Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling

ATMOSPHERIC ENVIRONMENT: X 5 (2020) 100054

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Temporal variability in odour emissions: To what extent this matters for the assessment of annoyance using dispersion modelling Marlon Brancher a, *, Werner Knauder b, Martin Piringer b, Günther Schauberger a a b

WG Environmental Health, Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Veterin€ arplatz 1, A-1210, Vienna, Austria Central Institute for Meteorology and Geodynamics, Hohe Warte 38, A-1190, Vienna, Austria

A R T I C L E I N F O

A B S T R A C T

Keywords: Odorous air pollution Environmental odours Impact assessment Dispersion model Variable emission rate Temporal variability

The annoyance potential of odours can be assessed using dispersion modelling, thereby delineating separation distances between pollution sources and nearby communities. It is common practice in this context to assume constant emissions over time, although odour emissions are often characterised by temporal variability. Here we show that the assumption of constant emissions can bias the separation distances towards underestimation, as compared to more realistic scenarios incorporating time-varying emissions. We identify three primary factors driving the level of such underestimation: wind direction frequency, degree of emission variability and percentile compliance level of odour impact criteria. Accordingly, the underestimation was more significant in the pre­ vailing wind directions. With greater variability of the odour emission rate, the separation distances tended to be larger. The higher the percentile, the greater the underestimation of separation distances. In particular, the 90th percentile showed superior skill in counteracting the source emission variability when compared to the 98th, 99th and 99.5th percentiles. The findings are achieved using a Lagrangian particle dispersion model. Meteoro­ logical input data are due to locally-derived wind and turbulence measurements at a site in Central Europe (Austria). Discrete representation of odour emissions over time (hourly resolution) was accounted for by employing a Monte Carlo-based method (inverse transform sampling). This work provides a better understanding of the extent to which accounting for temporal variability in odour emissions can be most useful.

1. Introduction Industrial activities and concentrated animal feeding operations can impact on the health and amenity of local communities due to emissions of odour (Hayes et al., 2014), bioaerosols (Robertson et al., 2019), dust (Machado et al., 2018), volatile organic and sulphur compounds (Fisher et al., 2018; Wu et al., 2018), noise (Hays et al., 2017) and light (Falchi et al., 2011). In many settings, odour is the main cause of community concerns and complaints (Cantuaria et al., 2017). Odour-emitting fa­ cilities and sensitive receptors are closer than in the past, principally due to higher population density (Gostelow et al., 2001). This reality has increased the potential for conflicts considerably so that odour com­ plaints continue to rise in number and severity (Cai et al., 2015; Hayes et al., 2017). As a result, odour pollution is receiving a great deal of attention in several countries. Central to this context is the improved quantification of odour levels to which residents are exposed, as a means of tackling odour pollution most effectively. Around the world, atmospheric dispersion modelling is the most

used method for assessing compliance with odour regulations (Brancher et al., 2017). Dispersion models offer the distinct benefit of not only being descriptive but also predictive (Capelli et al., 2013). A wide range of odour impact criteria (OIC) are currently in use for evaluating model outputs, i.e. the time series of ambient odour concentrations (Griffiths, 2014; Sommer-Quabach et al., 2014). Based on this evaluation, one can determine the distances needed for odour-emitting facilities to operate without causing excessive annoyance in the neighbourhood (Schau­ berger et al., 2018). By summarising the modelling chain, the separation distance thus specifies, in a practical manner, the odour annoyance footprint. Meteorological data and source information are two essential inputs for completing an odour modelling study. Within the latter, the current practice is to treat emissions as time-invariant. More specifically, it is common to consider a single odour emission rate (OER) value throughout the entire simulation period as input to dispersion models (Brancher et al., 2018; Hayes et al., 2006; Nicolas et al., 2008; Schau­ berger et al., 2014). In reality, however, it has long been acknowledged

* Corresponding author. E-mail address: [email protected] (M. Brancher). https://doi.org/10.1016/j.aeaoa.2019.100054 Received 4 June 2019; Received in revised form 13 November 2019; Accepted 16 November 2019 Available online 19 November 2019 2590-1621/© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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that odour emissions frequently fluctuate over time (Feilberg et al., 2010; Romain et al., 2013). If modelling is to be used more and more reliably for decision making, it is thus of relevance to advance our un­ derstanding of the potential influences posed by variable odour emis­ sions on assessment conclusions. Temporal variability in odour emissions can be due to several factors. Two important factors are the meteorological conditions and manage­ ment practices associated with a particular facility. For livestock buildings, odour emissions exhibit variability over the day and the year, which depends on the (i) animal growth, activity and diet, (ii) ambient temperature oscillation, (iii) ventilation rate, (iv) housing system and (v) barn cleaning plan (VDI 3894 Part 1, 2011). For many industrial sectors, odour emissions can be expected to vary owing to process-specific conditions such as work schedule, changes in produc­ tion and settings of air pollution control equipment. For example in the case of wastewater treatment plants, where liquid area sources are common, the wind flow over the release surface (Lucernoni et al., 2017; Prata et al., 2017b) and the liquid phase temperature (Invernizzi et al., 2019) have been reported as predictors of odour emissions. In this work, we aim to specify the influence of temporal variability in odour emissions on the assessment of odour annoyance via dispersion modelling. We focus on the use of a versatile Monte Carlo-based method to realise hourly time series of OERs. These served as the basis for constructing three time-resolved emission scenarios with gradually increasing OER variability. The conventional scenario with a constant OER was considered as a baseline. Furthermore, we select conceptually different OIC for the investigation. We then compare and discuss the results–in terms of separation distances–due to different emission sce­ narios and OIC.

2.2. Dispersion modelling We used the Lagrangian particle dispersion model LASAT in its version 3.4.5 for dispersion calculations. LASAT simulates the transport and turbulent diffusion of a representative sample of tracer particles using a stochastic process. LASAT follows the German Guideline VDI 3945 Part 3. Besides, it has been the basis for the development of the German regulatory dispersion model AUSTAL2000, which is the official reference model of the Technical Instruction on Air Quality Control (TA Luft). LASAT can be used as an alternative to AUSTAL2000 consistent with TA Luft and beyond (Janicke Consulting, 2019). Model evaluation has been reported for a variety of applications, in flat and complex terrain (Baumann-Stanzer et al., 2014; Baumann-Stanzer and Piringer, 2010; Hirtl and Baumann-Stanzer, 2007; Piringer and Baumann-Stanzer, 2009; Schatzmann et al., 2010). We defined a model domain of 4 � 4 km centred on the site, with a horizontal mesh width of 20 m. The current standard boundary layer model of LASAT (version 2.1) was applied. The more particles are simulated, the smaller is the sampling error, but the longer is the simulation. We set 100 particles per second because preliminary model runs indicated this rate to be a compromise between accuracy and computational time. To calculate the mean surface roughness length (z0) of the site, we applied the AERSURFACE utility (version 13016) (U.S. EPA, 2008) based on the CORINE Land Cover database for 2018. Briefly, the z0 was determined by a single sector with an upwind distance of 1 km radius relative to the location of the ultrasonic anemometer. A z0 ¼ 0.198 m was obtained from this procedure so that a value of 0.2 m was considered. We did not model obstacles and potential building down­ wash effects. If the stack height is assumed to be 2.5 times higher than any nearby building, downwash effects are unlikely to occur (U.S. EPA, 1985).

2. Methodology 2.1. Setting

2.3. Meteorological data

The work was set in Groβ-Enzersdorf, a municipality to the east of Vienna, Austria. We have conducted other studies at this location, however, for different aims (Brancher et al., 2019a, 2019b). Here the site was located at 48.199� N, 16.559� E (at 154 m ASL). It is predom­ inantly within flat terrain. Currently, the principal land use categories in the vicinity of the site are farmland and residential areas. According to €ppen Geiger Climate Classification (Kottek et al., 2006), the site the Ko falls within class Cfb (C: warm temperature, f: fully humid, b: warm summer). Fig. 1 shows the position of the site in Austria.

Sequential meteorological measurements of wind and turbulence data were sampled at 10 Hz with a three-axis ultrasonic anemometer (uSonic-3 Scientific, METEK GmbH, Elmshorn, Germany). This equip­ ment was mounted at 10 m height above terrain level. Atmospheric stability was characterised by the Obukhov length (L, in m). Standard deviations of the three wind components (σu, σv, σw) were also consid­ ered. Air temperature, measured at 2 m by an automatic weather station placed nearby the ultrasonic anemometer, was used. Data refer to the most recent year to date (2018). Data availability for all meteorological

Fig. 1. Topographic map of Austria with indication to the site in the municipality of Groβ-Enzersdorf. This map was built using a digital elevation model from airborne laser scan data (available at www.data.gv.at). Elevation of the terrain has a resolution of 10 � 10 m. 2

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parameters amounted to ~100% of the hours within the year. Modelling runs were thus undertaken using a one-year time series of site-specific meteorological observations averaged at hourly intervals.

variation (CV), and we selected the following values: CV ¼ 5%, CV ¼ 10%, and CV ¼ 20%. We used inverse transform sampling only once, specifically for ES05, as follows. We considered the Mersenne Twister pseudorandom number generator for Step 1 because it is widely used, tested and suitable for Monte Carlo simulations. Step 2 returns a realisation of logarithmically transformed emission rates ei ​ ¼ ​ logEi that are normally distributed with mean e (~4.3 or log 20; 000) and standard deviation adapted to the CV (s ​ ¼ ​ CV ​ e). We refer to the common logarithm with base 10. Next, we defined a filter to adjust extremes (an upper limit of 5.0 was set). For the set of random values generated herein, only 6 extreme values were discarded (~0.07%). Yet, this filter was applied with intention of further improving the fit of the samples to the lognormal distribution. Finally, we retained the OER values by applying the antilog. The realisation of OER values, in ouE s 1, was linked to the length (one year) and resolution (hourly) of the meteorological dataset. As a result, 8760 values for the OER were realised. ES10 and ES20 were derived from E05 by linearly increasing the dis­ tance of each hourly value e05;i from the mean e. For a CV ¼ 10%, the corresponding hourly value e10;i results from doubling the distance from the mean according to e10;i ¼ e þ 2ðe05;i eÞ. Similarly, for a CV ¼ 20%, the corresponding hourly value e20;i results from quadrupling the distance from the mean (Schauberger et al., 2016). Emissions were represented in LASAT as a single-point source (stack). Table 1 summarises the source parameters assumed for each emission scenario. To isolate and highlight the influence of time-varying OERs, which is the aim of this work, we held other source characteristics fixed.

2.4. Time-varying emissions To attain time-varying OERs, we used inverse transform sampling (Devroye, 1986). Inverse transform sampling allows generating random numbers from a specific probability distribution given the inverse of its cumulative distribution function (CDF) F 1. Here the fundamental assumption is that odour emissions follow a lognormal distribution. The theorem of the method states that with the knowledge of the probability distribution of a random variable X, there is a trans­ formation, mapping a uniformly distributed random variable u over the interval (0, 1) into the space of X (Bazrafkan et al., 2018). In short, let p be the probability distribution function, so that p(F 1(u)) ¼ p(X). It has been proved that the distribution of the output of the inverse of the CDF for u is the same as the distribution of X (Lemieux, 2009). Fig. 2 illus­ trates how the method works. In its essence, the method can be described by two steps as follows (Evangelidis et al., 2018): � Step 1: Generate a uniformly distributed random number u ∊ U(0, 1); � Step 2: Return X ¼ F 1(u). 2.5. Emission scenarios We defined four emission scenarios in this fashion: � ESCONV: this is the conventional scenario given by a constant emis­ sion rate of E ¼ 20,000 ouE s 1 and variance of zero. It forms the basis of the investigation and serves as a reference for the comparative analysis; � ES05, ES10 and ES20: these are the three time-resolved scenarios. The annual mean value (E ¼ 20,000 ouE s 1) was preserved, however, these scenarios were designed to show increasing emission vari­ ability. This variability was incorporated by the coefficient of

2.6. Odour impact criteria (OIC) OIC are specified by an odour concentration threshold (CT), the percentile compliance level of this threshold and the model averaging time (AT). The percentile indicates that a predefined CT may only be exceeded for a certain percentage of the time; hence it is also called tolerated exceedance probability (pT). The ratio between a short-term mean value (relevant for odour perception, in seconds) and the longterm mean value (predicted by a dispersion model, typically on an hourly basis), called peak-to-mean factor, is still broadly used to describe odour concentration fluctuations (Schauberger et al., 2012). Accordingly, we selected the following OIC as references for deter­ mining separation distances: � Ṟ1 (Queensland): 99.5th percentile (pT ¼ 0.5%), CT ¼ 0.5 ouE m 3, AT ¼ 1 h; � Ṟ2 (Norway): 99th percentile (pT ¼ 1%), CT ¼ 1.0 ouE m 3, AT ¼ 1 h; � Ṟ3 (Flanders): 98th percentile (pT ¼ 2%), CT ¼ 1.0 ouE m 3, AT ¼ 1 h; � Ṟ4 (Germany): 90th percentile (pT ¼ 10%), CT ¼ 0.25 ouE m 3, AT ¼ 1 h.

Ṟ1 is defined by the 99.5th percentile with a CT ¼ 5 ouE m 3 (as a peak value). This CT is scaled down to 0.5 ouE m 3 by using a peak-tomean factor of 10 (for wake-free stacks) reflecting the fact that model predictions relate to hourly averages. This criterion is enforced in Queensland (Australia). The 99.5th percentile is also used for example in New Zealand, some Canadian provinces and other Australian states. Ṟ2 is defined by the 99th percentile with a CT ¼ 1 ouE m 3 on an hourly basis (peak-to-mean factor ¼ 1). This criterion is enforced in Norway. Strictly speaking, in Norway this percentile has to be applied on a monthly basis, but here we apply it on an annual basis for purposes of comparison with the other selected criteria. The 99th percentile is also used in New South Wales (Australia) and Denmark (on a monthly basis as well), for example. Ṟ3 is defined by the 98th percentile with a CT ¼ 1 ouE m 3 with on an

Fig. 2. Illustration of the inverse transform sampling method. The random variable X takes values in a certain interval due to the inversion of F. This is shown exemplarily by the dotted arrow lines for F 1(0.5). If u is a uniform random number on (0,1), then X ¼ F 1(u) has the distribution function F. 3

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Table 1 Emissions scenarios and associated source characteristics. Emission scenario ESCONV ES05 ES10 ES20

Coefficient of variation

Flow rate 3

1

Stack height

Stack diameter

Exit velocity

[%]

[m s ]

[m]

[m]

[m s

0 5 10 20

7.6

8.0

1.8

3.0

hourly basis (peak-to-mean factor ¼ 1). This criterion is enforced in Flanders (Belgium). Most countries worldwide with odour regulations in place set the 98th percentile as compliance level. Ṟ4 is defined by the 90th percentile with a CT ¼ 1 ouE m 3 (nose response time). The factor of 4 is in use, which results in an hourly mean of CT ¼ 0.25 ouE m 3. This is the baseline criterion enforced in Germany. It has been often adopted in Austria and Switzerland too. All reference OIC are given on an hourly basis (AT ¼ 1 h) to allow a direct comparison of results. Different peak-to-mean factors however apply depending on the selected OIC, as described. References to the regulations in which the selected OIC are set can be found elsewhere (Brancher et al., 2017).

1

]

Exit temperature [� C] 20

unstable, neutral and stable conditions at the site were ~9, 68 and 23%, respectively. Good atmospheric ventilation implies a tendency to neutral stratifi­ cation so that this boundary layer is in a state of forced convention. Consistently, an abundance of near-neutral conditions occurred for the prevailing wind directions (NW and SE). The frequent neutral conditions (Fig. 4, central panel) resemble most of the wind rose shape (Fig. 3). Neutral stratification essentially dominated with wind speeds � 5.5 m s 1. Both unstable and stable conditions were observed more frequently with wind speeds � 2.8 m s 1. Yet, the results suggest that a few stable conditions occurred with wind speeds ~4 m s 1. Furthermore, low wind speeds favoured daytime unstable and night-time stable conditions. With the increase of wind speed, there was a propensity of near-neutral conditions to occur both during daytime and night-time. As expected, the diurnal evolution of the boundary layer, given by the dependence of turbulence on hour of the day and wind speed, has therefore been confirmed.

2.7. Statistical analyses We assessed the OER data for lognormality based on distribution analysis, thereby combining visual plots with hypothesis tests for this particular distribution. We measured the resultant separation distances (in full metres) from the stack towards the transport directions–those directions to which emissions spread. The separation distances due to ES05, ES10 and ES20 were then normalised by ESCONV-related distances. This ratio gave rise to scaling factors, dimensionless. The scaling factors were thus utilised to quantify the magnitude of possible differences in separation distances emerging from the different emission scenarios. A scaling factor equal to one demonstrates equality between the pair of distances. The further from one the scaling factor is, the more significant the difference.

3.2. Realisations of odour emission rates (OERs) Fig. 5 shows the realised OERs for the time-resolved emission sce­ narios [CV ¼ 5% (ES05); CV ¼ 10% (ES10); CV ¼ 20% (ES20)], besides the conventional emission scenario [CV ¼ 0% (ESCONV)]. Despite the pro­ portional increases in variability assigned to the three time-resolved emission scenarios, all scenarios have been designed to feature a matching annual mean value for the OER (i.e., E ¼ 20,000 ouE s 1). This can now be visualised by the dotted black line in Fig. 5, and it is vali­ dated in Section 3.3. The time-resolved emission scenarios comprise hours of low emis­ sions blended with hours of high emissions. The hourly time series of OERs follows an identical pattern because ES10 and ES20 are linearly dependent from ES05. Due to this dependence, the minimum and maximum OER values occur for ES20. All emission scenarios consider continuous emissions over the year.

3. Results 3.1. Wind regime and atmospheric stability The annual hourly-averaged wind speed and highest speed were 3.1 � 1.8 m s 1 and 10.8 m s 1 for the period (2018), respectively. The prevailing winds blew from northwest (NW) and southeast (SE), di­ rections for which the highest wind speeds occurred at this site (Fig. 3). As often observed in Central Europe, the site exhibits a bimodal distribution of wind directions. This wind regime is due to the west wind belt at these latitudes with alternating low and high-pressure influence. Whereas SE winds are regularly observed with fair weather (anticyclonic conditions), NW winds are mainly associated with cloudy or rainy pe­ riods (Brancher et al., 2019a). Winds characterised as calms (< 0.5 m s 1) amounted to ~0.6% of the observations. The annual hourly-averaged temperature was 12.4 � 9.2 � C. As described before, atmospheric stability was characterised by the Obukhov length L. The absolute value of L represents the height below which mechanically-generated turbulence dominates. For neutral stratification, L can approach positive or negative infinity so that the reciprocal of L (1/L, in m 1) is considered here. Hence, 1/L is positive for stable stratification and negative for unstable stratification. For neutral stratification, |1/L| values are nearly zero. Fig. 4 presents scatter wind roses (Cheynet, 2019) for the atmospheric stability in terms of 1/L. The dataset was divided by unstable (top panel), neutral (central panel) and stable (bottom panel) cases. In order to perform this division, we considered z0-dependent cut-off values for L (or its reciprocal) following Table 17, Annex 3 of TA Luft. The resultant annual frequencies of

3.3. Validity of emission scenarios We plotted the cumulative distribution function (CDF) of each emission scenario (Fig. 6, left panel). The higher the CV, the flatter the shape of the CDF. This was expected because the CV was adapted to the standard deviation of the distribution. Probability plots offer a way to visually compare the sample data to a specified distribution fitted to the data. As such, we also built probability plots (Fig. 6, right panel) to assess whether the realised OER data follow a lognormal distribution, as assumed for the three time-resolved scenarios ES05, ES10 and ES20. In a lognormal probability plot, if all the data points fall near the line of identity (a reference line that represents the theoretical distribution), an assumption of lognormality is thus reasonable. Otherwise, a presump­ tion of lognormality cannot be justified. Accordingly, strong evidence that the underlying distribution is lognormal has been provided. Note that a few outliers persisted even after adjusting extreme values (as described in Section 2.4); however, they have no statistical impact in a rather large sample dataset (n ¼ 8760). The probability plot correlation coefficient test further confirmed the goodness-of-fit of the timeresolved emission scenarios at the 5% significance level. Hence, good agreement between the empirical and theoretical distribution has been 4

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Fig. 3. Wind rose for Groβ-Enzersdorf, Austria. Hourly-averaged meteorological data for 2018. Legend denotes wind speed (WS) categories and their associated colours. The 3D ultrasonic anemometer was mounted at a height of 10 m from the ground. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article).

achieved. Also, both the CDFs and probability plots show that all sce­ narios have the same annual mean value of E ¼ 20,000 ouE s 1 for the OER because the curves touch each other at a probability of 0.5. Table 2 presents the cross-correlation matrix as a measure of the linearity between the emission scenarios. High Pearson correlation co­ efficients were attained due to the relatedness of the time-resolved scenarios. As expected, the correlation coefficient between the emis­ sion rate values of ES05 and those of ES10 is the highest. If all scenarios had been calculated in the same way as ES05, the cross-correlation would be close to zero because of their independent behaviour. The p-values were all much lower than the 5% significance level (p < 0.0001), thereby indicating the rejection of the hypothesis that no correlation exists between the pairs of scenarios.

ES10 and ES20. In this sense, the results reveal that the higher the percentile compliance level, the higher the underestimation. The prin­ cipal inference is that the assumption of a constant OER [CV ¼ 0% (ESCONV)] leads to an underestimation of separation distances. Moreover, the results show the robustness of the 90th percentile, given by Ṟ4, in reducing the influence of time-varying OERs on the separation distances up to a CV ¼ 10%. Namely, the separation distances due to ESCONV, ES05 and ES10 are analogous, making the enclosed areas of these scenarios to nearly overlap. To quantify changes in separation distances, we normalised the distances due to ES05, ES10 and ES20 by ESCONV, leading to scaling factors (Fig. 8). When using Ṟ1 and Ṟ2 for ES20, in some transport directions the resultant separation distances extended the model domain, so that the maximum measurable distance was taken. The main conclusion of Fig. 8, despite the oscillation of the scaling factors towards the transport directions, is that the use of a constant OER underestimates the sepa­ ration distances. This outcome is well captured by scaling factors greater than one, confirming the visual inspection of Fig. 7. As the OER vari­ ability increases, higher scaling factors occurred, up to a maximum factor of five. Again, the overall tendency is that the greater the vari­ ability of the OER, the higher the underestimation of the separation distances, as compared against the use of a constant OER. Fig. 8 also underlines that the separation distances which were calculated using Ṟ4 have no major differences among ESCONV, ES05 and ES10. Significant differences only appear for ES20. For example, the separation distances due to ES20 for the transport direction of 130� (caused by prevailing northwesterly winds) has a scaling factor of about 1.29. This represents an increase of 160 m (from 552 m for ES05 to 712 m for ES20). Similarly, the separation distances due to ES20 for the transport direction of 310� (caused by prevailing southeasterly winds) has a scaling factor of about 1.24. This represents an increase of 116 m (from 485 m for ES05 to 601 m for ES20).

3.4. Influence of time-varying odour emissions on separation distances Fig. 7 compares the separation distances as contour lines, enclosing the area of exceedance of the selected OIC. The extent displayed is relative to the model domain (4 � 4 km), with the stack located in the centre. First, there are systematic differences in separation distances across the selected OIC. These differences are not surprising. It has been shown that divergent distances can be determined even when the OIC relate to an equivalent level of protection (Brancher et al., 2019b; Sommer-Quabach et al., 2014). Second, the frequency of wind directions is a primary factor driving the odour dispersion pattern. The shape of the separation distances re­ sembles the wind regime of the site, which becomes more pronounced as the percentile is reduced. This outcome emphasises the significant lim­ itations of using simple measures of proximity as a proxy for odour exposure. Third, for all selected OIC, with increasing variability of the OER, the separation distances become larger, particularly in the prevailing wind directions. For the additional wind directions, this directionality influ­ ence is noticeably diminished. In general, the use of Ṟ1 gives rise to the largest differences in separation distances between ESCONV and ES05, 5

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4. Discussion 4.1. Summary of work and findings In this work, we used a Monte Carlo-based technique (inverse transform sampling) to generate random OER values at an hourly res­ olution. Three lognormally distributed, time-resolved emission sce­ narios with gradually increasing variability [CV ¼ 5% (ES05); CV ¼ 10% (ES10); CV ¼ 20% (ES20)] were designed. The case of constant emissions was represented by the conventional scenario [CV ¼ 0% (ESCONV)]. All scenarios were designed so as to have a matching annual mean value for the OER. Together with observational meteorological data, the four emission scenarios were inputted to a Lagrangian particle dispersion model (LASAT) to determine the separation distances for four OIC. In this manner, we studied whether temporal variability in odour emissions can influence the delineation of zones of expected annoyance, and if so, to what extent. The results indicate that the greater the variability of the OER, the larger the zone of expected annoyance, as shown by the calculated separation distances (Fig. 7). In other words, the separation distances tended to increase as a function of gradual increases in the OER vari­ ability. Consequently, the use of a constant OER was associated with a propensity to bias the separation distances towards underestimation. Concerning directionality, the level of underestimation was found to be more pronounced in the prevailing wind directions. The selection of four OIC allowed to explore the extent to which the use of a constant OER can potentially pose a risk of inadequate assessment conclusions. In general, the higher the percentile compliance level, the greater the un­ derestimates of separation distances. Scaling factors greater than one indeed occurred (Fig. 8). These results underlined the underestimation of the separation distances when using a constant value for the OER rather than accounting for temporal variability in odour emissions. For the conditions under investigation, the 90th percentile showed improved skill in counteracting the influence of time-varying OERs on the separation distances up to a CV ¼ 10%. In such a case, the separation distances due to ESCONV, ES05 and ES10 were very similar. Significant differences only arose for ES20. 4.2. Methodological choices Four methodological choices of this work deserve clarification. First, CV values within the range of 5–20% have been considered. This range was based on olfactometric measurements (for odour as a complex mixture of chemicals) or by using surrogates. For a waste thermal treatment plant, seven chemicals were used as surrogates for odour and showed a mean CV ¼ 17.9% (� 1.1%) (Schauberger et al., 2008). For a tannery wastewater treatment plant, H2S was used as a surrogate for odour with a CV ¼ 9.4% (Schauberger et al., 2013b). For fattening pigs, the CV for odour has been previously determined as 10.7% (Schauberger et al., 2013a) and 9.5% (Miller et al., 2004). Calculation of OERs for fattening pigs resulted in lower CV values between 4–6%, growing with the increased complexity of the emission model (Schauberger et al., 2014). For a pig nursery (3–10 weeks old), reported CV values were in the range 3.0–7.9% (Lim et al., 2001). The designated range for the CV can thus be considered demonstrative of the variability in odour emis­ sions. In addition, one should highlight that variability in odour emis­ sions can also be present in different plants belonging to the same sector. Regardless of representativeness with empirical emission data, that range has shown to be adequate to meet the aim of this work. Second, we adopted E ¼ 20,000 ouE s 1 for the OER. This annual mean value can represent different facilities. For example, it can be related to a variety of mechanically-ventilated livestock operations based on emission factors given in the German standard VDI 3894 Part 1. In this standard, emission factors are given by animal species, branches of production and housing techniques (VDI 3894 Part 1, 2011). Third, earlier studies have reported on the lognormality of OERs

Fig. 4. Scatter wind roses for the atmospheric stability as expressed by the reciprocal of Obukhov length (1/L). The top, central and bottom panels show the unstable, neutral and stable conditions, respectively.

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Fig. 5. Annual time course of the odour emission rate at an hourly resolution. ESCONV is the conventional scenario based on an annual mean value of E ¼ 20,000 ouE s 1 for the odour emission rate. ES05, ES10 and ES20 are the three time-resolved scenarios with the same mean value but with increasing variability. This variability was introduced by the coefficient of variation (CV) in the range of 5–20%.

(Akdeniz et al., 2012; Lim et al., 2001; Miller et al., 2004; Schauberger et al., 2014, 2013a, 2013b, 2008). This knowledge has been confirmed to be of great interest. Here it permitted for a generation of OER values grounded on the underlying probability distribution. The multiplicative modulation of the odour emission explains why a lognormal distribution for the OER is usually selected (Limpert et al., 2001; Limpert and Stahel, 2011). Lastly, we chose the OIC for this investigation based on a previous review of OIC in 28 countries (Brancher et al., 2017). The selected OIC are intended to cover, in particular, the wide range of percentiles currently in use.

time-resolved scenarios with a CV 6¼ 0 attempted to represent emissions from odour sources more realistically, as compared to the use of a constant value for the OER. If the OER is treated as constant throughout the entire simulation period, only variations in meteorological condi­ tions are reflected in the delineation of separation distances. As a consequence, the subsequent CDF of the time series of ambient odour concentrations is solely driven by meteorology. For long-term air quality standards, a linear function can accurately approximate the relationship between emission and ambient concen­ tration (Pisoni et al., 2017; Thunis et al., 2015). Annual average emis­ sions may thus be appropriate as model input in such a case (U.S. EPA, 2017). However, instead of an annual mean limit value, a wide range of high-end percentiles are utilised for annoyance prediction. Conse­ quently, an odour impact criterion follows a non-linear relationship. Discrete temporal representation of odour emissions should be therefore accounted for. A further strength is the use of a state-of-the-art, validated Lagrangian particle model (LASAT). We are aware of the many disper­ sion models available, but we chose LASAT for its capabilities, regula­ tory character (in Germany), and because it is well suited for the particular objectives of this work. Note that more sophisticated disper­ sion models demand more resources (run time, expertise and input data). If it is worth to spend additional efforts should be framed on a case-by-case basis (Capelli et al., 2013). Model performance is not purely dictated by model formulation. It

4.3. Strengths The initial value of this work is to look holistically at the odour impact assessment chain, which starts from the source emissions, transport and dilution in the atmosphere, to the evaluation of predicted time series of ambient odour concentrations by OIC. Besides, it in­ corporates various strengths in all parts of this chain. Temporal variability in odour emissions has long been acknowl­ edged. However, there is scarce literature systematically investigating how this issue may affect separation distances (Schauberger et al., 2016). Here we created and assessed four emission scenarios, including the conventional scenario with no more than an annual mean value for the OER and three scenarios with time-varying emissions. These 7

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Fig. 6. Cumulative distribution functions of the conventional emission scenario ESCONV based on an annual mean value of E ¼ 20,000 ouE s 1 for the odour emission rate and zero variability and of the three time-resolved scenarios ES05, ES10 and ES20 with the same mean value but with increasing variability (left). Probability plots of ES05, ES10 and ES20 which are based on Monte-Carlo simulated hourly odour emission rates as compared to the theoretical lognormal distributions (right).

4.4. Limitations

Table 2 Correlation coefficients as a measure of linear dependence between the emission scenarios. ES05 ES10 ES20

ES05

ES10

ES20

1 0.9413 0.6424

0.9413 1 0.8435

0.6424 0.8435 1

Our work also has limitations that should be noted. The major lim­ itation is that the statistical approach to generate time-varying OERs cannot take into account the exact dynamics of emissions. The temporal variability was purely introduced by considering, through the CV, an expected amount of variation. Even though the variability is incorpo­ rated and temporally redistributed, the variability itself is not driven by inherent predictors of odour emissions. The idea is, however, to embrace the net effect of variable emissions, rather than the exact behaviour of any individual OER value. The limitation described above is at least partially justified because of the current lack of time-resolved odour emission data. In theory, continuous monitoring could reflect the temporal variability in odour emissions. However, quantifying odour emissions in real-time is still a problem to be solved. Putting in place a sampling and analysis program based on currently available instruments and techniques–those related to dynamic olfactometry with human assessors–to obtain variable OERs is likely to be time and cost-prohibitive. There are also known limita­ tions when using analytical methods to derive so-called odour activity �n et al., 2019; Wu et al., 2016). values for scaling the emission (Rinco Time and cost constraints would similarly affect the use of reverse dispersion modelling for estimating variable OERs based on downwind odour measurements and meteorological data, as field observations are often infrequent and thus improbable to capture the expected variability in odour emissions over time. Moreover, these two approaches (dynamic olfactometry and reverse dispersion modelling) by definition cannot be applied to new premises or future modifications to existing premises. At this point predictive emissions models come into play. Emission models have been more and more used in the estimation of odour emissions. However, this method is process dependent and still has to be consolidated as noted in a previous study (Prata et al., 2017b). The application of a few process-specific odour/odorant emission models has been discussed in the literature, as for waste thermal treatment plants (Schauberger et al., 2008), fattening pigs (Schauberger et al., 2014) and liquid area sources in wastewater treatment plants (Prata et al., 2017a).

depends on input data quality, too. In the absence of direct measure­ ments of turbulence, a common practice is to categorise the turbulence using various schemes or to derive continuous turbulence estimates through the processing of surface and upper air meteorological data, for instance. More specifically, classified turbulence in the form of the KlugManier stability scheme is typically used as input to LASAT (Piringer et al., 2019). In this respect, another strong point of this work is the use of locally-derived wind and turbulence measurements from a cutting-edge technique (3D ultrasonic anemometer). In doing so, one of the most suitable meteorological input data available to date has been considered. When the CDF of a random variable X that is to be generated is known, and it can be inverted easily, inverse transform sampling be­ comes convenient (Evangelidis et al., 2018). Then, the applicability and effectiveness of this Monte Carlo-based method rely on how easy F 1 can be computed (Lemieux, 2009). For continuous distributions whose in­ verse functions have closed-form expressions or can be approximated, inverse transform sampling is perhaps the most direct method of sam­ pling (Fishman, 2003). The usefulness of the method has been shown for several applications in different fields (Blasius and Wang, 2018; Calì et al., 2018; Gunay et al., 2016), including environmental sciences (Schauberger et al., 2013b). As seen, a simple algorithm can represent the method, thereby allowing a relatively rapid consideration of the source emission variability. A final strength relates to the inclusion of four OIC. This enabled us to compare the resultant separation distances in terms of fundamentally different criteria that are currently in use worldwide.

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Fig. 7. Separation distances shown as contour lines enclosing the area of exceedance of four odour impact criteria (Ṟ1, Ṟ2, Ṟ3 and Ṟ4). ESCONV is the conventional scenario (CV ¼ 0%) and ES05 (CV ¼ 5%), ES10 (CV ¼ 10%), and ES20 (CV ¼ 20%) are the three time-resolved scenarios.

Regarding source characteristics, one source configuration has been considered in light of the intent of this work. The aim has been addressed by utilising a traditional case, and for this reason, we do not focus on multiple source characteristics. Owing to the lack of far-reaching consensus as to which OIC can delineate separation distances more reliably, we opt to explore the results in terms of different jurisdictional OIC. This choice allowed us to give new arguments for future harmo­ nisation of the concept of the odour impact criterion and components thereof, as shown next.

It has to be noted, however, that there is some evidence (Miedema, 1992) suggesting that higher percentiles could be better for tackling annoyance due to discontinuous emissions. Previously reported benefits of OIC with lower percentiles include: � Reduction of discrepancies in separation distances due to the use of different types of dispersion models (Piringer et al., 2015); � Counteracting of stack exit temperature uncertainty (Brancher et al., 2019b); � Potential lessening of inter-annual variability of separation distances (Brancher et al., 2019a); � Diminishing the impact of the climate change signal–and its poten­ tial modifications to dispersion parameters–on separation distances (Piringer et al., 2019); � Tendency to reflect in separation distances the most frequently occurring meteorological conditions at a site (Schauberger et al., 2006). In contrast, separation distances due to high-end percentiles are driven by the highest ambient odour concentrations experienced at receptors over the simulation period (Griffiths, 2014). These highest ambient odour concentrations are mainly attributable to stable stratification, low wind speeds (meaning unfavourable mete­ orological conditions to the dispersion of air pollutants);

4.5. Implications The findings have implications for policymaking and future epide­ miological studies on environmental odours. Although the selection of a suitable percentile to reflect odour exposure remains to be fully illuminated, there is growing evidence that OIC with relatively lower percentiles (or higher pT), such as the 90th percentile, are in general more skilled for delineating odour annoyance footprints. This work adds to this by showing that separation distances calculated with the 90th percentile are more robust against temporal uncertainties in odour emissions when compared to the 98th, 99th and 99.5th percentiles. With high-end percentiles, the assumption of con­ stant emissions becomes less and less valid, whereas the contrary can be inferred for percentiles that approximate the median of the distribution. 9

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Fig. 8. Scaling factors for the four odour impact criteria (Ṟ1, Ṟ2, Ṟ3 and Ṟ4) and four emission scenarios towards the transport directions. For this analysis, the separation distances due to the time-resolved scenarios [CV ¼ 5% (ES05); CV ¼ 10% (ES10); CV ¼ 20% (ES20)] were normalised by the separation distances due to the conventional scenario [CV ¼ 0% (ESCONV].

considering variable emissions can be most beneficial for assessing the annoyance potential of odours via dispersion modelling. Overall, the results indicate that the common practice of assuming a constant value for the OER can underestimate the separation distances to avoid annoyance, as compared to more realistic scenarios with hourly-varying emission rates. Such underestimation was observed to be more pro­ nounced in the prevailing wind directions. Besides, the separation dis­ tances tended to enlarge with gradual increases in emission variability, even though the same annual mean value for the OER was preserved. The results also show that the higher the percentile compliance level of a specific odour impact criterion, the more significant the separation distance underestimation. Thus, in addition to the wind direction fre­ quency, this work identifies the degree of emission variability and the percentile of OIC as two further factors influencing the level of separa­ tion distance underestimation. More in detail, the 90th percentile showed superior skill in counteracting emission variability influences when compared to the 98th, 99th and 99.5th percentiles. Therefore, the use of the 90th percentile offered a considerable advantage: the sepa­ ration distances are, to some extent, independent from the source emission variability.

o From this follows that the reliability of separation distances depends strongly on the quality of the dispersion model for such meteorological conditions; � A conceptual statistical issue is that uncertainty is more significant for high-end percentiles. Predictions then become more susceptible to systematic errors in the meteorological input data, for instance; � Lastly and crucial, only for those OIC with percentiles � 90th, model calculations can be formally checked against empirical field mea­ surements. OIC with percentiles > 90th revoke such an empirical comparison (Brancher et al., 2019b; EN 16841-1, 2016; Oettl et al., 2018; Piringer et al., 2015). Finally, epidemiological exposure–effect relationships are key for setting environmental criteria. Consequently, this work could also be of relevance for future epidemiological studies on odour pollution, as the assignment of odour exposure estimates is often based on dispersion modelling. The lack of resolution in emission input data may cause exposure misclassification and subsequently distort epidemiological inferences. Thus, one can expect that odour emissions specified in the form of time series will improve exposure estimates, and more accu­ rately assess the relationship between environmental odours and adverse effects.

Author contribution statement

5. Conclusions

GS and MB: Conceptualization, Methodology; MB and WK: Dispersion modelling, GS, MP, WK and MB: Preparation and processing of model input data; MB: Formal analysis, Writing- Original draft

This work provides a better understanding of the extent to which 10

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preparation; MB, GS and MP: Writing - Review & Editing, GS and MP: Supervision; MB: Project administration; GS, MP and MB: Funding acquisition.

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