A Low-Cost Virtual Sensor for Underwater pH Monitoring in Coastal Waters

In coastal water monitoring, abrupt pH changes might indicate different pollution sources. Existing sensors for pH monitoring in coastal waters at low cost are mainly based on a glass membrane and a reference electrode. Virtual sensors are elements capable of measuring certain parameters based on data from other parameters or variables. The aim of this paper is to propose the use of a virtual pH sensor based on measuring different physical effects of H+ on the electromagnetic field generated by an inductor. Double inductors based on two solenoids of 40 and 80 spires are used as sensing elements. Samples with pH from 4 to 11 are used, and the effect of temperature is evaluated using samples from 10 to 40 degrees C. The induced voltage and the delay of the signal are measured for powering frequencies from 100 to 500 kHz. These data of delay, induced voltage, frequency, and temperature are included in a probabilistic neural network to classify these data according to the pH. The results indicate low accuracy for samples with a pH of 11. A second analysis, excluding these data, offered correctly classified cases of 88.9%. The system can achieve considerable high accuracy (87.5%) using data gathered at a single frequency, from 246 to 248 kHz. The predicted versus observed data is correlated with a linear model characterized by an R2 of 0.69, which is similar to the ones observed in other virtual sensors.