Farmland soil quality is a crucial determinant for agricultural productivity, food safety, and human well-being. Among the various contaminants, heavy metals have emerged as pervasive factors significantly impacting farmland quality, attracting widespread societal concern. In this study, we systematically applied multivariate statistical analysis, geostatistical methods, and the positive matrix factorization (PMF) source apportionment technique to elucidate the sources and contributions of eight heavy metals (Cd, Hg, As, Pb, Cr, Cu, Zn, Ni) in farmland soils within an industrialized town. Our findings reveal that Cd, Hg, Pb, and Zn exhibit pollution or enrichment in farmland soils compared to natural background levels, with Hg and Cd surpassing 164.3% and 50.2%, respectively. Notably, Zn demonstrates discernible point-source pollution. Source apportionment results highlight industrial point sources, coal combustion, and agriculture as the primary anthropogenic contributors to heavy metal contamination, with zinc-plating enterprises being the predominant industrial point sources. Addressing the specific issue of point-source pollution from Zn in industrial activities, further analysis establishes a correlation between soil Zn content and the distance from zinc-plating enterprises. Utilizing an atmospheric transport model, we observe that the impact of industrial activities on soil Zn is limited when the distance exceeds 1.5 km, emphasizing the importance of monitoring Zn pollution within areas less than 1.5 km. This study pioneers a progressive source apportionment approach, considering the origins of different heavy metals, pollution levels, distance factors, and the cost-effectiveness of environmental measures. The insights gained provide scientifically sound strategies for future decision making in environmental protection.

The Xiashu loess exhibits expansion when in contact with water and contraction when water is lost, making it highly susceptible to the influence of rainfall. Therefore, it is essential to investigate the infiltration behavior of rainwater in Xiashu loess slopes under various conditions. The depth of infiltration in slopes directly affects the depth of landslide failure and serves as an important indicator for studying slope infiltration characteristics; only a handful of academics have delved into its study. This article is based on on-site rainfall experiments on Xiashu loess slopes, using three main factors, rainfall intensity, rainfall duration, and slope angle, as discrimination indicators for the infiltration depth of Xiashu loess slopes. The particle swarm optimization algorithm is employed to optimize the BP neural network and establish a PSO-BP neural network prediction model. The experimental data are accurately predicted and compared with the multivariate nonlinear regression model and traditional BP neural network models. The results demonstrate that the PSO-BP neural network model exhibits a better fit and higher prediction accuracy than the other two models. This model provides a novel approach for rapidly determining the infiltration depth of Xiashu loess slopes under different rainfall conditions. The results of this study lay the foundation for the prediction of the landslide damage depth and infiltration of Xiashu loess slopes.