| DC 欄位 |
值 |
語言 |
| DC.contributor | 土木工程學系 | zh_TW |
| DC.creator | 齊宛儒 | zh_TW |
| DC.creator | WAN-JU CHI | en_US |
| dc.date.accessioned | 2022-1-10T07:39:07Z | |
| dc.date.available | 2022-1-10T07:39:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=108322096 | |
| dc.contributor.department | 土木工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 由於對人體及環境的危害甚鉅，空氣污染為當今重要的研究議題，尤其是細懸浮微粒(PM2.5)。本研究建立了污染源及傳播特徵萃取流程，以分析台灣中部地區PM2.5複雜的環境系統之物理機制。首先以移動平均結合區域極值建立半自動選取污染事件機制；接著以交叉小波計算延遲時間空間分布(定為傳播情況)，透過主成分分析萃取出六個主要傳播特徵；最後，以各傳播特徵之主導事件探討小尺度氣象因子及大尺度天氣系統對於PM2.5傳播特徵之影響。
結果顯示PM2.5濃度日變動呈現雙峰模式—約於上午6:00~9:00及傍晚17:00~20:00左右開始累積，當污染事件發生時，PM2.5累積量約21.35μg/m3，而濃度平均升高時間(開始累積至峰值所需時間)約在8~9小時。第一至六主成分(PC1~6)對中部地區傳播情形解釋力分別為20.43%、14.36%、10.48%、10.10%、9.66%及8.15%，其中PC1為在東北風影響下，污染物由海岸往內陸傳遞的情況，其日均風速(1.6 m/s)與濃度升高百分率(82%)皆為六特徵之首；而污染狀況最嚴重PC5，處在大環境較弱的天氣條件，亦觀察到南風影響，有較高日PM2.5濃度(55.3μg/m3)及較低日風速(1.2m/s)，而其他傳播特徵主要受夾雜高壓出海及高壓迴流之天氣場影響，而台中火力發電廠(中火)在PC4~6等較為破碎的傳播特徵發生時，台中市區較高機會出現較差的日空氣品質，有較PC1~3高的PM污染。總結而言，本研究提供創新研究流程用以探討環境相關議題，並替污染排放政策提供科學依據。 | zh_TW |
| dc.description.abstract | Due to its adverse impact on the human body and environment, air pollution has been an important issue of study, particularly for fine particulate matter (PM2.5). We propose a novel sources and patterns detection technique to analyze the complex physical mechanisms of PM2.5 in central Taiwan. The procedure started with the auto-selecting events mechanism composed of moving average and local extrema calculations, followed by spreading pattern extraction, which combined the lag-time spatial distribution calculation results (spreading situation namely) from wavelet coherence with principal component analysis to yield the six main spreading patterns. Finally, representative events were analyzed to discuss the influence of meteorological conditions and weather systems on the PM2.5 spreading patterns.
The results showed that the general daily PM2.5 concentration variation displayed a bimodal pattern. Among the high PM2.5 events, the cumulative amount was ~21.35 μg/m3, and the average rise time was ~8–9 h. Principal Component 1 (PC1) shows the pattern from the coast to inland under the influence of the northeast wind with the highest daily average wind speed (1.6 m/s) and concentration increase percentage (82%); the most serious pollution situation happened in PC5, which is under the influence of weak synoptic, with the highest daily PM2.5 concentration (55.3 μg/m3) and minimum wind speed (1.2 m/s). PM2.5 events with other spreading patterns (PC2––6) were more likely under the influence of the continental high-pressure peripheral circulation and high-pressure reflux. In addition, the Taichung Power Plant had a higher chance of worsening the air quality in the Taichung urban area when PC4––6 occurred. Overall, the study provides a novel procedure to study environmental problems and a scientific basis for emission control strategies. | en_US |
| DC.subject | PM2.5 | zh_TW |
| DC.subject | 污染傳播特徵 | zh_TW |
| DC.subject | 小波訊號分析 | zh_TW |
| DC.subject | 天氣條件 | zh_TW |
| DC.subject | PM2.5 | en_US |
| DC.subject | pollutant spreading patterns | en_US |
| DC.subject | wavelet transform | en_US |
| DC.subject | meteorological conditions | en_US |
| DC.title | 以訊號分析資料探勘方法探討PM2.5污染傳播時空特徵及相應之天氣條件 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Investigation of the main PM2.5 sources and spreading patterns and corresponding meteorological conditions by the wavelet analysis approach | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |