Elsevier

Atmospheric Environment

Volume 155, April 2017, Pages 162-173
Atmospheric Environment

A simple model to assess odour hours for regulatory purposes

https://doi.org/10.1016/j.atmosenv.2017.02.022Get rights and content

Highlights

  • A novel method to compute so-called odour hours has been developed.

  • The method uses the concentration variance and a Weibull PDF.

  • It is fast and applicable for regulatory purposes.

  • The new method is independent from the type of dispersion model.

  • Computed quantities are in good agreement with observations.

Abstract

A novel methodology for estimating odour hours in the frame of licencing procedures is presented. In contrast to the widely used constant-factor-4 model, which is the prescribed method in Germany, a model based on computing concentration variances is proposed. It is derived upon the advection-diffusion equation for the concentration variance, but is strongly simplified by neglecting the transport and diffusion terms. In this way, the method becomes extremely efficient with regard to computation times. Furthermore, the model is independent on the type of dispersion model used to calculate average concentrations, which are necessary for subsequently computing concentration variances. In a second step, simulated concentration variances are used in combination with a slightly modified two-parameter Weibull probability density function to get the 90th percentile of the cumulative frequency distribution of odour-concentration fluctuations, which is required for computing a so-called odour hour. The model is operated in post-processing mode and can, thus, easily be implemented in existing dispersion models. It's validity has been tested against two tracer tests carried out in Germany and the U.S.

Introduction

In several countries (e.g. Austria, Germany, Switzerland, Italy) odour assessments are based on so-called odour-hours defined by at least 6 min of perceivable odour concentrations. It is well known that dispersion models typically provide mean concentrations for averaging times of about 30–60 min. Different models for odour estimation can be found in the literature (e.g. Mussio et al., 2001, Lo Iacono, 2009, Dourado et al., 2014, Murguia et al., 2014). Modelling odour hours requires the determination of the 90th percentile of the corresponding cumulative frequency distribution. Often the 90th percentile is normalized by the hourly-mean concentration by defining R90=C90/C¯, where C¯ is the hourly-mean concentration, and C90 the 90th percentile. In Germany, the regulatory odour dispersion model AUSTAL2000G (GIRL, 2009) uses the simple relationship R90=4, which is based on the work of Janicke and Janicke (2004). This assumption is broadly used in Austria, too, and is currently implemented in the Lagrangian particle model GRAL (Oettl, 2015a). The advantages of setting R90=4 are its robustness, and, as will be seen later in this work, its tendency to provide a conservative estimate for R90, which is generally eligible when applying (simple) models for regulatory purposes. Nevertheless, one might suppose that the magnitude of overestimation increases substantially with distance to sources or in case of overlapping plumes.

Piringer et al. (2016) use an empirical approach for assessing R90, which depends on atmospheric turbulence and the distance to the source. Though this model is certainly more appropriate from the theoretical point of view than using a constant factor, it has been strictly derived for a single point source only and cannot be applied for multiple sources. Furthermore it does not take into account cross-wind variability of R90.

Therefore, the aim of this work was to derive a simple model providing a better estimate for R90, but which is still applicable in licencing procedures. The concept of the approach is based on a simplified version of the advection-diffusion equation for the concentration variance c2¯, which serves determining R90 by utilizing a representative probability density function (PDF) for the odour concentration. Many models have been developed for the second- or higher-order moments of the concentration PDF, starting from the two-particle models proposed by Thomson (1990) and Borgas and Sawford (1994) for idealized conditions (homogeneous, isotropic, and stationary turbulence). Another interesting approach, the so-called PDF method, was brought forward by Pope (1985) and Cassiani et al. (2005). Unfortunately, it is difficult to apply the model to real cases due to the large computational time required. On the contrary, the fluctuating plume model (Yee et al., 1994a, Yee et al., 1994b; Yee and Wilson, 2000; Luhar et al., 2000, Franzese, 2003, Mortarini et al., 2009, Ferrero et al., 2013) is less time expensive and can be used in real-world dispersion conditions, i.e. non-homogeneous boundary layers. Recently, Manor (2014) proposed a Lagrangian model, able to simulate the concentration-variance trajectories, and Ferrero et al. (2016) introduced a new parameterization for the dissipation variance for that kind of models. Concerning Eulerian models, it is worth mentioning the paper of Mavroidis et al. (2015) that used a computational fluid dynamics (CFD) model to calculate the concentration fluctuations and compared model results with a wind-tunnel experiment suggesting that the magnitude of concentration fluctuations are comparable with those of mean concentrations.

Sect. 2.1 deals with the selection of a proper PDF by analysing concentration-fluctuation observations of three different field studies. In Sect. 2.2, the derivation of the basic equations of the concentration-variance model are outlined, and sect. 3 presents a comparison of modelled and observed data.

Section snippets

Selection of a proper probability density function (PDF) for concentration fluctuations

A number of field studies concerning concentration fluctuations are reported in literature. Different PDFs have been proposed for fitting observed data. Mylne and Mason (1991), by investigating dispersing plumes originating from a ground-level point source, compared a clipped-normal distribution with an exponential one and found that the clipped-normal PDF represents most of the observed features (e.g. concentration intensity), but underestimates concentration intensity i=c2¯/C¯ close to the

Model evaluation

The Uttenweiler and JU03 experiments were used to test the model's accuracy with regard to simulating the concentration-fluctuation intensity i and R90. It is planned to carry out similar model evaluations for other available datasets, such as the Sagebrush field campaign, in the future.

As tracer dispersion was influenced by buildings in both the Uttenweiler and JU03 experiments, microscale flow-field simulations had to be carried out prior to dispersion modelling. The GRAL model used in this

Conclusions

Based on previous works (Hsieh et al., 2007, Milliez and Carissimo, 2008, Manor, 2014, Ferrero et al., 2016) a simplified version for computing concentration variances has been derived. One of the crucial steps in the derivation was the neglecting of the transport and diffusion terms. The validity of this assumption has been tested by comparing modelled concentration-fluctuation intensities with observed ones, and by comparing the general spatial patterns with simulation results obtained by

Acknowledgements

We are very grateful to Prof. Dennis Finn, Air Resources Laboratory, Maryland, U.S., for providing the Sagebrush and Joint Urban 2003 data and for his useful directions. We would also like to thank all three anonymous reviewers for their precious comments, which helped a lot to improve this work.

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