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Sediment Sampling in Different Aquatic Environments: Statistical Aspects
Author(s) -
Håkanson Lars
Publication year - 1984
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr020i001p00041
Subject(s) - sediment , erosion , sampling (signal processing) , fraction (chemistry) , hydrology (agriculture) , loss on ignition , environmental science , alluvium , soil science , geology , environmental chemistry , geotechnical engineering , geomorphology , chemistry , organic chemistry , filter (signal processing) , computer science , computer vision
The aim of this paper is to discuss statistical aspects on sediment sampling and sample representativity. The study is based on empirical data from three different sedimentological environments: a river, a river mouth area, and a lake. The sediments have been analyzed for physical sediment character (water content and loss on ignition) and chemical/contaminational status (Pb, Cu, and Cd). The prevalent bottom dynamics influence the character of the sediments and the representativity and information value of sediment samples. An informative fraction is defined by the portion of a sediment sample that passes a 63‐μm mesh by wet sieving. This fraction corresponds approximately to deposits from areas of accumulation. Direct analysis of surficial sediment samples from areas of erosion or transportation, such as in rivers and river mouths, yield low information, i.e., many samples would be required to obtain a given statistical certainty. Correction with the water content (or similar parameters, e.g., grain size and bulk density) or organic content would improve the information but still not yield optimal results. Simple wet sieving through a 63‐μm mesh seems to yield best information, i.e., the lowest number of necessary analysis for the least amount of work. Fractionated centrifugation (or similar approach, e.g., ultrafiltration) would not improve the information value.