Airborne Haloacetic Acids
Bibliographic record
Abstract
Haloacetic acid (HAA) concentrations were measured in air samples from a semi-rural and a highly urbanized site in southern Ontario throughout 2000 to investigate their sources and gas-particle partitioning behavior. Denuders were efficient for collection of gaseous HAAs, and the particle phase was collected on a downstream quartz filter with negligible breakthrough. Total HAA concentrations (i.e., gas + particles) ranged between <0.025 and 19 ng m(-3) for individual HAAs at both sites. The dominant airborne HAA was monochloroacetic acid (MCA), followed in decreasing order by dichloroacetic acid (DCA), trifluoroacetic acid (TFA), and trichloroacetic acid (TCA). Difluoroacetic acid (DFA), monofluoroacetic acid (MFA), and chlorodifluoroacetic acid (CDFA) were also frequently detected at lower concentrations. Between sites, TFA, DFA, MFA, and TCA concentrations were significantly higher in Toronto, while CDFA concentrations were higher in Guelph. HAAs were primarily in the gas phase all year; however, during colder months, particle-phase HAA concentrations increased relative to the gas phase. Trichloroacetic acid had the highest particle fraction (phi) for all detected HAAs, with a mean phi of 0.51 and 0.56 for Guelph and Toronto, respectively, and both vapor pressure and acid strength appeared to influence gas-particle partitioning. Temporal trends at both sites were partially explained by temperature, short-wave radiation, and particle mass (PM10), leading to indications of the respective sources. A simple deposition model indicated that dry deposition of TFA and TCA should not be neglected in temperate mid-latitude environments and that precipitation concentrations can be successfully predicted by the Henry's law constant.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".