Sr isotope analysis of water samples at the Radiogenic Isotope Laboratory of the Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Napoli - Osservatorio Vesuviano (INGV-OV)
Bibliographic record
Abstract
Since 2000, a Thermal Ionisation Mass Spectrometer (Thermo ScientificTM Triton TI® Mass Spectrometer) and a clean laboratory are operating at the Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Napoli, Osservatorio Vesuviano (OV) to measure strontium (Sr) and neodymium (Nd) isotope compositions of volcanic products for scientific purposes. In 2014 particular attention has been dedicated to set up the analytical procedure for extracting Sr and Nd and measuring their isotope compositions from groundwater and mineral water, due to its growing interest on environmental topics. Strontium is considered a trace element and Sr2+ preferentially substitutes for Ca2+ in most rock types since it geochemically behaves like calcium. Groundwater is enriched in Sr during water-rock interaction processes occurring within the saturated and/or unsaturated zones. Conversely, Sr is removed from water as a result of mineral precipitation and ion exchange reactions. The most common removal process is the coprecipitation of Sr2+ ions with calcium carbonate. However, this latter process does not fractionate Sr2+ [Faure and Powell, 1972]. Therefore, the Sr isotopic composition of groundwater records an integrated signal of water-rock interaction along flow path and dissolution/precipitation events, and can be used as a dynamic tracer to constrain subsurface flow in volcanic, non volcanic and geothermal areas. Water is a natural resource, which is renewed by different processes. The aforementioned geochemical processes and reactions with dissolving/precipitating minerals have a profound effect on water quality. Since the 1990s, Sr isotopes have been extensively used as a natural tracer of groundwater flow [Peterman and Stuckless, 1992; Bullen et al., 1996; Johnson and De Paolo, 1994; McNutt et al., 1990; McNutt, 2000; Frost et al., 2002; Gosselin et al., 2004; Klaus et al., 2007] because groundwater progressively acquires the 87Sr /86Sr isotopic ratio from the rocks with which it has interacted [Prasanna et al. 2009]. Furthermore, variable Sr isotope ratios and concentrations characterize different groundwater sources and are tracers of groundwater mixing [eg. Carucci et al., 2012]. In this work we present the first results obtained by analyzing the Sr isotope composition of two mineral waters, selected groundwater samples from Mt. Etna and a certified water sample from North America (National Research Council Canada - NRC, Certified Reference Materials - CRM TM-25.4). On the contrary, the Nd isotope ratios of such water samples have been not measured due to the low Nd content of the investigated samples. A statistically representative data set on certified international standards (NIST SRM 987, La Jolla and JNdi-1) was used to evaluate the quality of the analytical data produced at the INGVOV Radiogenic Isotope Laboratory since 2014. This methodological approach will allow us to isotopically characterize different water systems with the aim to trace back the water-rock interaction and mixing processes in different environments. This analytical procedure could be exported to other geological contests and applied to other types of waters (e.g. surface and thermal water).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".