Portable electronic nose sniffs out wastewater plants

Wednesday, 17 November, 2021

Portable electronic nose sniffs out wastewater plants

Researchers from Spain have engineered a portable electronic nose (e-nose) that’s almost as sharp as a human nose at sniffing out the stink of wastewater treatment plants. Coupled with a drone, the lightweight e-nose can measure the concentration of different smells, predict odour intensity and produce a real-time odour map of the plant for management.

Conventionally, a wastewater plant’s odour is measured by dynamic olfactometry, where a human panel whiffs and analyses bags of air collected from the plant. Although the method has been considered the gold standard, the process is costly, slow and infrequent, which doesn’t allow operators to quickly respond to problems or pinpoint the root of the stench.

To better monitor wastewater plants’ odour emissions, Santiago Marco of the Institute for Bioengineering of Catalonia and his team designed a portable e-nose for real-time surveillance and data visualisation with the help of artificial intelligence (AI). The team collected bags of air from a plant and trained the e-nose to sniff out pungent chemicals such as hydrogen sulfide, ammonia and sulfur dioxide, which smell like rotten eggs, urine and burnt matches, respectively. The e-nose is also equipped with a sensor for carbon dioxide, an indicator of bacterial activity. In laboratory settings, the e-nose performed nearly as well as human noses.

The researchers then attached the 1.3 kg e-nose to a drone and sent it into the skies at a medium wastewater treatment plant in the south of Spain between January and June. Hovering over different facilities at the plant, the ‘sniffing drone’ sucks in air via a 10 m tube and analyses the air in a sensor chamber.

The results demonstrated that the e-nose on a drone was feasible for wastewater odour monitoring. When analysing the same air samples in a field test, 10 out of 13 measurements from the e-nose aligned with the human panel’s assessments. Aided by the drone’s mobility and the AI algorithm, the team also mapped out the temporal and spatial odour concentration and, for the first time, predicted the intensity of the scent from drone measurements.

“We are extremely happy with the results, but we need more validation and to make the device more robust for a real plant operation,” Marco said. The team plans to shave off some extra weight from the e-nose and develop a standardised process for the method. They are also planning to further optimise the device against the influence from temperature, humidity and other environmental conditions that can affect the accuracy.

“The work may also have implications for other facilities like landfills, composting plants or even large farms with cattle and pigs that are also known to produce all types of malodours,” Marco said.

The research paper has been published in the journal iScience.

Image credit: Maria Deseada Esclapez

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