Staff member

Fugitive odour emissions from wastewater treatment plants (WWTPs) present ongoing analytical and environmental challenges. Drone-mounted Instrumental Odour Monitoring Systems (IOMS) enable real-time, spatially resolved chemical sensing; however, large sensor arrays increase calibration complexity and cost. To address this, IOMS optimization is formulated as a machine-learning feature-selection problem. A two-stage selection strategy is introduced, combining Sequential Forward Selection (SFS) and Interval Partial Least Squares (iPLS) regression to identify minimal, information-rich sensor subsets and optimal temporal measurement windows. The approach is evaluated using data from a hexacopter-borne IOMS equipped with 21 sensors operating over an active WWTP. Sensors are ranked according to their incremental contribution to odour-concentration prediction error reduction, followed by refinement of measurement intervals to capture relevant temporal dynamics. Validation on independent flight data demonstrates that a configuration comprising only three sensors with optimized time windows retains or improves predictive performance relative to the full array. For quantification, the Bland-Altman limits of agreement improve from +/- 7 to +/- 5.3 dBod, and the Pearson correlation increases from 0.80 to 0.89. For odour-detection task, a single sensor achieves an AUC of 0.95, slightly outperforming the full sensor set (AUC = 0.93). Bootstrap analysis reveals variability in feature selection, though consistent trends are observed: ammonia sensors dominate quantitative models, whereas low-temperature MOX sensors are preferred in detection. The findings demonstrate the effectiveness of feature-selection strategies in simplifying IOMS hardware while preserving chemometric performance.

JTD