109A234

109

1,880 千元

數位建設-建構開放政府及智慧城鄉服務

2020/05/13

2020/12/31

余化龍

監資處

陳亦絢;江家慧

國立台灣大學

環境資訊系統、大氣空氣、環境監測及檢測

感測器、空氣品質、偏誤

sensor, air quality, bias

潘宗毅，李佩珍，林遠見

呂美君;陳以容

02-33663847#

meichunlu@gmail.com

本計畫針對臺灣的空污感測器感測PM2.5數據進行分析，建立篩選異常值的方法，提升數據品質，再進一步探討數據變動或偏誤的影響因子，並和標準測站PM2.5移動平均（0.5 × 前 12 小時平均 + 0.5 × 前 4 小時平均）比較，以了解感測器數據所代表的意義，為使大眾有感，後續以「即時AQI」代稱，並非單一PM2.5數據可以直接對應AQI等級。主要工作成果包括（1）建立異常值篩選程序、（2）了解影響感測器偏誤之因子、（3）了解感測器的分鐘值和標準測站用移動平均計算空氣品質之間的差異、（4）建立感測器感測數據、影響感測器的因子和標準測站監測數據之間的關聯性。 本計劃採用「民生公共物聯網」中智慧城鄉空品微型感測器之感測資料。以臺中市、高雄市為示範區，透過時空間群聚分析方法，找出並排除有明顯觀測誤差之異常值。進而篩選出與忠明、前金、橋頭測站相近之17、2與3個感測器，其感測器之間相關性分別為0.97~0.99、0.78~0.93和0.99，與忠明、前金、橋頭標準測站之間的相關性分別為0.81、0.70、0.68；透過自組特徵映射網路（Self-Organizing Map, SOM）探討其誤差成因，發現相對濕度、溫度、風速均是影響感測器偏誤的主要因子。 為釐清感測數據代表之意義，本計劃將感測器PM2.5感測數據分別以分鐘（民眾感知）、小時平均（相同尺度）與即時AQI（相同定義）和標準測站「即時AQI」比較其一致性，發現感測器之即時AQI（相同定義）表現最佳，分鐘值最差。空品較佳時，感測器易高估；空品較差時，感測器易低估。為增進對於感測器分鐘值觀測之不確定性判讀，本計劃綜整前述之分析，透過貝氏網路建構可以互動式方式，考慮不同氣象條件下，感測器PM2.5觀測來判讀標準測站「即時AQI」之不確定性，以利民眾可了解當下感測器的數據背後所代表的意義。

感測器、空氣品質、偏誤

109A234.pdf

18MB

成果報告

2021/1/1 上午 12:00:00

This project analyzes the PM2.5 data detected by air quality sensors in Taiwan, establishes a method for screening outliers, improves data quality, and further explores the effect factors of data variation or bias. To understand the meaning of the sensor data, compares them with PM2.5 Moving Average (0.5 × average of the previous 12 hours + 0.5 × average of the previous 4 hours) of the Taiwan Environmental Protection Administration (TWEPA)　standard station, subsequently name as "real-time AQI". The main work results include (1) establishing an outlier screening program, (2) understanding the factors that affect sensor bias, (3) understanding the difference between the minute value of the sensor and the moving average of the air quality at the TWEPA standard station, (4) Establish the correlation between the sensor data, the affecting-sensor factors, and the TWEPA standard station data. This project obtained air quality sensor data from Civil IoT Taiwan Data Service Platform - EPA air quality micro station. Taking Taichung City and Kaohsiung City as the demonstration areas, the outliers with obvious observation errors were identified and eliminated through temporal and spatial cluster analysis. Furthermore, 17, 2 and 3 sensors that are closed to Zhongming, Qianjin, and Qiaotou stations were screened out. The correlations between the sensors were 0.97~0.99, 0.78~0.93 and 0.99, respectively. The correlations between the sensors and Zhongming, Qianjin, and standard measuring stations are 0.81, 0.70, and 0.68 respectively; through the Self-Organizing Map (SOM)　to explore the cause of the bias, it is found that relative humidity, temperature, and wind speed are all affecting the sensor the main factor of bias. To clarify the meaning of the sensor data, this project compares the consistency between sensor PM2.5 sensor data with the scale of minute, hour average, the moving average and the standard station "real-time AQI". The sensor’s moving average performance is the best and the minute value is the worst. When the air quality is better, the sensor is easy to overestimate; when the air quality is poor, the sensor is easy to underestimate. To improve the interpretation of the uncertainty of the sensor's minute value observation, this project integrates the analysis, through the Bayesian Network construction, an interactive way, considering under different weather conditions, the data between sensor PM2.5 and TWEPA standard station. The Bayesian network Network allows the public to understand the meaning behind the current sensor data.

sensor, air quality, bias