The Use of Field Olfactometry in the Odor Assessment of a Selected Mechanical-Biological Municipal Waste Treatment Plant within the Boundaries of the Selected Facility-A Case Study

Odor management plans indicate the need to identify odor sources in waste management facilities. Finding the right tool for this type of task is a key element. This article covers a new approach for odor quantification and source identification at a selected waste management facility by coupling field olfactometry and the spatial interpolation method, such as inverse weighted distance. As the results show, this approach works only partially. Field olfactometry seems to be a suitable tool for odor identification that could be an instrument incorporated into odor management plans as it allowed for recognition of most odor-generating places at the selected facility, i.e., waste stabilization area, green waste storage area, and bioreactors. However, spatial distributions obtained by the selected interpolation method are characterized by high errors during cross-validation, and they tend to overestimate odor concentrations. The substantial weakness of the selected interpolation method is that it cannot handle points where the odor concentration is below the detection threshold. Therefore, the usefulness of such a method is questionable when it comes to odor management plans. Since field olfactometry is a reliable tool for odor measurements, further research into computational methods is needed, including advanced interpolation methods or dispersion modeling based on field olfactometry data.

Automated summary

Odor management plans require identifying odor sources in waste management facilities. This article presents a new approach using field olfactometry and spatial interpolation for odor quantification and source identification in a selected waste management facility. The results show that field olfactometry is a suitable tool for odor identification, but the selected interpolation method has high errors and tends to overestimate odor concentrations. Its inability to handle points below the detection threshold raises questions about its usefulness in odor management plans. Further research into computational methods, including advanced interpolation methods or dispersion modeling based on field olfactometry data, is needed.