OAV-Based Source Characterization of Designated Odor Compounds in Long-Term Continuous Monitoring Data with Severe Missingness

Odor management requires consideration of both compound concentrations and olfactory impacts determined by odor threshold concentrations. This study evaluated missing-data structures, interpolation performance, odor activity value (OAV)-based odor contributions, and seasonal compositional changes using 20 min monitoring data for 22 designated odor compounds collected from a livestock farm, a wastewater treatment facility, and an anonymized organic waste treatment facility in Eumseong, Republic of Korea (Site C), from 1 August 2022 to 30 June 2023. Because the dataset contained multi-week to multi-month block missing periods, compound-specific interpolation performance was assessed using block holdout validation with BiLSTM, BiGRU, TCN, Transformer, and HistGradientBoostingRegressor models. OAV, summed odor activity value (SOAV), and odor contribution (OC) were calculated primarily from observed concentrations, while fully interpolated series were additionally used for sensitivity comparison to evaluate interpolation-induced bias. Ammonia was excluded from OAV analysis owing to its low valid observation rate. Among the analyzable compounds, odor contribution was dominated by volatile fatty acids and trimethylamine rather than hydrogen sulfide. The livestock farm and wastewater treatment facility showed n-valeric-acid-dominated profiles, whereas Site C showed a mixed profile involving valeric acids and trimethylamine. Seasonal analysis indicated relatively consistent fatty-acid-dominated compositions at the livestock farm and wastewater treatment facility, while Site C showed a possible shift from fatty-acid dominance in warm seasons to trimethylamine dominance in cold seasons. These results provide an OAV-based framework for identifying odor management priorities in long-term continuous odor datasets with severe missingness.