Comparisons of forward-in-time and backward-in-time Lagrangian stochastic dispersion models for micro-scale atmospheric dispersion
Bibliographic record
Abstract
Lagrangian stochastic dispersion models are sometimes run in backward mode to estimate air emissions from different types of sources including area sources. The forward-in-time and backward-in-time Lagrangian stochastic (fLS and bLS) dispersion models may not result in the same estimates. The two models were compared under different atmospheric conditions in micro-scale applications. They are equivalent in a steady-state and horizontally homogeneous atmosphere in many features including estimating concentration at a point, using surface receptor, and prerunning the models. Although bLS shows better computational efficiency, it has a larger uncertainty in results due to the use of surface receptors. In a non-steady-state wind field, the two models show opposite transition trends when the wind fields experience a step change. Under sinusoidal-varying winds, the two models show different shapes of the predicated concentration curves. The normalized differences of the mean concentrations mainly increase with the receptor height when the source-receptor distance is fixed. A controlled methane release experiment was conducted to investigate the behaviors of the two models driven by real wind fields. The correlation coefficient between model-predicted concentrations is 0.95. The model-predicted (forward model) and measured concentrations show similar trend with a correlation coefficient of 0.70. The bLS model estimates larger peak concentrations than that fLS model under the same emission rate. The best-fitted results of the fLS and bLS models give recovery ratios of 1.1558 and 0.9675, respectively, which are better than that using a constant 15-min averaged wind (0.7922).Implications: There are large uncertainties and difficulties in quantification of fugitive air emissions from area sources such as landfills, agriculture, and industry sections. Lagrangian stochastic dispersion model is a versatile tool for these applications with the capability of near-field description and good efficiency. Backward models are usually used to estimate emission rates from area sources due to high computing efficiencies. But they may not result in the same estimate as the forward models due to factors involving model realization and input parameters. It is necessary to investigate the discrepancies to select the best model with minimal uncertainty in the results.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
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".