劉威呈 結合衛星影像及空氣污染擴散模式評估河灘裸露地對空氣品質之影響 Air Pollution Impact Evaluation for Barren Riverbed by Integrating Remote Sensing
Technology and Air Quality Modelling 摘要 臺東縣卑南溪流域為臺灣發生揚塵問題較嚴重之河川之ㄧ，由於卑南溪於豐水期 與枯水期之河川流量差異甚大，又加上臺東縣地勢崎嶇多變化，導致季風之風向 改變，常將卑南溪河灘裸露地之泥沙催吹往河川下游方向移動，使得周遭空氣品 質下降，影響下游民眾之環境品質。然而，對於此類型之大型裸露地之污染類型， 目前仍少有快速而有效的方法，可以評估此類污染逸散之問題，本研究乃利用衛 星影像之特性，具有即時性、空間性及時間性等特性，可快速了解大型污染物之 範圍，並且結合空氣污染擴散模式，進行裸露地污染源之擴散模擬。結果顯示： 模擬濃度與監測站濃度之變化趨勢有不錯的吻合度，由等濃度圖顯示污染物逸散 的方向皆往臺東市的方向擴散，亦與現況相符。在該地區河灘裸露地污染源之貢 獻比例設定為1.81%時，此項技術評估之準確率可提升至 60%~80%，顯示利用衛 星影像及擴散模式，評估大型裸露地之污染逸散問題之可行性。
Beinan River located on Taitung County is one of the rivers which cause dust-raising severely. Due to the wide variation of flow rate between dry and wet seasons as well as the rugged geography, which changes the direction of the monsoon and silt of barren riverbed therefore flows to the downtown of Taitung County on the downstream area. Accordingly, the ambient air quality is decreased and then the living environment of the residents is affected. However, few efficient measurements were made to evaluate the spreading pollution caused by the pollutant from
large-sized barren riverbed. This study try to utilize the properties of the remote sensing, immediacy, spatiality and temporality, to promptly capture the regions of the priority pollutant. Moreover, the air quality model was used to simulate the diffusion of the pollution sources. The results showed that the variation trend of pollutant density between model simulation and monitoring datas weres moderately matched. Besides, the concentration concour illustrated that the pollutant diffusion was flow to Taitung City, which was same as the actual observation. When the proportion of pollutant contribution from barren riverbed in the region surrounding Taitung County was set for 1.81, the evaluation accuracy could be increased about 60 to 80 percentage, which suggested that it is tangible and workable to investigate the diffusion of the pollution in large-sized barren riverbed by using the remote sensing technology and air quality model.
