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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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在土壤中模拟PFAS吸附,使用机器学习.

Joel Fabregat-Palau1, Amirhossein Ershadi1, Michael Finkel1

  • 1Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, Tübingen 72076, Germany.

Environmental science & technology
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PubMed
概括
此摘要是机器生成的。

一个新的机器学习工具,PFASorptionML,准确地预测了土壤中的和多基物质 (PFAS) 吸附. 它确定了分子量和有机碳等关键因素,有助于环境风险评估.

关键词:
Kd 灵敏度 Kd 的灵敏度数据集数据集数据集.空间地图空间地图种类的变化 种类的变化堆叠模型的堆叠模型

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科学领域:

  • 环境化学环境化学
  • 土壤科学 土壤科学
  • 计算化学的计算化学

背景情况:

  • 和多基基物质 (PFAS) 是持久的环境污染物.
  • 了解 PFAS 在土壤中的吸收对于风险评估至关重要.
  • 预测PFAS吸附系数 (Kd) 的现有模型存在局限性.

研究的目的:

  • 开发和验证一种新的机器学习 (ML) 工具,PFASorptionML,用于预测PFAS在土壤中的固体-液体分布系数 (Kd).
  • 确定影响吸附行为的关键PFAS和土壤特性.
  • 为环境风险评估提供一个用户友好的平台.

主要方法:

  • 开发了PFASorptionML,使用了1,274Kd条目的数据集,用于土壤和沉积物中的各种PFAS.
  • 纳入的PFAS属性 (分子量,疏水性,pKa) 和土壤特征 (pH,质地,有机碳,CEC).
  • 进行灵敏度分析以确定不同参数对Kd值的影响.

主要成果:

  • PFASorptionML表现出高预测性能,RPD>3.16,超过现有工具的性能.
  • 分子重量,疏水性和土壤有机碳含量被确定为影响PFAS吸附的最重要的因素.
  • PFAS链条长度和功能组显著影响Kd,链条更长,水性更高,增加吸附.

结论:

  • PFASorptionML是一种可靠且准确的工具,用于预测土壤中的PFAS Kd.
  • 该模型集成特定位置数据的能力使空间Kd映射成为可能.
  • PFAS吸收ML是环境风险评估和PFAS污染管理的宝贵资源.