Unveiling underutilized public data on Canadian industrial pollutant transfers and disposals
Bibliographic record
Abstract
Over 50 nations worldwide have Pollutant Release and Transfer Registers (PRTRs), including Canada’s National Pollutant Release Inventory (NPRI), which comprise large public datasets of chemical releases to air, water, and land and also transfers to various on and off-site waste management practices. These inventories aim to support a myriad of audiences in pollution-related decision-making. While the Organisation for Economic Cooperation and Development (OECD) framed a role for PRTRs as indicators for Sustainable Development Goal (SDG) 12 - the sound management of chemicals and wastes, research to date has focused only on air and water releases, omitting vast PRTR data on pollutant transfers to waste management. For Canada’s NPRI, 30 years of waste management transfers data on 250+ chemicals has been collected but rarely used in environmental research. Here we show how this overlooked NPRI data may be used to inform snapshots and trends in progress towards SDG 12 using the OECD’s framework. Results show that over 28 million (M) tonnes (t) of NPRI pollutants have been transferred from industrial facilities to various waste management practices from 2006 to 2021, of which ~10M t were transferred off-site for waste management operations both within and outside Canada. Time trends show pollutant transfer quantities are increasing, driven by on-site disposals to tailings and waste rock management (of mainly phosphorous, manganese and other metals) and underground injection (of mainly hydrogen sulphide). New route maps reveal that interprovincial and international pollutant transfers are common, and that chain of custody analyses is a burgeoning opportunity but hampered by data limitations. The findings create a state of the knowledge launching point for mainstreaming the use of this overlooked data from both Canada’s NPRI and PRTRs around the world, to better track both national and international progress towards sound management of chemicals in waste and SDG 12. <i>Implications</i>: Pollutant transfers and disposals data has not previously been widely used in environmental research. This paper shows how it can be, in the context of SDG 12. Doing so can inspire uptake by researchers and a range of other public users, both strengthening the justification for collecting this data, and bolstering public participation in environmental decision-making from a local to global scale. Doing so also provides the foundation for more in-depth analysis on the domestic and international transboundary movement of Canadian industrial pollutants in waste in the lens of SDG 12 – a topic that was beyond scope here but addressed elsewhere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.280 | 0.003 |
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; both teacher heads agree on what is shown here.
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".