| 英文摘要 |
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.
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