Development and External Validation of a Multi-Component Odorant Detection-Alarm Algorithm Using Hydrogen Sulfide and Ammonia

This study developed a machine-learning-based detection-alarm algorithm using hydrogen sulfide (H2S) and ammonia (NH3) as core chemical inputs to determine the analytical detection status of 20 odor compounds and evaluated its external applicability at an independent facility. Data collected at 20 min intervals from three odor-emitting facilities were used, with detection defined as a concentration at or above the compound-specific detection limit. Data from a livestock farm and a wastewater treatment facility were used to develop a meteorological–temporal baseline model (M0b) and an alarm model additionally incorporating H2S and NH3 (M4). Model selection, probability calibration, and compound-specific thresholds were determined using nested site–week cross-validation, and the finalized models were applied unchanged to Site C. In the development dataset, mean average precision (AP) increased from 0.362 for M0b to 0.463 for M4 (ΔAP = +0.101), with improvements for 17 of 20 compounds. At Site C, mean AP increased from 0.424 to 0.443 (ΔAP = +0.019), with improvements for 13 compounds. Excluding exploratory methyl isobutyl ketone, mean ΔAP was +0.106 and +0.020, respectively. External evaluation included only four calendar-week groups, yielding a minimum attainable two-sided permutation p-value of 0.125. No compound-specific improvement reached Benjamini–Hochberg-adjusted significance (q < 0.05) in either dataset; the external findings are therefore exploratory. Under fixed-threshold operation, dimethyl sulfide achieved a precision of 0.921 and a false-positive rate of 0.057, but recall was 0.182. These results suggest that H2S and NH3 may provide useful additional information for screening multiple odor compounds, while emphasizing the need for site-specific threshold assessment and further independent validation.