【摘要】 在工业化和城镇化加速背景下,新污染物不断涌现,由于其具有环境持久性、生物累积性和生物毒性等特性,严重威胁人群健康,已成为全球关注的重大公共卫生问题之一.本文系统梳理了新污染物的人群暴露特征、多终点健康危害、效应分子机制及预测预警体系,深入剖析国内外研究现状与核心挑战,提出未来研究的关键方向.针对我国新污染物人群研究面临全生命过程健康危害证据链断裂、健康导向管控体系缺失等难题,未来研究亟需聚焦四大关键领域:在暴露评估方面,发展多介质全景式检测技术,建立新污染物多维动态监测网络;在健康效应方面,突破单一污染物评价范式,揭示复合暴露危害,构建全人群健康效应谱和生物标志物体系;在毒性机制方面,利用新途径技术方法(new approach methodologies, NAMs)阐明非传统作用靶点与信号通路,建立本土化毒理数据库;在风险预警方面,融合多源异构数据与人工智能算法,开发机器学习驱动的健康风险预测模型,构建数智化预警平台及响应机制.未来应以人群健康为中心、以科学发展为引擎、以技术创新为基石,拓展新污染物人群健康危害研究的深度和广度,推动以健康为导向的新污染物治理高质量发展,为全球环境与健康协同发展贡献中国智慧和力量.更多还原

【Abstract】 Accelerated industrialization and urbanization have precipitated the global emergence of persistent, bioaccumulate, and toxic environmental contaminants—collectively termed emerging contaminants(ECs). These substances pose increasingly complex, transboundary, and systemic threats to public health. Therefore, they demand urgent, interdisciplinary, and innovation-driven interventions at the science–policy interface. Against this backdrop, this review synthesizes current evidence on human-exposure patterns, multi-endpoint health effects, molecular toxicodynamics, and predictive risk frameworks for ECs, and critically examines knowledge gaps and methodological limitations in existing research. In the context of China, effective EC management is hindered by fragmented evidence of life-course health effects and inadequate health-oriented regulatory infrastructure. The rapidly evolving chemical landscape and insufficient integration of health data exacerbate these challenges. To address them, we propose an integrated research framework prioritizing the following four transformative domains.(1) Advanced exposure surveillance. Multi-media, high-resolution analytical platforms and nationwide spatiotemporal monitoring networks are needed to capture dynamic EC profiles across environmental matrices and biological samples. This requires overcoming bottlenecks in ultra-trace detection and non-targeted screening, and harmonizing multi-pathway exposure assessments, particularly for vulnerable populations and high-risk occupational cohorts. Leveraging next-generation exposure measurement technologies and realtime data analytics will enable adaptive, precise monitoring and provide early warnings for EC hotspots. Such networks will enhance our ability to track and predict the spread of ECs and allow for targeted interventions.(2) Holistic health impact assessment. A transition from reductionist, single-pollutant models to systems toxicology approaches is essential. Key priorities include constructing population-scale health-effect spectra and exposome-wide biomarker panels to comprehensively account for real-world mixture exposures. Leveraging functional exposomics, integrated with longitudinal cohort studies and multi-omics technologies, will elucidate critical life-course exposure windows and latent health risks. Integrating environmental, clinical, and socio-behavioral data will enable nuanced risk stratification and tailored public health responses. This comprehensive approach will be crucial for developing effective public health strategies.(3) Mechanistic toxicology innovation. New approach methodologies(NAMs) integrating organoids, CRISPR screening, multi-omics, and computational biology can be used to explore non-monotonic dose responses, identify novel toxicity pathways, and decipher mixture interactions. Establishing a robust, open-access localized toxicological database is essential to support next-generation risk assessment and adverse outcome pathway development. Synergizing in vitro, in vivo, and in silico models will accelerate mechanism-based discovery and translational application to improve understanding of the mechanisms underlying EC effects on human health for more precise interventions.(4) Intelligent risk forecasting. Implementing artificial intelligence(AI)-driven predictive frameworks that assimilate multi-omics data streams, environmental monitoring datasets, and population health records through deep-learning architectures will facilitate real-time risk stratification, early intervention, and the development of dynamic health-based guidance values. Concurrently, quantification of ECattributable disease burdens and intervention benefits is vital to inform evidence-based policy. AI-driven approaches can enhance the speed and accuracy of risk assessments to facilitate proactive public health measures. We advocate for paradigm shifts in environmental health research and governance from hazard identification to prevention-oriented governance, from isolated investigations to transdisciplinary convergence(e.g., exosomomics–AI integration), and from reactive regulation to anticipatory intelligence. Future efforts must prioritize high-risk ECs(e.g., per-and polyfluoroalkyl substances, microplastics, and endocrine disruptors), strengthen “big-data” national biomonitoring alongside “deep-mechanism” granular studies, and foster global data/knowledge sharing. This strategic roadmap aligns scientific rigor, digital innovation, and public health imperatives to catalyze sustainable EC mitigation, safeguard population health, and position China at the forefront of next-generation environmental governance and global stewardship. Embracing these changes will protect public health and promote a sustainable future.更多还原