Development of an in-situ detector for classification and regression of dissolved gases in liquid waste with application to wastewater monitoring

Monitoring volatile compounds in sewer systems is of high importance due to the toxic and corrosive nature of various nuisance chemicals generated such as hydrogen sulfide (H 2 S). Hotspot monitoring facilitates identification of the location of the generated H 2 S, and thereby targeted treatment can be applied which eventually minimizes the use of chemicals and lowers the environmental effect within the sewer system. Here, we developed a portable detector that automatically extracts and delivers sewer contents to a microfluidic-based detector, fabricated by a selective microchannel embedded with a metal oxide semiconductor (MOS) sensor. Using a wide concentration range of H 2 S and ammonia (NH 3 ) dissolved in water (i.e., two components to which the MOS sensor has potential cross-selectivity), a database for a machine learning model was developed. The model could classify between NH 3 and H 2 S with 96.4% and 96.9% overall recall in separate and mixture aqueous solutions, respectively. Overall regression precisions of 84.6% and 88.8% were obtained in separate and mixture aqueous solutions, respectively. The developed setup was used in a field test (at Annacis Island (Delta, BC)) wastewater treatment plant where the results showed that the device could identify H 2 S and NH 3 in raw influent samples and measuring the concentrations via regression with 94.6% and 83.5% overall recall and precision for H 2 S and NH 3 , respectively. These results demonstrate the promise of the developed automated detector and machine-learning data processing methodology for applications in in-situ wastewater monitoring or treatment through the detection of H 2 S hotspots for targeted mitigation efforts. • An automated microfluidic-based gas detector identifies and measures hydrogen sulfide and ammonia in raw influent. • A machine learning model is used to classify the presence and the amount of each gas in a liquid wastewater sample. • The automated device facilitates the detection of hotspots and reduces the treatment cost.