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Record W7000001534

Effects of urban pollution on stream ecosystem functioning

2019· dissertation· en· W7000001534 on OpenAlexfundno aff

Bibliographic record

VenueCommunities in ADDI (University of the Basque Country) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsnot available
FundersEuskal Herriko UnibertsitateaEusko JaurlaritzaMinisterio de Ciencia e InnovaciónCanadian Institute for Advanced Research
KeywordsUrban streamEcosystemPollutionUrban ecosystemUrbanizationClimate changeEcosystem health
DOInot available

Abstract

fetched live from OpenAlex

During the last century pollution generated in urban areas has become a prevalent impact in most streams and rivers worldwide.Although many waste water treatment plants (WWTP) have been implemented to reduce pollution, they do not eliminate it completely and their effluents still contribute complex mixtures of pollutants.The ecological effects of these effluents depend on their chemical composition and the final concentration in the receiving water body, which varies with the water flow of the receiving river or stream.While the effects of urban effluents on stream water quality are relatively well studied, we still lack a clear picture about their effects on ecosystem processes.This dissertation analysed the effects of urban pollution on stream ecosystem functioning, by combining observational and manipulative experiments.The effects of pollution can be exacerbated when stressors operate in concert.Among these, hydrological alterations are of special concern, either natural or human-induced, as low flows reduce the dilution capacity of the streams.Thus, I first assessed the joint effects of urban pollution and water stress on stream functioning.I studied several ecosystem processes in 13 Mediterranean streams of contrasting dilution capacity, and defined in each stream a control site and an impact site, upstream and downstream from the sewage inputs, respectively.Urban effluents caused complex effects on ecosystem functioning, but most ecosystem rates increased in proportion to pollutant subsidies of labile organic matter, nutrients and pharmaceutically active compounds.The main driver of the variability observed in ecosystem functioning, were variations in water chemistry, especially in the concentration of pharmaceutical drugs.These results show the effects of pollution to depend on the effluent nature and its dilution in the receiving stream, and that even highly concentrated urban effluents tend to subsidize biological activity.The effects of WWTP effluents are difficult to measure in the field by comparing reaches upstream and downstream from the effluent input, as the upstream sites are usually affected by other sources of pollution.This situation calls for a manipulative experiment, which I performed following a Before-After/Control-Impact design.Part of the effluent of a large tertiary urban WWTP was diverted into a small nearly unpolluted stream and the effects were assessed during a whole year.Despite of being highly diluted, the effluent subsidized most of the measured processes, only a few remained unaffected, and biofilm nutrient uptake capacity was the only being reduced.These results show that even highly diluted effluents can exert significant effects on stream ecosystem functioning.Theoretically, we expected WWTP effluents to cause a subsidy-stress response on ecosystem functioning depending on their final dilution, but the two studies so far discussed showed no such a pattern.Therefore, we performed a laboratory experiment in which a series of artificial streams were subjected to a gradient of WWTP effluent concentration, ranging from pure stream water to pure effluent.Biofilm biomass accrual was the only process following the subsidy-stress pattern, whereas WWTP effluent subsidized most processes, although they showed complex responses to their

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.158
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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