臺灣博碩士論文加值系統

近年來人們越來越重視空氣汙染，當中懸浮微粒的討論尤其熱烈。懸浮微粒在日常生活中無所不在，海風中所夾帶的鹽粒、工廠所排放的廢氣、汽機車排放及揚起的沙塵，都是懸浮微粒生成的原因。懸浮微粒具有高環境變異性，依靠政府所設立的環境監測網是遠遠不足的，但受限於儀器價格昂貴、測站設立困難，勢必降低儀器的大小及成本，達到提高觀測密度的目的。市面上有許多懸浮微粒的感測器，他們皆具有低成本、便攜性等特性，但大多都僅在室內校驗。本次我們將測試低成本感測器在戶外觀測的可行性。
本次實驗主要利用Shinyei PPD42NS整合Arduino Uno控制板進行懸浮微粒的觀測，PPD42NS是利用光散射原理的懸浮微粒感測器，感測器安置於中央大學科二館頂樓，觀測時間為2016/03/23～2017/03/06共349天。實驗中使用環保署的中壢及平鎮測站的即時值資料進行相關性分析，並利用中央大學10米塔的氣象資料，將相關性的比較分成溫度、相對濕度、風向、雨量四個面向去進行探討。分析後發現，溫度的改變對感測器並無顯著的影響；相對濕度對感測器則有較大的影響，當處在高濕度（RH>95%）環境下感測器會出現異常輸出；降雨事件則會造成感測器故障；當風向為北風或東風時會因建築物遮蔽而出現濃度偏低的情形。

In recent years, people pay more and more attention to air pollution, which is particularly enthusiastic discussion of particles matter (PM). PM spread everywhere in our daily life. Sea salt, exhaust gas emissions from industries, vehicle emissions and raised dust, are the main reasons of the formation of PM. PM has high environmental variability. It is far less enough to rely on the government set up by the environmental monitoring network. Because of the limitation of the expensive costs of the instruments and the difficulties of the setting of the stations, reducing the size and cost of the instrument is the main purpose to improve the observation density. There are many PM sensors with low-cost portability, but most PM sensors are calibrated indoor. This time we will test the feasibility of low-cost sensors in outdoor observation.
This work mainly uses Shinyei PPD42NS use with Arduino Uno microcontroller board for PM observation. PPD42NS is PM sensor based on light scattering, the sensor installation location at the National Central University of the Science Building#2, the observation time is 2016/03/23 to 2017/03/06 for 349 days. In this work, the correlation analysis was using the real-time data of the EPA Zhongli and Pingzhen station, and the meteorological data of the NCU station. We will be divided into four for the temperature, relative humidity, wind direction, rainfall to discuss. The results show that the temperature change has no significant effect. The relative humidity has a great influence. In high relative humidity (RH> 95%), the sensor will output abnormally. Rainfall will cause the sensor to malfunction. When the east or north wind occurred, lower value appeared for the shelter of the building.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
附表目錄 vi
附圖目錄 vii
一、 前言 1
1-1 研究背景 1
1-2 文獻回顧 2
1-3 研究動機與目的 4
二、 資料彙集及研究方法 5
2-1 測站位置 5
2-1-1 環保署空氣品質監測站 5
2-1-2 中央大學測候站 6
2-1-3 中央大學懸浮微粒感測器 6
2-2 儀器介紹 6
2-2-1 VEREWA F701 6
2-2-2 PPD42NS 7
2-3 資料種類及內容 8
2-3-1 環保署空氣品質監測站懸浮微粒資料 8
2-3-2 中央大學懸浮微粒資料 8
2-3-3 中央大學測候站風向風速濕度及降水資料 9
2-4 分析方法 9
三、 結果 10
3-1 原始資料 10
3-1-1 原始資料小時平均 10
3-1-2 夜間日平均 11
3-1-3 夜間月平均 12
3-2 離散圖分析 12
3-2-1 每日離散圖 12
3-2-2 每月離散圖 12
3-2-3 全年離散圖 13
3-3 診斷分析 13
3-3-1 相對濕度過濾分析 13
3-3-2 溫度過濾分析 14
3-3-2-1 溫度與相對濕度過濾分析 14
3-3-3 降雨過濾分析 15
3-3-4 風向過濾分析 15
3-3-4-1 風向與相對濕度過濾分析 16
四、 結論與未來展望 17
4-1 結論 17
4-2 未來展望 18
五、 參考文獻 19
5-1 參考文獻 19
5-2 參考網頁 20
附表 21
附圖 24
附錄一、 PPD42NS與中壢測站日R2統計表 49
附錄二、 PPD42NS與平鎮測站日R2統計表 51
附錄三、 NCU與平鎮測站、NCU與中壢測站的每日離散圖 53

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