用无监督学习发现加纳土壤重金属异常，精准定位高风险污染点。
Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment
- 采用孤立森林与主成分重构误差检测异常样本
- 6个稳健异常点集中于单一点位，健康风险指数超1倍以上
- 可帮助精准识别污染热点，支持环境风险管控决策
加纳快速城市化区域的土壤重金属污染是持续存在的环境与公共健康问题，尤其在非正规垃圾处置点。本研究应用无监督机器学习框架，分析加纳中部地区12个垃圾点及居民区对照点共78份土壤样本中8种金属（As、Cd、Cr、Cu、Hg、Ni、Pb、Zn）的浓度，并结合健康风险指数（HI和ILCR）。孤立森林与主成分重构误差各识别出12个异常样本（占15.4%），而DBSCAN未检测到密度孤立噪声点。共识方法最终确定6个稳健异常样本（7.7%），全部集中于单一采样点S3。异常样本的平均HI值比正常样本高出70%–80%，所有共识异常均超过HI=1阈值。主成分重构误差与HI呈强正相关（r≈0.8），表明多变量偏离与健康风险一致。识别出三类异常：S3处极端铜富集，S4/S5处镍异常偏低，S9–S12处铅-锌协同升高。结果表明，无监督学习能提供超越整体指标的精细、客观洞察，助力污染点靶向优先排序与风险导向管理。
原文摘要 · Abstract (English)
Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at unregulated waste disposal sites. This study applies an unsupervised machine learning framework to detect and characterise anomalous heavy metal contamination patterns in soils from twelve waste sites and residential controls in the Central Region, of Ghana. Concentrations of eight metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) were analysed alongside standard health risk indices, including the Hazard Index (HI) and Incremental Lifetime Cancer Risk (ILCR). Isolation Forest and PCA reconstruction error each identified $12$ anomalous samples ($15.4\%$ of $78$ samples), while DBSCAN detected no density-isolated noise points. A consensus approach isolated six robust anomalies ($7.7\%)$, all spatially concentrated at a single site (S3). Anomalies exhibited approximately $70$--$80\%$ higher mean HI values than normal samples, with all consensus anomalies exceeding the HI$=1$ threshold. PCA reconstruction error showed a strong positive association with HI ($r \approx 0.8$), indicating consistency between multivariate deviation and health risk. Three distinct anomaly types were identified: extreme Cu enrichment at S3, anomalously low Ni at S4/S5, and moderate multi-metal (Pb--Zn) co-elevation at S9--S12. The results demonstrate that unsupervised machine learning provides granular, objective insight beyond aggregate indices, enabling targeted site prioritisation and risk-informed environmental management.
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