Detection of Water Pollutants With a Nonuniform Array of Microwave Sensors
Reza K. Amineh, Maryam Ravan, Dhara Tandel
Abstract
Detection of pollutants in water is conventionally performed in wet chemistry labs for samples that are collected manually. This task is time-consuming, costly, and difficult to expand to large water bodies and perform frequently. Thus, to facilitate water quality testing, a new methodology is presented based on the use of a non-uniform array of microwave sensors and applying machine learning to the collected data. The sensor elements resonate at different frequencies which cover a broad bandwidth, providing sufficient information for machine learning algorithms to determine the type of pollutant. Here, as a proof-of-concept, the proposed methodology is tested with water samples including Phosphate (PO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> ), Lead (Pb), Mercury (Hg), and Chromium <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+6</sup> (Cr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+6</sup> ) with various concentrations. The proposed technology is fast, cost-effective, repeatable, and expandable for detecting a larger number of pollutants.