The Ranch Poultry Production Complex - Carrathool · PDF fileThe Ranch Poultry Production...

Appendix B

Air Quality Impact Assessment (SLR 2015a)

The Ranch Poultry Production Complex

Farm 3

Air Quality Impact Assessment

Report Number 630.11282-R6

19 October 2015

Scolexia Pty Ltd

16 Learmonth Street

Moonee Ponds

VIC 3039

Version: Revision 0

Scolexia Pty Ltd The Ranch Poultry Production Complex Farm 3 Air Quality Impact Assessment

Report Number 630.11282-R6 Revision 0

19 October 2015 Page 2

SLR Consulting Australia Pty Ltd

The Ranch Poultry Production Complex

Farm 3

Air Quality Impact Assessment

PREPARED BY:

SLR Consulting Australia Pty Ltd ABN 29 001 584 612

2 Lincoln Street Lane Cove NSW 2066 Australia

(PO Box 176 Lane Cove NSW 1595 Australia)

T: 61 2 9428 8100 F: 61 2 9427 8200

E: [email protected] www.slrconsulting.com

This report has been prepared by SLR Consulting Australia Pty Ltd

with all reasonable skill, care and diligence, and taking account of the

timescale and resources allocated to it by agreement with the Client.

Information reported herein is based on the interpretation of data collected,

which has been accepted in good faith as being accurate and valid.

This report is for the exclusive use of Scolexia Pty Ltd.

No warranties or guarantees are expressed or should be inferred by any third parties.

This report may not be relied upon by other parties without written consent from SLR.

SLR disclaims any responsibility to the Client and others in respect of any matters outside the agreed scope of the work.

DOCUMENT CONTROL

Reference Status Date Prepared Checked Authorised

630.11282-R6 Revision 0 19 October 2015 Varun Marwaha Martin Doyle Adam Williams

FINAL




Table of Contents


1 INTRODUCTION 6

2 DEVELOPMENT OVERVIEW 7

2.1 Regional Context 7

2.2 Local Context 9

2.3 Development Summary 10

2.4 Surrounding Land Use 11

2.5 Sensitive Receptors 13

2.6 Hours of Operation 15

2.7 Local Topography 15

3 IMPACT ASSESSMENT CRITERIA 17

3.1 Odour 17

3.2 Particulate Matter 19

3.2.1 Total Suspended Particulate 19

3.2.2 PM10 and PM2.5 19

3.2.3 Deposited Dust 19

3.3 Summary of Project Air Quality Goals 20

4 POLLUTANT EMISSION ESTIMATION 21

4.1 Odour 21

4.1.1 Methodology 21

4.1.2 Odour Emissions Estimation 21

4.2 Particulates 25

5 DISPERSION MODELLING CONFIGURATION 26

5.1 Model Selection 26

5.2 Accuracy of Modelling 26

5.3 Meteorological Modelling 27

5.3.1 Selection of the Meteorological Year 27

5.3.2 TAPM 28

5.3.3 CALMET 28

5.3.4 Meteorological Data Used in Modelling 29




Table of Contents


5.4 Odour Peak-to-Mean Ratios 34

5.5 Emission Sources 35

5.5.1 Odour 35

5.5.2 Particulates 35

5.6 Building Wake Effects 35

5.7 Regional Odour Sources 35

5.8 Scenarios Assessed 36

6 RESULTS AND DISCUSSSION 37

6.1 Odour Impact Assessment 37

6.1.1 Scenario 1 37

6.1.2 Scenario 2 39

6.2 Dust Impact Assessment 41

6.3 Mitigation and Monitoring Measures 42

7 CONCLUSIONS 43

8 REFERENCES 44

TABLES

Table 1 Key Elements of The Ranch Farm 3 10 Table 2 Location of the Identified Sensitive Receptors 13 Table 3 Impact Assessment Criteria for Complex Mixtures of Odorous Air Pollutants 18 Table 4 Adopted Odour Impact Assessment Criteria 18 Table 5 OEH Goals for Allowable Dust Deposition 19 Table 6 Project Air Quality Goals 20 Table 7 Parameters used in the Odour Emissions Estimation for The Project Site 22 Table 8 Shed Ventilation as a Percentage of Maximum Ventilation 23 Table 9 A Summary of the Schedule for Bird Removal from Each Poultry Shed 23 Table 10 Meteorological Parameters used for the AQIA - TAPM 28 Table 11 Meteorological Parameters used for this Study – CALMET (v 6.42) 29 Table 12 Description of Atmospheric Stability Classes 32 Table 13 Parameters for each Vent type for each Poultry Shed 35 Table 14 Summary of the Parameters used in Modelling of Background Odour 36 Table 15 Predicted 99

th Percentile Nose-Response Odour Concentrations (OU) – Scenario 1 37

Table 16 Predicted 99th Percentile Nose-Response Odour Concentrations (OU) – Scenario 2 39




Table of Contents


FIGURES

Figure 1 Regional Location of the Development Site 8 Figure 2 Local Setting of the Development Site 9 Figure 3 Layout of The Ranch Poultry Production Complex 12 Figure 4 Residential and Industrial Receptors Surrounding the Project Site (UTM Zone 55) 14 Figure 5 Local Topographical Features 15 Figure 6 Varying Bird Density and Ventilation Rate Profile for the Project Site 22 Figure 7 Modelled Shed Odour Emission Rates through the Birds Cycle 24 Figure 8 Modelled Shed Odour Emission Rates throughout the Year – Sheds 1, 2 and 3 25 Figure 9 Predicted Seasonal Wind Roses for the Development Site (CALMET predictions, 2012)31 Figure 10 Predicted Stability Class Frequencies at the Development Site (CALMET predictions,

2012) 32 Figure 11 Predicted Mixing Heights at the Development Site (CALMET predictions, 2012) 33 Figure 12 Predicted Temperatures at Development Site (CALMET predictions, 2012) 34 Figure 13 Predicted 99

th Percentile Nose-Response Odour Concentration (OU) –Scenario 1 38

Figure 14 Predicted 99th Percentile Nose-Response Odour Concentration (OU) – Scenario 2 40

APPENDICES

Appendix A Selection of Representative Meteorological Year





1 INTRODUCTION

SLR Consulting Australia Pty Ltd (SLR) has been commissioned by Scolexia Pty Ltd (Scolexia), on behalf of VOAG 3 Pty Ltd (VOAG 3), to undertake an air quality impact assessment (AQIA) for a proposed intensive poultry broiler production farm within a rural property in the area known as Tabbita, approximately 26 kilometres (km) north-west of Griffith in south-western New South Wales (NSW).

Three new farms, including The Ranch Farm 3 (the subject of this AQIA), and two additional farms known as The Ranch Farm 1 and The Ranch Farm 2, are being proposed as part of “The Ranch Poultry Production Complex”. Each of these farms will be the subject of a separate development application and a separate AQIA will be prepared as part of the respective Environmental Impact Statement (EIS) to be lodged with Carrathool Shire Council (the Council).

For the purposes of this AQIA, the overarching development site comprising the three farms is being referred to as “The Ranch Development Site”, while the development site subject to this particular AQIA is being referred to as “The Ranch Farm 3 Site” (the Development Site).

The aim of this AQIA is to quantify the change in potential dust and odour impacts in the region due to the operation of The Ranch Farm 3 in isolation, as well as the cumulative impacts of The Ranch Poultry Production Complex (i.e. the three farms) and other surrounding poultry developments. This AQIA forms a part of the overall EIS for the Development Site.

This report has been prepared with reference to the guideline document Approved Methods for the Modelling and Assessment of Air Pollutants in New South Wales (DEC 2005) (hereafter Approved Methods). For the assessment of potential odour emissions, this report also refers to the Technical Framework: Assessment and management of odour from stationary sources in NSW (DEC 2006a) (hereafter the Odour Framework) and the Technical Notes: Assessment and management of odour from stationary sources in NSW (DEC 2006b) (hereafter the Technical Notes).

The Approved Methods and the Odour Framework outline the parameters that need to be determined in any AQIA as follows:

All potential emission sources - materials, equipment or activities (including transport, waste management and maintenance) (Section 2.3).

All nearby receptors potentially affected by dust and odour emissions (both current and future); this is particularly important where there is a potential for rezoning or subdivision (Section 2.5).

Operating hours and times when intermittent emission generating activities are likely to occur (Section 2.6).

Site features that may affect pollutant propagation and dispersion, including topography, vegetation, buildings and surrounding land uses (Section 2.7).

The pollutant assessment criteria that were used to assess the proposal under current and future circumstances (for example, where possibility of a change in land use exists) (Section 3).

Weather conditions particular to the site (including prevailing wind directions and the likelihood of inversions or katabatic drift) (Section 5.3).

Likely air quality impacts (including odour impacts predicted using level 2 assessment) (Section 6).





2 DEVELOPMENT OVERVIEW

2.1 Regional Context

The Development Site is located on the Back Hillston Road approximately 3.9 km north-east of Tabbita and 20 km south-east of Goolgowi in the Riverina region of south-western NSW (see Figure 1).





Figure 1 Regional Location of the Development Site





2.2 Local Context

The Development Site is positioned within Lot 78 in Deposited Plan (DP) 720258 and comprises approximately 50 hectares (ha) of vacant rural land within the Carrathool Local Government Area (LGA) (see Figure 2).

Figure 2 Local Setting of the Development Site





2.3 Development Summary

The Ranch Farm 3 will comprise eight tunnel-ventilated fully-enclosed climate-controlled poultry sheds, along with two new dwellings to accommodate the farm manager and assistant farm manager and various ancillary infrastructure items and improvements.

On each shed, air extraction fans mounted at one end will uniformly draw air into the shed through mini-vents along the sides of the shed and later in the growing cycle across cooling pads and through tunnel vents. The air will be pulled over the chickens and exhausted through a series of ventilation fans that will be housed within a fan box that is designed to direct exhaust vertically. Temperature sensors within the sheds will allow the ventilation to be adjusted as required.

The release of odorous air in a horizontal direction tends to ‘pool’ the odorous air in the atmosphere, especially during the calm wind conditions (early morning and late night), which results in higher odour concentrations in the vicinity of the odorous air. The release of odorous air from the sheds using a fan box is likely to improve the vertical dispersion of odorous air in the atmosphere decreasing the odour concentrations in the vicinity of the source.

Tunnel ventilation is easier to manage than natural ventilation and enables the grower to provide close to optimum conditions for bird health, growth and performance throughout the year. Additional benefits include better control over shed moisture levels, which is directly related to odour production.

Each shed will have the capacity to house a maximum of 50,000 birds at any one time, equating to a farm population of up to 400,000 birds.

The disturbance footprint for The Ranch Farm 3 will be relatively small and the commercial activities associated with the poultry operation will be largely confined to this area. The key elements of the development are summarised in Table 1.

Table 1 Key Elements of The Ranch Farm 3

Development Characteristic Proposed Development

Purpose Birds grown for human consumption

Number of poultry sheds 8, each measuring 172 metres (m) long by 18 m wide

Type of poultry sheds Tunnel-ventilated, fully-enclosed, climate-controlled

Maximum shed population 50,000 birds

Maximum farm population 400,000 birds

Maximum bird density within sheds 18 birds per square metre (m

2)

(and will not exceed a density of 34 kg/m2)

Hours of operation 24 hours a day, 7 days a week

Production cycle length Approximately 9 weeks (63 days), comprising 7.5 weeks (53 days) of bird occupation and a 1.5 week (10 days) cleaning phase

Number of production cycles per year On average, approximately 5.7

Source: SLR 2015





2.4 Surrounding Land Use

The surrounding neighbourhood is primarily characterised by traditional agricultural production. Additional land uses within the area include a privately owned and operational silo on Tysons Road, located approximately 3.6 km south-west of the Development Site, GrainCorp’s Tabbita Silo, which is currently closed, approximately 3.8 km to the south-west, and JBS Australia’s Tabbita Feedlot (cattle feedlot) approximately 13 km to the west. There is also a low density of surrounding residential dwellings. Additional detail regarding sensitive receptors has been provided in Section 2.5.

As advised in Section 1, The Ranch Farm 3 is part of a larger development known as The Ranch Poultry Production Complex. This Complex will also comprise The Ranch Farm 1 and The Ranch Farm 2 (subject of separate development applications), which will each comprise 8 poultry sheds to accommodate up to 400,000 birds. The layout of The Ranch Poultry Production Complex is shown in Figure 3.

At the time of preparing this AQIA, the following additional poultry developments were proposed (i.e.

development application lodged) within the vicinity of the Development Site1:

Tabbita Farm 1 – poultry production complex proposed approximately 9 km west of the Development Site. The proposal comprises 20 poultry sheds to accommodate up to 1,020,000 birds.

Tabbita Farm 2 – poultry production complex proposed approximately 9 km west of the Development Site. The proposal comprises 20 poultry sheds to accommodate up to 1,020,000 birds.

Maylands Farm A - poultry production complex proposed approximately 20 km northwest of the Development Site. The proposal comprises 24 poultry sheds to accommodate up to 1,360,800 birds.

ProTen’s Jeanella South Poultry Production Complex – poultry production complex proposed approximately 24 km north-west of the Development Site. The proposal comprises 16 poultry sheds to accommodate up to 856,000 birds.

The background odour impacts from the aforementioned land uses are discussed in detail in Section 5.7.

1 Source: Carrathool Shire Council available online at http://www.carrathool.nsw.gov.au/planning/public-

exhibition, on 18 August 2015.

http://www.carrathool.nsw.gov.au/planning/public-exhibition






Figure 3 Layout of The Ranch Poultry Production Complex





2.5 Sensitive Receptors

The Development Site is removed from any urban areas. Council has advised that Tabbita, which is located approximately 3.9 km south-west of the Development Site, comprises five residences. The nearest sensitive receptors lie 3.9 km to the southwest. The receptor locations adopted within this assessment are listed in Table 2 and shown in Figure 4.

Table 2 Location of the Identified Sensitive Receptors

Receptor ID Location

X (km) UTM 56

Y (km) UTM 56

Elevation (m, AHD)

Approximate Distance from Development Site (km)

R1 Tysons Road, Tabbita 398.741 6,225.241 142.4 3.9

R2 Back Hillston Road, Tabbita 398.938 6,224.469 144.0 4.7

R3 Back Hillston Road, Tabbita 396.509 6,222.022 116.6 6.6

R4 Kidman Way, Tabbita 394.968 6,222.975 113.7 5.9

R5 Tabbita Lane, Tabbita 392.512 6,223.637 115.4 6.2







R12 Youngs Lane, Tabbita 391.297 6,232.258 125.9 5.6

R13 Kidman Way, Tabbita (workshop/shed on Lot 40 DP756045)

392.469 6225.940 119.0 4.5

The dwelling identified on Figure 4 located approximately 2 km south-west of the site is derelict and uninhabitable. A complying development certificate was issued by Council on 15 June 2015 for the demolition of this dwelling (CDC 2015_016).





Figure 4 Residential and Industrial Receptors Surrounding the Project Site (UTM Zone 55)





2.6 Hours of Operation

While the development will operate 24 hours a day, seven days a week, the majority of activity will be carried out between 7.00 am and 7.00 pm. As the birds reach their desired slaughter weight they will be removed from the sheds and transported from the site between 8.00 pm and 2.00 pm, although for reasons of livestock welfare they will generally be removed overnight and in the morning when it is cooler and the birds are more settled.

There will typically be one daily shift for farm workers commencing at 7.00 am and finishing at 4.00 pm.

2.7 Local Topography

The topographical data used in the modelling assessment was sourced from the United States Geological Service’s Shuttle Radar Topography Mission database that has recorded topography across Australia with a 3 arc second (approximately 90 m) spacing. The topography of the region surrounding the Development Site is shown in Figure 5.

Figure 5 Local Topographical Features





The Development Site is located within a terrain ranging from approximately 138 m to 147 m Australian Height Datum (AHD).





3 IMPACT ASSESSMENT CRITERIA

3.1 Odour

Impacts from odorous air contaminants are often nuisance-related rather than health-related. Odour performance goals guide decisions on odour management, but are generally not intended to achieve “no odour”.

The detectability of an odour is a sensory property that refers to the theoretical minimum concentration that produces an olfactory response or sensation. This point is called the odour threshold and defines one odour unit (OU). An odour goal of less than 1 OU would theoretically result in no odour impact being experienced.

In practice, the character of a particular odour can only be judged by the receiver’s reaction to it, and preferably only compared to another odour under similar social and regional conditions. Based on the literature available, the level at which an odour is perceived to be a nuisance can range from 2 OU to 10 OU depending on a combination of the following factors:

Odour Quality: whether an odour results from a pure compound or from a mixture of compounds. Pure compounds tend to have a higher threshold (lower offensiveness) than a mixture of compounds.

Population sensitivity: any given population contains individuals with a range of sensitivities to odour. The larger a population, the greater the number of sensitive individuals it may contain.

Background level: whether a given odour source, because of its location, is likely to contribute to a cumulative odour impact. In areas with more closely-located sources it may be necessary to apply a lower threshold to prevent offensive odour.

Public expectation: whether a given community is tolerant of a particular type of odour and does not find it offensive, even at relatively high concentrations. For example, background agricultural odours may not be considered offensive until a higher threshold is reached than for odours from a landfill facility.

Source characteristics: whether the odour is emitted from a fan box (point source) or from an area (diffuse source). Generally, the components of point source emissions can be identified and treated more easily than diffuse sources. Emissions from point sources can be more easily controlled using control equipment. Point sources tend to be located in urban areas, while diffuse sources are more often located in rural locations.

Health Effects: whether a particular odour is likely to be associated with adverse health effects. In general, odours from agricultural activities are less likely to present a health risk than emissions from industrial facilities.

Experience gained through odour assessments from proposed and existing facilities in NSW indicates that an odour performance goal of 7 OU is likely to represent the level below which “offensive” odours should not occur (for an individual with a ‘standard sensitivity’ to odours). The NSW Environment Protection Authority (EPA) recommends within the Odour Framework that, as a design goal, no individual be exposed to ambient odour levels of greater than 7 OU. This is expressed as the 99

th

percentile value, as a nose response time average (approximately one second).





Odour performance goals need to be designed to take into account the range in sensitivities to odours within the community, and provide additional protection for individuals with a heightened response to odours, using a statistical approach which depends on the size of the affected population. As the affected population size increases, the number of sensitive individuals is also likely to increase, which suggests that more stringent goals are necessary in these situations. In addition, the potential for cumulative odour impacts in relatively sparsely populated areas can be more easily defined and assessed than in highly populated urban areas. It is often not possible or practical to determine and assess the cumulative odour impacts of all odour sources that may impact on a receptor in an urban environment. Therefore, the proposed odour performance goals allow for population density, cumulative impacts, and anticipated odour levels during adverse meteorological conditions and community expectations of amenity.

A summary of odour performance goals for various population densities, as referenced in the Technical Notes is shown in Table 3.

Table 3 Impact Assessment Criteria for Complex Mixtures of Odorous Air Pollutants

Population of Affected Community Impact Assessment Criteria for Complex Mixtures of Odours (OU)

Urban area (> 2000) 2.0

~300 3.0

~125 4.0

~30 5.0

~10 6.0

Single residence (< 2) 7.0

Source: DEC, 2006

The Odour Framework states that the impact assessment criteria for complex mixtures of odorous air pollutants must be:

Applied at the nearest existing or likely future off-site sensitive receptor(s);

The incremental impact (predicted impact due to the pollutant source alone) must be reported in units consistent with the impact assessment criteria (OU), as peak concentrations (i.e. approximately 1 second average) and as the 99

th percentile of dispersion model predictions for

Level 2 impact assessments.

Council has advised that Tabbita, which is located approximately 3.9 km south-west of the Development Site, comprising five residences. The Australian Bureau of Statistics (ABS) Census data for 2011 gave an average population per house of 2.4 people for rural communities in NSW, while the EPA uses 2.8 people per house. Adopting the EPA’s value, Tabbita has an estimated population of around 14 people. On this basis, SLR has adopted an odour assessment criterion of 5 OU for the four residences located in Tabbita. A criterion of 6 OU is considered to be appropriate for the isolated rural residences scattered surrounding the Development Site. The adopted odour impact assessment criteria are summarised in Table 4.

Table 4 Adopted Odour Impact Assessment Criteria

Receptor Impact Assessment Criteria for Complex Mixtures of Odours (OU)

R1 to R5 6.0

R6 to R10 5.0

R11 to R13 6.0





3.2 Particulate Matter

3.2.1 Total Suspended Particulate

Airborne contaminants that can be inhaled directly into the lungs can be classified on the basis of their physical properties as gases, vapours or particulate matter. In common usage, the terms “dust” and “particulates” are often used interchangeably. The term “particulate matter” refers to a category of airborne particles, typically less than 30 microns (μm) in diameter and ranging down to 0.1 μm and is termed total suspended particulate (TSP). The annual average goal for TSP recommended by the EPA is 90 micrograms per cubic metre of air (μg/m

3).

3.2.2 PM10 and PM2.5

Emissions of particulate matter less than 10 μm and 2.5 μm in diameter (referred to as PM10 and PM2.5 respectively) are considered important pollutants due to their potential health implications.

The EPA’s PM10 assessment goals set out in the Approved Methods are as follows:

A 24-hour maximum of 50 µg/m3; and

An annual average of 30 µg/m3.

The Approved Methods do not set any assessment goals for PM2.5. In December 2000, the National Environment Protection Council (NEPC) initiated a review to determine whether a national ambient air quality criterion for PM2.5 was required in Australia, and the feasibility of developing such a criterion.

The review concluded that there is sufficient community concern regarding PM2.5 to consider it an entity separate from PM10.

As such, in July 2003, a variation to the Ambient Air Quality National Environment Protection Measure (NEPM) was made to extend its coverage to PM2.5, setting the following Interim Advisory Reporting Standards for PM2.5:

A 24-hour average concentration of 25 µg/m3; and

An annual average concentration of 8 µg/m3.

It is noted that the advisory reporting standards relating to PM2.5 particles are interim guidelines only at the present time and are not intended to represent air quality criteria.

3.2.3 Deposited Dust

The preceding section is concerned in large part with the health impacts of airborne particulate matter. Nuisance impacts need also to be considered in relation to deposited dust. In NSW, accepted practice regarding the nuisance impact of dust is that dust-related nuisance can be expected to impact on residential areas when annual average dust deposition levels exceed 4 g/m

2/month.

Table 5 presents the impact assessment goals set out in the Approved Methods for dust deposition, showing the allowable increase in dust deposition level over the ambient (background) level to avoid dust nuisance.

Table 5 OEH Goals for Allowable Dust Deposition

Averaging Period Maximum Increase in Deposited Dust Level Maximum Total Deposited Dust Level

Annual 2 g/m2/month 4 g/m

2/month

Source: Approved Methods, DEC 2005.





3.3 Summary of Project Air Quality Goals

The air quality goals adopted for this assessment, which conform to current EPA and Federal air quality criteria, are summarised in Table 6.

Table 6 Project Air Quality Goals

Pollutant Averaging Time Goal

Odour 1-second nose response 99

th percentile

6 OU (R1 to R5 and R11 to R13) 5 OU (R6 to R10)

TSP Annual average 90 µg/m3

PM10 Maximum 24 Hours Annual average

50 µg/m3

30 µg/m3

PM2.5 Maximum 24 Hours Annual average

25 µg/m3

(interim advisory reporting standard at the present time) 8 µg/m

3 (interim advisory reporting standard at the present time)

Dust Deposition Annual Maximum incremental increase of 2 g/m

2/month

Maximum cumulative of 4 g/m2/month (Project and other sources)





4 POLLUTANT EMISSION ESTIMATION

4.1 Odour

4.1.1 Methodology

Estimation of odour emissions from a poultry shed is a complex matter and depends on a number of inter-related parameters including, but not limited to, bird age/weight, ambient temperature, shed target temperature and ventilation rate. A literature review showed that a range of odour emission estimation methodologies are available to calculate the potential odour emission rate from a typical climate-controlled (tunnel ventilated) poultry shed in publicly available odour impact assessment reports for different farms. For this assessment, the widely accepted odour emissions model of Ormerod et al (2005) has been adopted.

The methodology presented by Ormerod et al (2005) takes into account a number of factors which have an impact on the odour generation within the chicken sheds, such as:

the number of birds, which varies later in the batch as harvesting takes place;

the stocking density of birds, which is a function of bird numbers, bird age and shed size;

ventilation rate, which depends on bird age and ambient temperature; and

design and management practices, particularly those aimed at controlling litter moisture.

The odour emissions model of Ormerod et al (2005) generates an hourly varying emission rates from meat chicken farm sheds and is represented by the following equation:

𝑂𝐸𝑅 = 0.025 × 𝐾 × 𝐴 × 𝐷 × 𝑉0.5

where:

OER = hourly odour emission rate (OU.m³/s)

K = scaling factor between 1 and 5, where a value of 1 represents a very well designed and managed shed operating with minimal odour emissions, and a value of 4-5 would represent a shed with a serious odour management issues.

A = total shed floor area (m²)

D = average bird density (kg/m²)

V = ventilation rate (m³/s)

4.1.2 Odour Emissions Estimation

As discussed in Section 2.3, there will be a total of 8 sheds each capable of housing a total of 50,000 birds. The parameters used for the estimation of odour emissions from each shed are summarised in Table 7.





Table 7 Parameters used in the Odour Emissions Estimation for The Project Site

Parameter Value Units Notes

K 2.2 - New farms conforming to best practice (Ormerod et al 2005)

A 3,096 m2 Calculated based on the shed dimensions (see Table 1)

D hourly varying

kg/m2

Based on the number of birds and weight of each bird in the bird cycle (see Figure 6)

V hourly varying

m3/s

Based on bird age and target temperature inside the shed (see Figure 6)

The estimated varying bird density and the varying ventilation rate for a typical bird growth cycle during summer months are shown in Figure 6.

Figure 6 Varying Bird Density and Ventilation Rate Profile for the Project Site

Note: Ventilation rate representative of summer season.

It is noted that the bird density (D) is related to the age of the birds (and hence bird weight) and the stocking density (i.e. the number of birds placed per unit area).

The ventilation rate (V) at any given time is a function of the age of the birds and the ambient temperature. Table 8 provides an estimate of the ventilation required for a tunnel ventilated shed as a percentage of the maximum for summertime conditions.





Table 8 Shed Ventilation as a Percentage of Maximum Ventilation

Bird Age (Weeks) 1 2 3 4 5 6 7 8

Temperature (°C) above Target

Ventilation Rate (as a percentage of the Maximum)

<1 1.7 2.6 5.1 7.7 9.8 11.5 17 17

1 1.7 12.5 12.5 25 25 25 25 25

2 1.7 25 25 37.5 37.5 37.5 37.5 37.5

3 1.7 37.5 37.5 50 50 50 50 50

4 1.7 37.5 37.5 50 50 50 50 50

6 1.7 37.5 37.5 62.5 75 75 75 75

7 1.7 37.5 37.5 62.5 75 75 87.5 100

8 1.7 62.5 62.5 62.5 75 75 100 100

9 1.7 62.5 62.5 87.5 100 100 100 100

Source: Ormerod et al 2005

It is noted that the ventilation rate may vary depending on the ambient temperature, for example during the winter months lower ventilation rates may be required to maintain the target temperature inside the shed. The varying ventilation rate profile shown in Figure 6 represents the bird cycle during the summer season.

As discussed in Section 2.3, the total bird cycle is assumed to be of 63 days, including a 10 day cleanout period. The bird thinning will be carried out to maintain the stocking density below 34 kg/m² (RSPCA 2013). The Proponent anticipates that 25% of the total birds (i.e. 12,500 per shed) would be removed on day 32, day 38 and day 44, with the remaining birds removed around day 53. A summary of the periodic removal of birds from each of the sheds is shown in Table 9.

Table 9 A Summary of the Schedule for Bird Removal from Each Poultry Shed

Day of Cycle Variation in Number of Birds Number of Birds Remaining

1 +50,000 50,000

32 -12,500 37,500

38 -12,500 25,000

44 -12,500 12,500

53 -12,500 0

63 0 0

Based on previous experience, it is noted that bird mortality at day 32 is normally at least 2%. However, due to the uncertainty associated with estimating bird mortality, the data presented in Table 9 are based on a 0% mortality rate which will result in a conservative assessment of potential odour impacts. It is expected that in reality, the number of birds in each shed will be slightly less than as presented in Table 9.

The variability of odour emissions for each shed during a full bird growth cycle is shown in Figure 7.





Figure 7 Modelled Shed Odour Emission Rates through the Birds Cycle

From Figure 7, the decline in emissions after day 53 represents the clean-out of the sheds. It is noted that the shed clean-out may result in elevated odour release during disturbance of the litter, but odour emissions from the sheds can be easily managed by minimising the amount of air exchange through the shed during clean-out and cleaning only during the daytime when atmospheric dispersion is most effective (Ormerod et al 2005).

The variability of estimated odour emissions for each shed for a year of operations is shown in Figure 8.

Due to the size of the farm, the sheds will take around 3 days to place with birds. That is, a number of sheds will be placed each day until the farm is full. This means the birds are expected to range in age from 1 to 3 days of age (based on the placement taking 3 days). On this basis, in regards to the bird cycle for each shed, the cycle was started one day apart for every fourth shed. For instance:

For sheds 1, 2 and 3, the cycle was assumed to be starting on day 1;

For sheds 4, 5 and 6 the cycle was assumed to be starting on day 2; and

For sheds 7 and 8, the cycle was assumed to be starting on day 3.

It is noted that the varying odour emission shown in Figure 8 represents the odour emissions for sheds 1, 2 and 3 only. It is also noted that this variation in odour emissions will vary from shed to shed.

The drop in the overall emissions midway through the year corresponds to lower temperatures in the late autumn and winter months which result in lower ventilation rates and therefore less odour emissions from the sheds.





Figure 8 Modelled Shed Odour Emission Rates throughout the Year – Sheds 1, 2 and 3

4.2 Particulates

In addition to emissions of odour, intensive agricultural operations such as poultry farms have the potential to give rise to emissions of particulate matter (or ‘dust’). Dust from poultry farms can be generated from a range of sources including:

Earthworks and construction of the sheds during the construction phase;

Operational emission from the chicken sheds;

Vehicle movements on site;

Feed delivery; and

Shed cleaning.

Fugitive emissions of dust from construction activities, vehicle movements and shed cleaning operations are most appropriately managed by good site management and the implementation of dust suppression measures as outlined in Section 6.3. The significant separation distance between the farm and the nearest sensitive receptors would also reduce the risk of any off-site nuisance dust impacts.

Notwithstanding the above, to assess the potential off-site impacts of particulate emissions due to the operations of The Ranch Farm 3, reference is made to a study conducted by the Australian Poultry Cooperative Research Centre “Dust and Odour Emissions from Meat Chicken Sheds” (APCRC 2011). As part of this study, semi continuous dust measurements were conducted on 50 separate days at 3 broiler farms using DustTrak

TM and reported in terms of mass concentrations of PM10 and PM2.5.

It was noted in the findings of this study that the concentration of particulate matter in the air exiting the sheds was highly variable and was influenced by ventilation rate, farm, bird age, season, microenvironment, litter management practices and other factors. The majority of the PM10 emission rates measured ranged from 5 mg/s to 50 mg/s per shed.





5 DISPERSION MODELLING CONFIGURATION

5.1 Model Selection

Odour emissions from The Ranch Farm 3 have been modelled using the US EPA’s CALPUFF (Version 6) modelling system, as recommended by the EPA. CALPUFF is a transport and dispersion model that ejects “puffs” of material emitted from modelled sources, simulating dispersion and transformation processes along the way. In doing so it typically uses the fields generated by a meteorological pre-processor CALMET, discussed further below. Temporal and spatial variations in the meteorological fields selected are explicitly incorporated in the resulting distribution of puffs throughout a simulation period. The primary output files from CALPUFF contain either hourly concentration or hourly deposition fluxes evaluated at selected receptor locations. The CALPOST post-processor is then used to process these files, producing tabulations that summarise results of the simulation for user-selected averaging periods.

The advantages of using CALPUFF (rather than using a steady state Gaussian dispersion model such as Ausplume) is its ability to handle calm wind speeds (<0.5 m/s), complicated terrain and cumulative pollution impacts. Steady state models assume that meteorology is unchanged by topography over the modelling domain and may result in significant over or under estimation of air quality impacts.

More advanced dispersion models (such as CALPUFF) are approved for use by many regulatory authorities in situations where these models may be more appropriate than use of steady-state models and assumptions.

5.2 Accuracy of Modelling

Atmospheric dispersion models, such as CALPUFF, all represent a simplification of the many complex processes involved in the dispersion of pollutants in the atmosphere. To obtain good quality results it is important that the most appropriate model is used and the quality of the input data (meteorological, terrain, source characteristics) is adequate.

The main sources of uncertainty in dispersion models, and their effects, are discussed below.

Oversimplification of physics: This can lead to both under-prediction and over-prediction of ground level pollutant concentrations. Errors are greater in Gaussian plume models as they do not include the effects of non-steady-state meteorology (i.e., spatially- and temporally-varying meteorology).

Errors in emission rates: Ground level concentrations are proportional to the pollutant emission rate. In addition, most modelling studies assume constant worst case emission levels or are based on the results of a small number of stack tests, however operations (and thus emissions) are often quite variable. Accurate measurement of emission rates and source parameters requires continuous monitoring.

Errors in source parameters: Plume rise is affected by source dimensions, temperature and exit velocity. Inaccuracies in these values will contribute to errors in the predicted height of the plume centreline and thus ground level pollutant concentrations.

Errors in wind direction and wind speed: Wind direction affects the direction of plume travel, while wind speed affects plume rise and dilution of plume. Errors in these parameters can result in errors in the predicted distance from the source of the plume impact, and magnitude of that impact. In addition, aloft wind directions commonly differ from surface wind directions. The preference to use rugged meteorological instruments to reduce maintenance requirements also means that light winds are often not well characterised.

Errors in mixing height: If the plume elevation reaches 80% or more of the mixing height, more interaction will occur, and it becomes increasingly important to properly characterise the depth of the mixed layer as well as the strength of the upper air inversion.





Errors in temperature: Ambient temperature affects plume buoyancy, so inaccuracies in the temperature data can result in potential errors in the predicted distance from the source of the plume impact, and magnitude of that impact.

Errors in stability estimates: Gaussian plume models use estimates of stability class, and 3D models use explicit vertical profiles of temperature and wind (which are used directly or indirectly to estimate stability class for Gaussian models). In either case, errors in these parameters can cause either under-prediction or over-prediction of ground level concentrations. For example, if an error is made of one stability class, then the computed concentrations can be off by 50% or more.

The US EPA makes the following statement in its Modelling Guideline (US EPA, 2005) on the relative accuracy of models:

“Models are more reliable for estimating longer time-averaged concentrations than for estimating short-term concentrations at specific locations; and the models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For

example, errors in highest estimated concentrations of 10 to 40% are found to be typical, i.e., certainly well within the often quoted factor-of-two accuracy that has long been recognised for these models. However estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable.”

This AQIA utilises the CALPUFF dispersion model in full 3D mode, incorporating 3D meteorological output from TAPM and CALMET (refer Section 5.3). The meteorological dataset has been compiled using observations from nearby automatic weather stations and a five year period of meteorological data was reviewed to ensure that the year selected for use in the modelling is representative of long-term meteorological conditions. The use of the 99

th percentile (88

th highest) predictions given by the

model in assessing off-site impacts (in accordance with the relevant odour impact assessment guideline) also means that uncertainties associated with the peak model predictions for rare or unusual meteorological conditions are removed.

5.3 Meteorological Modelling

Meteorological mechanisms govern the dispersion, transformation and eventual removal of pollutants from the atmosphere. The extent to which pollution will accumulate or disperse in the atmosphere is dependent on the degree of thermal and mechanical turbulence within the earth’s boundary layer. Dispersion comprises vertical and horizontal components of motion. The stability of the atmosphere and the depth of the surface-mixing layer define the vertical component. The horizontal dispersion of pollution in the boundary layer is primarily a function of the wind field. The wind speed determines both the distance of downwind transport and the rate of dilution as a result of plume ‘stretching’. The generation of mechanical turbulence is similarly a function of the wind speed, in combination with the surface roughness. The wind direction, and the variability in wind direction, determines the general path pollutants will follow, and the extent of crosswind spreading.

Pollution concentration levels therefore fluctuate in response to changes in atmospheric stability, to concurrent variations in the mixing depth, and to shifts in the wind field (Oke 2004).

To adequately characterise the dispersion meteorology of the Development Site, information is needed on the prevailing wind regime, mixing depth and atmospheric stability and other parameters such as ambient temperature, rainfall and relative humidity.

5.3.1 Selection of the Meteorological Year

The Bureau of Meteorology (BoM) maintains automatic weather stations (AWS) throughout Australia. The closest such station was identified as Griffith Airport (Station # 75041).





In order to determine a representative meteorological year for use in dispersion modelling, the last five years of meteorological data (2010-2014) from Griffith Airport were analysed against the long term meteorological conditions. Specifically, the following parameters were analysed:

Percentage of calm wind speed events (wind speed <0.5 m/s). Calm wind conditions are conducive to higher concentrations of odour due to poor dispersion of the odour plume.

Hourly wind speeds observed at 9:00 am and 3:00 pm.

Hourly temperature at 9:00 am and 3:00 pm.

Hourly relative humidity at 9:00 am and 3:00 pm.

Based on this analysis, it was concluded that the year 2012 was representative of the last five years of meteorological conditions experienced at the Development Site and hence the 2012 calendar year was adopted for use in this AQIA. A detailed analysis is shown in Appendix A.

5.3.2 TAPM

The TAPM prognostic model, developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was used to generate the three dimensional upper air data required for CALMET modelling (Section 5.3.3).

TAPM predicts wind speed and direction, temperature, pressure, water vapour, cloud, rain water and turbulence. The program allows the user to generate synthetic observations by referencing databases (covering terrain, vegetation and soil type, sea surface temperature and synoptic scale meteorological analyses) which are subsequently used in the model input to generate one full year of hourly meteorological observations at user-defined levels within the atmosphere.

Additionally, TAPM may assimilate actual local wind observations so that they can optionally be included in a model solution. The wind speed and direction observations are used to realign the predicted solution towards the observation values. Available observed meteorological data from the nearby BoM station located at Griffith Airport were incorporated into the TAPM setup. Table 10 details the parameters used in the TAPM meteorological modelling for this assessment.

Table 10 Meteorological Parameters used for the AQIA - TAPM

TAPM (v 4.0.4)

Number of grids (spacing) 4 (30 km, 10 km, 3 km and 1 km)

Number of grid points 30 x 30 x 35

Year of analysis 1 January 2012 to 31 December 2012

Centre of analysis 380,690 m E 6,238,940 m S

Data assimilation Griffith Airport BOM Station (#75041)

5.3.3 CALMET

In the simplest terms, CALMET is a meteorological model that develops hourly wind and other meteorological fields on a three-dimensional gridded modelling domain that are required as inputs to the CALPUFF dispersion model. Associated two dimensional fields such as mixing height, surface characteristics and dispersion properties are also included in the file produced by CALMET. The interpolated wind field is then modified within the model to account for the influences of topography, sea breeze, as well as differential heating and surface roughness associated with different land uses across the modelling domain. These modifications are applied to the winds at each grid point to develop a final wind field. The final hourly varying wind field thus reflects the influences of local topography and land uses.





CALMET modelling was conducted using the nested CALMET approach, where the final results from a coarse-grid run were used as the initial guess of a fine-grid run. This has the advantage that off-domain terrain features including slope flows, blocking effect can be allowed to take effect and the larger –scale wind flow provides a better start in the fine-grid run.

The outer domain (70 km × 70 km) was modelled with a resolution of 1.4 km. TAPM-generated three dimensional meteorological data was used as the initial guess wind field and the local topography and available surface weather observations in the area were used to refine the wind field predetermined by TAPM data. Hourly surface meteorological data from the BoM station located at Griffith Airport were incorporated in the outer domain modelling.

The output from the outer domain CALMET modelling was then used as the initial guess field for the mid domain CALMET modelling. The mid domain encompassed an area of 25 km × 25 km and a horizontal grid spacing of 0.5 km was used to adequately represent the important local terrain features and land use. The local topography and land use information was used in this run to refine the wind field parameters predetermined by the coarse CALMET run.

The output from the mid domain CALMET modelling was then used as the initial guess field for the inner domain CALMET modelling. The inner domain encompassed an area of 20 km × 20 km and used a horizontal grid spacing of 0.2 km.

As mentioned in Section 2.7, the topographical data was sourced from the United States Geological Service’s Shuttle Radar Topography Mission database that has recorded topography across Australia with a 3 arc second (approximately 90 m) spacing. The land use data file was created using the latest publically available aerial imagery.

Table 11 details the parameters used in the meteorological modelling.

Table 11 Meteorological Parameters used for this Study – CALMET (v 6.42)

Outer Domain

Meteorological grid 70 km × 100 km

Meteorological grid resolution 1.4 km

Surface station data Griffith Airport BoM Station

Initial guess filed 3D output from TAPM modelling

Mid Domain



Initial guess field 3D output from ‘Outer’ domain model run

Inner Domain



Initial guess field 3D output from ‘Mid’ domain model run

5.3.4 Meteorological Data Used in Modelling

Wind Speed and Direction

A summary of the annual wind behaviour predicted by CALMET at the Development Site is presented as wind roses in Figure 9.





Figure 9 indicates that winds experienced at the Development Site are predominantly light to moderate (between 1.5 m/s and 8 m/s). Calm wind conditions (wind speed less than 0.5 m/s) were predicted to occur approximately 1.0% of the time throughout the modelling period.

The seasonal wind roses indicate that typically:

In summer, winds are moderate to high and are experienced almost evenly from all quadrants with the exception of the north-western quadrant from which a low percentage of winds are experienced. Calm winds were experienced approximately 0.4% of the time during summer.

In autumn, winds are moderate to high and are experienced almost evenly from all quadrants, with a slightly higher percentage of winds from the north-eastern and south-eastern quadrant and a lower percentage of winds from the north-western quadrant. Calm winds were experienced approximately 1.0% of the time during autumn.

In winter, winds are moderate to strong and are experienced predominantly from the northern quadrant, with very few winds from the eastern quadrant. Calm winds were experienced approximately 0.7% of the time during winter.

In spring, winds are moderate to strong predominantly from the north-eastern and south-western quadrants with very few winds from the eastern and west-north western quadrant. Calm winds were experienced approximately 1.3% of the time during spring.





Figure 9 Predicted Seasonal Wind Roses for the Development Site (CALMET predictions, 2012)





Atmospheric Stability

Atmospheric stability refers to the tendency of the atmosphere to resist or enhance vertical motion. The Pasquill-Turner assignment scheme identifies six Stability Classes, A to F, to categorise the degree of atmospheric stability (see Table 12). These classes indicate the characteristics of the prevailing meteorological conditions and are used as input into various air dispersion models.

Table 12 Description of Atmospheric Stability Classes

Atmospheric Stability Class

Category Description

A Very unstable - Low wind, clear skies, hot daytime conditions

B Unstable - Clear skies, daytime conditions

C Moderately unstable - Moderate wind, slightly overcast daytime conditions

D Neutral - High winds or cloudy days and nights

E Stable - Moderate wind, slightly overcast night-time conditions

F Very stable - Low winds, clear skies, cold night-time conditions

The frequency of each stability class predicted by CALMET, extracted at the Development Site during the modelling period is presented in Figure 10. The results indicate a high frequency of conditions typical to Stability Class F. Stability Class F is indicative of very stable conditions, conducive to a low level of pollutant dispersion due to mechanical mixing, resulting in higher predicted odour concentrations.

Figure 10 Predicted Stability Class Frequencies at the Development Site (CALMET predictions, 2012)





Mixing Heights

Diurnal variations in maximum and average mixing depths predicted by CALMET at the Development Site during the 2012 modelling period are illustrated in Figure 11.

As would be expected, an increase in mixing depth during the morning is apparent, arising due to the onset of vertical mixing following sunrise. Maximum mixing heights occur in the mid to late afternoon, due to the dissipation of ground based temperature inversions and growth of the convective mixing layer.

Figure 11 Predicted Mixing Heights at the Development Site (CALMET predictions, 2012)

Temperature

The modelled temperature variations as predicted at the Development Site during the year 2012 are illustrated in Figure 12. The maximum temperature (41.9°C) was predicted on 29 November 2012 and the minimum temperature (0.9°C) was predicted on 2 September 2012.





Figure 12 Predicted Temperatures at Development Site (CALMET predictions, 2012)

5.4 Odour Peak-to-Mean Ratios

The Approved Methods (DEC 2005) states that peak-to-mean ratios should be incorporated when conducting atmospheric dispersion modelling of odour.

It is commonly recognised that dispersion models such as CALPUFF need to be supplemented to accurately simulate atmospheric dispersion of odours. This is because the instantaneous perception of odours by the human nose typically occurs over a time scale of approximately one second but dispersion model predictions are typically valid for time scales equivalent to ten minutes to one hour averaging periods. To estimate the effects of plume meandering and concentration fluctuations perceived by the human nose, it is possible to multiply dispersion model predictions by a correction factor called a “peak-to-mean ratio”. The peak-to-mean ratio (P/M60) is defined as the ratio of peak 1-second average concentrations to mean 1-hour average concentrations.

To estimate peak concentrations, this assessment has used data presented in Table 6.1 of the Odour Framework. Specifically, to establish a conservatively high estimate of peak odour concentrations, the following peak-to-mean ratio (P/M60) has been adopted, corresponding to near-field receptors:

A peak-to-mean ratio (P/M60) of 2.3 has been applied to the emission rate for all sources which is consistent with the wake-affected point sources and volume sources for all stability classes.





5.5 Emission Sources

5.5.1 Odour

As discussed in Section 2.3, eight tunnel-ventilated fully-enclosed climate-controlled poultry sheds. On each shed, air extraction fans mounted at one end will uniformly draw air into the shed through mini-vents along the sides of the shed and later in the growing cycle across cooling pads and through tunnel vents. The air will be pulled over the chickens and exhausted horizontally through the extraction fans. Temperature sensors within the sheds will allow the ventilation to be adjusted as required.

The estimated hourly-varying odour emission rates for the horizontal vents, calculated using the methodology presented in Section 4, were provided as input to the CALPUFF dispersion model via hourly-varying emission files. The Plume Rise Model Enhancements (PRIME) building downwash algorithm was also used to incorporate building wake effects (from the proposed infrastructures/sheds) in the dispersion model (see Section 5.6).

The parameters for the horizontal vents were provided by the Proponent based on the engineering design and are presented in Table 13.

Table 13 Parameters for each Vent type for each Poultry Shed

Parameter Height (m)

Diameter (m)

Exit Velocity (m/s)

Exit Temperature (°C)

Horizontally emitting fans ( 6.0 2.2 0.1 Daily varying1

Note: 1

Assumed to be equal to target shed temperature relevant to bird age (ranging from 21-32°C) as per Section 4.1.2.

5.5.2 Particulates

The majority of the PM10 emission rates measured from poultry sheds ranged from 5 mg/s to 50 mg/s per shed (APCRC 2011). In order to provide a conservative and worst case screening-level assessment of particulate emissions from the Development Site, the CALPUFF model was configured with a single stack source located at the centre of the farm, emitting at 6 m above ground (same as odour release) level with an exit velocity of 10 m/s and an emission rate of 0.4 g/s (based on 8 sheds emitting 50 mg/s PM10 each).

5.6 Building Wake Effects

The BPIP (Building Profile Input Program) PRIME algorithm was used to compile building height and width data so that building wake effects could be accounted for in the dispersion model. Building wake is only included in the dispersion model when vertical exhausts have been represented.

5.7 Regional Odour Sources

As noted in Section 2.4, the surrounding neighbourhood is primarily characterised by traditional agricultural production. As advised in Section 2.4, other odour generating industries identified in the area are:

Privately owned silo on Tysons Road. It is noted that the odour from a silo is likely to be significantly different from the odour being generated from a poultry production farm, hence it is not considered any further in this assessment.

GrainCorp’s Tabbita Silo, which is currently closed, hence not considered any further in this assessment.

JBS Australia’s Tabbita Feedlot (cattle feedlot), located approximately 13 km west of the Project Site. Based on the large separation distance from the Development Site, it is not considered any further in this assessment.





Proposed Maylands Farm 1 – proposed poultry production complex approximately 20 km to the northwest of the Development Site. Based on the large separation distance from the Development Site, it is not considered any further in this assessment.

Proposed Tabbita Farm 1 – proposed poultry production complex approximately 9 km to the west of the Development Site.

Proposed Tabbita Farm 2 – proposed poultry production complex approximately 9 km to the west of the Development Site.

Proposed The Ranch Poultry Production Complex comprising The Ranch Farm 1 and The Ranch Farm 2 (in addition to The Ranch Farm 3, the subject of this AQIA).

The odour emissions from the aforementioned regional odour sources were estimated using the same methodology as described in Section 4.1.1. A summary of key parameters used in the emissions estimation is shown in Table 14.

Table 14 Summary of the Parameters used in Modelling of Background Odour

Site Number of Farms

Number of Sheds

Number of Birds/shed

Shed Dimensions

Source Representation

Development Application

1

Proposed Tabbita Farm 1

1 20 51,000 161 m x 17.7 m

1 source per shed

DA 2015/040

Proposed Tabbita Farm 2

1 20 51,000 161 m x 17.7 m

1 source per shed

DA 2016/005

The Ranch Farm 1 1 8 50,000 176 m x 18 m

1 source per shed

DA 2016/017

The Ranch Farm 2 1 8 50,000 176 m x 18 m

1 source per shed

Not lodged yet

Source: 1


The dispersion modelling to account for the cumulative impacts for these developments was conducted using the same meteorological set and methodology adopted for The Ranch Farm 3 as outlined in the above sections.

5.8 Scenarios Assessed

To assess the impacts from the Development Site and the regional odour source, two scenarios are proposed:

Scenario 1 – The Ranch Farm 3 only; and

Scenario 2 – The Ranch Farm 3 plus other regional odour sources (see Table 14).





6 RESULTS AND DISCUSSSION

6.1 Odour Impact Assessment

6.1.1 Scenario 1

The 99th percentile nose-response odour concentrations predicted at the surrounding sensitive

receptors for Scenario 1 are presented in Table 15.

Table 15 Predicted 99th

Percentile Nose-Response Odour Concentrations (OU) – Scenario 1

Receptor ID

Odour Concentration (OU)

Odour Criterion (OU)

R1 0.3 6.0

R2 0.3 6.0

R3 0.1 6.0

R4 0.5 6.0

R5 0.5 6.0

R6 0.7 5.0

R7 0.7 5.0

R8 0.8 5.0

R9 0.9 5.0

R10 0.9 5.0

R11 0.5 6.0

R12 0.6 6.0

R13 0.7 6.0

Note 1: As predictions are the 99th percentile (i.e. 88

th highest), the total impact will not be a sum of the background and

increment

The results presented in Table 15 show that the predicted 99th percentile nose-response odour

concentrations are well below the adopted criteria at all receptors assessed. It should be noted that the predicted results are based on a number of conservative assumptions, which include zero mortality rate within the sheds and continuous release of emissions throughout any hour of the year.

The dispersion modelling performed for this assessment was performed using the ‘standard’ hourly time step (assuming continuous emission release throughout the hour) and hourly average meteorological data and the predicted hourly odour concentrations were then converted to nose response level (approximately 1-second peak concentration) using the appropriate peak to mean ratio as outlined in Section 5.4.

The predicted 99th percentile odour concentrations (for nose-response time, 1-second average) are

presented as a contour plot in Figure 13. It is noted that the odour contour plot does not reflect odour concentrations occurring at any particular instant in time, but rather illustrates a compilation of the predicted 99

th percentile (88

th highest) odour concentration at all locations downwind, taking into

account all combinations of meteorological conditions modelled across the entire year.

Based on the above, the predicted nose response odour concentrations presented in this report are concluded to represent the conservative worst case impacts at surrounding sensitive receptors. Therefore, the actual odour concentrations at surrounding receptors are expected to be lower than the predicted odour concentrations presented in Table 15.





Figure 13 Predicted 99th

Percentile Nose-Response Odour Concentration (OU) –Scenario 1





6.1.2 Scenario 2

The 99th percentile nose-response odour concentrations predicted at the surrounding sensitive

receptors for Scenario 2 are presented in Table 16.

Table 16 Predicted 99th

Percentile Nose-Response Odour Concentrations (OU) – Scenario 2

Receptor ID

Odour Concentration (OU)

Odour Criterion (OU)

R1 1.2 6.0

R2 1.2 6.0

R3 0.6 6.0

R4 1.4 6.0

R5 1.5 6.0

R6 1.7 5.0

R7 1.7 5.0

R8 1.8 5.0

R9 2.0 5.0

R10 2.2 5.0

R11 3.4 6.0

R12 2.0 6.0

R13 2.3 6.0

Note 1: As predictions are the 99th percentile (i.e. 88

th highest), the total impact will not be a sum of the background and

increment

The results presented in Table 16 show that the predicted 99th percentile nose-response odour

concentrations are well below the adopted criteria at all receptors assessed.

The predicted 99th percentile odour concentrations (for nose-response time, 1-second average) are

presented as a contour plot in Figure 14. It is noted that the odour contour plot does not reflect odour concentrations occurring at any particular instant in time, but rather illustrates a compilation of the predicted 99

th percentile (88

th highest) odour concentration at all locations downwind, taking into

account all combinations of meteorological conditions modelled across the entire year.

It is noted that the concentrations presented in Table 16 and Figure 14 represent the cumulative odour impacts from The Ranch Poultry Production Complex and The Tabbita Farm 1 and Tabbita Farm 2. The highest impacts are predicted to occur at receptor R11 (located on Kidman Way, Tabbita) which represents approximately 56% of the respective criterion.

It should be noted that the predicted results are based on a number of conservative assumptions, which include zero mortality rate within the sheds and continuous release of emissions throughout any hour of the year.

Based on the above, the predicted nose response odour concentrations presented in this report are concluded to represent the conservative worst case impacts at surrounding sensitive receptors. Therefore, the actual odour concentrations at surrounding receptors are expected to be lower than the predicted odour concentrations presented in Table 16.





Figure 14 Predicted 99th

Percentile Nose-Response Odour Concentration (OU) – Scenario 2





6.2 Dust Impact Assessment

Screening level dispersion modelling was conducted using the methodology discussed in Section 4.2 and Section 5.5.2.

The results of the screening level modelling provided a maximum 24-hour PM10 concentration at the nearest sensitive receptor of 0.8 µg/m

3.

This is well below the EPA criterion for ambient PM10 concentrations of 50 µg/m3 as a maximum 24-

hour average concentration and would not be expected to significantly impact on existing air quality given that PM10 concentrations in rural areas in Australia typically range from 10 to 30 µg/m

3, with

short-term elevated concentrations occurring as a result of regional events such as bushfires and dust storms.

In addition, given that the screening assessment used a continuous worst-case PM10 emission rate it would be expected that actual PM10 concentrations resulting from the operation of the poultry farm at the nearest sensitive receptors would be lower than the results given by the modelling. It would therefore be expected that the concentrations of particulate experienced at the nearest sensitive receptors (the closest of which is approximately 3.9 km from the Development Site) would be dominated by regional background sources.

Recent work performed for the Jeanella South Poultry Production Complex (856,000 birds) and the Euroley Poultry Production Complex (3,920,000 birds) (PEL, 2015) indicated that detailed dispersion modelling of particulates yielded a maximum 24 hour average PM10 concentration at a distance of approximately 2 km from operations of between 5 µg/m

3 and 10 µg/m

3. Given that the Ranch Farm 3

will house a population of 400,000 birds, concentrations of PM10 at 2 km would be anticipated to be significantly lower than 5 µg/m

3. Also, given that the Ranch Development Site (Ranch Farms 1, 2 & 3)

will house a total population of 1,200,000 birds, the cumulative concentrations of PM10 at approximately 2 km would be anticipated to be lower than 10 µg/m

3.

The results of the screening level dispersion modelling study are therefore considered to appropriately reflect the minor risk associated with particulates resulting from the operation of the Ranch Farm 3.

Although no dispersion modelling of nuisance dust (dust deposition) or longer term averages of concentration (annual average PM10 and TSP) have been performed, the use of the peak concentration of PM10 as an indicator of broader compliance is considered to be appropriate.

Based on these initial screening level results and the identified level of risk associated with particulate matter from other similar studies, any further detailed modelling is not considered to be warranted.





6.3 Mitigation and Monitoring Measures

The following design features, best management practices and mitigation measures will be employed at The Ranch Farm 3 to minimise the potential for dust and odour emissions:

The poultry sheds will be fully enclosed, have adequate roof overhang (wide eaves) and be surrounded by concrete bund walls to prevent rainwater entering the sheds and to allow for the controlled discharge of wash down water from the sheds. These measures are likely to reduce the level of moisture within the poultry sheds.

The feed silos will be fully enclosed to both prevent the entry of rainwater and minimise emissions of dust/particulate matter when loading and unloading.

The poultry sheds will be tunnel-ventilated, which will allow control over the moisture levels and promote optimum growing conditions and bird health. The increased airflow and improved feed conversion in tunnel-vented sheds helps to maintain bedding material within the optimal moisture range.

All sheds will be fitted with nipple drinkers with drip cups, as opposed to traditional cup drinkers, to minimise water spillage and reduce the risk of increased shed moisture.

Regular monitoring and maintenance of the tunnel ventilation systems and bird drinkers will be undertaken to avoid spillage, leaks and uneven distribution.

Stocking densities and bird health within each of the poultry sheds will be regularly checked and, if necessary, appropriate corrective measures will be implemented.

Daily monitoring and maintenance of the bedding material will occur to identify, remove and replace any caked material beneath drinking lines and/or areas with excessive moisture content.

Poultry litter (spent bedding material) will be promptly removed from the sheds and generally transported off-site in covered trucks at the end of each production cycle during the clean-out phase. Wherever possible the handling of the material will be avoided during adverse climatic conditions, such as strong winds. The shed ventilation systems will not be used during the removal of bedding material.

Dead birds will be collected from the sheds on a daily basis and stored in on-site chillers prior to removal from site.

The insides of the poultry sheds and the surrounds will be maintained at all times to ensure a clean and sanitary environment.

During sanitisation, the amount of air released from the sheds while any sanitising scent is present will be minimised and, if possible, a low scent sanitiser will be utilised.

Internal access roads will be appropriately maintained to minimise dust emissions and speed restrictions (<40 km/h) will be implemented on any unsealed internal roads.





7 CONCLUSIONS

SLR was commissioned by Scolexia on behalf of VOAG 3, to undertake an AQIA for The Ranch Farm 3.

The aim of this assessment was to quantify the change in potential dust and odour impacts in the region due to the operation of The Ranch Farm 3 in isolation and cumulatively with other surrounding developments, including The Ranch Farm 1 and The Ranch Farm 2 (as part of the Ranch Poultry Production Complex). Potential odour emissions from the proposed poultry farm were estimated based on site specific design parameters and publicly available research studies on the odour emissions generation from poultry farms. Potential hourly varying odour emission rates were estimated based on a number of factors in a bird growth cycle, such as:

the number of birds, which varies later in the batch as harvesting takes place;

the stocking density of birds, which is a function of bird numbers, bird age and shed size;

ventilation rate, which depends on bird age and ambient temperature; and

design and management practices, particularly those aimed at controlling litter moisture.

The CALPUFF dispersion model was used to predict the potential odour levels at surrounding sensitive receptors based on an hourly, 1-year site-representative meteorological file compiled using observational data from Griffith Airport BoM Station during 2012. A peak-to-mean ratio of 2.3 was used to convert the predicted hourly odour concentrations to nose-response (1-second average) odour levels.

The modelling results showed that The Ranch Farm 3 (increment) and combined (cumulative) with The Ranch Farm 1, The Ranch Farm 2, Tabbita Farm 1 and Tabbita Farm 2 are predicted to comply with the adopted odour criteria at all identified surrounding sensitive receptors. Based on the modelling results it has been concluded that the proposed poultry farm operation is unlikely to cause any significant odour nuisance at the nearby surrounding sensitive receptor locations.

The screening level particulate emissions modelling showed that the PM10 levels were well below the EPA criterion of 50 µg/m

3. Notwithstanding the above, potential dust emissions from the proposed

development will be minimised through the application of best management practice measures. Considering the distance between the Development Site and surrounding sensitive receptors the proposed development is unlikely to cause any significant elevation of particulate levels at the nearest sensitive receptor locations.





8 REFERENCES

APCRC 2011, Dust and Odour Emissions from Meat Chicken Sheds, Australian Poultry CRC, Project No – 04-45

DEC 2005, Approved Methods for the Modelling and Assessment of Air Pollutants in New South Wales, NSW Department of Environment and Conservation, 26 August 2005.

DEC 2006a, Technical Framework - Assessment and Management of Odour from Stationary Sources in New South Wales, NSW Department of Environment and Conservation, November 2006.

DEC 2006b, Technical Notes - Assessment and Management of Odour from Stationary Sources in New South Wales, NSW Department of Environment and Conservation, November 2006.

SLR 2015, The Ranch Poultry Production Complex – Farm 3, Request for SEARs Supporting Document, Report number 630.11282, 29 June 2015.

Ormerod R and Holmes G 2005, Description of PAE Meat Chicken Farm Odour Emissions Model, Brisbane: Pacific Air and Environment.

Oke 2004, Boundary Layer Climates, Second Edition, Routledge, London and New York, 435 pp.

Appendix A Report Number 630.11282-R6

Page 1 of 4

_________________________________________________________________________________


Selection of Representative Meteorological Data

In dispersion modelling, one of the key considerations is the representative nature of the meteorological data used. Once emitted to atmosphere, emissions will:

rise according to the momentum and buoyancy of the emission at the discharge point relative to the prevailing atmospheric conditions;

be advected from the source according to the strength and direction of the wind at the height which the plume has risen in the atmosphere;

be diluted due to mixing with the ambient air, according to the intensity of turbulence; and

possibly be chemically transformed and/or depleted by deposition processes.

Dispersion is the combined effect of these processes.

Dispersion modelling is used as a tool to simulate the air quality effects of specific emission sources, given the meteorology typical for a local area together with the expected emissions. Selection of a year when the meteorological data is atypical means that the resultant predictions may not appropriately represent the corresponding air quality impacts.

The year of meteorological data used for the dispersion modelling was selected by reviewing the most recent five years of historical surface observations at Griffith Airport AWS [station number 075041] (2010 to 2014 inclusive) to determine the most representative year of long-term conditions. Wind speed, ambient temperature and relative humidity were compared to long term averages for the region to determine the most representative year.

Data collected from 2010 to 2014 is summarised in Figure A1 to Figure A6. Examination of the data indicates the following:

Figure A1 and Figure A2 indicate that 2012 exhibit wind speeds that are closest to the long term average.

Figure A3 and Figure A4 show that temperatures in 2012 and 2013 more appropriately reflect the long term average. Temperatures in 2012 are slightly lower than the long term average at 9 am in autumn/winter however 2013 temperatures are slightly higher than the long term average at 3 pm in autumn/winter.

Figure A5 and Figure A6 indicate that relative humidity in 2012 appropriately reflect the long term average.

Years 2012 and 2013 indicate average wind speeds that are slightly lower than the long term average, especially at 3 pm. Using these years as the representative year would be a conservative approach because low wind speeds are associated with less effective plume dispersion. No other parameters significantly deter the use of any one of these years of data. Consequently, 2012 was selected as a representative year of meteorology.


Page 2 of 4

_________________________________________________________________________________


Figure A1 Wind Speed at 9 am at Griffith Airport AWS for 2010 – 2014

Figure A2 Wind Speed at 3 pm at Griffith Airport AWS for 2010 – 2014


Page 3 of 4

_________________________________________________________________________________


Figure A3 Temperature at 9 am at Griffith Airport AWS for 2010 – 2014

Figure A4 Temperature at 3 pm at Griffith Airport AWS for 2010 – 2014


Page 4 of 4

_________________________________________________________________________________


Figure A5 Relative Humidity at 9 am Griffith Airport AWS for 2010 – 2014

Figure A6 Relative Humidity at 3 pm Griffith Airport AWS for 2010 – 2014

The Ranch Poultry Production Complex - Carrathool · PDF fileThe Ranch Poultry Production...

Documents

Transcript of The Ranch Poultry Production Complex - Carrathool · PDF fileThe Ranch Poultry Production...