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Validation of Odor Modeling using Field Surveys

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Odor modeling is entrenched within the regulatory framework in Australia and other countries. There has been much time and effort spent on emissions estimation and modeling methods, with a view to improving the prediction of odor impacts. Predicted odor levels are expressed in terms of concentration, percentile and averaging time.

Recently there has been increased focus on to reverse amenity assessments, as opposed to the impacts of new or expanding operations. This Reverse amenity occurs where sensitive land uses encroach on existing odor-emitting activities and restrict the flexibility of the existing industry. This results in conflict between property users. Reverse amenity is particularly common in Queensland, Australia, where traditional industrial and farming areas have been affected by rapid urban and peri-urban growth. This has led to land use conflicts where none previously existed.

In addition to dispersion modeling, many regulatory agencies allow the use of “alternative tools”, reducing the need for intensive site-specific modeling and provision of additional information. These “alternative” tools include odor diaries, complaint data, field odor surveys, and odor nuisance surveys.

This paper looks at three cases where alternative tools have been used; 1) a site where a property owner proposed subdividing a land parcel adjacent to an existing intensive livestock operation, 2) another site where development next to an intensive livestock operation is proposed and dispersion modeling has been performed and 3) a proposed refinery expansion for which where dispersion modeling indicates an existing odor nuisance, but no complaints have ever been received.

The worked performed and the outcomes of each of the three scenarios is discussed, and comment is provided with regard to the methodology both in terms of practically validating the modeling, and also the practicality of the alternative tools.

Document Type: Research Article


Publication date: 2012-01-01

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