The release of siloxane from landfill leachate has attracted wide attention. In this study, dodecamethylcyclohexasiloxane (D6) and dodecamethylpentasiloxane (L5) were chosen as the target pollutants to investigate the adsorption characteristics of cyclosiloxane and linear siloxane on municipal solid waste (MSW) under the landfill circumstance. The adsorption behavior could be well described by the Langmuir–Freundlich model, with a maximum adsorption capacity of 0.677 μg g⁻¹ and 15.864 μg g⁻¹ for L5 and D6, respectively. It seems that D6 has a stronger affinity to MSW compared with L5. The maximum adsorption was observed when the pH was 5.0 and 5.5 for D6 and L5. The optical temperature was 30 °C. The organic acid and inorganic ions in the leachate could restrict the adsorption to a low level. The results suggested that the adsorption of siloxane on MSW could be limited at the methanogenic and mature stages, which may promote the distribution of siloxane in the leachate. The results could help to understand the release behavior of siloxane from landfill leachate during the running of a landfill.

The activated sludge process serves as the core barrier in pharmaceutical wastewater treatment, yet its stability is inherently challenged by the extreme complexity of influent composition and the unpredictability of toxic shocks, particularly under contract development and manufacturing organization (CDMO) operations. Current biotoxicity assessment methods face inherent trade-offs among timeliness, specificity, and matrix robustness, resulting in fragmented, reactive management that lacks predictive capacity. In response, this review critically synthesizes evidence on toxicity pathways and monitoring technologies, systematically evaluating their mechanistic basis and engineering applicability. Building on these findings, we propose a conceptual perception–cognition–response architecture that structures decision-making across three adaptive tiers: (i) a perception layer that tolerates false positives for rapid anomaly detection; (ii) a cognition layer that requires effect-based biological verification; and (iii) a response layer that authorizes resilience-oriented interventions. Rather than a linear pipeline, the three tiers form an adaptive feedback cycle that dynamically aligns monitoring intensity, verification depth, and response authority with real-time risk gradients and site-specific constraints. By explicitly linking biological mechanisms to assessment limitations and tiered decision rules, this review provides a hypothesis-generating roadmap that orients biotoxicity management from episodic, composition-based assessment toward adaptive, effect-driven control. The proposed framework is intended to guide future pilot validation, multi-sensor integration, and context-specific calibration, offering a unified narrative for advancing proactive biotoxicity control in complex pharmaceutical wastewater systems.