Towards highly economical and accurate wastewater sensors by reduced parts of the LED-visible spectrum

Interest is growing in simple, fast and inexpensive systems to analyze urban wastewater quality in real time. In this research project, a methodology is presented for the characterization of COD, BOD5, TSS, TN, and TP of wastewater samples, without the need to alter the samples or use chemical reagents, from a few wavelengths, belonging to the different color groups that compose the visible spectrum in isolation: (380-700 nm): violet (380-427 nm), blue (427-476 nm), cyan (476-497 nm), green (497-570 nm), yellow (570-581 nm), orange (581-618 nm), and red (618-700 nm). In this study, about 650 raw and treated urban wastewater samples from over 43 WWTPs and a total of 36 estimation models based on genetic algorithms have been calculated. Seven models were calculated for each pollutant parameter; one model for each color group of the visible spectrum, except for TN, which includes an additional model combining the wavelengths of the violet and red region of the spectrum. All the calculated models showed high accuracy, with an R2 between 80 and 85 % for COD, BOD5 and TSS, and 66–74 % for TN and TP. The tests carried out have shown the accuracy of the models of the different color groups to be very close to each other. However, it is noted that the models making use of the wavelengths between 497 and 570 nm (green) were the ones that showed the best performance in all the parameters under study. This research work lays the foundations for the development of cheaper, faster, and simpler wastewater monitoring and characterization equipment. [Display omitted] •Accurate and real-time wastewater characterization using reduced part of the visible spectrum.•36 models created using genetic algorithms for analyzing 650 samples from 43 WWTPs.•Prediction of COD, BOD5, TSS, TN, TP with a R2 of 80–85 %•Low-cost, high-precision wastewater monitoring using LED spectrophotometry.•Methodology enables real-time monitoring of wastewater pollutant levels at low cost.