Detection of Water Pollutants With a Nonuniform Array of Microwave Sensors

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.