Using net anthropogenic nutrient inputs at fine spatial scales benefits decision‐making in watersheds to protect water quality
Bibliographic record
Abstract
Abstract Excessive nutrient loadings from various human activities on land to inland and coastal waters result in water quality degradation through cultural eutrophication. Accurately identifying and quantifying the main nitrogen and phosphorus sources on land is essential to implement proper intervention strategies to reduce loadings in order to preserve water quality for the long term. The Net Anthropogenic Nitrogen/Phosphorus Inputs (NANI/NAPI) mass balance approach has successfully been used to quantify nutrients added on land to sustain human activities and predict the fraction exported to water. However, the rich detail of information used to generate the mass balance is typically merged and upscaled to whole watersheds, averaging out highly heterogeneous patterns on land, which would be key to identify regions that disproportionately load nutrients to receiving waters. Here we present NANI and NAPI at increasingly finer scales (watershed, county, and municipal) in eight catchments of the Saint‐Lawrence Basin in Québec, Canada, and compare input changes between two different decades (1981 and 2021). In 2021, NANI ranged from 828 to 6602 kg km−2 at the watershed scale whereas the range at the municipal one was much greater from near 0 to 27,413 kg km−2, highlighting the importance of using the municipal scale to pinpoint specific regions of high inputs. We then show how partitioning individual NANI components (the same could be done for NAPI) could reveal the dominant input type among urbanisation, crop‐intensive, or livestock‐intensive agriculture, which can help decision makers choose the most effective intervention strategy based on dominant input type. Finally, we discuss how information derived over time can further be used to enhance understanding around mitigation strategies. Policy implications. The use of the detailed components of NANI and NAPI at the finest scale possible provides relevant quantitative information and acts as an effective communication tool between scientists and decision makers. We suggest that the use of this approach can better enable targeted nutrient reduction strategies based on scientific evidence to protect water quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".