Community multiscale air quality (CMAQ) modeling of the atmospheric quality and pollutant deposition over the Terrace-Kitimat Valley of northwestern British Columbia, Canada
Bibliographic record
Abstract
Tracking the effects of air pollution from industries is important for developing management strategies under changing emissions. However, computational tools for air pollution assessment often do not elucidate modeling uncertainty, making it difficult for environmental policy-makers to know how much confidence to put in model results, which also hampers aspects that may need improving. This study examined how the WRF-SMOKE-CMAQ modeling system with various planetary boundary-layer (PBL) schemes and atmospheric datasets mimics the local meteorology, air quality and acidic deposition at 1 km horizontal resolution over the industrializing Terrace-Kitimat Valley of northwestern British Columbia. Quantitative and qualitative correspondence of model outputs with observational data varied with station location, the nature of pollutant emissions, and quantity of chemical species. Valid model outputs were used to delineate present compliance with objectives on ambient fine particulate matter, and baseline exceedance of critical loads of sulfur and nitrogen deposition for the forest ecosystem. Spatial impacts of anticipated industrial emissions on the environment were also assessed. An additional 15 tonnes day-1 permissible SO2 emission from an aluminum smelter in Kitimat was projected to result in 50–88 % increase in aerial exceedance of the limit for protection of lichen, and 37–67 % increase in spatial exceedance of acidic deposition to soils. Cumulatively, 16–18 km2 of plant habitat, and 10–11 km2 of soil in an area contiguous with the smelter site will likely be damaged by its SO2 emission under the latest regulation. Should two Liquefied Natural Gas projects commence operations, cumulative NOx concentrations are expected to remain below harmful levels, while pre-existing areal exceedance of nitrogen deposition will barely increase (0–1 km2). An additional 4 km2 area will be exposed to SO2 concentrationsiii that are directly harmful to vegetation, while 13–14 km2 total area with an average of 29.7–35.0 kg ha-1 yr-1 excess sulfur deposition was estimated. These projections assumed all future emissions of NOx, SO2 and other air pollutants will be from elevated point sources.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".