Trace heavy metals in water represent a significant health hazard due to their bioaccumulation in the human body. Current detection methods for these contaminants typically require sophisticated laboratory equipment and procedures, limiting their applicability in the field. In this paper, we show that Terahertz (THz), a prospective frequency band for wireless communication technologies, exhibits distinctive pine-like structures (termed “THz-Pine”) in the three-dimensional space when sensing liquids. Moreover, our measurements reveal that this structure possesses superior capabilities for characterizing trace physical information compared to conventional 2D spectrum methods. Leveraging above findings, we introduce Terahertz Trace Sensing,i.e.,TraSen, a contactless method for detecting and quantifying trace heavy metals in water.TraSenoperates by emitting Terahertz signals vertically above a water sample, capturing the reflected signals, and analyzing the resultingTHz-Pineto determine the type and concentration of trace heavy metals present. To quantify trace heavy metals concentrations, we develop an embedded Physics-Informed 3D point cloud deep learning model. Our extensive dataset, comprising 102,500 Terahertz signal samples across 40 trace heavy metals, demonstrates thatTraSencan effectively identify trace heavy metals with 95.4% accuracy in type identification and 94.3% accuracy in concentration quantification. Specifically, our preliminary findings indicate that the detection limit of Terahertz signals for heavy metals in water is approximately 0.1$\mu g/dL$.