中国P站

Abstract

Ecological risk assessment (ERA) is a critical tool for evaluating the potential impacts of chemical contaminants on ecosystems. Toxicological data, derived from laboratory studies on individual organisms, provide the foundational evidence for understanding chemical toxicity. However, translating these data into meaningful ecological risk predictions remains a significant challenge due to differences in scale, complexity, and ecological interactions. This article explores the methodologies and frameworks used to link toxicological data to ERA, emphasizing the integration of species sensitivity, exposure pathways, and ecosystem-level effects. We discuss key approaches such as species sensitivity distributions (SSDs), probabilistic risk assessment, and the incorporation of field data to bridge the gap between controlled experiments and real-world outcomes. Challenges, including data variability, extrapolation across species, and the influence of environmental factors, are examined alongside emerging solutions like computational modeling and multi-tiered assessment frameworks. We conclude that while significant progress has been made, interdisciplinary collaboration and advancements in data integration are essential for enhancing the predictive power of ERA and ensuring effective environmental management.