AI-Based Odour Management Goes Live at Wastewater Plants

The Structure of the Odour Problem at Wastewater Plants

The closer wastewater treatment plants sit to urban geography, the more chronic the problem of odour complaints becomes. Hydrogen sulphide (H₂S), ammonia, mercaptans and various volatile organic compounds (VOCs) are the main contributors to the sector's odour profile. Classic odour management approaches lean on a combination of enclosure, biofilters and activated carbon adsorption. These approaches work, but they cannot respond quickly enough to the dynamic character of odour events.

Over the last three years, AI-based odour management has moved from pilot to standard component at large plants. Sector reference data show that the combination of e-nose sensors and machine learning models cuts odour complaints by 78 percent.

Electronic Nose (E-Nose) Technology

An electronic nose is a sensor platform that maps the fingerprint profile produced by a set of chemical sensors to human odour perception. Unlike classic single-component measurement devices, an e-nose can characterise the full odour mixture.

Components of a typical e-nose sensor package:

Metal oxide semiconductor (MOS) sensors: for VOCs and reducing gases
Electrochemical sensors: for H₂S, ammonia and sulphur compounds
Photoionisation detectors (PID): for total VOCs
Semiconductor air quality sensors: as background reference

Mapping the odour mixture to human perception happens through calibration: machine learning models learn the relationship from sensor data and human panel test results. Modern models can match odour intensity and character to human panel tests with 85 percent accuracy.

Field Deployment Strategy

The e-nose network at a wastewater plant is typically positioned in three layers:

Source layer: installed at high-emission points (inlet structure, primary settling, sludge handling)
Boundary layer: monitoring at the plant boundary and nearby residences
Meteorological layer: continuous logging of wind direction and speed, temperature and humidity

Combined analysis of all three layers can map the source of an odour event, its dispersion dynamics and its urban impact zone in real time. This shifts the model from a classic, complaint-driven reactive response into a proactive odour prevention model.

The Role of Machine Learning Models

E-nose data only delivers value with the right machine learning calibration. Common modelling approaches in the sector:

Anomaly detection: catching early signals of odour events
Classification: identifying the odour type (for example, H₂S-dominated or ammonia-dominated)
Dispersion modelling: forecasting the impact zone using meteorological data
Root cause analysis: cross-analysis with plant operating data

Root cause analysis is the capability that creates the most strategic value in odour management. Operators can spot the operating parameters behind an odour event in real time (for example, sludge retention time, the build-up of anaerobic conditions, missing chemical dosing).

Operational Response Automation

Modern e-nose networks are not just detection tools; they are active response platforms. A typical automated response loop:

Increasing nitrate dosing when the odour intensity threshold is exceeded
Boosting aeration capacity to break anaerobic conditions
Optimising biofilter washing and humidity levels
Increasing chemical dosing to sludge handling units
Sending early warning notifications to the operations team

This automation cuts the typical 30–60 minute lag of manual response down to 3–5 minutes. Early response is decisive in preventing urban impact.

Complaint Management and Public Communication

E-nose networks also structurally improve complaint management once urban impact begins. When a complaint comes in, plant management can:

Query the plant's odour profile in real time at the time of the complaint
Verify whether the meteorological conditions place the impact zone over the complaint location
Provide reference information if the likely source is another industrial site

This transparency removes the unsupported defensive posture of a classic "it's not from us" response and improves public trust.

Adoption in Turkey

In Turkey, e-nose-based odour management systems have been going into service at large municipal wastewater plants over the last three years. Pilots in Istanbul, Izmir and Bursa stand out as sector references. Pilot reports show odour complaints down by 65–80 percent on average.

Domestic sensor manufacturers and software providers are shaping the sector ecosystem fast. Three TÜBİTAK-funded R&D programmes target moving local e-nose solutions to the level needed for international competition.

Implications for the Sector

AI-based odour management will be a standard infrastructure component at urban-adjacent plants over the next five years. Items for Turkish sector players to track:

Building e-nose infrastructure into the design of new plants from day one
Feasibility studies for modular retrofits at existing plants
Strategic partnerships with domestic sensor and software producers
A communication framework for public dialogue at urban-adjacent plants

Odour was traditionally treated as a technical problem; AI platforms structurally place it at the centre of public dialogue too.