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相关概念视频

Gravimetry: Overview01:05

Gravimetry: Overview

Gravimetric analysis is a quantitative method where the analyte is isolated and weighed directly or after conversion into a substance of known composition. Gravimetric analysis can be classified as precipitation, electrogravimetry, volatilization, and particulate gravimetry, based on the method used to isolate the analyte.
In precipitation gravimetry, the analyte is converted into a precipitate and weighed. For example, the silver content in a sample can be estimated by precipitating and...
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Microbial leaching, also known as bioleaching, is an environmentally favorable method for extracting metals from low-grade ores using specific microorganisms. This biotechnological approach is particularly valuable for mining operations targeting copper, gold, and uranium, where traditional extraction methods may be economically or environmentally impractical.Copper Leaching and Microbial CatalysisIn copper bioleaching, crushed ore is arranged into heaps and irrigated with a dilute sulfuric...

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相关实验视频

Updated: Jun 3, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation
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用机器学习模型预测和评估大米土壤中的重金属污染的一般方法框架.

Unurnyam Jugnee1,2, Le Jiao3,4,5, Sainbayar Dalantai1

  • 1Division of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.

Heliyon
|January 1, 2026
PubMed
概括

这项研究开发了一种机器学习框架,用于预测大米田中的重金属污染,识别关键驱动因素并绘制污染地区的地图. 结果突出了中度到严重的污染,从西到东恶化,对土壤保护至关重要.

关键词:
这是一种重金属,重金属.湖南省 湖南省 湖南省机器学习 机器学习这里是大米土壤.污染预测 污染预测

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科学领域:

  • 环境科学 环境科学
  • 土壤科学 土壤科学
  • 地质化学 地质化学

背景情况:

  • 田中的重金属污染威胁着生态和人类健康.
  • 现有的研究主要集中在源分配上,缺乏强大的空间预测模型.
  • 准确预测重金属的空间分布和驱动机制对于有效管理至关重要.

研究的目的:

  • 开发和评估一个一般的方法框架,用于预测和评估在田中的重金属污染.
  • 为了确定重金属空间分布背后的驱动机制.
  • 想象重金属污染的空间分布,并评估污染负载指数.

主要方法:

  • 使用的机器学习算法:随机森林 (RF),额外树回归器 (ETR),极端梯度增强回归 (XGBR) 和梯度增强回归树 (GBRT).
  • 利用沙普利增量解释 (SHAP) 来确定各种因素对预测模型的贡献.
  • 分析了气候变量作为重金属含量的潜在预测因素.

主要成果:

  • 射频在预测 (As), (Cr),铜 (Cu) 和 (Hg) (R2 > 0.70) 方面表现出色.
  • 对于 (Cd), (Zn) 和 (Pb) (R2 > 0.40),ETR表现良好.
  • 对于 (Ni) (R2 = 0.61),GBRT是有效的. 气候变量是重要的预测因素. 空间分析显示,79.8%的区域中等污染和20.2%严重污染,污染向东增加.

结论:

  • 开发的机器学习框架提供了准确的预测重金属在田的空间分布.
  • 气候变量在重金属积累中起着重要作用.
  • 该研究发现了广泛的中度至严重污染,强调需要有针对性的土壤污染控制和保护工作,特别是在东部地区.