Chlorination Kinetics of 11-Nor-9-carboxy-Δ<sup>9</sup>-tetrahydrocannabinol: Effects of pH and Humic Acid
Bibliographic record
Abstract
The main psychoactive compound in marijuana, Δ 9 -tetrahydrocannabinol (THC), and its metabolites are emerging organic contaminants that have been detected in waste and surface waters. As legalization of marijuana for medical and recreational use continues, the effects of increased use and potency of marijuana on water and wastewater treatment processes and the environment should be considered. This study examined degradation kinetics of the main urinary metabolite of THC, 11-nor-9-carboxy-Δ 9 -tetrahydrocannabinol (THC–COOH) with chlorine. THC–COOH was rapidly removed from both deionized (DI) water at pH 5.6 ± 0.2 and Suwannee River humic acid (SRHA) at pH 5.1 ± 0.2 using low doses of chlorine (0.1 to 0.50 mg free Cl 2 /L), with half-lives calculated from second-order kinetics constants ( k 2 ) of 8 s for DI and 42 s for DI with SRHA. Kinetic rates increased with an increase in pH from 5 to 9 in both DI water and SRHA and no interference from phosphate was observed. The chlorination pathway of electrophilic substitution of Cl at the ortho or para position of the phenol structure of THC–COOH was confirmed by detection of monochlorinated byproduct fragmentation ions using flow injection analysis with orbitrap mass spectrometry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".