Temperature Dependence of the Characteristic Travel Distance
Bibliographic record
Abstract
The effect of temperature variation on the environmental fate of organic chemicals can be evaluated in steady-state multimedia box models by expressing chemical partitioning data and reaction rate coefficients as functions of temperature. Using such a modelthetemperature dependence of the characteristic travel distance in air L(A), which is a measure for the atmospheric long-range transport potential of organic chemicals, is calculated. Simulations are reported for a set of 40 chemicals of environmental interest. Increasing temperature is shown to have two opposing effects on L(A). Rates of chemical transformations in the atmosphere (k(air)) and surface media are increased, which reduces L(A). Rates of atmospheric deposition (k(dep)) are reduced leading to increased mobility and L(A). Accordingly, L(A) can monotonically increase or decrease with increasing temperature, or it can have a maximum in the modeled temperature range, but it cannot have a minimum. For chemicals with a strong temperature dependence of k(air) relative to k(dep), L(A) will increase with increasing temperature. Results for selected polychlorinated biphenyls are compared to monitoring data yielding qualitative agreement when chemical properties are adjusted to mean temperatures for the measurement period. The results demonstrate that the temperature dependence of the characteristic travel distance is highly dependent on chemical characteristics and can be counterintuitive. The use of mass balance models is thus essential. The difference between the L(A) values at 5 degrees C and 30 degrees C can be up to a factor of 6. Accordingly, chemical ranking with respect to L(A) can change significantly if performed at different temperatures. Implications of the different temperature dependencies on long-range transport to polar regions are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".