Detection of Water Pollutants With a Nonuniform Array of Microwave Sensors

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 nonuniform 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 (MLAs) to determine the type of pollutant. Here, as a proof of concept, the proposed methodology is tested with water samples, including phosphate (PO4), lead (Pb), mercury (Hg), and chromium
$^{+6}$
(Cr
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) with various concentrations. The proposed technology is fast, cost-effective, repeatable, and expandable for detecting a larger number of pollutants.