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<p>Emerging contaminants are reshaping ecosystems, yet our understanding of their community- and system-level impacts is rather limited, because conventional assessments oversimplify the real problem by focusing on single chemicals or species. A big-data-integration framework that links chemical measures with biological responses to elucidate hidden drivers of emerging contaminants’ environmental risks can help address the challenge. This Perspective discusses how to make such a framework actionable by employing high-throughput techniques, using data-driven tools that handle high-dimensional data and sharing paired chemical–biological datasets. Such integration can enable earlier intervention on key emerging contaminants to improve the sustainability of ecosystems.</p>

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Big data integration for environmental risk assessment of emerging contaminants

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Han-Lin Cui,
  • Shu-Hong Gao,
  • Hong-Cheng Wang,
  • Li-Ying Zhang,
  • Yi Luo,
  • Guang-Guo Ying,
  • Wei-Ling Sun,
  • Yun-Jiang Yu,
  • Bin Liang,
  • Ai-Jie Wang

摘要

Emerging contaminants are reshaping ecosystems, yet our understanding of their community- and system-level impacts is rather limited, because conventional assessments oversimplify the real problem by focusing on single chemicals or species. A big-data-integration framework that links chemical measures with biological responses to elucidate hidden drivers of emerging contaminants’ environmental risks can help address the challenge. This Perspective discusses how to make such a framework actionable by employing high-throughput techniques, using data-driven tools that handle high-dimensional data and sharing paired chemical–biological datasets. Such integration can enable earlier intervention on key emerging contaminants to improve the sustainability of ecosystems.