使用数字映射评估金属污染物的空间变性
Younes Garosi1, Mohsen Sheklabadi2, Shamsollah Ayoubi1
1Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Archives of environmental contamination and toxicology
|September 17, 2025
概括
数字土壤绘图 (DSM) 通过机器学习模型有效预测有毒金属的分布. 土壤特性和水文是关键因素,使污染监测和整治战略成为可能.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 土壤科学 土壤科学
背景情况:
- 有毒金属污染对环境和健康构成重大风险.
- 了解有毒金属的空间分布对于有效管理至关重要.
- 数字土壤绘图 (DSM) 为环境评估提供了一个强大的框架.
研究的目的:
- 使用DSM. 调查戈尔韦平原有毒金属的空间预测.
- 确定影响有毒金属分布的环境共变量.
- 开发对有毒金属污染的准确预测模型.
主要方法:
- 使用了数字土壤绘图 (DSM) 方法.
- 分析了150个土壤样本的有毒金属度和土壤特性.
- 使用遗传算法来识别相关的环境共变量.
- 应用机器学习算法 (随机森林,立方体,回归树) 用于空间预测.
主要成果:
- 随机森林 (RF) 模型展示了最优的预测性能.
- 土壤特性和水文因素被确定为有毒金属分布的主要驱动因素.
- 与引导集成的射频模型产生了强大的预测和不确定性地图.
结论:
- DSM与机器学习相结合,为绘制有毒金属污染的地图提供了一种可行的方法.
- 开发的模型可以帮助监测和优先考虑污染地区的整治工作.
- 确定关键的环境共变量可以提高对污染源和传播的理解.
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