Studies on the early detection of wastewater’s toxicity using a microbial sensing system

A microbial sensing system with the built-in special processing algorithm was developed for detecting the toxicity or over-nutritivity of physically or chemically pretreated wastewater before been dispensed into a reservoir for further biological treatment. The electrical current is the output of the system varied according to both bio-chemical and electro-chemical reactions between the bacteria-attached electrode and wastewater. The toxic substances may inhibit the oxygen uptake of bacteria distinctively sampled from the activated sludge. The formation of reduction–oxidation reactions on the membrane was considered owing to bio-chemical activities of the oxygen uptake rate, which induce charge transfer effects. Present microbial sensing system is capable of measuring different degrees of toxicity correlated with the inhibition rates of the bacteria. The system is a portable and compact design primarily including a sensor, a signal amplifier, a low pass filter, and a data acquisition system. The wastewater reacted with the modified electrode that contained approximately 20 millions of bacteria controlled under flow culture condition at an optimized interval of ca. 9 h. Experimental results obtained from three varied concentrations of 40 ml specified toxic solutions indicated that the degree of toxicity in different wastewater was roughly correlated with their electro-induced bioactivities within 8 min. Only data acquired between 1 and 500 s were empirically found to be sufficient for regression calculation and computation to grade the degrees of wastewater’s toxicity. Each testing result measured by the newly developed microbial sensing system was verified with the simultaneous measurement using an oxygen-dissolved analyzer. A highly unified relation between measurements using the microbial sensing system and oxygen-dissolved analyses was obtained according to slope of the fitted first order linear equation to be 0.9921±0.0433. The correlation coefficient is 0.9767.