Heavy metals with a high concentration value in the soil are detrimental to the soil ecosystem. In this study, 96 samples were taken from two soil sections (A-A, B-B) in the mouth of the Juanshui River to analyze heavy metal pollution. Soil pollution status and the ecological risk were investigated using Nemerow and the potential ecological risk index. The sources affecting pollution and pollution distribution characteristics were exhibited based on Inverse Distance Weighted (IDW), principal component analysis (PCA), and Statistics. Pollution in the soil of the mouth of the Juanshui River had reached a moderate level. Hg and Cd were the main potential ecological risk contributions. Coal burning and agricultural chemicals were the predominant pollution source in the study area. In order to avoid the soil of the river mouth becoming a secondary pollution source for water bodies and resulting in a vicious circle of heavy metal pollution, heavy metals in the soil of the river mouth of China should be mainly managed and controlled.

Euler deconvolution is a widely used automatic or semi-automatic method for potential field data. However, it yields many spurious solutions that complicate interpretation and must be reduced, eliminated, recognized, or ignored during interpretation. This study proposes a post-processing algorithm that converts Euler solutions produced by tensor Euler deconvolution of gravity data with an unprescribed structural index into probability values (p values) using the B-spline series density estimation (BSS) method. The p values of the Euler solution set form a probability density distribution on the estimation grid. The BSS method relies on the fact that while spurious solutions are sparse and ubiquitous, Euler deconvolution yields many similar or duplicate solutions, which may tightly cluster near real sources. The p values of the Euler solution clusters form multi-layered isosurfaces that can be used to discriminate neighboring target sources because the p values of spurious solutions are vanishingly small, making it simple to remove their interference from the probability density distribution. In all synthetic cases, the geometric outlines of anomaly sources are estimated from probability density isosurfaces approximating synthetic model parameters. The BSS method was then applied to airborne gravity data from Mount Milligan, British Columbia, Canada. Subsequently, results from synthetic models and field data show that the proposed method can successfully localize meaningful geological targets.