Targeted Source Detection for Environmental Data
This work addresses the challenge of distinguishing between natural and anthropogenic contamination sources for environmental management, though it appears incremental as it builds on existing methods for a specific interdisciplinary application.
The paper tackles the problem of identifying sources of contaminants in coupled natural and human systems, proposing a technique for simultaneous source detection and prediction that outperforms other approaches in a case study on groundwater contamination in a shale gas development region.
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, wastewater transport, and urbanization are included. In addition to the occurrence of anthropogenic contamination, the presence of some contaminants (e.g., methane, salt, and sulfate) of natural origin is not uncommon. Therefore, scientists sometimes find it difficult to identify the sources of contaminants in the coupled natural and human systems. In this paper, we propose a technique to simultaneously conduct source detection and prediction, which outperforms other approaches in the interdisciplinary case study of the identification of potential groundwater contamination within a region of high-density shale gas development.