Quantifying and characterizing major DOC fractions in water treatment processes: A simplified SPE method without recovering sorbed compounds
Bibliographic record
Abstract
absorption (SUVA) as an indicator of hydrophobic (HPO) dissolved organic carbon (DOC) to evaluate removal efficiency and estimate trihalomethane formation potential (THMFP). However, current fractionation methods, such as solid-phase extraction (SPE), primarily focus on recovering sorbed compounds, and assume that treatment impacts only the quantity, not the characteristics, of DOC fractions. Additionally, varying recovery rates and inconsistent fractionation pH definitions complicate cross-study comparisons of hydrophilic and HPO DOC composition. To address these issues, we tested three pH fractionation approaches (pH 3, pH 7, and sequential adjustment) and observed significant differences in DOC content, SUVA, and specific THMFP (STHMFP) between SPE filtrates at pH 3 and pH 7, which were most likely because of ionizable acidic DOC compounds becoming HPO at lower pH levels. Based on these findings, we developed a new fractionation method to estimate the quantity and characteristics of major DOC fractions-hydrophilic neutral (HPIN), total acidic (TA = HPOA + HPIA), and hydrophobic neutral (HPON)-without the need to recover sorbed fractions. Applying this method in a conventional coagulation/softening plant revealed HPON decreased while the relative amounts of HPI and TA increased after the treatment. However, the treated water HPI exhibited significantly higher STHMFP and contained approximately twice the proportion of low-molecular-weight compounds than raw water HPI, highlighting significant changes in both the content and properties of DOC fractions throughout the treatment process. Our study indicates that the contribution of HPI DOC fraction to SUVA and STHMFP in treated water is greater than that of HPO DOC. PRACTITIONER POINTS: A two-stage ENV to estimate major DOC fractions without recovering sorbed compounds. One ENV cartridge at pH 3 can effectively isolate HPI DOC, replacing sequential ENV. Coagulation and lime/soda softening altered characteristics of DOC fractions. HPI DOC in treated water contributes to SUVA and STHMFP more than HPO fraction.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".