An environmental sensor for detection of subsurface contamination

地下常见的污染物，如溶解的重金属及其络合物、硝酸盐-亚硝酸盐等，由于其对人类、植物和动物的潜在影响，引起了地质环境工程师的极大关注。通常用于检测地下这些污染物的方法是从钻孔中提取土壤样本，从监测井中提取地下水样本，然后进行化学分析。这种常规采样和分析可能成本高、耗时长，并且不能提供对污染物迁移的实时监测。本研究将基于电介质、物理化学、微波工程和人工智能等科学与工程领域的最新发展，以及近十年来PI的研究，研制出一种用于地下污染监测和检测的环境传感器。实验观测和理论分析证明，土壤-水系统的频率相关复介电常数随其物理和化学性质的变化而变化。因此，本研究将根据土壤-水系统的物理和化学性质的变化，开发一种用于地下污染监测的环境传感器测量土壤和地下水原位复介电常数的传感器指示地下的特征。传感器测量的复介电常数数据将由人工神经网络进行处理，用于定量和半定量地圈定地下污染。传感器采用短路同轴线技术，快速、可靠、健壮，易于现场实施。将开发基于人工神经网络的数据处理软件，将复介电常数与地下污染的类型和浓度联系起来。在NSERC发现赠款的支持下，可能会开发一种环境传感器技术，用于广泛的环境工程应用。

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.