在低精度采样条件下优化对土壤微量金属的健康风险评估:农业土壤的案例研究
Yafeng Liu1, Feng Xu1, Huijuan Wang2
1School of Resources and Environment, Anqing Normal University, Anqing 246133. China.
The Science of the total environment
|June 11, 2024
概括
低精度的土壤采样可以扭曲微量金属 (TM) 的健康风险评估. 这项研究引入了使用实证贝叶斯战争 (EBK) 和蒙特卡洛模拟 (MCS) 改进的健康风险评估 (HRA) 模型,以提高准确性.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 风险评估 风险评估
背景情况:
- 由于成本的限制,在大面积的土壤采样网往往需要更低的精度.
- 这可能导致不准确的微量金属 (TM) 度数据和错误的健康风险评估.
- 现有的方法在数据限制下难以准确评估风险.
研究的目的:
- 开发一个增强的健康风险评估 (HRA) 模型,在低精度土壤采样下提高准确性.
- 将蒙特卡洛模拟 (MCS) 与实证贝叶斯式战争 (EBK) 结合起来,以获得更可靠的TM度预测.
- 为了减轻与不同采样尺度相关的健康风险的高估或低估.
主要方法:
- 开发了一个增强的HRA模型,集成MCS和EBK.
- 应用该模型来预测不同采样尺度 (500m,1000m) 的土壤TM度.
- 评估模型在减少风险估计错误方面的表现.
主要成果:
- 增加的抽样规模高估了儿童的非致癌风险 (危险指数高达1.64).
- EBK有效地预测了土壤TM度,产生了更接近大规模实际度的值.
- 结合EBK-MCS HRA模型减少了风险估计偏差,提高了评估准确性和可靠性.
结论:
- 增强的HRA模型提供了强大的方法论,以有限的数据进行土壤TM健康风险评估.
- EBK适用于大规模土壤采样中预测TM度.
- 即使使用较低精度的采样网,也可以通过先进的建模实现准确的风险评估.
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