High-resolution quantification of atmospheric CO <sub>2</sub> mixing ratios in the Greater Toronto Area, Canada
Bibliographic record
Abstract
Abstract. Many stakeholders are seeking methods to reduce carbon dioxide (CO2) emissions in urban areas, but reliable, high-resolution inventories are required to guide these efforts. We present the development of a high-resolution CO2 inventory available for the Greater Toronto Area and surrounding region in Southern Ontario, Canada (area of ∼ 2.8 × 105 km2, 26 % of the province of Ontario). The new SOCE (Southern Ontario CO2 Emissions) inventory is available at the 2.5 × 2.5 km spatial and hourly temporal resolution and characterizes emissions from seven sectors: area, residential natural-gas combustion, commercial natural-gas combustion, point, marine, on-road, and off-road. To assess the accuracy of the SOCE inventory, we developed an observation–model framework using the GEM-MACH chemistry–transport model run on a high-resolution grid with 2.5 km grid spacing coupled to the Fossil Fuel Data Assimilation System (FFDAS) v2 inventories for anthropogenic CO2 emissions and the European Centre for Medium-Range Weather Forecasts (ECMWF) land carbon model C-TESSEL for biogenic fluxes. A run using FFDAS for the Southern Ontario region was compared to a run in which its emissions were replaced by the SOCE inventory. Simulated CO2 mixing ratios were compared against in situ measurements made at four sites in Southern Ontario – Downsview, Hanlan's Point, Egbert and Turkey Point – in 3 winter months, January–March 2016. Model simulations had better agreement with measurements when using the SOCE inventory emissions versus other inventories, quantified using a variety of statistics such as correlation coefficient, root-mean-square error, and mean bias. Furthermore, when run with the SOCE inventory, the model had improved ability to capture the typical diurnal pattern of CO2 mixing ratios, particularly at the Downsview, Hanlan's Point, and Egbert sites. In addition to improved model–measurement agreement, the SOCE inventory offers a sectoral breakdown of emissions, allowing estimation of average time-of-day and day-of-week contributions of different sectors. Our results show that at night, emissions from residential and commercial natural-gas combustion and other area sources can contribute > 80 % of the CO2 enhancement, while during the day emissions from the on-road sector dominate, accounting for > 70 % of the enhancement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".