Use of the National Pollutant Release Inventory in environmental research: a scoping review
Bibliographic record
Abstract
The National Pollutant Release Inventory (NPRI), which has been collecting and disseminating pollutant data since 1994, is Canada’s legislated, publicly accessible inventory of pollutant releases (to air, water, and land), disposals, and transfers (for treatment, recycling, or energy recovery). The public availability of NPRI data is a key program output, initially driven by the community-right-to-know movement and now also compelled by the need to support a myriad of environmental science and policy efforts at various scales. Twenty-five years after the inception of the NPRI, this scoping review of peer-reviewed literature (up to 2019) was undertaken to better understand the nature and extent of uptake of NPRI information by researchers, namely, who are using it and how. The findings show that NPRI use in peer-reviewed research has increased steadily since 1997. NPRI information is implicated in 225 scholarly journal articles between 1994 and 2019. The main users are from the Government of Canada and Canadian universities, though many users from diverse backgrounds beyond these categories and beyond Canada were also noted. Researchers were primarily leveraging NPRI data on pollutants released to air, the focus of which was most often on the criteria air contaminants and metals (mercury). Less popular were data on water releases, land releases, and disposal data, while there were no examples of researchers using data on transfers. Seven prominent themes arose pertaining to the area(s) of interest of studies that use NPRI information, including geospatial analyses, environmental monitoring, predictive modelling, industrial sectors, other pollutant inventories, human health outcomes, and policy or program analysis. Several other study themes were also noted relating to socioeconomic issues, waste treatment and remediation, climate change, indigenous groups, and biomonitoring. Future opportunities to increase NPRI use in research in general, and in understudied areas in particular, as well as to increase the use of underutilized NPRI variables, remain.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".