Odour Emissions from Tunnel Ventilated Poultry Housing

In Australia, factors such as local planning processes, urban encroachment into rural areas and intensification of the poultry industry have increased the potential for odour and dust nuisance. At present, accurate estimates of odour emissions from mechanically ventilated poultry housing systems do not exist for Australian conditions. This has made the poultry industry vulnerable to unsubstantiated criticism. Recently, the Australian poultry industry have made a significant investment in research to obtain accurate estimates of odour, dust and volatile chemical emission rates from typical poultry housing systems. This paper describes the measurement of odour emissions from tunnel ventilated poultry housing systems in different climatic zones in Queensland and Victoria, Australia (humid sub-tropical and Mediterranean respectively) during two seasons (summer and winter). Samples were collected at defined intervals over typical batch production cycles to define the odour emission profiles. These samples were analysed using dynamic olfactometry according to the Australian Standard 4323.3 to derive the odour concentration values. Ventilation rates were measured concurrently, allowing the calculation of odour emission rates. Odour concentration and emission rates were assessed in terms of ventilation rate, ambient and shed air temperature and relative humidity and litter moisture status. Odour emission rates varied with bird age. Seasonal differences in odour emission rate were also observed.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Comparison of odour emission rates measured from various sources using two sampling devices

Two commonly used sampling devices (a wind tunnel and the US EPA dynamic emission chamber), were used to collect paired samples of odorous air from a number of agricultural odour sources. The odour samples were assessed using triangular, forced-choice dynamic olfactometry. The odour concentration data was combined with the flushing rate data to calculate odour emission rates for both devices on all sources. Odour concentrations were consistently higher in samples collected with a flux chamber (ratio ranging from 10:7 to 5:1, relative to wind tunnel samples), whereas odour emission rates were consistently larger when derived from wind tunnels (ratio ranging from 60:1 to 240:1, relative to flux chamber values). A complex relationship existed between emission rate estimates derived from each device, apparently influenced by the nature of the emitting surface. These results have great significance for users of odour dispersion models, for which an odour emission rate is a key input parameter.

Odour emissions from tunnel-ventilated broiler sheds: case study of nine Queensland farms.

Odour emission rates were measured from nine tunnel-ventilated broiler farms in south-eastern Queensland, Australia. At one farm, odour emission rates were measured over two sequential batches approximately weekly, while at the remaining farms, odour emission rates were measured just before the first pickup (around Day 35 of the batch) when bird liveweight was greatest and peak odour emission rates were expected. Odour samples were analysed using dynamic olfactometry (to AS/NZS 4323.3:2001), and an artificial olfaction system was used to continuously monitor odour emission rates at one farm. Odour emission rates ranged from 330 to 2960 ou/s per 1000 birds and from 0.19 to 2.12 ou/s.kg, with a significant amount of variability observed throughout the batch and throughout each sampling day. While the wide range in odour emission rates was primarily due to changes in bird liveweight and ventilation requirements, other factors were also involved. The artificial olfaction system proved useful for quantifying the range and variability of odour emission rates, especially when olfactometry analysis was impractical.

Odour, dust and non-methane volatile organic-compound emissions from tunnel-ventilated layer-chicken sheds: a case study of two farms

An observational study was undertaken to measure odour and dust (PM10 and PM2.5) emission rates and identify non-methane volatile organic compounds (NMVOCs) and odorants in the exhaust air from two tunnel-ventilated layer-chicken sheds that were configured with multi-tiered cages and manure belts. The study sites were located in south-eastern Queensland and the West Gippsland region of Victoria, Australia. Samples were collected in summer and winter on sequential days across the manure-belt cleaning cycle. Odour emissions ranged from 58 to 512 ou/s per 1000 birds (0.03-0.27 ou/s.kg) and dust emission rates ranged 0.014-0.184 mg/s per 1000 birds for PM10 and 0.001-0.190 mg/s per 1000 birds for PM2.5. Twenty NMVOCs were identified, including three that were also identified as odorants using thermal desorption-gas chromatography-mass spectrometry/olfactometry analysis. Odour emission rates were observed to vary with the amount of manure accumulation on the manure belts, being lowest 2-4 days after removing manure. Odour emission rates were also observed to vary with diurnal and seasonal changes in ventilation rate. Dust emissions were observed to increase with ventilation rate but not with manure accumulation. Some NMVOCs were identified at both farms and in different seasons whereas others were observed only at one farm or in one season, indicating that odorant composition was influenced by farm-specific practices and season.

Rapid continuous chemical analysis of meat chicken shed emissions by SIFT–MS

Assessing and addressing odour impacts from poultry production is extremely difficult and subjective because the odorants involved and their dynamics over time and space are poorly understood. This knowledge gap is due, in part, to the lack of suitable analytical tools for measuring and monitoring odorants in the field. The emergence of Selected Ion Flow Tube – Mass Spectrometry (SIFT–MS) and similar instruments is changing that. These tools can rapidly quantify targeted odorants in ambient air in real time, even at very low concentrations. Such data is essential for developing better odour abatement strategies, assessment methods and odour dispersion models. This project trialled a SIFT–MS to determine its suitability for assessing the odorants in meat chicken shed emissions over time and space. This report details evaluations in New Zealand and Australia to determine the potential of SIFT–MS as a tool for the chicken meat industry, including odour measurement (as a proxy for dynamic olfactometry). The report is specifically targeted at those funding and conducting poultry odour research. It will be of interest to those involved with environmental odour monitoring and assessment in general. The high upfront cost of SIFT–MS will lead to potential users wanting compelling evidence that SIFT–MS will meet their needs before they invest in one.