集成机器学习和正矩阵因子化用于源特定污染和多用途土壤中潜在有毒元素的预测风险评估,围绕活跃煤矿的多用途土壤
Zahid Bashir1, Deep Raj1, Rangabhashiyam Selvasembian1,2
1Department of Environmental Science and Engineering, School of Engineering and Sciences, SRM University-AP Amaravati Andhra Pradesh 522240 India Zahidbashir5175@gmail.com deepraj2587@gmail.com rambhashiyam@gmail.com.
RSC advances
|February 23, 2026
概括
采矿活动污染土壤有毒元素,对人类健康和生态系统构成风险. 这项研究整合了先进的方法,以精确确定污染源并评估针对性环境管理的风险.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 风险评估 风险评估
背景情况:
- 矿山土壤中的潜在有毒元素 (PTEs) 构成环境和健康风险.
- 传统的评估缺乏综合的空间,来源和预测方法.
- 了解PTE分布和来源对于有效管理至关重要.
研究的目的:
- 调查由开放式煤矿影响的土壤中的PTE分布,来源和风险.
- 开发一个源特异性风险评估的综合框架.
- 为目标的土壤整治和环境管理战略提供信息.
主要方法:
- 在印度一座煤矿附近,从五种土地使用类型收集了120个土壤样本.
- 应用正矩阵因数分解 (PMF) 来进行源分配.
- 利用机器学习 (随机森林) 和地理空间分析进行预测和绘图.
- 进行了生态和概率健康风险评估.
主要成果:
- 发现严重的多金属污染,其中Co,Cd和Zn被显著丰富.
- 混合工业采矿活动被确定为主要污染源 (~49%).
- 与Cd和Hg相关的高生态风险,特别是在农业和路边土壤中.
- 对当地居民,特别是儿童而言,发现了不可接受的健康风险,由Cr and Co.驱动.
结论:
- 综合框架有效地识别了污染源,并评估了采矿影响土壤中的风险.
- 煤矿和相邻的农业区是污染的关键热点.
- 需要紧急的补救和管理策略,以减轻健康和生态风险,特别是在儿童等弱势群体.
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