Cost of reactive nitrogen release from human activities to the environment in the United States
Bibliographic record
Abstract
Leakage of reactive nitrogen (N) from human activities to the environment can cause human health and ecological problems. Often these harmful effects are not reflected in the costs of food, fuel, and fiber that derive from N use. Spatial analyses of damage costs attributable to source at management-relevant scales could inform decisions in areas where anthropogenic N leakage causes harm. We used recently compiled data describing N inputs in the conterminous United States (US) to assess potential damage costs associated with anthropogenic N. We estimated fates of N leaked to the environment (air/deposition, surface freshwater, groundwater, and coastal zones) in the early 2000s by multiplying watershed-level N inputs (8-digit US Geologic Survey Hydrologic Unit Codes; HUC8s) with published coefficients describing nutrient uptake efficiency, leaching losses, and gaseous emissions. We scaled these N leakage estimates with mitigation, remediation, direct damage, and substitution costs associated with human health, agriculture, ecosystems, and climate (per kg of N) to calculate annual damage cost (US dollars in 2008 or as reported) of anthropogenic N per HUC8. Estimates of N leakage by HUC8 ranged from <1 to 125 kg N ha −1 yr −1 , with most N leaked to freshwater ecosystems. Estimates of potential damages (based on median estimates) ranged from $1.94 to $2255 ha −1 yr −1 across watersheds, with a median of $252 ha −1 yr −1 . Eutrophication of freshwater ecosystems and respiratory effects of atmospheric N pollution were important across HUC8s. However, significant data gaps remain in our ability to fully assess N damages, such as damage costs from harmful algal blooms and drinking water contamination. Nationally, potential health and environmental damages of anthropogenic N in the early 2000s totaled $210 billion yr −1 USD (range: $81–$441 billion yr −1 ). While a number of gaps and uncertainties remain in these estimates, overall this work represents a starting point to inform decisions and engage stakeholders on the costs of N pollution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".