This paper reviews the application of lake sediment sampling in the recently glaciated terrain (last 100,000 years) of Canada where organic-rich lake sediments have been accumulating in lakes since deglaciation during the last 10,000 or so years. Lake sediment is a useful exploration sample medium for detecting geochemical dispersion from mineralized bedrock and/or metal-rich glacial dispersal trains. The method is used in areas of low to moderate topographic relief and poor to moderately developed drainage systems in the Canadian Shield and some parts of the Appalachian regions of Canada, as well as in low relief areas in the Canadian Cordillera that are covered by thick glacial sediments and have abundant lakes. Government organizations and exploration companies have conducted more than 100 lake sediment surveys across Canada since the 1970s and some of these are used to illustrate key concepts. The paper summarizes protocols for execution and interpretation of lake sediment surveys, serving as a practical guide for both the mineral exploration industry and government agencies. Methods for sample collection, processing, and analysis, quality assurance/quality control (QA/QC) procedures, and data reporting are reviewed. Much of the information presented here is based on the methods used by the Geological Survey of Canada, British Columbia Geological Survey, and Ontario Geological Survey to conduct lake sediment surveys in Canada.
Geochemical exploration is vital for mineral prospecting as it provides direct evidence of mineralization. Regionally, stream sediment surveys are commonly used to detect geochemical anomalies; however, traditional anomaly detection methods often fail to separate subtle anomalies due to complex geology, high-dimensional data and strong spatial heterogeneity. This study introduces a new method that uses a random forest regression model integrated with multi-source environmental factors to simulate the geochemical background and identify anomalies. The random forest regression model demonstrated high accuracy for the copper background field, achieving 78.4% explained variance on the test set. Compared with traditional methods, the new approach significantly enhances the spatial consistency between anomalies and known deposits, raising the anomaly verification effectiveness rate from 45.3 to 67.8%. Furthermore, feature importance analysis indicates that copper distribution is mainly influenced by lithological background and temperature. This study demonstrates that random forest regression effectively integrates multi-source environmental factors to accurately simulate the spatial variation of geochemical background fields. It provides a powerful solution to the challenge of distinguishing anomalies from background in the presence of lithological variations and topographic dilution.
This study investigates the geochemical distribution and behaviour of rare earth elements (REEs) in stream sediments from Southeast Spain, where REE enrichment is generally scarce except for the El Hoyazo garnet placer deposit. Using data from the Geochemical Atlas of Spain, 337 stream sediment samples were analysed to assess REE concentrations, spatial patterns, and elemental associations through geochemical mapping and multivariate statistics. Median REE concentrations are comparable to national averages but lower than those reported in other regions, as Hercynian domains. Normalised REE patterns show an overall depletion relative to NASC, with a marked enrichment of heavy REE (HREE) along the coastal sector. Factor analysis differentiates lithological, mineralisation, and supergene controls, identifying a specific HREE fractionation statistically associated with Fe-Mn. In contrast, no significant statistical relationships were observed between REE and carbonate-related elements, suggesting that carbonate complexation is unlikely to represent a primary control on REE behaviour. Partial correlation analysis indicates that Fe shows the strongest association with HREE behaviour, suggesting a key role of Fe-bearing phases in supergene HREE enrichment under predominantly neutral to alkaline conditions. Mn also shows significant, although generally weaker, associations with HREE. In the Cabo de Gata volcanic domain, HREE enrichment appears to be primarily controlled by lithological and mineralogical factors. The influence of Fe–Mn phases is most clearly expressed in Neogene sediments, where secondary processes may partially overprint primary signatures. This study highlights the combined influence of lithology and post-depositional Fe–Mn phases on REE mobility and fractionation in a semi-arid Mediterranean environment.
Reanalysis of 6636 organic-rich lake-sediment samples, for 65 elements, from the archive of the Geological Survey of Canada, collected from a large region of northern and northeastern Labrador, has improved the definition of five previously known anomalous signatures. It has also enabled the identification of two additional anomalies of unknown or partly known provenance. In most cases, the signatures are defined by multiple elements. Profiling, filtering and smoothing techniques were applied to the data. Some known mineral occurrences, including the Voisey's Bay Ni–Cu–Co deposit and rock types of distinctive composition, such as the Florence Lake greenstone belt, have characteristic dispersion patterns of varying strength. In contrast, the response to other well-documented mineralized occurrences, such as those around the U occurrences at Moran Lake, are weak to absent. Potential bedrock sources for other strong anomalies have not yet been identified. It is important to take into account that the anomalous results occur in, or are derived from, glacial sediments formed from one or more complex erosion and deposition events that the region has undergone. The new geochemical analyses have added value to archived lake-sediment samples by improved detection limits, and include elements not analysed previously, such as suites of rare earth and other critical elements such as Bi, Ge, In, Pd, Pt, Sn and Te. They should find application in future exploration programmes, as demand for these commodities continues.
The Qinghai–Tibet Plateau hosts salt lakes enriched in strategic metals, yet the behaviour of rubidium (Rb) and caesium (Cs) remains poorly understood. Based on 169 water samples from 44 lakes on the Qinghai–Tibet Plateau, this study integrates multivariate statistics with spatial hotspot analysis (Getis-Ord Gi*, 1000 m threshold). Rubidium (0.004–13.05 mg l −1 ) and Cs (0.0001–2.07 mg l −1 ) exhibit a SE-high, NW-low pattern. Principal component analysis shows that Rb loads strongly on the evaporative concentration factor (principal component (PC) 1, loading 0.853). It correlates significantly with K + (r = 0.803) and total dissolved solids (TDS) (r = 0.758), indicating conservative behaviour driven by evaporation. In contrast, Cs loads most strongly on a decoupled factor (PC3, loading 0.749) and shows weaker correlation with TDS (r = 0.471), suggesting non-conservative enrichment influenced by deep hydrothermal input and clay mineral adsorption–desorption. Hotspot analysis identifies Mang Co as a shared Rb–Cs hotspot, Gangzhang Co and Qiagang Co as Cs-specific, and Eya Co as Rb-specific. These findings provide a quantitative framework for understanding rare alkali metal enrichment in high-altitude salt lake systems.
Multi-element geochemical data derived from systematic surveys from a variety of media within a geospatial continuum contain information that provides insight into processes that reflect mineralogy, paragenesis, alteration and economic mineralization. The relationships of the elements and corresponding attributes using a range of data analytical methods, including univariate and multivariate statistical methods along with machine learning, provide the framework for building knowledge from which extended methods of artificial intelligence can enhance model building and mineral systems discovery. This contribution provides the elements for the design of workflows that meet the requirements of enhancing knowledge from geochemical and mineralogical surveys for the purposes of geological mapping, mineral resource prediction or environmental management. Further, it outlines a framework for a systematic evaluation of geochemical data plus attributes that enable the discovery of processes from which models can be constructed and tested using machine learning methods. Methods are described for ‘process discovery’ followed by additional methods for ‘process validation/prediction’. Six case studies are presented that highlight different approaches to the discovery of processes that assist in mineral exploration and geological mapping at various scales. The workflow includes caveats and flags potential issues or limitations on what can be discovered and validated.
Exploratory data analysis (EDA) involves graphical and spatial interpretation of the distributions of individual elements, an assessment of correlation between two or more elements, and unsupervised multivariate analysis of compositional data. It is an essential step in data analysis. Its purpose is to gain an understanding of the processes influencing geochemical data. An understanding of raw data distributions allows for the selection of appropriate data transformations as a precursor to parametric or robust statistical analysis of the data. Log transforms are used to de-skew mainly trace element distributions, although it should be recognised that many data sets contain multiple populations. Data may be levelled to correct for those processes identified through EDA. Compositional data is subject to geochemical closure which has the effect of creating false correlations, particularly where major elements vary due to fractionation, hydrothermal alteration or weathering. These effects are removed using the logs of element ratios. Correlation and regression analysis are used to infer geochemical processes from the data. The use of residuals following regression of a dependent variable against a controlling variable may be more informative than analysis of raw data. Statistical outliers, known as geochemical anomalies, can be identified in the data using well-established techniques.
Lithium (Li) is a strategic element in the global energy transition, largely driven by its use in lithium-ion batteries for electric vehicles. This study examines the distribution of Li and associated elements (Cs, Rb, K, Be, Sn) in Minas Gerais State, southeastern Brazil, through a low-density stream sediment geochemical survey combined with robust statistical approaches. Baseline values were defined using different calculation methods, multivariate statistics, and compositional data analysis, aiming to delineate Li-rich zones and evaluate the Ara & ccedil;ua & iacute; Fold Belt, a key domain of the Eastern Brazilian Pegmatite Province. Results show that lithological diversity across the seven geotectonic units strongly controls geochemical baselines. The Ara & ccedil;ua & iacute; Fold Belt exhibits the largest concentration range and highest correlation coefficients between Li and related elements, reflecting a common source in pegmatites and parental granites. Among the tested methods, median +/- 2MAD, or mMAD (where MAD is median absolute deviation), provided more consistent baselines and thresholds than Tukey's inner fence. Staged factor analysis (centered log-ratio and isometric log-ratio transformations) characterized main geochemical associations and supported the generation of the Geochemical Mineralization Probability Index mineralization map, a predictive layer integrated into a Mineral Prospectivity Map. The study demonstrates how stream sediment geochemistry, treated with advanced statistical methods, can effectively guide mineral exploration strategies for critical elements such as Li.
The arid alpine landscape on the northern margin of the Qaidam Basin, northern Tibetan Plateau, presents a challenge for conventional geochemical background modeling. Leveraging a unique multi-scale geochemical dataset (1:200 000, 1195 samples; 1:50 000, 18 855 samples; 1:25 000, 35 675 samples) from the Qinghai Geological Exploration Fund, this study systematically evaluates scale effects on background estimation. We compared the performance of four prevalent methods - iterative exclusion, median absolute deviation, exploratory data analysis, and the concentration-area fractal method - and found that a multi-method approach effectively balances the comprehensiveness and precision of the background modeling framework. To address the spatial heterogeneity of backgrounds caused by complex geology, we propose a novel subzone robust background method (SRBM). This method calculates robust median background values within distinct geological units and generates an adaptive background field through area-weighted fusion. In the Banhongshan area, application of the SRBM precisely calibrated the gold background to 0.47 ng g-1, successfully identifying two concealed gold anomalies obscured by traditional methods; one anomaly coincides perfectly with known industrial orebodies. This research demonstrates that high-density sampling (1:25 000) significantly enhances the signal-to-noise ratio for chalcophile elements in arid alpine terrains. The SRBM, by integrating geological knowledge with robust statistics, provides a powerful and reliable tool for weak geochemical signal extraction in both mineral exploration and environmental assessment.
Tree canopy fluids, transpired through leaf stomata, can be collected and readily analyzed for their elemental concentrations. However, these transpired fluids have rarely been considered as a sample medium in the context of exploration geochemistry. The objective of this proof-of-concept study was to test sampling and analytical protocols for transpired fluids from Norway spruce, and to examine whether they contain a geochemical signal of the underlying geology. Seventeen samples were collected over two Au-Co prospects and the surrounding background calc-silicate and mafic rocks in the glaciated terrain of northern Finland. A polyethylene plastic bag was tied over a bundle of sun-lit branches for four days in mid-July. Under partially cloudy conditions, 10 ml of fluid was collected, sufficient for single quadrupole inductively coupled plasma mass spectrometry analysis after laboratory filtering. The quality of the uncensored data was adequate for 27 elements, the concentrations of which ranged from a maximum of 1.5 g l(-1) for Sn to 75.5 mg l(-1) for Ca. The framework of compositional data analysis was used to detect element log-ratios that discriminate samples according to the underlying lithology and mineralization. The results indicate that lithological units can be discriminated with log-ratios of Al, B, and Li. Cesium, La, and Ce exhibit anomalies on top of the subcropping prospects. Transpired fluids from the Norway spruce canopy are a practical non-invasive sampling medium, making them suitable for the exploration of environmentally sensitive and other restricted regions. Further research is necessary to validate the effectiveness of transpired fluids from Norway spruce as a sampling medium in mineral exploration.
In remote semi-arid regions like the San Juan River watershed, determining the distribution and sources of aluminum (Al), arsenic (As), and lead (Pb) can inform water managers and users in mitigating potentially negative effects on human and ecosystem health. Water samples were collected from 33 tributaries and sites along the San Juan River during the monsoon seasons of 2021 and 2022. Synoptic water quality samples collected during baseflow along the San Juan River in February 2021 had Al, As, and Pb concentrations below U.S. Environmental Protection Agency (EPA) drinking water thresholds (50-200 & micro;g l(-1) Al, 10 & micro;g l(-1) As, 15 & micro;g l(-1) Pb). Total water concentrations of Al, As, and Pb were generally higher in 2022 than in 2021 with average Al concentrations 9.74-98.45% higher in 2022 than in 2021. Average As and Pb concentrations were 18.27-96.29% higher and 11.54-98.66% higher in 2022 than 2021, respectively. Total water concentrations of Al, As, and Pb were greater than EPA thresholds at most sites and highest at Chinle Creek near Bluff, La Plata River, Comb Wash, and Gallegos Canyon. Most sites also had higher particulate Al, As, and Pb concentrations in total water compared to filtered concentrations. A combination of water chemistry, geology, water use, and geomorphology suggested that the source(s) of Al, As, and Pb in total water is likely geogenic non-point. This study enhances understanding of the distribution and potential sources of Al, As, and Pb in the San Juan River watershed, bringing awareness to the community, and providing tools to evaluate metals concentrations in remote semi-arid regions worldwide.
We assessed the spatial extent and source signatures of metal contamination in the Sudbury region, Ontario, Canada, using three lichen species (Stereocaulon spp., Cladonia rangiferina, Evernia mesomorpha), moss (Dicranum scoparium), humus forms, mineral soils, and snowpack. Baseline concentrations were established for 23 elements across these 7 monitors. Of the biomonitors, Stereocaulon spp. generally shows the highest baseline concentrations for most metals, C. rangiferina the lowest, and E. mesomorpha intermediate. In contrast, the moss D. scoparium has higher concentrations of nutrient elements (e.g. Ca, Mg, K) but comparatively low metal concentrations relative to the lichen species. Distances from the city to the baseline concentrations for As, Cd, Co, Cu, Ni, and Pb were compared across media types. Elemental concentrations in snow particulate matter (PM) returns to baseline within 9-15 km of the city (with one exception at 30 km for Ni), whereas soils remain elevated to 25-46 km. Lichens exhibit a steep 'principal' contamination gradient like snow PM but remain elevated until 26-38 km, roughly matching soil patterns and suggesting resuspended legacy contamination. The Bioaccumulation Scale was applied to lichens for cross-species comparison and determining environmentally relevant elements. The scale reduces cross-species variability but does not eliminate species-specific effects. Around the smelters, 13 of 32 elements exceed the environmentally relevant threshold (Bioaccumulation ratio >3.4). This work provides updated baseline values and baseline distances for a region historically impacted by mining activity, and integrates multiple media to reveal relationships between modern and legacy contamination.
The efficient extraction of geochemical signatures related to mineral deposits presents a challenging task. Here, we introduce a new multi-technique framework for the detection of multi-element geochemical footprints through the sequential combination of four methods: accumulation coefficient analysis, the receiver operating characteristics curve, principal component analysis, and machine learning techniques. The proposed framework is evaluated using stream sediment geochemical data collected during porphyry-copper exploration in the north Baft district, SE Iran. The results indicate that the newly devised framework is valid and effective with regards to processing high-dimensional multivariate geochemical data and to identifying true positive geochemical anomalies. As such, the newly devised approach has implications for mineral exploration targeting.
A biogeochemistry (Picea mariana bark) orientation case study consisting of three transects over two known gold zones was completed in the McFaulds Lake region ('Ring of Fire') located in the far north of Ontario, Canada. The well-known proprietary weak/selective Mobile Metal Ion (MMI (TM)) leach with inductively coupled plasma mass spectrometry (ICP-MS) finish was utilized, rather than a conventional technique such as acid digestion and ICP-MS on macerated bark tissue. This approach is based on the need to generate biogeochemical data that more accurately represent the labile phases of elements that may be released from hidden mineral deposits. Extensive peatlands characterize the study area, below which an similar to 8 m thick sequence of till covers the underlying Archean bedrock that hosts the Triple J gold zones. The MMI (TM) results include relevant patterns with respect to the gold zones, in particular for the metals In and Mo. Horizons of massive chromite of the Blackbird deposit also appear to be influencing tree bark biogeochemistry, in particular for Bi, U and rare earth elements. These biogeochemical signals are possible evidence for the presence of a metal accretion zone in the soil substrate, perhaps due to a redox column developed above the mineralized zones. Peat thickness appears to affect metals concentrations in bark tissue; I infer that mineral soil substrates provide more metal signal for uptake to the spruce trees compared to thicker peat areas, where significant portions of the root systems are within organic peat. Comparison to a nearby aqua-regia digest bark geochemistry dataset helps illustrate that landscape drainage and soil/substrate moisture content present additional variables, which can have significant control on the uptake and translocation of some metals to the outer tissues of spruce trees. Using a weak or selective leach technique on black spruce outer bark may improve the signal-to-noise ratio compared to conventional digestion methods by targeting any weakly bound labile phases.
Recent research on carbonatite-hosted rare earth element (REE) deposits has highlighted the diversity of REE-bearing minerals and their origins. This study investigates the mineralogy and geochemistry of a 1 km-long drill core intersecting unweathered carbonatite of the Mt Weld REE deposit in Western Australia, using hyperspectral reflectance spectroscopy and whole-rock geochemistry. Six distinct zones are identified within the central carbonatite: (1) weathering profile; (2) magnesiocarbonatite; (3) phosphate-siderite-rich magnesio- and ferrocarbonatite (MF); (4) phosphate-rich MF carbonatite; (5) phosphate-poor MF carbonatite; and (6) calciocarbonatite. The integration of visible near-infrared (VNIR) and thermal infrared (TIR) reflectance spectra reveal that the primary Nd-related absorption features occur at c. 745 nm when hosted by phosphates (Zones II-IV), and shift to c. 740 nm when hosted by carbonates. Reassessment of the whole-rock geochemistry, based on the newly defined zones, reveals the gradual variation of lambda values (a quantitative descriptor of REE patterns), reflecting the progressive magmatic fractionation from Mg-rich to Fe-rich carbonatite magma, with the decreasing of general light REE (LREE)/heavy REE (HREE) fractionation (La/Yb). The total concentration of REE is initially enriched in Mg-rich carbonatite but subsequently decreases as phosphorus concentrates within Fe-rich carbonatite. REE precipitate as monazite during the hydrothermal evolution of each magmatic stage, transitioning to REE-fluorocarbonates in the late-stage magma when REE and phosphorus become depleted. The MF carbonatite is interpreted to undergo multiple magmatic stages, each influenced by brine-melt and hydrothermal processes that control the distribution of LREE. In contrast, localized HREE enrichment occurred during late-stage interactions between MF carbonatite and pre-existing calciocarbonatite.
Ionic geochemistry is a selective extraction approach developed to target weakly adsorbed or exchangeable elements in soils using a mixed-ligand solution. The method enables the simultaneous extraction of 57 elements, subsequently quantified by inductively coupled plasma mass spectrometry. While reliable and accurate, the initial protocol required large sample masses (50 g) and extended leaching times (6 hours), which limited its practicality for large-scale surveys and environmental assessments. Moreover, the high sample mass prevented the possibility of producing multiple replicates, splitting samples for inter-laboratory or cross-method QA/QC testing, and was not suitable in field contexts where soil collection yields insufficient material. In this study, we optimized the method using a Box-Behnken experimental design applied to soil samples enriched with copper as a tracer. Sample characterization was conducted by X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy prior to leaching. The optimization significantly reduced the sample mass (to 10 g) and leaching time (to 5 hours) while maintaining high analytical accuracy and precision: R = 0.999; half absolute relative deviation < 10%; half deviation relative between -10 and +10% for all 57 elements before and after optimization. Structural analyses confirmed that the extraction process did not alter the soil matrix despite an 80% reduction in sample mass. The optimized protocol improves the applicability of ionic geochemistry for both geochemical exploration and environmental monitoring by lowering resource requirements, enabling replicates and QA/QC cross-checks, and ensuring reliable detection of ultra-trace elements. It thus provides a more efficient and sustainable framework for assessing mineralization-related anomalies and soil contamination.
Geochemical exploration criteria for relatively light rare earth element (LREE)-enriched iron oxide-copper-gold (IOCG) deposits have been tested and are applicable across multiple Proterozoic terranes in eastern Australia. The criteria were established using monazite from E1 (Cloncurry District, Queensland) and compared to existing data from Prominent Hill and Carrapateena (Gawler Craton, South Australia) and are effective in rare earth element (REE)-rich deposits. The criteria use La, Ce and Nd chemistry in combination with Th and Y, where La + Ce > 65 wt%, Nd < 12.5 wt%, and Y and Th are both <1 wt%. However, the criteria are not as effective in REE-poor deposits such as Osborne and SWAN (Cloncurry District). For these deposits, other trends in the monazite REEs, such as changing Y and Dy contents, are better to use, which can discriminate between mineralization-associated hydrothermal monazite that formed at low temperatures (low Y and Dy) from metamorphic monazite that formed at high temperatures and are not associated with mineralization (high Y and Dy). In metamorphosed terranes like the Cloncurry District, where metamorphism is well constrained, chemical and temperature information using monazite chemistry can give information on the formation conditions of monazite, potentially hinting at a nearby IOCG system.
Geochemical data for the UK Lake District including both Geochemical Baseline Survey of the Environment (G-BASE) stream sediment data and newly collected samples are shown here as a tool for modelling whole-rock geochemistry at a regional scale and as a case study for identifying potential As-Bi-Co-Cu-Fe-Ni mineralization. Regional whole-rock concentrations for the Skiddaw Group and Borrowdale Volcanic Group were modelled using G-BASE stream sediment data and found to align closely with newly collected in situ X-ray fluorescence measurements of host-rock samples. Average concentrations of elements such as Ag, Al, As, Fe, Ni and Ti differed by only 1-2 wt% or similar to 20 ppm between the two datasets. Six areas identified by G-BASE as potential As-Co-Cu-Ni targets were visited. Of these, Keld and Devoke Water showed evidence for sulfide dissemination within the host rock rather than visible veins, while Black Combe, Seathwaite, Coniston and Tilberthwaite were confirmed to host vein-type, quartz-sulfide mineralization geochemically similar to known deposits at Dale Head North, Scar Crag and Ulpha. This study highlights the successful application of G-BASE data for regional geochemical modelling and exploration targeting. The workflow could be adapted for other areas covered by preexisting stream sediment geochemical data or integrated into exploration strategies for new regions.
Limited studies comparing geochemical patterns in vegetation with those in regolith materials over mineral deposits have been undertaken in arid to semi-arid regions. Biogeochemical responses to base metal mineralization in two contrasting regolith settings are assessed. The first examined the response of Pinus radiata, Acacia dealbata (green wattle) and Baeckea utilis to areas of contamination and outcropping mineralization in felsic volcanic units at the historic Sunny Corner Ag-Pb-Zn mine in eastern New South Wales, Australia. The second involved Atriplex vesicaria (bladder saltbush) composition over the deeply weathered, gneiss-hosted cobaltiferous pyrite deposit at Thackaringa in far western New South Wales. At Sunny Corner, foliage and other organs display elevated concentrations of Pb, Zn and As over mineralization and the extensive zone of mine tailings. P. radiata needles also exhibit regional variation in the concentration of nutrient elements depending on the underlying lithology. Saltbush at Thackaringa displays elevated Co and Zn above and downslope of outcropping gossans, whereas Mn and Zn values are more elevated along drainage lines. Both A. vesicaria and P. radiata exhibit controls on uptake of Cu, S and other nutrients that is likely to be a mechanism to prevent accumulations reaching toxic levels. For some elements there is weak direct correlation between element concentrations in the plants and adjacent shallow soils, although overall spatial patterns display similarity. Where the distribution of selected species permits sampling at the requisite scale, biogeochemistry offers an alternative and potentially preferable sampling media to regolith, although there are advantages in sampling both media.
The Florida River in southwestern Colorado, USA, flows through the Southern Ute Reservation and is a cultural and water resource to Tribal and non-Tribal communities. High concentrations of total Al, up to 16 400 & micro;g l(-1), have been detected in the Florida River. Elevated Al concentrations in water may affect aquatic organisms' ability to regulate ions and inhibit respiratory functions. Water chemistry collected during three sampling events in 2022 from the Florida River and two of its tributaries are used here to better understand the spatial distribution, seasonality, and sources of Al concentrations in the Florida River. Streambed sediment and rock chemistry is also used to infer geologic sources of Al in the watershed. A five-stage sequential extraction was performed on streambed sediment and rock samples to help determine Al transport processes. We assessed relations between major and trace elements in water using a principal component analysis, finding that high Al concentrations in the Florida River (>3500 & micro;g l(-1) total Al) are caused by the erosion of Al-laden sediments. We used a stepwise multiple linear regression to investigate the relationship between land use and Al concentrations. Land use did not reliably predict Al concentration, though increased streamflow connected to reservoir releases - particularly in the Salt Creek tributary - were found to contribute Al to the Florida River during the irrigation season. In this study, we demonstrate that successful metal source appropriation may be achieved through limited field sampling when performed with sufficient water chemistry analysis, sequential chemical extraction of stream sediments, and statistical and land-use analysis.
