環保專案成果報告查詢系統

專案計畫編號	EPA-99-FA14-03-A195
經費年度	099
計畫經費	3,600 千元
專案開始日期	2010/09/16
專案結束日期	2011/09/15
專案主持人	林昭遠
主辦單位	空保處
承辦人	隋婉君
執行單位	國立中興大學
專案分類	化學物質管理
中文關鍵字	河川揚塵、遙測科技、濁水溪
英文關鍵字	river aeolian dust, remote-sensing technologies, Zhoushui River
協同主持人	黃隆明、錢滄海、林文賜、周文杰、林彥伯
共同主持人
計畫聯絡人	林淑玲
計畫聯絡電話	04-22840381#509
計畫聯絡信箱	sllin@water.nchu.edu.tw

中文摘要	本計畫目的為彙整濁水溪沿岸鄰近測站歷年空氣品質資料，分析揚塵發生與氣候狀況之關係，調查濁水溪易發生揚塵河段環境歷史資料，建立揚塵潛在區位變遷歷史資訊，分析濁水溪易發生揚塵區域河床土地覆蓋變遷，了解發生揚塵潛在區位之變遷，應用遙測科技確認潛在揚塵發生區位，建立揚塵發生機率預測模式。分析崙背空品測站歷年PM10之變化趨勢，自1994年起秋冬時期最大日平均PM10皆達155μg/m3以上，且日平均PM10大於125μg/m3之天數除2000~2002年較少外，其餘各年皆大於10日以上，顯示高汙染PM10危害並非近年才發生。進一步將測站資料予以篩選，2005-2009年間共有50天河川揚塵事件日，分佈於9月至3月間，利用氣象因子(風速、溫度及溼度)，進行揚塵量與氣象因子相關性分析，顯示日平均PM10峰值與各氣象因子之峰值間具有遲滯現象，風速約2小時、溫度與溼度約有3小時之時間差，此時間差可作為揚塵預測模式之預警時間；各時間尺度分析彙整如下表，顯示風速及溫度對河川揚塵量之貢獻較具影響性。以自強大橋與西濱大橋間之河段為樣區，利用航照圖及衛星影像進行土地利用判釋與分析，樣區內造成地表變動之主要因素為河道變遷與人為耕作等影響，低灘地退水裸露後可能成為揚塵潛在發生區位，配合水位重現期分析，河道凸岸堆積區位約1.1年會發生淹水、約1.5年會發生乾旱，此為植生復育困難之所在；高灘地由於泥砂粒徑較粗且具有耕地防風措施，對揚塵貢獻量相對較小；南岸保安林區位植生覆蓋良好，但植物防風遮蔽效應約樹高10~15倍距離，對南岸保全對象之保護效果有限。利用遙測影像處理技術分析各河川揚塵事件日15天內之衛星影像，將樣區之地覆類別區分為：植生、水域、裸地及濱水灘地等四種；探討河床裸地之變遷及其與揚塵發生之關係。裸地面積增加初期雖會造成揚塵量增加，但裸地之風蝕區位具自動癒合能力，細粒徑河床質被吹走後；殘留表層之粗粒徑砂粒將具覆蓋功能，可避免河床裸地區位繼續遭受風蝕，致河床裸地面積與日平均PM10呈二次曲線關係。衛星影像常態化差異水指標(NDWI)，除可萃取河床表層水分含量之空間變化外；因河床較高區位，退水時水位較高流速較快，所沉積下來之河床質表面粒徑較粗，容易乾燥不易保水，而河道低水位區位，因水位低流速較小，所沉積下來之河床質表面粒徑較細，其保水性較佳不易乾旱，可見NDWI也能間接反映出河床質表面粒徑之空間分布，甚至可反映河床高程之空間變化。據此，可利用NDWI將河床裸地再細分為粗粒徑、中粒徑、與細粒徑等三個類別，進一步與氣象因子結合探討其與揚塵發生之關係，建立揚塵發生機率預測模式；並劃定河川揚塵潛在發生區位。將裸地之NDWI值依K-mean群集分析，細分成粗、中、細三種不同表面粒徑分區，再依各分區面積為自變數分別與日平均PM10進行相關性分析，顯示日平均PM10與粗粒徑裸地面積呈負相關；而與細粒徑裸地面積呈正相關。因此，河床細粒徑裸地區位之空間分布即為揚塵潛在區位。揚塵發生機率預測模式係採用2005/10/13~2008/11/18共64筆氣候資料進行建模，再利用2008/11/19~2009/12/3共28筆資料進行驗證，整體精確度為71.43%。另整合2005~2009年氣象資料與土地利用資料建立揚塵量推估模式，所建置之揚塵預測模式可作為濁水溪河川揚塵預警之參考。
中文關鍵字	河川揚塵、遙測科技、濁水溪

中文摘要

本計畫目的為彙整濁水溪沿岸鄰近測站歷年空氣品質資料，分析揚塵發生與氣候狀況之關係，調查濁水溪易發生揚塵河段環境歷史資料，建立揚塵潛在區位變遷歷史資訊，分析濁水溪易發生揚塵區域河床土地覆蓋變遷，了解發生揚塵潛在區位之變遷，應用遙測科技確認潛在揚塵發生區位，建立揚塵發生機率預測模式。分析崙背空品測站歷年PM10之變化趨勢，自1994年起秋冬時期最大日平均PM10皆達155μg/m3以上，且日平均PM10大於125μg/m3之天數除2000~2002年較少外，其餘各年皆大於10日以上，顯示高汙染PM10危害並非近年才發生。進一步將測站資料予以篩選，2005-2009年間共有50天河川揚塵事件日，分佈於9月至3月間，利用氣象因子(風速、溫度及溼度)，進行揚塵量與氣象因子相關性分析，顯示日平均PM10峰值與各氣象因子之峰值間具有遲滯現象，風速約2小時、溫度與溼度約有3小時之時間差，此時間差可作為揚塵預測模式之預警時間；各時間尺度分析彙整如下表，顯示風速及溫度對河川揚塵量之貢獻較具影響性。以自強大橋與西濱大橋間之河段為樣區，利用航照圖及衛星影像進行土地利用判釋與分析，樣區內造成地表變動之主要因素為河道變遷與人為耕作等影響，低灘地退水裸露後可能成為揚塵潛在發生區位，配合水位重現期分析，河道凸岸堆積區位約1.1年會發生淹水、約1.5年會發生乾旱，此為植生復育困難之所在；高灘地由於泥砂粒徑較粗且具有耕地防風措施，對揚塵貢獻量相對較小；南岸保安林區位植生覆蓋良好，但植物防風遮蔽效應約樹高10~15倍距離，對南岸保全對象之保護效果有限。利用遙測影像處理技術分析各河川揚塵事件日15天內之衛星影像，將樣區之地覆類別區分為：植生、水域、裸地及濱水灘地等四種；探討河床裸地之變遷及其與揚塵發生之關係。裸地面積增加初期雖會造成揚塵量增加，但裸地之風蝕區位具自動癒合能力，細粒徑河床質被吹走後；殘留表層之粗粒徑砂粒將具覆蓋功能，可避免河床裸地區位繼續遭受風蝕，致河床裸地面積與日平均PM10呈二次曲線關係。衛星影像常態化差異水指標(NDWI)，除可萃取河床表層水分含量之空間變化外；因河床較高區位，退水時水位較高流速較快，所沉積下來之河床質表面粒徑較粗，容易乾燥不易保水，而河道低水位區位，因水位低流速較小，所沉積下來之河床質表面粒徑較細，其保水性較佳不易乾旱，可見NDWI也能間接反映出河床質表面粒徑之空間分布，甚至可反映河床高程之空間變化。據此，可利用NDWI將河床裸地再細分為粗粒徑、中粒徑、與細粒徑等三個類別，進一步與氣象因子結合探討其與揚塵發生之關係，建立揚塵發生機率預測模式；並劃定河川揚塵潛在發生區位。將裸地之NDWI值依K-mean群集分析，細分成粗、中、細三種不同表面粒徑分區，再依各分區面積為自變數分別與日平均PM10進行相關性分析，顯示日平均PM10與粗粒徑裸地面積呈負相關；而與細粒徑裸地面積呈正相關。因此，河床細粒徑裸地區位之空間分布即為揚塵潛在區位。揚塵發生機率預測模式係採用2005/10/13~2008/11/18共64筆氣候資料進行建模，再利用2008/11/19~2009/12/3共28筆資料進行驗證，整體精確度為71.43%。另整合2005~2009年氣象資料與土地利用資料建立揚塵量推估模式，所建置之揚塵預測模式可作為濁水溪河川揚塵預警之參考。

中文關鍵字

河川揚塵、遙測科技、濁水溪

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	期末報告.pdf	15MB		2011/12/1 上午 12:00:00

摘要	The purposes of this project are to integrate the air quality data derived from the monitoring stations nearby the bank of the Zhuoshui river, analyze the relationship between aeolian dust occurrence and the meteorologic condition, investigate the historical environment data of the river section which is vulnerable to aeolian dust occurrence, establish historical information of potential aeolian dust occurrence, analyze land cover change of the areas which are vulnerable to aeolian dust occurrence, understand the change of the aeolian dust occurrence site, recognize the sites of the potential aeolian dust occurrence, and establish the models for estimating the probability of aeolian dust occurrence. Analyzing the variation trend of the historical PM10 data derived from the Lunbei air quality station shows that from 1994 the daily maximum PM10 can reach above the value of 155μg/m3, and the daily mean PM10 greater than 125μg/m3 can occur more than 10 days except the years of 2000~2002. This means that Lunbei has been experienced the harm of PM10 for a long time. Fifty days of riverbed aeolian dust occurrence event distributing from September to March were selected after screening the data of the station during 2005-2009. The meteorologic factors (wind velocity, temperature, and relative humidity) were used to understand the relationship of aeolian dust emission. The results indicate that the lag phenomenon exists in the arriving time of the peak value, the delay time of daily mean PM10 is about 2 hours compared with wind velocity and 3 hours delay to the temperature and relative humidity measured. The delay time can be used for establishing the warning systems of the aeolian dust forecast model. The models compiled are listed as follows, and show that wind velocity and temperature play an important role on aeolian dust emission of the river bed. The river section, from bridge Ziqiang to bridge Xibin, was taken as a sample area. Air photos and satellite images are employed to interpret the landuse, the annual channel change and tillage are the main factors which affecting the variations of river bed on land utilization. The areas which belong to low water level could be the potential areas of aeolian dust occurrence after water recession. The sedimentation sites of convex bank are susceptible to flooding (return period of 1.1 years) and drought (return period of 1.5 years) and are the hardly possible in revegetation. The high water level areas with the properties of having coarser soil particles and practices of windbreak show contribution to less aeolian dust. The protection forest which distributed in the southern bank with good coverage condition depicts limit effects on the protection targets due to the short protected length. Satellite images within 15 days of the aeolian dust event were collected to analyze the land cover corresponding to each event using image processing technology. The land cover of the sample area can be primarily classified as vegetation, water, bare land, and riparian strip to explore the relationship of bare land change and aeolian dust emission. The initial phase of increasing area of bare land can result in the raise of the amount of aeolian dust emission. However, wind erosion areas of bare land have the ability of healing itself; the surface residues of coarse particles will increase and play the function of mulching to slow down the erosion rate when the fine particles are removed gradually. There is a parabolic correlation between changes of area of bare land and daily mean PM10. Normalized difference water index (NDWI) derived from satellite image can display the spatial distribution of moisture content in the surface of soil layers. In addition, NDWI can also be employed to delineate the spatial distribution of particle size and/or elevation due to higher river beds usually with the properties of more coarse sedimentation and easier to be dried compared with that of those located at the lower site. Since the bare land can be further classified into coarse particles, medium particles, and fine particles, they can then combine with meteorologic factors to understand their contribution to aeolian dust emission, establish forecast models for estimating the probability of aeolian dust occurrence, and delineate the potential areas of aeolian dust emission. Grouping the NDVI values of bare land into 3 categories (coarse particles, medium particles, and fine particles) by using K-mean cluster analysis, the subareas of bare land can be used as independent variables to study their contribution to PM10. The results indicate that the bare land with coarse particles has a negative correlation with daily mean PM10, while the bare land with fine particles shows a positive correlation with daily mean PM10. Therefore, the spatial distribution of bare land with fine particles can be the potential areas of aeolian dust emission. The probability model for forecasting the aeolian dust emission was established by using the meteorologic data of 2005/10/13 ~ 2008/11/18. The probability model with the overall accuracy 71.43% was verified by the data of 2008/11/19 ~ 2009/12/3. Compiling the meteorologic and landuse data of 2005 ~ 2010, the estimated models for the daily mean PM10 and the daily maxium PM10 were also established. The models can be further used as the reference to set up the aeolian dust emission warning system.
英文關鍵字	river aeolian dust, remote-sensing technologies, Zhoushui River

摘要

The purposes of this project are to integrate the air quality data derived from the monitoring stations nearby the bank of the Zhuoshui river, analyze the relationship between aeolian dust occurrence and the meteorologic condition, investigate the historical environment data of the river section which is vulnerable to aeolian dust occurrence, establish historical information of potential aeolian dust occurrence, analyze land cover change of the areas which are vulnerable to aeolian dust occurrence, understand the change of the aeolian dust occurrence site, recognize the sites of the potential aeolian dust occurrence, and establish the models for estimating the probability of aeolian dust occurrence. Analyzing the variation trend of the historical PM10 data derived from the Lunbei air quality station shows that from 1994 the daily maximum PM10 can reach above the value of 155μg/m3, and the daily mean PM10 greater than 125μg/m3 can occur more than 10 days except the years of 2000~2002. This means that Lunbei has been experienced the harm of PM10 for a long time. Fifty days of riverbed aeolian dust occurrence event distributing from September to March were selected after screening the data of the station during 2005-2009. The meteorologic factors (wind velocity, temperature, and relative humidity) were used to understand the relationship of aeolian dust emission. The results indicate that the lag phenomenon exists in the arriving time of the peak value, the delay time of daily mean PM10 is about 2 hours compared with wind velocity and 3 hours delay to the temperature and relative humidity measured. The delay time can be used for establishing the warning systems of the aeolian dust forecast model. The models compiled are listed as follows, and show that wind velocity and temperature play an important role on aeolian dust emission of the river bed. The river section, from bridge Ziqiang to bridge Xibin, was taken as a sample area. Air photos and satellite images are employed to interpret the landuse, the annual channel change and tillage are the main factors which affecting the variations of river bed on land utilization. The areas which belong to low water level could be the potential areas of aeolian dust occurrence after water recession. The sedimentation sites of convex bank are susceptible to flooding (return period of 1.1 years) and drought (return period of 1.5 years) and are the hardly possible in revegetation. The high water level areas with the properties of having coarser soil particles and practices of windbreak show contribution to less aeolian dust. The protection forest which distributed in the southern bank with good coverage condition depicts limit effects on the protection targets due to the short protected length. Satellite images within 15 days of the aeolian dust event were collected to analyze the land cover corresponding to each event using image processing technology. The land cover of the sample area can be primarily classified as vegetation, water, bare land, and riparian strip to explore the relationship of bare land change and aeolian dust emission. The initial phase of increasing area of bare land can result in the raise of the amount of aeolian dust emission. However, wind erosion areas of bare land have the ability of healing itself; the surface residues of coarse particles will increase and play the function of mulching to slow down the erosion rate when the fine particles are removed gradually. There is a parabolic correlation between changes of area of bare land and daily mean PM10. Normalized difference water index (NDWI) derived from satellite image can display the spatial distribution of moisture content in the surface of soil layers. In addition, NDWI can also be employed to delineate the spatial distribution of particle size and/or elevation due to higher river beds usually with the properties of more coarse sedimentation and easier to be dried compared with that of those located at the lower site. Since the bare land can be further classified into coarse particles, medium particles, and fine particles, they can then combine with meteorologic factors to understand their contribution to aeolian dust emission, establish forecast models for estimating the probability of aeolian dust occurrence, and delineate the potential areas of aeolian dust emission. Grouping the NDVI values of bare land into 3 categories (coarse particles, medium particles, and fine particles) by using K-mean cluster analysis, the subareas of bare land can be used as independent variables to study their contribution to PM10. The results indicate that the bare land with coarse particles has a negative correlation with daily mean PM10, while the bare land with fine particles shows a positive correlation with daily mean PM10. Therefore, the spatial distribution of bare land with fine particles can be the potential areas of aeolian dust emission. The probability model for forecasting the aeolian dust emission was established by using the meteorologic data of 2005/10/13 ~ 2008/11/18. The probability model with the overall accuracy 71.43% was verified by the data of 2008/11/19 ~ 2009/12/3. Compiling the meteorologic and landuse data of 2005 ~ 2010, the estimated models for the daily mean PM10 and the daily maxium PM10 were also established. The models can be further used as the reference to set up the aeolian dust emission warning system.

英文關鍵字

river aeolian dust, remote-sensing technologies, Zhoushui River