[Paper Review] Concept to assess the human perception of odour by estimating short-time peak concentrations from one-hour mean values. Reply to a comment by Müller et al

Günther Schauberger, Martin Piringer+8 more|arXiv (Cornell University)|Jan 4, 2012

Odor and Emission Control Technologies24 references3 citations

TL;DR

This paper refines a method to estimate short-term peak odour concentrations from one-hour average values using a variable peak-to-mean ratio, challenging the assumption of a constant factor (e.g., 4) used in models like AUSTAL2000. It argues that peak-to-mean ratios depend on atmospheric stability, source geometry, and distance, and should not be treated as a universal constant, thereby improving the accuracy of odour nuisance assessments in environmental dispersion modeling.

ABSTRACT

Biologically relevant exposure to environmental pollutants often shows a non-linear relationship. For their assessment, as a rule short term concentrations have to be determined instead of long term mean values. This is also the case for the perception of odour. Regulatory dispersion models like AUSTAL2000 calculate long term mean concentration values (one-hour), but provide no information on the fluctuation from this mean. The ratio between a short term mean value (relevant for odour perception) and the long term mean value (calculated by the dispersion model), called the peak-to-mean value, is usually used to describe these fluctuations. In general, this ratio can be defined in different ways. Müller et al. (2012), in a comment to Schauberger et al. (2012) which includes a statement that AUSTAL2000 uses a constant factor of 4, argue that AUSTAL2000 does not apply a peak-to-mean factor and does not calculate odour exceedance probabilities. Instead it calculates the frequency of so-called odour-hours by applying the relation between the 90-percentile of the instantaneous concentration and the hourly mean (Janicke and Janicke, 2007a), not between some peak value and the mean. According to Janicke and Janicke (2007a), the 90-percentile of the instantaneous concentration can in practice be estimated with sufficient accuracy from the hourly mean by using a factor of 4. Having so far replied to Müller et al. (2012) we take additionally the opportunity to elaborate a little more on the peak-to-mean concept, especially pointing out that a constant factor independent of the stability of the atmosphere, the distance from and the geometry of the source, is not appropriate. On the contrary it shows a sophisticated structure which cannot be described by only one single value.

Motivation & Objective

• To address the limitation of regulatory dispersion models like AUSTAL2000, which report only one-hour mean concentrations without capturing short-term fluctuations relevant to human odour perception.
• To challenge the assumption that a constant peak-to-mean factor (e.g., 4) universally applies to estimate short-term odour peaks from long-term averages.
• To clarify that AUSTAL2000 does not use a fixed peak-to-mean factor but instead calculates odour-hours based on the 90th percentile of instantaneous concentrations relative to the hourly mean.
• To demonstrate that the peak-to-mean ratio is not a single universal value but varies with atmospheric stability, source distance, and geometric configuration.
• To improve the accuracy of odour nuisance prediction by replacing oversimplified constants with a more nuanced, context-dependent estimation of short-term odour exposure.

Proposed method

• The method proposes replacing the use of a constant peak-to-mean factor with a variable ratio derived from the relationship between the 90th percentile of instantaneous odour concentrations and the one-hour mean concentration.
• It relies on empirical data and dispersion modeling principles to show that the 90th percentile of instantaneous concentrations can be estimated from the hourly mean using a factor of 4, as suggested by Janicke and Janicke (2007a).
• The approach integrates atmospheric stability classes and source geometry into the estimation of peak-to-mean ratios, recognizing that these factors significantly influence concentration fluctuations.
• The paper uses a reply format to critique and refine earlier modeling assumptions, particularly the claim that AUSTAL2000 applies a fixed factor of 4 for peak estimation.
• It emphasizes that the 90th percentile of instantaneous values—rather than arbitrary peak values—is the relevant metric for odour exposure assessment.
• The method is grounded in the principle that biologically relevant exposure to odours depends on short-term peaks, not long-term averages, and thus requires dynamic, context-sensitive estimation.

Experimental results

Research questions

• RQ1 Does the AUSTAL2000 dispersion model apply a constant peak-to-mean factor of 4 to estimate short-term odour peaks from one-hour mean values?
• RQ2 Is the 90th percentile of instantaneous odour concentrations a valid and sufficient proxy for short-term peak exposure in odour dispersion modeling?
• RQ3 How does the peak-to-mean ratio vary with atmospheric stability, source distance, and geometric configuration?
• RQ4 Can a single universal constant adequately represent the fluctuation between short-term peaks and long-term mean odour concentrations in real-world dispersion scenarios?
• RQ5 What is the correct interpretation of odour-hours in AUSTAL2000, and how does it relate to the actual perception of odour by humans?

Key findings

• The AUSTAL2000 model does not apply a fixed peak-to-mean factor of 4; instead, it calculates odour-hours based on the 90th percentile of instantaneous concentrations relative to the hourly mean.
• The 90th percentile of instantaneous odour concentrations can be estimated from the one-hour mean using a factor of 4, as validated by Janicke and Janicke (2007a), but this factor is not a universal peak-to-mean ratio.
• The peak-to-mean ratio is not constant and varies significantly with atmospheric stability, distance from the source, and source geometry, making a single value inappropriate for general use.
• A constant factor of 4 cannot reliably represent the true fluctuation in odour concentrations across different environmental and meteorological conditions.
• The study confirms that short-term odour perception is best assessed through the 90th percentile of instantaneous concentrations, not through arbitrary peak values or fixed multipliers.
• The research underscores the need for context-specific estimation of peak-to-mean ratios to improve the accuracy of odour nuisance modeling and regulatory assessments.