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◆ Environmental Science & Technology2026-02-04· Environmental science

Temporal Evolution in Toxicity Drivers of Shale Gas Flowback and Produced Water: Bridging Compositional Dynamics to Risk Prediction

Yingqi Du, Zengwen Liu, Shuru Chen, Keshuo Zhang, Ling Zhang, Fan Wu, Xiangying Zeng, Zhiqiang Yu, Jing You

原始摘要(英文原文)· Original abstract
Flowback and produced water (FPW) from shale gas extraction poses significant environmental risks due to its complex chemical composition. To address critical knowledge gaps regarding the temporal dynamics of FPW composition and toxicity, we conducted one-year intermittent sampling at an active shale gas site. By integrating whole-effluent toxicity testing with zebrafish embryos, toxicity identification evaluation, and compositional analysis, we identified temporal-specific toxicity transitions driven by temporal chemical variation. During flowback, embryo toxicity (96h-LC 50: 3–45% dilution) showed a nonmonotonic decline, characterized by distinct toxicological stages: organic pollutants dominated the early-stage toxicity (>70% contribution), while joint effects of metals and oxidants governed midflowback toxicity. Principal component analysis resolved divergent chemical trajectories, demonstrating progressive organic contaminant decline alongside cumulative inorganic accumulation. Leveraging these insights, we developed a rapid risk classification system using an optimized LightGBM model with SHAP interpretability. This framework employs three routine monitoring parameters (TDS, TOC, pH) to stratify FPW risk levels with 81.3% accuracy, enabling field-deployable risk management strategies. By elucidating mechanistic shifts in FPW toxicity during continuous flowback, our work established stage-specific treatment priorities while bridging dynamic toxicity mechanisms with practical monitoring parameters to advance sustainable practices for shale gas operations.
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Temporal Evolution in Toxicity Drivers of Shale Gas Flowback and Produced Water: Bridging Compositional Dynamics to Risk Prediction — 科研速览 Science Skim