Target analysis and suspect screening of wastewater derived contaminants in receiving riverine and coastal areas and assessment of environmental risks
Bibliographic record
Abstract
The main objective of this thesis is to evaluate the impact of wastewater discharges on receiving riverine and coastal ecosystems by studying the occurrence and fate of emerging contaminants (ECs). Since there is a myriad of ECs present in the environment, this thesis mostly focused on two main groups of compounds: (i) endocrine disrupting compounds (EDCs), including natural and synthetic hormones, alkylphenols (APs), such as nonylphenol (NP) and octylphenol (OP), and the plasticizer bisphenol A (BPA), and (ii) pharmaceutically active compounds (PhACs). These compounds were selected because they are one of the ECs of major input into the environment, thus being considered as “pseudo-persistent” pollutants, and for their potential deleterious effects to non-target organisms. Besides these target ECs, this thesis also evaluated the occurrence of other groups of contaminants by using high resolution mass spectrometry (HRMS). Finally, an assessment of the environmental risks posed by the identified ECs has been done in order to select the compounds of major ecological concern and those that could be used as relevant markers of wastewater contamination in freshwater and marine ecosystems. To accomplish the thesis objectives, two different analytical approaches have been used: (i) quantitative target analytical methods to determine the two groups of selected ECs (13 target EDCs and 81 PhACs that belong to nineteen different therapeutic groups) using reference analytical standards, and (ii) suspect screening using HRMS and exact mass compound databases (360 ECs)
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".