Estimates of Exceedances of Critical Loads for Acidifying Deposition in Alberta and Saskatchewan
Bibliographic record
Abstract
Abstract. Estimates of potential harmful effects to ecosystems in the Canadian provinces of Alberta and Saskatchewan due to acidifying deposition were calculated, using a one year simulation of a high resolution implementation of the Global Environmental Multiscale – Modelling Air-quality and Chemistry (GEM-MACH) model, and estimates of aquatic and terrestrial ecosystem critical loads. The model simulation was evaluated against two different sources of deposition data; total deposition in precipitation and total deposition to snowpack in the vicinity of the Athabasca oil sands. The model captured much of the variability of observed ions in wet deposition in precipitation (observed versus model sulphur, nitrogen and base cation R2 values of 0.90, 0.76 and 0.72, respectively), while being biased high for sulphur deposition, and low for nitrogen and base cations (slopes 2.2, 0.89 and 0.40, respectively). Aircraft-observation-based estimates of fugitive dust emissions, shown to be a factor of ten higher than reported values (Zhang et al., 2017), were used to estimate the impact of increased levels of fugitive dust on model results. Model comparisons to open snowpack observations were shown to be biased high, but in reasonable agreement for sulphur deposition when observations were corrected to account for throughfall in needleleaf forests. The model-observation relationships for precipitation deposition data, along with the expected effects of increased (unreported) base cation emissions, were used to provide a simple observation-based correction to model deposition fields. Base cation deposition was estimated using published observations of base cation fractions in surface collected particles (Wang et al., 2015). Both original and observation-corrected model estimates of sulphur, nitrogen and base cation deposition were used in conjunction with critical load data created using the NEG-ECP (2001) and CLRTAP (2004, 2016, 2017) protocols for critical loads, using variations on the Simple Mass Balance model for forest and terrestrial ecosystems, and the Steady State Water Chemistry and the First-order Acidity Balance models for aquatic ecosystems. Potential ecosystem damage at 2013/14 emissions and deposition levels was predicted for regions within each of the ecosystem critical load datasets examined here. The spatial extent of the regions in exceedance of critical loads varied between 1 × 104 and 3.3 × 105 km2, for the more conservative observation-corrected estimates of deposition, with the variation dependant on the ecosystem and critical load protocol. The larger estimates (for aquatic ecosystems) represent a substantial fraction of the area of the provinces examined. Base cation deposition was shown to have a neutralizing effect on acidifying deposition, and the use of the aircraft and precipitation observation-based corrections to base cation deposition resulted in reasonable agreement with snowpack data collected in the oil sands area. However, critical load exceedances calculated using both observations and observation-corrected deposition suggest that the neutralization effect is limited in spatial extent, decreasing rapidly with distance from emissions sources, due to the rapid deposition of emitted primary particles dust particles as a function of their size.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".