Full text loading...
-
Geophysical And Geostatlstical Approach To Characixrization Of Contaminated Sediments In An Urban Waterway: Potential And Limitations
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 8th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems, Apr 1995, cp-206-00080
Abstract
The detection and characterization of heavy metal contaminants in urban waterways at parts per million (ppm)<br>concentrations using standard geophysical techniques is a difficult undertaking. The essentially microscopic<br>nature of the target is outside the range of resolution available with typically employed geophysical tools.<br>Those methods which might produce useful results, such as electrical resistivity, electromagnetic, or magnetic<br>surveys are limited in their effectiveness by sources of interference generated by the urban environment. The<br>time and cost involved in sending samples to laboratories for analysis can be prohibitive, especially in view of<br>limited budgets for sediment sampling programs. In spite of these difficulties, there are geophysical<br>technologies which can contribute useful information to the problem of sediment characterization in urban<br>waterways. Methods such as side scan sonar and subbotom profiling cannot directly detect ppm concentrations<br>of metal contaminants, but they can provide useful information allowing for the most effective use of sampling<br>budgets. Modeling software can furnish indirect but useful secondary information by determining the type of<br>sediment on the river bottom from reflection data. Geotechnical properties such as density and porosity can be<br>estimated using the reflection data, and the relationship between particle size and the affinity for contaminants to<br>accumulate in sediments of a certain size may be exploited. Geostatistical analysis can be useful in determining<br>the minimum sample spacing necessary to adequately characterize the levels of pollutants in the river, using the<br>fact that samples taken too close together tend to spend sampling dollars inefficiently and those taken too far<br>apart are not representative of true conditions in the river bottom.