Insights into groundwater hydrochemistry and origin of the hydrocarbon contaminated karst aquifers, SW Iran
Bibliographic record
Abstract
Khuzestan, Ilam, Lorestan, and Chaharmahal and Bakhtiari provinces in southwest of Iran. In this study, conventional geochemical, stable isotopes of H 2 O (δ 2 H, δ 18 O), SO 4 (δ 34 S, δ 18 O), and dissolved inorganic carbon (δ 13 C DIC ) were employed on the samples collected in February 2021, August 2021, and February 2022 from SW Iran. By the use of hydrogeochemical diagrams, sulfuric samples (SS), non-sulfuric samples (NSS), and oil field water (OFW) samples in karst aquifers have been distinguished. The findings reveal that: (1) the major chemical composition of the SS, NSS, and OFW samples consist of CaSO 4 , CaHCO 3 , and NaCl types, respectively; (2) the δ 2 H H2O values range between −17.37 and −15.86 ‰ V-SMOW and the δ 18 O H2O values range between −4.49 and −4.18 ‰ V-SMOW for the SS and NSS, indicating that they originated from local rainwater; (3) the δ 34 S SO4 and δ 18 O SO4 values of dissolved sulfate of SS, NSS, and OFW indicate that the sulfate resulted from interactions between water and rocks, particularly evaporitic rocks such as gypsum and anhydrite; and (4) the δ 13 C DIC values vary extensively, ranging from −16 ‰ to + 7 ‰ V-PDB. Higher δ 13 C DIC signatures are consistent with biogeochemical processes (i.e., methanogenesis) or exchange with marine carbonates. (5) The concentration of total petroleum hydrocarbons (TPH) ranged from 26.3 to 19,670 μg/L in the groundwater samples.Toluene was the most abundant species of BTEX in all samples. • The concentration of total petroleum hydrocarbons (TPH) ranged from 26.3 to 19,670 μg/L in the groundwater samples. • Hydrogeochemistry and multiple isotopes uses to study karst waters in SW Iran. • Hydrochemical characteristics of karst groundwaters are different. • Halite and gypsum dissolution are the main salinization processes. • Methanogenesis is supported by the δ 13 C DIC in NSS.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".