Zhao Yangfan, Wang Yinggang, Wu Hao, Wang Hui, Yue Jingpeng, Gao Ziyang, Deng Jinliang, Li Xiaojun
Key Laboratory of Eco-Restoration of Regional Contaminated Environment, Ministry of Education, Shenyang University, Shenyang, 110044, China.
Environmental College, Shenyang University, Shenyang, 110044, China.
Environ Geochem Health. 2025 Aug 7;47(9):364. doi: 10.1007/s10653-025-02683-7.
Soil contamination is a significant threat to global food security and public health. Accurate apportionment of pollutant sources is a prerequisite for developing science-driven pollution control protocols. This research was undertaken in Huanren Manchu Autonomous County, located in Northeast China. With a resident population of approximately 216,000, the county boasts abundant natural resources including mineral deposits, biodiversity, and water reserves. Data were preprocessed using Principal Component Analysis (PCA) to enhance interpretability for subsequent modeling. Abbreviated Principal Component Score Multilinear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) were cross-validated to ensure robust source attribution, thereby addressing the limitations of single-method uncertainty. This triangulation approach, combined with probabilistic Monte Carlo Simulation and health risk assessment, enabled a multi-dimensional evaluation of contamination pathways and risks. This aspect has been underexplored in heavy metal (HM) studies of mining-impacted agricultural soils. The average concentrations of eight heavy metals were as follows: Cr (74.0 mg/kg), Ni (32.1 mg/kg), Cu (118.9 mg/kg), Zn (541.7 mg/kg), Cd (2.2 mg/kg), Pb (202.0 mg/kg), Hg (0.3 mg/kg), and As (12.0 mg/kg). Quantitative pollution source analysis revealed three primary contributors to soil HMs: industrial point sources (contributing 46.1%), which is the most significant contributor to pollution; agricultural sources (contributing 22.2%) and natural sources (contributing 31.7%). Industrial sources, as the primary local pollution contributors, will effectively guide relevant government departments in formulating targeted management policies and measures. Probabilistic risk evaluation yielded two crucial findings: (1) Non-carcinogenic hazard indices for adults and children remained below 1, indicating acceptable risks from the presence of HMs in agricultural soils, however, (2) Carcinogenic risks surpassed the 1 × 10⁻ cancer risk benchmark for 100% of children and 32.3% of adults. Carcinogenic risks to the human population arising from individual HMs showed the following sequence: Cr > Ni > As > Zn > Cd. This research has not only revealed an alarmingly high risk of cancer in the study region due to HMs accumulation in its agricultural soils but also, by identifying the crucial sources, provided a scientific basis for controlling this harmful pollution.
土壤污染是对全球粮食安全和公众健康的重大威胁。准确划分污染源是制定科学的污染控制方案的前提。本研究在中国东北的桓仁满族自治县开展。该县常住人口约21.6万，拥有丰富的自然资源，包括矿藏、生物多样性和水资源储备。使用主成分分析（PCA）对数据进行预处理，以增强后续建模的可解释性。对简化主成分得分多元线性回归（APCS-MLR）和正定矩阵因子分解（PMF）进行交叉验证，以确保可靠的源归因，从而解决单一方法不确定性的局限性。这种三角测量方法，结合概率蒙特卡罗模拟和健康风险评估，实现了对污染途径和风险的多维度评估。在受采矿影响的农业土壤重金属（HM）研究中，这一方面尚未得到充分探索。八种重金属的平均浓度如下：铬（74.0毫克/千克）、镍（32.1毫克/千克）、铜（118.9毫克/千克）、锌（541.7毫克/千克）、镉（2.2毫克/千克）、铅（202.0毫克/千克）、汞（0.3毫克/千克）和砷（12.0毫克/千克）。定量污染源分析揭示了土壤重金属的三个主要来源：工业点源（贡献率46.1%），是最主要的污染源；农业源（贡献率22.2%）和自然源（贡献率31.7%）。工业源作为当地主要的污染源，将有效指导相关政府部门制定有针对性的管理政策和措施。概率风险评估得出两个关键结果：（1）成人和儿童的非致癌危害指数均低于1，表明农业土壤中重金属的存在风险可接受，然而，（2）致癌风险超过了1×10⁻⁶的癌症风险基准，儿童的超标率为100%，成人的超标率为32.3%。个体重金属对人群的致癌风险排序如下：铬＞镍＞砷＞锌＞镉。本研究不仅揭示了研究区域因农业土壤中重金属积累而导致的惊人高癌症风险，还通过确定关键来源，为控制这种有害污染提供了科学依据。
