Trichloroethylene, tetrachloroethylene and carbon tetrachloride in an urban atmosphere: mixing ratios and temporal patterns
Bibliographic record
Abstract
The measurement of halogenated hydrocarbons in the atmosphere is a matter of great interest owing to their adverse effects on the human health and the environment. This work is focused on the measurement of three toxic chlorinated hydrocarbons: trichloroethylene (TCE), tetrachloroethylene (PCE), and carbon tetrachloride (CTC). Moreover, CTC is a greenhouse gas and an ozone depleting gas, restricted under the Montreal Protocol. Owing to their low reactivity, the target chlorinated hydrocarbons are considered to be persistent and, thus, many measurements only address their mean mixing ratios (a concentration measure expressed as mol/mol). Consequently, most of the reported data have low temporal resolution as daily, seasonal or yearly mean mixing ratios, obtained with few measurements. In the study reported in this paper hourly measurements were performed for a long period of time: almost two years for TCE and PCE, and one year for CTC. The main objective was to study the temporal variability of the chlorinated hydrocarbons with high temporal resolution in order to identify their main sources and to enhance the understanding of their atmospheric processes. During the measurement period, March 2007–February 2008 with N = 3290 valid data, CTC showed a mean mixing ratio of 0.16 ppbv (SD = 0.13) with lower temporal variability than the majority of non-methane hydrocarbons (NMHCs), being very well mixed in the urban atmosphere owing to its long lifetime. TCE and PCE mean mixing ratios for the May 2006–February 2008 period, were 0.13 ppbv (SD = 0.42, N = 4601) and 0.25 ppbv (SD = 0.54, N = 4709) respectively, with a larger temporal variability. The study of the sources of TCE and PCE reveals that both compounds have industrial and/or commercial origin, but with different main sources.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".