Dispersion model evaluation of PM2.5, NOx and SO2 from point and major line sources in Nova Scotia, Canada using AERMOD Gaussian plume air dispersion model
Bibliographic record
Abstract
AERMOD was used to model the air dispersion of point and major line emissions of PM2.5 in Halifax and Pictou, NOX in Halifax and SO2 in Halifax, Sydney and Port Hawkesbury, Nova Scotia, Canada. Emission inventory data for 2004 were used in simulations within four, 50 km x 50 km, domains over annual, monthly and 1–hour averaging periods. Annual averaged surface concentration maps are reported. Modeled versus observed comparisons were made within each domain at the Government, National Air Pollution Surveillance (NAPS) monitoring sites (discrete receptors). Evaluation of the model was conducted on the annual, monthly and hourly results using a number of statistical methods that included R2, fractional bias, normalized mean square error and the fraction of predictions within a factor of two of the observations. The AERMOD model evaluation showed that there was good agreement between the modeled and observed SO2 concentration for the annual and monthly comparison but less skill at estimating the hourly comparisons for SO2 in Halifax and Sydney. AERMOD showed poor model skill at predicting SO2 in Port Hawkesbury over the same averaging periods. The model evaluation for PM2.5 in Halifax, PM2.5 in Pictou and NOX in Halifax showed poor agreements and model skill. The surface concentrations from the point and major lines sources in all domains from all metrics were found to be well below the National Air Quality Standards. AERMOD has shown its utility as a suitable model for conducting dispersion modeling from point and line sources in Nova Scotia with good model skill for estimating annual and monthly SO2 concentrations in Halifax and Sydney. The study highlights the validity of using emission inventory data to estimate the surface impact of major point and line sources within domains containing complex terrain, differing land use types and with large variability within the annual meteorology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".