ABSTRACT
Heavy metal(loid)s pollution of industrial legacies has become a severe environmental issue worldwide. Linking soil pollution to groundwater contaminant plumes would make invisible pollution features visible across the site, but related studies are lacking and require the convergence of multiple technologies. This study uniformly managed the soil and groundwater data in a 3D visualization model to pellucidly assess the spatial distribution of critical contaminants beyond simple drilling information. The distribution of Pb, Zn, As, and Cd in soil-groundwater system has a strong correlation to historical production, substance type, soil property, and groundwater flow direction. Over 2600 measurements of High-density electrical resistivity tomography (ERT) data were used to guarantee the exactness of soil structures. Hydraulic conductivity showed a strongest correlation (R2 = 0.86), yielding a calibrated model to reveal the anisotropic and contaminant transport in the region, with the consequent minimize the drilling tests. This study provides a template for the description of a verifiable scenario of hydrogeological conditions and pollution characteristics at smelting sites, coupled with traditional exploration and non-invasive techniques. The findings highlight the significance of visualizing the internal state of the soil-groundwater system under consideration, thus providing a basis for targeted control measures against site contamination.
ABSTRACT
A social interaction consists of contributions by the individual, the environment and the interaction between the two. Ideally, to enable effective assessment and interventions for social isolation, an issue inherent to depressive and psychotic illnesses, the isolation must be identified in real-time and at an individual level. However, research addressing sociability deficits is largely focused on determining loneliness, rather than isolation, and lacks focus on the richness of the social environment the individual revolves in. In this paper, We describe the development of an automated, objective and privacy-preserving Social Ambiance Measure (SAM) that converts unconstrained audio recordings collected from wrist-worn audio-bands into four levels, ranging from none to active. The ambiance levels are based on the number of simultaneous speakers, which is a proxy for overall social activity in the environment. Results show that social ambiance patterns and time spent at each ambiance level differed between participants with depressive or psychotic disorders and healthy controls. Individuals with depression/psychosis spent less time in diverse environments and less time in moderate/active ambiance levels. Moreover, social ambiance patterns are found associated with the severity of self-reported depression, anxiety symptoms and personality traits. The results in this paper suggest that objectively measured social ambiance can be used as a marker of sociability, and holds potential to be leveraged to better understand social isolation and develop effective interventions for sociability challenges, thus improving mental health outcomes.
