A 150-year phosphorus budget for the Thames catchment, UK
Bibliographic record
Abstract
Phosphorus (P) is critical for food production but rising P inputs to agricultural land have contributed to eutrophication of fresh and marine waters. Concurrently, wastewater effluent from increasing populations has also become a major P input to natural waters, particularly in urbanised catchments. This study considers the long-term phosphorus budget of the River Thames catchment from 1867 to the present. We combine databases of agricultural land use, human population and river monitoring to develop a phosphorus budget model for the gauged catchment area (9,948 km2) and identify key inputs, outputs and transfers over the period. We quantify P imports and exports of fertilizer, food, feedstuffs, and industrial products (1867-2017), along with direct discharge of fluvial P at the tidal limit (1936-2017). Net P input to land from animal production was essentially stable at ~1,700 tonnes P until 1940, after which there was a steady rise, peaking at approximately 3,800 tonnes P in the early 1970s. Since then, P inputs to land have fallen to a current stable level of ~2,200 tonnes P. This represents a cumulative net input to land of 350 kT P since 1867. Whilst this input is somewhat counterbalanced by losses to the fluvial system and crop harvest, there is nevertheless a large P legacy in catchment soils. Net inputs from wastewater (urine and faeces) rose steadily from 0.8 kT in 1936 to 2 kT in 2010, whilst the marked change occurred in relation to P in detergents rising from zero in 1950 to a peak of ~2kT in 1987, since when there has been a gradual decline to <1 kT at present. The total wastewater effluent contribution rose from 0.8 kT in 1936 to a peak of 3.4 kT at the end of the 1980s. The Urban Waste Water Treatment Directive (91/271/EEC) enforced enhanced removal of P in wastewater from the early 1990s, which led to an immediate, sharp decrease in wastewater contribution of 1 kT P since when there has been a steady decline to 0.4 kT at present. This has shifted the environmental pathway of wastewater P from discharge to rivers to accumulation in sludge which is now largely disposed of by application to agricultural land thus adding to the P legacy in catchment soils. Our analysis of the Thames P budget will end with a discussion of uncertainties in the P model, and the sensitivity of our overall conclusions to assumptions about model structure and parameters applied to our historical records.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".