Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments
- Colorado School of Mines, Golden, CO (United States). Hydrologic Sciences and Engineering Program
- Colorado School of Mines, Golden, CO (United States). Dept. of Civil and Environmental Engineering
- Colorado School of Mines, Golden, CO (United States). Dept. of Applied Mathematics and Statistics
- Desert Research Inst. (DRI), Reno, NV (United States). Division of Hydrologic Sciences
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
A better understanding of how microbial communities interact with their surroundings in physically and chemically heterogeneous subsurface environments will lead to improved quantification of biogeochemical reactions and associated nutrient cycling. This paper develops a methodology to predict potential elevated rates of biogeochemical activity (microbial “hotspots”) in subsurface environments by correlating microbial DNA and aspects of the community structure with the spatial distribution of geochemical indicators in subsurface sediments. Multiple linear regression models of simulated precipitation leachate, HCl and hydroxylamine extractable iron and manganese, total organic carbon (TOC), and microbial community structure were used to identify sample characteristics indicative of biogeochemical hotspots within fluvially-derived aquifer sediments and overlying soils. The method has been applied to (a) alluvial materials collected at a former uranium mill site near Rifle, Colorado and (b) relatively undisturbed floodplain deposits (soils and sediments) collected along the East River near Crested Butte, Colorado. At Rifle, 16 alluvial samples were taken from 8 sediment cores, and at the East River, 46 soil/sediment samples were collected across and perpendicular to 3 active meanders and an oxbow meander. Regression models using TOC and TOC combined with extractable iron and manganese results were determined to be the best fitting statistical models of microbial DNA (via 16S rRNA gene analysis). Finally, fitting these models to observations in both contaminated and natural floodplain deposits, and their associated alluvial aquifers, demonstrates the broad applicability of the geochemical indicator based approach.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC02-05CH11231; 737 AC02-05CH11231
- OSTI ID:
- 1471023
- Alternate ID(s):
- OSTI ID: 1324027
- Journal Information:
- Science of the Total Environment, Vol. 563-564; ISSN 0048-9697
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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