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Record W4411277621 · doi:10.1016/j.envint.2025.109602

Short-chain PFASs dominance and their environmental transport dynamics in urban water systems: Insights from multimedia transport analysis and human exposure risk

2025· article· en· W4411277621 on OpenAlexaboutno aff
Kunfeng Zhang, Abdul Qadeer, Sheng Chang, Xiang Tu, Hongru Shang, Moonis Ali Khan, Yingying Zhu, Qing Fu, Yanling Yu, Yujie Feng

Bibliographic record

VenueEnvironment International · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsnot available
FundersChinese Research Academy of Environmental SciencesKey Research and Development Program of Heilongjiang
KeywordsDominance (genetics)Environmental scienceEcologyBiologyGenetics

Abstract

fetched live from OpenAlex

The increasing prevalence of short-chain per- and polyfluoroalkyl substances (PFASs) poses significant challenges for urban water systems (UWS) due to their persistence, high mobility, and widespread occurrence. This study provides a comprehensive assessment of the occurrence, environmental transport, influencing factors, removal efficiency, and human health risks of PFASs across UWS impacted by industrial activities. Regulatory restrictions on long-chain PFASs have led to their replacement with short-chain analogues, resulting in their dominance in effluents from manufacturing plant parks (MPPs), with concentrations ranging from 30.28 to 3738.51 ng/L (mean: 557.68 ± 1072.03 ng/L). Wastewater treatment plant (WWTP) serve as both sources and sinks of PFASs, with a negative average removal efficiency (–47.4 %) and an estimated annual discharge of 12.29 kg of PFASs into the environment. Downstream of WWTP, PFASs concentrations in rivers decrease exponentially due to dilution and sediment partitioning; however, short-chain PFASs persist over long distances due to their high aqueous mobility. While the detected PFAS levels in rivers pose low health risks to humans, they present low to medium ecological risks to aquatic organisms, particularly algae, invertebrates, and fish. Advanced statistical analyses using piecewise structural equation modeling (piecewiseSEM) and co-occurrence network analysis (CNA) identified key environmental drivers of PFASs behavior, including heavy metals (effect size: 0.70), nutrient levels (0.36), and physicochemical parameters (–0.52). Furthermore, drinking water treatment plants (DWTPs) demonstrated limited removal efficiency, with tap water concentrations ranging from 27.95 to 84.72 ng/L, exceeding the regulatory limits set by the US EPA (2022) (PFOA: 0.004 ng/L, PFOS: 0.02 ng/L) and Health Canada (Σ 25 PFAS: 30 ng/L). These findings underscore the urgent need for enhanced regulations, the development of sustainable alternatives, and the implementation of advanced treatment technologies to mitigate the environmental and public health risks associated with short-chain PFASs.

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 score0.021
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.208
Teacher spread0.203 · 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

Citations49
Published2025
Admission routes1
Has abstractyes

Explore more

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