Impact of Averaging Time on Meeting Odor Standards


  • Phyllis Diosey - Hazen and Sawyer

As noted in an earlier paper (Diosey, 2016), numerous jurisdictions around the globe have elevated the concept of odor impact from a mere nuisance to the status of an actual environmental pollutant, and have developed different approaches for determining what constitutes an acceptable odor impact in the community. These different approaches vary not simply on how odor is defined (i.e., on a pollutant-specific basis, such as hydrogen sulfide, and/or in the level of odor, such as dilution-to-threshold (D/T)), but in averaging and exposure times (see Tables 1 and 2). . Since using standard regulatory dispersion modeling is frequently used to assess the potential for future odor impacts, and since these models, generally designed to assess short and long-term health effects, are often based on a 1-hour averaging time, different approaches have been developed to convert the standard model output to the regulatory odor standard format, such as use of conversion factors, a power law, hourly mean percentile, and other techniques (e.g., “odor hour”). This lack of consensus on what constitutes an odor impact, how to define it, how to measure it, and how to model it often seems to put the focus and effort on the methodology used to conform to the averaging and frequency criteria rather than on defining and solving the odor problem. The problem arises in that these different approaches often lead to different solutions and controls for what may be often very similar facilities and exposure scenarios.

This paper investigates the effect that various averaging time approaches have on identifying significant offsite odor impacts, along with the determination of the amount of control needed to eliminate these offsite impacts.

Preliminary Findings
As part of this study, the sources at an urban wastewater treatment facility were modeled using the USEPA regulatory model AERMOD and five years of local meteorological data. Hydrogen sulfide emission rates were modeled. Table 3 presents initial results using a peak to mean factor of 0.2 to convert the 1-hour model output to various averaging times from 1-hour to 15 minutes. While facility-wide impacts are generally compared to standards for compliance, a review of these results indicate that individual sources will result in exceedances of the standards in several U.S. jurisdictions and would need to be controlled in order to be in compliance; however, the control strategies for the same types of sources and emissions would need to be adjusted to meet the different criteria. The paper will present the results of modeling analyses using the different averaging time and frequency approaches currently applied, comparing the resulting impacts to the different regulations, and comparing the different control requirements that would be required in the different jurisdictions for the same source types.

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