Effects of nitrogen removal from wastewater on phytoplankton in eutrophic prairie streams
Bibliographic record
Abstract
Abstract Biological nutrient removal (BNR) may be an effective strategy to reduce eutrophication; however, concerns remain about effects on receiving waters of removing both nitrogen (N) and phosphorus (P), rather than P alone. Phytoplankton abundance (as µg chlorophyll a/L) and community composition (as nmol biomarker pigment/L) were quantified over 6 years in two connected eutrophic streams to determine how algae and cyanobacteria varied in response to a shift from tertiary (P removal) to BNR (N and P removal) wastewater treatment. Phytoplankton were sampled biweekly at nine stations May to September and were analysed using generalised additive models (GAMs) to quantify landscape patterns of phototrophs and identify potential causal relationships both before (2010–2012) and after (2017–2019) BNR installation in 2016. Analysis with GAMs showed that 69%–79% of deviance in phytoplankton abundance and composition could be explained by date‐ and site‐specific variance in stream flow, temperature, and solute concentrations (mainly nutrients), whereas similar GAMs using only effluent N content (δ15Nwater) as a predictor explainedc. 60% of phototroph deviance. Prior to BNR, phytoplankton levels (mainly chlorophytes) increased with ‐rich effluent, whereas their abundance declined with δ15N after BNR (diatoms, chlorophytes). Overall, declines in total effluent release of N (67%–97%) but not P (c. 0%) due to BNR resulted in a 52 ± 7% decline in phytoplankton abundance relative to upstream values, despite high inter‐annual variation in discharge and baseline chlorophyll a concentration. Nitrogen removal by BNR improved water quality in N‐limited ecosystems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".