關鍵字資料搜尋

"109年度空污感測器數據分析與數據品質提升計畫""監資處""""109th year project on air quali

「"109年度空污感測器數據分析與數據品質提升計畫""監資處""""109th year project on air quali」於資料集「環保專案摘要資料」由單位「行政院環境保護署」的郭先生所提供，聯繫電話是02-2311-7722#2399，最近更新時間為：2022-01-15 01:13:31。欄位專案登錄碼的內容是"1091399185" ，欄位中文標題的內容是"109年度空污感測器數據分析與數據品質提升計畫" ，欄位中文摘要的內容是"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。 " ，欄位中文關鍵字的內容是"感測器、空氣品質、偏誤" ，欄位英文標題的內容是"109th year project on air quality sensor data analysis and data quality improvement" ，欄位英文摘要的內容是"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." ，欄位英文關鍵字的內容是"sensor, air quality, bias" ，欄位施政分類的內容是"" ，欄位專案類型說明的內容是"科技研究類專案" ，欄位專案計劃編號的內容是"109A234" ，欄位專案年度的內容是109 ，欄位專案經費的內容是"1880" ，欄位合約開始日期的內容是2020-05-13 ，欄位合約結束日期的內容是2020-12-31 ，欄位主辦單位的內容是"監資處" ，欄位承辦人的內容是"陳亦絢;江家慧" ，欄位執行單位的內容是"國立台灣大學" ，欄位專案主持人的內容是"余化龍" ，欄位協同主持人的內容是"" ，欄位共同主持人的內容是"潘宗毅，李佩珍，林遠見" ，欄位專案報告下載URL的內容是https://epq.epa.gov.tw/project/filedownload.aspx?fid=237121 。

專案登錄碼

"1091399185"

中文標題

"109年度空污感測器數據分析與數據品質提升計畫"

中文摘要

"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。 "

中文關鍵字

"感測器、空氣品質、偏誤"

英文標題

"109th year project on air quality sensor data analysis and data quality improvement"

英文摘要

"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."

英文關鍵字

"sensor, air quality, bias"

施政分類

專案類型說明

"科技研究類專案"

專案計劃編號

"109A234"

專案年度

109

專案經費

"1880"

合約開始日期

2020-05-13

合約結束日期

2020-12-31

主辦單位

"監資處"

承辦人

"陳亦絢;江家慧"

執行單位

"國立台灣大學"

專案主持人

"余化龍"

協同主持人

共同主持人

"潘宗毅，李佩珍，林遠見"

專案報告下載URL

https://epq.epa.gov.tw/project/filedownload.aspx?fid=237121