Lithium and strontium isotope dynamics in a carbonate island aquifer, Rottnest Island, Western Australia

Applying Machine Learning to investigate metal isotope variations at the watershed scale: A case study with lithium isotopes across the Yukon River Basin

Constraining the multiple climatic, lithological, topographic, and geochemical variables controlling isotope variations in large rivers is often challenging with standard statistical methods. Machine learning (ML) is an efficient method for analyzing multidimensional datasets, resolving correlated processes, and exploring relationships between variables simultaneously. We tested four ML algorithms to elucidate the controls of riverine δ7Li variations across the Yukon River Basin (YRB). We compiled (n = 102) and analyzed new samples (n = 21), producing a dataset of 123 river water samples collected across the basin during the summer including δ7Li and extracted environmental, climatological, and geological characteristics of the drainage area for each sample from open-access geospatial databases. The ML models were trained, tuned, and tested under multiple scenarios to avoid issues such as overfitting. Random Forests (RF) performed best at predicting δ7Li across the basin, with the median model explaining 62 % of the variance. The most important variables controlling δ7Li across the basin are elevation, lithology, and past glacial coverage, which ultimately influence weathering congruence. Riverine δ7Li has a negative dependence on elevation. This reflects congruent weathering in kinetically-limited mountain zones with short residence times. The consistent ranking of lithology, specifically igneous and metamorphic rock cover, as a top feature controlling riverine δ7Li modeled by the RFs is unexpected. Further study is required to validate this finding. Rivers draining areas that were extensively covered during the last glacial maximum tend to have lower δ7Li due to immature weathering profiles resulting in short residence times, less secondary mineral formation and therefore more congruent weathering. We demonstrate that ML provides a fast, simple, visualizable, and interpretable approach for disentangling key controls of isotope variations in river water. We assert that ML should become a routine tool, and present a framework for applying ML to analyze spatial metal isotope data at the catchment scale.

Lithium isotopic fingerprints of sources and processes in surface waters of the Ebro River Basin (Spain)

The Ebro River in north-eastern Spain is among the largest contributors of freshwater to the Mediterranean Sea and ends in the Ebro delta, one of the major wetlands in Europe. The bedrock of the Ebro River basin mainly consists of carbonate rocks and evaporites of Palaeozoic and Mesozoic age, and the river flows through several large cities, and agricultural and industrial areas. The Ebro outlet at Amposta was sampled once a month for a year (2006), and a field campaign in April of the same year sampled the Ebro along its main course as well as its principal tributaries. In the present study, the behaviour of Li and its isotopes was investigated at basin scale, with the objective of elucidating the processes controlling the lithium-isotope signatures of a large river draining mostly sedimentary bedrock. δ⁷Li values show a narrow range from +17.1 ‰ to +18.3 ‰ along the Ebro main stream, and between +16.3 and +18.9 ‰ at the outlet. In the major tributaries, the δ⁷Li values ranged from +12.9 ‰ to +20.9 ‰, with bedrock values ranging from +0.5 to +29.3 ‰. Comparing Li concentrations with Cl and SO₄ ones, it appears that evaporite weathering plays an important role in controlling Li concentrations, but no anthropogenic agricultural or industrial influence on Li concentrations was detected. The Na/Li, Cl/Li and SO₄/Li ratios clearly reflect the role of halite dissolution for some tributaries (Gallego, Ega and Aragon), gypsum dominating others (Guadalope, Matarrana, Huerva and Segre), and little influence of carbonate in all tributaries, the Ebro itself being a mixture of all tributaries. We tentatively applied the simple Rayleigh fractionation model, but most δ⁷Li values of the Ebro water samples plotted away from the fractionation line, reinforcing the major role of mixing processes in the Ebro basin, rather than fractionation processes during water/rock interactions. A comparison of δ⁷Li values and ⁸⁷Sr/⁸⁶Sr ratios further demonstrates the role of gypsum/anhydrite and limestone in the Ebro and its tributaries. Sr-isotopes show a non-negligible role of carbonate dissolution, generally considered to be weak in the control of the lithium cycle in catchments.

Assessment of Lithium, Macro- and Microelements in Water, Soil and Plant Samples from Karst Areas in Romania

Lithium is a critical element for the modern society due to its uses in various industrial sectors. Despite its unequal distribution in the environment, Li occurrence in Romania was scarcely studied. In this study a versatile measurement method using ICP-MS technique was optimized for the determination of Li from various matrixes. Water, soil, and plant samples were collected from two important karst areas in the Dobrogea and Banat regions, Romania. The Li content was analyzed together with other macro- and microelement contents to find the relationship between the concentration of elements and their effect on the plants' Li uptake. In Dobrogea region, half of the studied waters had high Li concentration, ranging between 3.00 and 12.2 μg/L in the case of water and between 0.88 and 11.1 mg/kg DW in the case of plants, while the Li content in the soil samples were slightly comparable (from 9.85 to 11.3 mg/kg DW). In the Banat region, the concentration of Li was lower than in Dobrogea (1.40-1.46 μg/L in water, 6.50-9.12 mg/kg DW in soil, and 0.19-0.45 mg/kg DW in plants). Despite the high Li contents in soil, the Li was mostly unavailable for plants uptake and bioaccumulation.