished and accounted for 16.3% of 3,300 deployed sensors. The quality index of Tai-chung was only 50% since the MNE criteria of 30% is more rigorous as the sensors have been used for more than one year. In comparison, the MNE cri-teria of 50% is used for other counties or cities within the first service year. In the warm season sensor quality index ranged between 77.6% to 96.4%, on-ly the sensor quality of Hsinchu county and Kaohsiung city was lower than 65%. In the cold season, sensor quality ranged between 86.3% to 100%, only that of Miaoli county was lower than 65%. The sensor quality of Taichung city was only 50% in warm season. After using the calibration equation de-veloped in this research, the data quality was improved substantially by over 40% in cold season demonstrating the significance of using the correct cali-bration equation. This research proposed a data divergence statiscal method to judge the abnormal sensors and found that there are 5 and 8 abnormal sensors with very high probability in Da Yuan and Guanyin industry areas, respectively, which account for 13% and 6% in the total deployed number of sensors of 37 and 137, respectively. In addition, by using the ANN based on sensor PM2.5 concentrations, temperature and RH as the input layer, R2 was improved from 0.73 to 0.81 which is still lower than 0.85 obtained by the present the calibration equation. There were over 1000 people attending public education and expert training workshops, which improved the understanding of the deployment status of PM2.5 sensors and the limitation of the sensor data. This hopes to enhance the data quality of PM2.5 sensors, and increase the domestic people’s confidence in sensor data quality.
英文關鍵字	PM2.5 sensor, low cost air sensor, Air quality monitoring, Environ-mental internet of things, ANN, Multiple variable regression

摘要

This project aims to compare and calibrate PM2.5 sensors using the exist-ing BAM-1020 monitors, audit the randomly selected sensors deployed at 14 counties or cities, analyze and apply the sensor data. Final goal is to pro-mote the smart IoT sensor network and the proper application of its data through public education, training workshops and expert advisory meetings to disseminate the applicable range and the factors influencing the readings of the sensors. It was found that there was a great influence of the relative humidity (RH) on PM2.5sensor readings. As RH increased, the sensor readings also in-creased, which justifies the need of calibration. Using BAM-1020 data, PM2.5 sensor data (manufacturer calibration) were calibrated by linear regression with R2 of 0.64(Taoyuan), 0.73(Zhongming), 0.75(Tainan), 0.74(Keelung) and 0.68(Pingtung), respectively. In this research, after multi-variable regression based on PM2.5 concentration and relative humidity, R2 was increased to 0.79(Taoyuan), 0.85(Zhongming) 0.85(Tainan), 0.84(Keelung) and 0.81 (Pingtung), respectively. MNB was decreased substantially to ±10% from +37.8~+57.7% and MNE was decreased to 30% from 46.47~69.8%. An increase in wind speed would decrease the measured value because of the decrease in inlet sampling flowrate and the number of sampled particles. When the sen-sors are used for more than eighteen months, it was found the data quality starts to degrade. But this aging problem needs to be investigated further. To sum up, factors such as PM2.5 concentrations, relative humidity, wind speed, sampling flow and ser-vice duration affect the measured values which should be considered when using sensor data in the future. This project audited the performance of PM2.5 sensors setup in 14 coun-ties or cities in two seasons. Until now, the inspection has been finished and accounted for 16.3% of 3,300 deployed sensors. The quality index of Tai-chung was only 50% since the MNE criteria of 30% is more rigorous as the sensors have been used for more than one year. In comparison, the MNE cri-teria of 50% is used for other counties or cities within the first service year. In the warm season sensor quality index ranged between 77.6% to 96.4%, on-ly the sensor quality of Hsinchu county and Kaohsiung city was lower than 65%. In the cold season, sensor quality ranged between 86.3% to 100%, only that of Miaoli county was lower than 65%. The sensor quality of Taichung city was only 50% in warm season. After using the calibration equation de-veloped in this research, the data quality was improved substantially by over 40% in cold season demonstrating the significance of using the correct cali-bration equation. This research proposed a data divergence statiscal method to judge the abnormal sensors and found that there are 5 and 8 abnormal sensors with very high probability in Da Yuan and Guanyin industry areas, respectively, which account for 13% and 6% in the total deployed number of sensors of 37 and 137, respectively. In addition, by using the ANN based on sensor PM2.5 concentrations, temperature and RH as the input layer, R2 was improved from 0.73 to 0.81 which is still lower than 0.85 obtained by the present the calibration equation. There were over 1000 people attending public education and expert training workshops, which improved the understanding of the deployment status of PM2.5 sensors and the limitation of the sensor data. This hopes to enhance the data quality of PM2.5 sensors, and increase the domestic people’s confidence in sensor data quality.

英文關鍵字

PM2.5 sensor, low cost air sensor, Air quality monitoring, Environ-mental internet of things, ANN, Multiple variable regression