Review of Trace-Element Field-Blank Data Collected for the California Groundwater Ambient Monitoring and Assessment (GAMA) Program, May 2004-January 2008

Lisa D. Olsen; Miranda S. Fram; Kenneth Belitz

doi:10.3133/sir20095220

Home/Archive/USGS

USGS · sir20095220

Review of Trace-Element Field-Blank Data Collected for the California Groundwater Ambient Monitoring and Assessment (GAMA) Program, May 2004-January 2008

Lisa D. Olsen·Miranda S. Fram·Kenneth Belitz

View original source ↗Export metadata ↓

Abstract

Trace-element quality-control samples (for example, source-solution blanks, field blanks, and field replicates) were collected as part of a statewide investigation of groundwater quality in California, known as the Priority Basins Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basins Project is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB) to assess and monitor the quality of groundwater resources used for drinking-water supply and to improve public knowledge of groundwater quality in California. Trace-element field blanks were collected to evaluate potential bias in the corresponding environmental data. Bias in the environmental data could result from contamination in the field during sample collection, from the groundwater coming into contact with contaminants on equipment surfaces or from other sources, or from processing, shipping, or analyzing the samples. Bias affects the interpretation of environmental data, particularly if any constituents are present solely as a result of extrinsic contamination that would have otherwise been absent from the groundwater that was sampled. Field blanks were collected, analyzed, and reviewed to identify and quantify extrinsic contamination bias. Data derived from source-solution blanks and laboratory quality-control samples also were considered in evaluating potential contamination bias. Eighty-six field-blank samples collected from May 2004 to January 2008 were analyzed for the concentrations of 25 trace elements. Results from these field blanks were used to interpret the data for the 816 samples of untreated groundwater collected over the same period. Constituents analyzed were aluminum (Al), antimony (Sb), arsenic (As), barium (Ba), beryllium (Be), boron (B), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), lithium (Li), manganese (Mn), mercury (Hg), molybdenum (Mo), nickel (Ni), selenium (Se), silver (Ag), strontium (Sr), thallium (Tl), tungsten (W), uranium (U), vanadium (V), and zinc (Zn). The detection frequency and the 90th percentile concentration at greater than 90 percent confidence were determined from the field-blank data for each trace element, and these results were compared to each constituent's long-term method detection level (LT-MDL) to determine whether a study reporting level (SRL) was necessary to ensure that no more than 10 percent of the detections in groundwater samples could be attributed solely to contamination bias. Only two of the trace elements analyzed, Li and Se, had zero detections in the 86 field blanks. Ten other trace elements (Sb, As, Be, B, Cd, Co, Mo, Ag, Tl, and U) were detected in fewer than 5 percent of the field blanks. The field-blank results for these constituents did not necessitate establishing SRLs. Of the 13 constituents that were detected in more than 5 percent of the field blanks, six (Al, Ba, Cr, Mn, Hg, and V) had field-blank results that indicated a need for SRLs that were at or below the highest laboratory reporting levels (LRL) used during the sampling period; these SRLs were needed for concentrations between the LT-MDLs and LRLs. The other seven constituents with detection frequencies above 5 percent (Cu, Fe, Pb, Ni, Sr, W, and Zn) had field-blank results that necessitated SRLs greater than the highest LRLs used during the study period. SRLs for these seven constituents, each set at the 90th percentile of their concentrations in the field blanks, were at least an order of magnitude below the regulatory thresholds established for drinking water for health or aesthetic purposes; therefore, reporting values below the SRLs as less than or equal to (=) the measured value would not prevent the identification of values greater than the drinking-water thresholds. The SRLs and drinking-water thresholds, respectively, for these 7 trace elements are Cu (1.7 ?g/L and 1,300

Explore related subjects

Scientific Investigations Report Report

Source-reported bounding extent: 34.25° to 42.333333333333336° latitude; -125.25° to -113.41666666666667° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Lisa D. Olsen, Miranda S. Fram, Kenneth Belitz. 2010. Review of Trace-Element Field-Blank Data Collected for the California Groundwater Ambient Monitoring and Assessment (GAMA) Program, May 2004-January 2008. https://doi.org/10.3133/sir20095220

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Karst hydrologic processes in carbonate fensters of the western Great Smoky Mountains, Tennessee, 2017–18

The geology of the Great Smoky Mountains in Tennessee and North Carolina is dominated by metamorphic strata. Along the western extent of the range, however, several carbonate fensters expose the Ordovician Jonesboro Limestone along the western edge of the Great Smoky Fault. The fensters (Cades Cove, Tuckaleechee Cove, and Wear Cove) create opportunities for allogenic recharge at the contact between the surrounding, relatively insoluble, strata and the underlying soluble formations, resulting in the development of karst systems. Karst environments have a high degree of connectivity between surface streams and groundwater systems; thus, karst aquifers are inherently vulnerable to contamination from surface activities. Karst environments also provide a specific environment for sensitive biota. Protection of the unique resources of karst environments requires an understanding of the behavior of karst aquifers, knowledge of karst recharge areas, and the mapping of groundwater flow paths. A study began in 2017 to characterize the hydrologic behavior of the karst systems in Great Smoky Mountains National Park through hydrologic monitoring, dye tracing, and seepage investigations. Hydrologic monitoring equipment was installed at several karst hydrogeologic features: two in-cave sites, two major springs, Abrams Creek in Cades Cove, and a karst depression wetland. Dye tracing was conducted to determine resurgences for the cave systems, to delineate spring recharge areas, and to estimate karst groundwater velocities. Seepage investigations along Abrams Creek helped determine if streams were sinking into the Jonesboro Limestone, either at the contact with insoluble strata or as streams flowed across the carbonate fenster. Study results indicate considerable differences in groundwater behavior between the conduit-dominated flow systems in Tuckaleechee Cove compared to the lower-gradient, colluvium-covered karst systems in Cades Cove. Study results also indicate that contaminants in the area of Bull sink and Whiteoak Sink could be rapidly transported through karst groundwater towards springs in Tuckaleechee Cove. Karst resources in Cades Cove are most likely to be affected by local events or activities taking place within the cove itself or in upper Abrams Creek upstream of the Great Smoky Fault. This study provides information on the vulnerability and uniqueness of the karst systems in the western Great Smoky Mountains, which are characterized by complex geology, high-gradient subterranean streams, deep caves, and carbonate fensters.

Tennessee↗

USGS2026

Analysis of groundwater geochemistry in the Lansing, Michigan, area

Groundwater resources in the Lansing, Michigan, area are protected by a wellhead protection plan, but previous studies have indicated that groundwater in this area may be susceptible to surface contamination. The U.S. Geological Survey, in cooperation with the Lansing Board of Water & Light, analyzed geochemical and aquifer characteristics to assess the potential for contamination of groundwater resources. Recharge age categories were determined from tritium concentrations for some wells, and by using ternary diagrams and principal component analysis for other wells. These age classifications, combined with hydrogeologic and geochemical data, were analyzed by using statistical methods to identify trends that may indicate areas of increased groundwater susceptibility. Wells with modern recharge tended to have higher sodium, chloride, and chloride:bromide ratios than did wells with mixed recharge, suggesting greater surface connectivity and potential influence from anthropogenic sources such as road salt. In contrast, wells in the mixed age category exhibited more stable geochemistry and lower constituent concentrations. Classification and regression tree models identified aquifer geometry—particularly the thickness of the uppermost bedrock unit and presence of confining layers—as key predictors of recharge age and geochemical indicators. The trends identified from these analyses could help to reveal locations in the Lansing area that are among the most susceptible to contamination.

Michigan↗

USGS2026

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

California, Idaho, Nevada, Oregon, Utah↗