Incorporation of RLINE into AERMOD: An update and evaluation for mobile source applications
Bibliographic record
Abstract
The R-LINE model, which was released in 2013 as a stand-alone model for roadway-type applications and was based on a set of newly developed dispersion curves, exhibited favorable model performance in a limited set of evaluations (Heist et. al, 2013, Snyder et al. Citation2013, Venkatram et al. Citation2013). In 2019, the R-LINE model was incorporated as the RLINE source type in EPA’s preferred near-field dispersion model AERMOD. Since its inclusion in AERMOD, the RLINE source type has been tested and compared to other AERMOD source types using multiple data sets and transportation studies. The outcome of these tests is a need to revisit the dispersion parameters used in the original RLINE dispersion curves to address performance issues suggested by comparisons to AREA and VOLUME source types in AERMOD. The work presented here includes corrections to the RLINE vertical wind profiling, harmonization of several aspects of the RLINE formulation with AERMOD’s AREA and VOLUME source types (i.e. the addition of terrain and meander weighting), and updates to the RLINE dispersion parameterization based on a computational optimization routine. The updated RLINE source type is compared with AREA and VOLUME estimates for two hot-spot transportation studies. RLINE modeled estimates are also reevaluated with two of the previous evaluation studies and two additional tracer studies. The updated RLINE formulation leads to improved performance in most cases and closer comparison with the AREA and VOLUME sources.Implication Statement The RLINE source type was recently added by the EPA to the AERMOD model as a “preferred” model option. Thus, the RLINE source type is now available to the air quality modeling community as a modeling option without any approval required. This paper explains recent changes to the model formulation and provides both an updated and expanded model evaluation. For the updated evaluation, we compare the three AERMOD source types (RLINE, AREA, and VOLUME) for two tracer databases used when the RLINE source was initially created (Caltrans 99 and Idaho Falls). We also add model evaluations for two “new” databases (GM Sulfate and Berkeley Freeway Experiment) to expand the assessments of model performance. Additionally, two model intercomparisons are examined, comparing design concentrations for two real-world highway hot-spot projects for RLINE against the AREA and VOLUME sources, which shows much better agreement between the three source types with the updated RLINE model. The work is essential for dispersion model practitioners to understand the specifics of RLINE’s model formulation as well as its performance against the other two AERMOD source types typically used for modeling roadway emissions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".