Ground Truthing CALPUFF and AERMOD for Odor Dispersion from Swine Barns using Ambient Odor Assessment Techniques

A collaborative research effort by several institutions investigated the dispersion of odors from a swine production facility. Trained human receptors measured downwind odor concentrations from four tunnel-ventilated swine finishing barns near Story City, Iowa, during twenty measurement events conducted between June and November 2004. Odor concentrations were modeled for short time steps using CALPUFF and AERMOD atmospheric dispersion models to compare predicted and measured odor levels. Source emission measurements and extensive micrometeorological data were collected along with ambient odor measurements using the Nasal Ranger® device (St. Croix Sensory, St. Paul MN), Mask Scentometer, odor intensity ratings, and air sample analysis by dynamic triangular forced-choice olfactometry (DTFCO). AERMOD predictions fit the odor measurements slightly better than CALPUFF with predicted concentrations being about half those predicted by CALPUFF. The Mask Scentometer and Nasal Ranger® measurements related best to the dispersion model output, and scaling factors of 3.0 for CALPUFF and 2.4 for AERMOD suggested for the Nasal Ranger® and 0.5 for the Mask Scentometer (both models). Measurements obtained using the Nasal Ranger®, Mask Scentometer, and odor intensity ratings correlated well to each other, had the strongest linear relationships, and provided slopes (measured: modeled) closest to 1.0. Converting intensity ratings to a dilution to threshold concentration did not correlate and relate as well, and this method was deemed less desirable for ambient odor assessment. Collection of ambient air samples for analysis in a olfactometry laboratory displayed poor correlations with other methods and should not be used to assess ambient odors

Comparison of on-pond measurement and back calculation of odour emission rates from anaerobic piggery lagoons

Odours are emitted from numerous sources and can form a natural part of the environment. The sources of odour range from natural to industrial sources and can be perceived by the community dependant upon a number of factors. These factors include frequency, intensity, duration, offensiveness and location (FIDOL). Or in other words how strong an odour is, at what level it becomes detectable, how long it can be smelt for, whether or not the odour is an acceptable or unacceptable smell as judged by the receptor (residents) and where the odour is smelt. Intensive livestock operations cover a wide range of animal production enterprises, with all of these emitting odours. Essentially, intensive livestock in Queensland, and a certain extent Australia, refers to piggeries, feedlots and intensive dairy and poultry operations. Odour emissions from these operations can be a significant concern when the distance to nearby residents is small enough that odour from the operations is detected. The distance to receptors is a concern for intensive livestock operations as it may hamper their ability to develop new sites or expand existing sites. The piggery industry in Australia relies upon anaerobic treatment to treat its liquid wastes. These earthen lagoons treat liquid wastes through degradation via biological activity (Barth 1985; Casey and McGahan 2000). As these lagoons emit up to 80 per cent of the odour from a piggery (Smith et al., 1999), it is imperative for the piggery industry that odour be better quantified. Numerous methods have been adopted throughout the world for the measurement of odour including, trained field sniffers, electronic noses, olfactometry and electronic methods such as gas chromatography. Although these methods all have can be used, olfactometry is currently deemed to be the most appropriate method for accurate and repeatable determination of odour. This is due to the standardisation of olfactometry through the Australian / New Zealand Standard for Dynamic Olfactometry and that olfactometry uses a standardised panel of 'sniffers' which tend to give a repeatable indication of odour concentration. This is important as often, electronic measures cannot relate odour back to the human nose, which is the ultimate assessor of odour. The way in which odour emission rates (OERs) from lagoons are determined is subject to debate. Currently the most commonly used methods are direct and indirect methods. Direct methods refer to placing enclosures on the ponds to measure the emissions whereas indirect methods refer to taking downwind samples on or near a pond and calculating an emission rate. Worldwide the odour community is currently divided into two camps that disagree on how to directly measure odour, those who use the UNSW wind tunnel or similar (Jiang et al., 1995; Byler et al., 2004; Hudson and Casey 2002; Heber et al., 2000; Schmidt and Bicudo 2002; Bliss et al., 1995) or the USEPA flux chamber (Gholson et al., 1989; Heber et al., 2000; Feddes et al., 2001; Witherspoon et al., 2002; Schmidt and Bicudo 2002; Gholson et al., 1991; Kienbusch 1986). The majority of peer reviewed literature shows that static chambers such as the USEPA flux chamber under predict emissions (Gao et al., 1998b; Jiang and Kaye 1996) and based on this, the literature recommends wind tunnel type devices as the most appropriate method of determining emissions (Smith and Watts 1994a; Jiang and Kaye 1996; Gao et al., 1998a). Based on these reviews it was decided to compare the indirect STINK model (Smith 1995) with the UNSW wind tunnel to assess the appropriateness of the methods for determining odour emission rates for area sources. The objective of this project was to assess the suitability of the STINK model and UNSW wind tunnel for determining odour emission rates from anaerobic piggery lagoons. In particular determining if the model compared well with UNSW wind tunnel measurements from the same source; the overall efficacy of the model; and the relationship between source footprint and predicted odour emission rate

The validation of a simple Gaussian dispersion model for determining odour emission rates from area sources

The accuracy of dispersion models is a widely debated topic. The goal of this research was to assess if the odour emission rates (OER) predicted using a simple Gaussian model were comparable to the more intensive and expensive wind tunnel-based odour measurement technique. A Gaussian dispersion model (STINK) was used to determine odour emission rates from piggery ponds. These values were then compared to those measured simultaneously on the pond surfaces using a wind tunnel, and analysed using dynamic olfactometry. Results of linear regressions showed a moderate relationship for two of the three seasons studied, and a very good relationship for the other season studied. Overall, the model showed a good correlation between calculated downwind, and calculated on-pond odour emission rates. The use of the STINK model shows promise as an alternative to the more labour-intensive direct emission rate measurement techniqu

Quantification of odours from piggery effluent ponds using an electronic nose and an artificial neural network

An Artificial Neural Network (ANN) and an electronic nose, AromaScan, were used to predict the piggery odour concentrations emanating from an effluent pond and to develop a confident, rapid, and cost-effective technique for odour measurement. Odour samples from five different piggery effluent ponds were analysed using the AromaScan and dynamic dilution olfactometry. The resulting sensor data were used to train the artificial neural network to correlate the responses to the odour concentrations measured by olfactometry. Effectiveness was evaluated through simulation with various pre-processing techniques and network architectures. The simulation results have shown that a two-layer back-propagation neural network, which has a tan-sigmoid transfer function in the hidden layer and a linear transfer function in the output layer, could be trained to predict piggery odour concentrations with high value of the correlation coefficient R of 0.984 under the best network performance. The results from the application of scaling and principal component analysis suggest that these techniques are necessary not only to avoid the failure of the network caused by saturation but also to enhance performance. An early stopping technique was shown to provide benefits to the network performance in terms of a decrease in computation time and overfitting. It was found that the optimal number of hidden neurons for the network was 20. Odour concentration of unknown samples were able to be predicted with significant accurac

Odorous VOC emission following land application of swine manure slurry

Swine manure is often applied to crop land as a fertilizer source. Odor emissions from land-applied swine manure may pose a nuisance to downwind populations if manure is not applied with sufficient forethought. A research project was conducted to assess the time decay of odorous volatile organic compound (VOC) emissions following land application of swine manure. Three land application methods were compared: surface application, incorporation 24 h after surface application, and injection. Emission rates were measured in field plots using a small wind tunnel and sorbent tubes. VOCs including eight volatile fatty acids, five aromatics, and two sulfur-containing compounds were quantified by gas chromatography-mass spectrometry. In most cases, a first order exponential decay model adequately described the flux versus time relationship for the 24 h period following land application, but the model sometimes overestimated flux in the 6-24 h range. The same model but with the time term squared adequately predicted flux over the entire 24 h period. Three compounds (4-methylphenol, skatole, and 4-ethylphenol) accounted for 93 percent of the summed odor activity value. First order decay constants (k) for these three compounds ranged from 0.157 to 0.996 h-1. When compared to surface application, injection of swine manure resulted in 80e95 percent lower flux for the most odorous aromatic compounds. These results show that VOC flux decreases rapidly following land application of swine manure, declining below levels of detection and near background levels after 4 to 8 h

Ground Truthing CALPUFF and AERMOD for Odor Dispersion from Swine Barns using Ambient Odor Assessment Techniques

A collaborative research effort by several institutions investigated the dispersion of odors from a swine production facility. Trained human receptors measured downwind odor concentrations from four tunnel-ventilated swine finishing barns near Story City, Iowa, during twenty measurement events conducted between June and November 2004. Odor concentrations were modeled for short time steps using CALPUFF and AERMOD atmospheric dispersion models to compare predicted and measured odor levels. Source emission measurements and extensive micrometeorological data were collected along with ambient odor measurements using the Nasal Ranger® device (St. Croix Sensory, St. Paul MN), Mask Scentometer, odor intensity ratings, and air sample analysis by dynamic triangular forced-choice olfactometry (DTFCO). AERMOD predictions fit the odor measurements slightly better than CALPUFF with predicted concentrations being about half those predicted by CALPUFF. The Mask Scentometer and Nasal Ranger® measurements related best to the dispersion model output, and scaling factors of 3.0 for CALPUFF and 2.4 for AERMOD suggested for the Nasal Ranger® and 0.5 for the Mask Scentometer (both models). Measurements obtained using the Nasal Ranger®, Mask Scentometer, and odor intensity ratings correlated well to each other, had the strongest linear relationships, and provided slopes (measured: modeled) closest to 1.0. Converting intensity ratings to a dilution to threshold concentration did not correlate and relate as well, and this method was deemed less desirable for ambient odor assessment. Collection of ambient air samples for analysis in a olfactometry laboratory displayed poor correlations with other methods and should not be used to assess ambient odors.This proceeding is from International Symposium on Air Quality and Manure Management for Agriculture Conference Proceedings, 13-16 September 2010, Dallas, Texas 711P0510cd.</p