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 (δ 15 N water ) as a predictor explained c . 60% of phototroph deviance. Prior to BNR, phytoplankton levels (mainly chlorophytes) increased with ‐rich effluent, whereas their abundance declined with δ 15 N 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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".