An environmental sensor for detection of subsurface contamination

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Contaminants commonly encountered in subsurface, such as dissolved heavy metals and their complexes, nitrate-nitrite, etc., are of great concern to geo-environmental engineers because of their potential impacts to human, plants and animals.  The methods commonly used to detect these contaminants in subsurface involve taking soil samples from boreholes and groundwater samples from monitoring wells and then performing chemical analyses. Such routine sampling and analysis can be costly, time consuming, and cannot provide real time monitoring of contaminant migration. In this study, an environmental sensor for subsurface contamination monitoring and detection will be developed based on the state-of-the-art development in science and engineering, including dielectrics, physical chemistry, microwave engineering and artificial intelligence, as well as on the PI's research in the past decade.  It has been evidenced from experimental observations and theoretical analyses that the frequency-dependent complex permittivity of soil-water systems varies with changes in their physical and chemical properties.  Therefore,  sensors that measure the in-situ complex permittivity of soil and groundwater indicate characteristics of the subsurface.  The complex permittivity data measured from the sensors will be processed by artificial neural networks for quantitative and semi-quantitative delineation of subsurface contamination.  The sensor adapts the short-circuit coaxial line technology, which is fast, reliable, robust and can be readily employed for in-situ implementation. Artificial neural networks based data processing software will be developed to link the complex permittivity with the type and concentrations of subsurface contamination.  With the support of NSERC Discovery Grant, an environmental sensor technology may be developed for broad environmental engineering applications.