The main objectives of the current research were source identification and quantification of the relationship between land use pattern and heavy metals (HMs) (Cr, Ni, Cd, Hg, Pb, Co, Zn, Cu, As) in soil samples collected in the border of Republic of Ireland and Northern Ireland. For the first goal, positive matrix factorization (PMF), principal component analysis with absolute principal component scores (PCA/APCS) and Unmix were utilized whereas, for the second objective, redundancy analysis (RDA) was employed. The results of source apportionment indicated that the geological formations (e.g. parent rocks), mineral explorations along with application of fertilizers in agriculture were the most influential contributing factors for the elevated levels of HMs. In this context, PCA/APCS and Unmix identified 3 sources compared to 4 sources detected by PMF with R² values larger than 0.7, except for As and Hg, indicating the reasonable accuracy of these receptor models for source identification. Among the 9 HMs considered, the performance of both PMF and PCA/APCS for As and Hg were poor with R² values equal to 0.23 and 0.51 for PMF versus 0.71 and 0.48 yielded by PCA-APCS. According to the findings of RDA; Cr, Co, As, Ni and Cu appeared to be the primary elements having strong correlations with pH and land use types. Additionally, the results of RDA demonstrated that Zn and Cu are the most probable elements that may be influenced by the amount of phosphorus in soil whereas Hg, Pb, Cr, Co and Ni are less likely to be affected.

当前研究的主要目标是对土地利用模式与土壤样品中重金属（Cr、Ni、Cd、Hg、Pb、Co、Zn、Cu、As）之间的关系进行来源识别和量化。爱尔兰共和国和北爱尔兰的边界。对于第一个目标，使用了正矩阵分解 (PMF)、具有绝对主成分分数的主成分分析 (PCA/APCS) 和 Unmix，而对于第二个目标，采用了冗余分析 (RDA)。源解析结果表明，地质构造（如母岩）、矿产勘探以及农业中的化肥施用是HMs水平升高的最有影响的因素。在这种情况下，²值大于 0.7，除了 As 和 Hg，表明这些受体模型用于源识别的合理准确性。在考虑的 9 种 HM 中，PMF 和 PCA/APCS 对 As 和 Hg 的性能都很差，PMF 的R ²值分别为 0.23 和 0.51，而 PCA-APCS 产生的R ²值分别为 0.71 和 0.48。根据 RDA 的调查结果；Cr、Co、As、Ni 和 Cu 似乎是与 pH 值和土地利用类型有很强相关性的主要元素。此外，RDA 的结果表明，Zn 和 Cu 是最可能受土壤中磷含量影响的元素，而 Hg、Pb、Cr、Co 和 Ni 不太可能受到影响。