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Title: Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments

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 ofmore » 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.« less
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [3] ;  [1] ;  [4] ;  [5]
  1. Colorado School of Mines, Golden, CO (United States). Hydrologic Sciences and Engineering Program
  2. Colorado School of Mines, Golden, CO (United States). Dept. of Civil and Environmental Engineering
  3. Colorado School of Mines, Golden, CO (United States). Dept. of Applied Mathematics and Statistics
  4. Desert Research Inst. (DRI), Reno, NV (United States). Division of Hydrologic Sciences
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
Science of the Total Environment
Additional Journal Information:
Journal Volume: 563-564; Journal ID: ISSN 0048-9697
Publisher:
Elsevier
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 59 BASIC BIOLOGICAL SCIENCES; microbial DNA; extractable metals; floodplain geochemistry
OSTI Identifier:
1471023
Alternate Identifier(s):
OSTI ID: 1324027

Kenwell, Amy, Navarre-Sitchler, Alexis, Prugue, Rodrigo, Spear, John R., Hering, Amanda S., Maxwell, Reed M., Carroll, Rosemary W. H., and Williams, Kenneth H.. Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments. United States: N. p., Web. doi:10.1016/j.scitotenv.2016.04.014.
Kenwell, Amy, Navarre-Sitchler, Alexis, Prugue, Rodrigo, Spear, John R., Hering, Amanda S., Maxwell, Reed M., Carroll, Rosemary W. H., & Williams, Kenneth H.. Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments. United States. doi:10.1016/j.scitotenv.2016.04.014.
Kenwell, Amy, Navarre-Sitchler, Alexis, Prugue, Rodrigo, Spear, John R., Hering, Amanda S., Maxwell, Reed M., Carroll, Rosemary W. H., and Williams, Kenneth H.. 2016. "Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments". United States. doi:10.1016/j.scitotenv.2016.04.014. https://www.osti.gov/servlets/purl/1471023.
@article{osti_1471023,
title = {Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments},
author = {Kenwell, Amy and Navarre-Sitchler, Alexis and Prugue, Rodrigo and Spear, John R. and Hering, Amanda S. and Maxwell, Reed M. and Carroll, Rosemary W. H. and Williams, Kenneth H.},
abstractNote = {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.},
doi = {10.1016/j.scitotenv.2016.04.014},
journal = {Science of the Total Environment},
number = ,
volume = 563-564,
place = {United States},
year = {2016},
month = {5}
}