在严重污染的采矿环境中,以数据驱动的高精度模拟酸度行为
Ana Barroso1, Teresa Maria Valente2, Amélia Paula Marinho Reis2,3
1ICT - Institute of Earth Sciences, Pole of University of Minho, University of Minho, Braga, Portugal. id9873@uminho.pt.
Scientific reports
|September 30, 2025
概括
精确估计酸性矿井排水 (AMD) 酸度至关重要. 使用多重线性回归 (MLR) 的新特定场所模型准确预测AMD酸度,优于传统方法.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 评估水质水质的评估.
背景情况:
- 准确的酸度估计对于酸性矿井排水 (AMD) 的表征和修复至关重要.
- 像定位和铁物种化模型这样的传统方法与特定地点的地化学复杂性作斗争.
- 在复杂的AMD环境中,由于空间和季节性水化学变异性,一般化模型往往会失败.
研究的目的:
- 开发一个高精度的,特定地点的经验模型,用于预测AMD影响的水域中的酸度.
- 在复杂的AMD环境中评估传统酸度估计方法的局限性.
- 为了证明针对特定地点校准的多变量模型的实用性.
主要方法:
- 在Trimpancho矿山综合体 (西班牙伊比利亚金矿带) 收集现场数据.
- 多重线性回归 (MLR) 的应用在模型开发中.
- 与标准的海丁方法对比开发模型的性能.
主要成果:
- 使用Cu,Al,Mn,Zn和pH (R2=99.2%) 建立了一个强大的AMD酸度预测模型.
- 特定地点的MLR模型显著优于Hedin方法 (13%对29%的平均绝对百分比误差).
- 观察到强大的空间和季节性水化学变异性,突出显示了对局部化模型的需求.
结论:
- 现场校准的多变量模型为复杂的AMD系统中酸度预测提供了实用和准确的方法.
- 开发的模型为特林潘乔遗址提供了优越的替代方案,而不是一般化的方法.
- 这项研究强调了在AMD管理中考虑特定地点的地质化学的重要性.
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