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Jellyfish Compliance with SEQ MUSIC Modelling Guidelines · MCU15/0122
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Page | 2 [OCR of page images] Table of Contents INCFOGUCTION......sssssssssssssssssssssssssssssssssssssssssssssssisnnnnsssssessssssssssssssssssssssussssssnsesseesssesssssssssssssssssssssssssssssssssssssnsnsssssensenssen® 3 Technology OVErVieW......essescccscssssssssscsssssssssssesssessssssnuesmusessecssssssssusussesecessssssesssssunssessessessssnnueseeseeseeesssnseess 4 Components of the Jellyfish Filter. Membrane Filtration Cartridge...... Operation of the Jellyfish Filter...... Self-cleaning FUNCTIONS Wes cesssseeeessccssessssssnesesscsssssssssseescessessccsesssssnueecsessssssuununsssnunessesssssssnseeeseetees 5 MAaiNteNn ane ......cssssssesessscscssssssssunsssnsneescscsssssssnssssssesessseeesssssnunssesssessssssssunesesseeesessssnunsesseeesssssnsssssnnueeeseeeessane 6 Water Quality Objectives... seeccccsssssssssssssssmeessssseessssmmeeseceesssssssnsessesessssunuseuseesesessssssnsesecsesessssessanee 7 Humes Research Data and Published Papers.........csesessssssssssssssseeccsssssssssnssscssessscsessssssnnsesssceesesssnnnneesees 8 MUSIC Modelling Guidelines... eeeccsssssssssssssssseessscsessssssussscseesecseessssnnmmeesscesssssssnsetessessesssssnsneesseessssane 8 Generic Node: POiMt Ln ssessssssssssssssssssssssennsnnssnessessessesssssesssssssssssssssssnnnsssssssssssessssssssesesseessessessesseseseeseessneeesees 9 Generic Node: POM 2...sesssessssssssssssssssssssssennsnnssssseessessesssssessssssssssssssssssnsssssssnsssssssssssseeseeseesessesseeseseeceseeseneeesses 9 Generic Node: POIMt 3 ....ssssssssssssssssssssssssssennesnsnssssessessessssessessstsssssssssssenssnssssssssssssssssssesesseesessessessesesseseessesees 10 CONCIUSION....ssssssssesssessssssesssssessessesssssessesseeseesesesceneessrnnessssssssssssssssssssesssssussssssssssssssssssssssssssessseseessessetsesseeseeseseseeeees 12 Appendix A: Jellyfish Filter Technical Manual... ssssessccscssscscsssssssneecssssssssssnssesessesssssnmneesssessane 13 Appendix B: Published Papel... eeesesscssssssssssenssesssccessssssssseesecsesssssssnuussesesssssnsssesseeseeeceseesssnnuneeeseeesssse 14 Appendix C: MUSIC Transform FUNCTIONS... ecccsssssssssesessssteesccscsssssnueeeseccssssssssessssunnsesesseesssssnneessessses 15 Page | 2
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Page | 3 [OCR of page images] Introduction Humes is one of Australia’s leading provider of engineered concrete solutions for the civil construction industry and has a long history of engineering precast and prestressed concrete solutions, with over 100 years of manufacturing experience. Since the 1990’s, Humes has been involved with developing and implementing stormwater treatment devices, and is now recognised as one of the leading suppliers of stormwater quality improvement devices (SQIDs). Humes has committed extensive amount of resources and time into the research and development of its SOQID product line. These include treatment devices such as: e¢ HumeGard (Primary Treatment — Gross Pollutants) e¢ HumeCeptor (Secondary Treatment — Sediment/Oil removal) e Jellyfish Filter (Tertiary Treatment — Sediment and Nutrient Removal) Testing has been set up for these products in order to prove the performance of the device in an Australian climate. Specifically, the Jellyfish filter has been exposed to rigorous local testing at a commercial test site in Ipswich. Currently, many local councils and regulators in Australia use MUSIC modelling as a method of demonstrating compliance of WSUD to State Planning Policy (2014). The MUSIC Modelling Guidelines (Water by Design, 2010) assists with guiding users and regulatory authorities in using the software with conditions applicable to South East Queensland (SEQ). This document demonstrates compliance of the Jellyfish Filter with the relevant sections of the MUSIC Modelling Guidelines for SEQ, to assist the development application process. Page | 3
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Maintenance High-flow Cartridges
Access Wall
Backwash Pool
Inlet
Outlet
Draindown
Cartridge Deck
Cartridge
Separator Skirt
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Page 5
Head Plate
Tentacles
Inlet
Outlet
Gravitational
Particles
Flow Rotation
Settling
Membrane Filtration
Page | 5
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Page | 6 [OCR of page images] Maintenance The primary purpose of the JellyFish® filter is to remove pollutants from stormwater runoff. As with any filtration system, periodically these pollutants must be removed to restore the JellyFish® filter to maximum treatment performance. Regular inspection and maintenance are required to ensure proper functioning of the system. Maintenance of the Jellyfish Filter is generally conducted annually. The maintenance procedure can be summarised into two steps: 1. Vacuum sediments from the sump (as per standard GPTs) 2. Rinse down filter cartridges with only a low pressure hose before recommissioning them back into the device With proper maintenance, it is recommended that the filter cartridges be replaced with new ones (unless cartridges are damaged beforehand) every 5 years. This ensures the Jellyfish Filter is operating in an optimal environment, and ensures that maintenance and on-going costs are kept low. Page | 6
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Page | 7 [OCR of page images] Water Quality Objectives The State Planning Policy for 2014 states the following water quality objectives are to be achieved for applicable developments in Queensland. Table B: Post construction phase—stormwater management design objectives Application (a) Applies to western Queensland and Cape York/ Far North Queensland, for population centres greater than 25,000 persons, and (b) For all other climatic regions with population centres greater than 3000 persons. Climatic Design objectives eal] UT imeem LLL Ue TET Cer Ce Cd east) unmitigated development (%) Application itr) Mapp System) South East 8 = Development for urban purposes within population Queensland ” 45 9° centres greater than 3000 persons. Central Queensland 85 60 45 9a As above. (south) Central As above. Queensland 75 60 40* ga * Mackay Regional Council has adopted a 35% (north) reduction for TN. As above. Dry Tropics 80 60 * 40 90 * Townsville City Council has adopted a 65% reduction for TP. Wet Tropics 80 60 40 90 As above. Development for urban purposes within Cape York/ENQ | 80 bo ae oe population centres greater than 25,000 persons. Western Queensland 85 60 45 90 As above. Excludes development that is less than 25% impervious. N/A N/A N/A N/A In lieu of modelling, the default bio-retention treatment area to comply with load reduction targets for all Queensland regions is 1.5% of the contributing catchment area. > All ¢ i L i Catchments contributing to un-lined receiving Waterway stability management waterway. Local government may not require * Limit the peak 2-year ARI event discharge within Compliance if the waterway is degraded. the receiving waterway to the pre-development For peak flow for the 1-year ARI event, use peak 1-year ARI event discharge. co-located storages to attenuate site discharge rate of stormwater. Figure 4 - SPP 2014 Water Quality Objectives Specifically for South East Queensland (SEQ), the water quality objectives are (from the table above): ¢ 80% reduction in Total Suspended Solids (TSS) © 60% reduction in Total Phosphorous (TP) e 45% reduction in Total Nitrogen (TN) e 90% reduction in Gross Pollutants (GP) Page | 7
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Page | 8 [OCR of page images] Humes Research Data and Published Papers In 2012, Humes established a local field monitoring program of the Jellyfish Filter at a commercial facility in West Ipswich. Humes provided a JF1200-2-1 to the site under approval from Ipswich City Council, along with sampling equipment in order to evaluate the treatment performance of the Jellyfish Filter under Australian conditions, specifically in SEQ. The study was independently conducted by QUT, and published in a peer reviewed journal (Australian Journal of Water Research) by Humes (See Appendix B). Another research paper was published in the Australian Water Association Journal in May 2015 by Humes which demonstrated the statistical robustness of the data collected at the Ipswich test site for the Jellyfish Filter. This paper confirmed that enough samples had been collected, but also that the quality of samples collected was statistically significant. This was critical in demonstrating that the field data collected was statistically robust and accurately represented the performance of the Jellyfish Filter under local conditions (See Appendix B). The research conducted in Australia also confirms results from overseas testing. The University of Florida performed comprehensive testing on the Jellyfish Filter, and found similar removal rates as those published in Australia. MUSIC Modelling Guidelines The Music Modelling Guidelines for SEQ (Water by Design, 2010) provides a comprehensive guide on acceptable modelling practice for the SEQ region. The purpose of the guidelines is to ensure a consistent and uniform approach to stormwater quality modelling and assessment by providing preferred MUSIC parameters to demonstrate compliance with the State Planning Policy. By utilising MUSIC, it is possible to select and size an appropriate Jellyfish Filter in order to meet these water quality objectives. The Jellyfish targets nutrients as a tertiary filtration device (as opposed to gross pollutants — although gross pollutant removal is possible). Therefore it is accurately represented in MUSIC as a “Generic Node”. Products which are represented by these types of nodes need to demonstrate the following three (3) points, as described below. Page | 8
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Page | 9 [OCR of page images] Generic Node: Point 1 4.14 Generic nodes The MUSIC User Manual describes the parameters that can be defined for the generic node. The structure of the generic node is similar to the GPT node. If the pollutant reduction function is used, users should express caution in adopting reliable pollutant reduction efficiency rates. Similarly, assessment authorities should not accept models which use the pollutant reduction function unless the applicant has demonstrated that: the proposed treatment measure operates in a manner which cannot be represented using one of the other MUSIC treatment nodes the proposed reduction efficiencies are justified by rigorous scientific testing and results are published in an credible engineering/scientific journal the modelled pollutant reduction efficiency reflects the published figures. With reference to the first point highlighted, as the primary function of the Jellyfish is fine sediment and nutrient removal, it cannot be modelled by any other MUSIC node available. The closest node may be the GPT Node, however, while the Jellyfish filter is capable of removing gross pollutants, its primary function is nutrient removal, and is often paired with a GPT upstream of the Jellyfish, hence improving the longevity of the Jellyfish Filter. This demonstrates compliance with point 1 of the Generic Node. Generic Node: Point 2 Page | 9 4.14 Generic nodes The MUSIC User Manual describes the parameters that can be defined for the generic node. The structure of the generic node is similar to the GPT node. If the pollutant reduction function is used, users should express caution in adopting reliable pollutant reduction efficiency rates. Similarly, assessment authorities should not accept models which use the pollutant reduction function unless the applicant has demonstrated that: + the proposed treatment measure operates in a manner which cannot be represented using one of the other MUSIC treatment nodes the proposed reduction efficiencies are justified by rigorous scientific testing and results are published in an credible engineering/scientific journal the modelled pollutant reduction efficiency reflects the published figures.
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Page | 10 [OCR of page images] With reference to the second point highlighted above, Humes has published one peer reviewed journal articles with respect to the Jellyfish testing site and results, and another justifying the statistical significance of the data collected. The papers are: e Evaluation of Treatment Performance of a Stormwater Treatment Membrane Filter under Australian Conditions (Australian Journal of Water Research) e A Statistical Approach to Assess Stormwater Treatment Device Performance Data (Australian Water Association Journal) Both of these papers can be used to demonstrate compliance with the guidelines. The papers are attached in Appendix B for further reference. Generic Node: Point 3 4.14 Generic nodes The MUSIC User Manual describes the parameters that can be defined for the generic node. The structure of the generic node is similar to the GPT node. If the pollutant reduction function is used, users should express caution in adopting reliable pollutant reduction efficiency rates. Similarly, assessment authorities should not accept models which use the pollutant reduction function unless the applicant has demonstrated that: + the proposed treatment measure operates in a manner which cannot be represented using one of the other MUSIC treatment nodes * the proposed reduction efficiencies are justified by rigorous scientific testing and results are published in an credible engineering/scientific journal * the modelled pollutant reduction efficiency reflects the published figures. With reference to the third point highlighted above, this can be justified by using MUSIC modelling in accordance with the MUSIC Modelling Guidelines for SEQ (2010). A MUSIC model was created to simulate the site based on the site details provided in the published papers (see Appendix B) Page | 10
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Page | 11 [OCR of page images] eR Commercial ROOF (0.027 ha, 100% Imp) m™ M™ ; 1G ‘Commercial ROOF (0.027 ha, 190% imp} Humes fecha eehina ade” | Treatment Train : Node a Sources Residual Load % Reduction | Flow (ML/yr) 4.13 Lo 10.2 } Total Solids (kg/yr) 310 488 843 }— Total Phosphorus (kg/yr) 0.593 0.228 61.6 t— Total nitrogen (kg/yr) 3.58 1.38 36 4 | Gross Pollutants (kg/yr) 28.5 0.739 974 , Commercial ROAD (0.443 hs, 400% imp) | &| & | aay a a a Figure 5 - MUSIC Modelling for Humes Test Site at Ipswich Figure 5 shows the results of the MUSIC modelling for the Ipswich test site that the Jellyfish Filter was treating. The details of the model are: MUSIC Version 6 Rainfall Station 40659 Greenbank (West of Brisbane) with 6 minute time step from 1/01/1980 to 31/12/1989 (10 years) Commercial Source Nodes (as described in the MUSIC Modelling Guidelines) Modified Generic Node to model the Jellyfish Filter with the high flow bypass set to the treatment flow rate for JF1200-2-1 (13L/s) The table below compares the differences between the actual field test data collected and the MUS IC modelling. Table 1- Comparisons between Published Results and Modelling Pollutant Published Load | Modelling Load | Difference (%) Reductions (%) | Reductions (%) TSS 89 84.3 47 TP 65 61.6 3.4 TN 55 56 1 Given the approximations and accuracies within MUSIC and the above minor differences in load reductions between the field data and modelling, the parameters modelled accurately reflect the published field data as per the guidelines. The results suggest that MUSIC modelling presents a more conservative approach to using the Jellyfish Filter as all the above pollutants in table 1 resulted in slightly less than was actually recorded at the test site (and almost exactly for TN). The transform functions used in MUSIC can be found in Appendix C Page | 41
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Page | 12 [OCR of page images] Conclusion Given that the MUSIC Modelling Guidelines for SEQ require justification for proprietary devices with regards to tertiary treatment, Humes has conducted local research on the Jellyfish Filter. The results of the research prove that the Jellyfish filter can achieve the reduction efficiencies to be used in a Generic Node in MUSIC of: e TSS 89% © TP 65% e ™N55% This demonstrates that the Jellyfish Filter meets the MUSIC Modelling Guidelines for SEQ. Page | 12
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Page | 13 [OCR of page images] Appendix A: Jellyfish Filter Technical Manual Page | 13
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JellyFish® filter
Technical manual
Issue 5
Filter
[OCR of page images]
Strength. Performance. Passion.
JellyFish® filter
Technical manual
TAO)
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“3
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Page 15
Contents JellyFish® filter 1 System components 2 Membrane filtration cartridge 4 System operation 5 Self-cleaning functions 6 System performance 7 Treatment efficiency 7 Inlet and outlet pipes 7 System options 8 Design information 9 Configurations and design capacities 9 Bypass design 10 Dry sump option 11 Submerged (tidal/tailwater) installations 11 Series JellyFish® filter 11 System installation 12 Inspection and maintenance 12 Replacement parts 12 Appendices 13 Precast solutions 20 Contact information 21
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JellyFish® filter
JellyFish® filter
The JellyFish® filter is a tertiary stormwater treatment system featuring membrane
filtration to provide exceptional pollutant removal at high treatment flow rates with
minimal head loss and low maintenance costs.
The JellyFish® filter uses gravity, flow rotation, and • The system provides tertiary level performance with a
up-flow membrane filtration to provide tertiary small footprint
treatment to stormwater in an underground structure. The proven performance of the JellyFish® filter and
Using unique filtration cartridges, each JellyFish® filter high flow rate membranes enables water quality
provides a large membrane surface area, resulting in high objectives to be met with a smaller footprint system
flow rates and pollutant removal capacity. than typical bioretention systems.
• It has been independently researched and proven
The JellyFish® filter efficiently captures a high level of
The JellyFish® filter has been independently researched
stormwater pollutants, including:
under both laboratory and field conditions in the
• Total Suspended Solids (TSS), median removal United States and Australia. In the United States, it has
efficiency of 89%, including particles down to received verification under the stringent New Jersey
two microns Corporation for Advanced Technology (NJCAT) protocol.
• Total Nitrogen (TN), median removal efficiency of 55%
• It treats higher flow rates than most filters
• Total Phosphorous (TP), median removal efficiency
Each filter cartridge has an effective filter area of
of 65%
35.4 m2 designed to treat 5 litres per second (L/s)
• Total Copper (Cu), median removal efficiency of 61%
during operation.
• Total Zinc (Zn), median removal efficiency of 91%.
• Above-ground land use is maintained
Designed as a polishing device for constrained sites, the The system is assembled within a fully-trafficable,
JellyFish® filter is available in a range of sizes to cater for precast concrete structure for underground
both at-source and end-of-pipe solutions. installations on constrained sites, allowing maximum
use for above-ground activities.
• Maintenance is easy
The filter backwashes after peak flows so it can
self-clean several times in each storm event. Manual
backwash is recommended annually. When cartridge
replacement is required (usually every three to five
years), it is a safe and simple process.
• We provide world class treatment solutions
Humes has a team of water specialists dedicated
to delivering sustainable solutions, creating
maximum value for your project, accommodating
your site conditions, design requirements and
construction factors.
JellyFish® filter 1
Page 17
System components • Filter cartridges are designated as either high-flow or
draindown cartridges, depending on their location in
The JellyFish® filter is comprised of several structural and the cartridge deck. High-flow cartridges placed within
functional components: the backwash pool are automatically backwashed after
each storm event.
• A cylindrical precast concrete structure which is
available in a range of diameters and depths that • Draindown cartridges located outside the backwash
serves as a vessel providing structural support pool facilitate the draindown of the backwash
for a 50 year design life and provides storage for pool. The design flow rate (2.5 L/s) of a draindown
accumulated filtered pollutants. cartridge is controlled by the lid orifice. The lower
design flow rate of the draindown cartridge ensures
• A rigid high-strength fibreglass cartridge deck
the membranes last for longer periods between
separates the vessel into a lower chamber and upper
scheduled maintenance.
chamber. This houses the filter cartridges, provides
a surface and flow path for treated water to the • Cartridge lids are fastened into the deck to securely
effluent pipe, and provides containment of oil and anchor the filter cartridges. The lids are removable to
other hydrocarbons below the deck and also provides allow manual rinsing and replacement of the filter
a platform for maintenance personnel to safely service cartridges when required. Cartridge lids contain a
the filter cartridges. flow control orifice that is specifically sized for use
with high-flow and draindown cartridges. Blank lids
• The lower chamber provides storage for pollutant
have no orifice and are used to cover unoccupied deck
separation and membrane filtration.
apertures in systems that do not use the full rated flow
• The upper chamber provides adequate clearance for capacity of the system.
inspection and maintenance.
• A rigid fibreglass backwash pool weir extends
• A rigid high-strength fibreglass Maintenance Access 150 mm above the cartridge deck and encloses the
Wall (MAW) attenuates influent water velocity and high-flow cartridges. During inflow, filtered water
directs flow into the lower chamber through a large leaving the high-flow cartridges forms a pool inside
opening in the cartridge deck. In addition, it provides the weir. When the water level in the pool exceeds
storage for floatable pollutants. It also serves as an the weir height it overflows and spills to the cartridge
inspection and maintenance access point. deck where it then flows to the outlet pipe. As the
• JellyFish® membrane filtration cartridges are secured inflow event subsides and pressure decreases, water
to the deck by the cartridge lids. Each filter cartridge in the backwash pool reverses flow direction and
consists of multiple membrane filter tentacles, which automatically backwashes the high-flow cartridges,
treat the stormwater by filtering out fine suspended cleaning the membrane surfaces. Water in the lower
particulates (TSS) and particulate-bound pollutants on chamber (below deck) is displaced through the
the membrane of each tentacle. Filtered water passes draindown cartridges.
through the membranes, up the centre tube of each This self-cleaning mechanism may occur multiple
tentacle and exits through the top. times during a single storm event as rainfall/runoff
intensities rise and fall, thereby significantly
extending the service life of the cartridges and the
maintenance interval.
2 JellyFish® filter
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• A separator skirt encloses the filtration cartridge • Large diameter access lids are installed at the surface
JellyFish® filter
and defines the filtration zone. The separator skirt and are removed to allow access for maintenance
extends the full length of the filtration tentacles and of the system. The upper chamber is designed
prevents contamination of the membranes with oil with tapered surrounds to match with finished
and floatable debris. The separator skirt also forces surface grades.
water to enter the filtration zone under low velocities.
The separator skirt is attached to the underside of the The JellyFish® filter and components are depicted in
cartridge deck. Figure 1 below.
• As an option, the inlet pipe can be located below the
deck for drainage networks with deep invert levels.
In these systems, a deflector plate is installed across
the inlet pipe to induce tangential water flow
through the channel between the chamber wall and
separator skirt.
Figure 1 – JellyFish® filter components
Maintenance access wall
High-flow cartridges
Inlet Backwash pool
Outlet
Draindown cartridge
Cartridge deck
Separator skirt
JellyFish® filter 3
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Membrane filtration cartridge Figure 2 – JellyFish® membrane filtration cartridge
Head plate Lifting eyes
The JellyFish® filter utilises multiple lightweight
Attachment Gasket
membrane filtration cartridges. Each cartridge consists of nuts
multiple removable filter elements (“filtration tentacles”)
attached to a cartridge head plate. Each filtration
tentacle consists of a central perforated tube surrounded
by a specialised membrane. A removable oil-resistant
polymeric gasket provides a watertight seal between the
cartridge and the deck. A JellyFish® membrane filtration Tentacles
cartridge is depicted in Figure 2.
The cartridge length is 1,372 mm. The dry weight of
a new cartridge is less than 9 kg, and the wet weight
of a used cartridge is less than 23 kg. No heavy lifting
equipment is required during exchange.
The filtration tentacle membranes provide a large surface
area, resulting in high flow and suspended sediment These values translate to a conservative design
removal capacities. A typical JellyFish® cartridge with membrane filtration flux rate (flow per unit surface area)
11 filtration tentacles has 35.4 m of membrane
2
of 0.14 L/s/m2 for the high-flow cartridge and 0.07 L/s/m2
surface area. Hydraulic testing on clean filter cartridges for the draindown cartridge.
demonstrated a flow rate of 11.3 L/s at 455 mm of
driving head. In addition, the filtration membrane has been treated
to allow biofilm to grow but not clog the pores of the
Extensive independent field testing, including testing membrane. The flow rating of a particular JellyFish® filter
at an urban site with high intensity rainfall and runoff, cartridge is based on the membrane filtration surface
has demonstrated consistently high pollutant removal area of the cartridge and data collected from both
performance with a conservative design flow rate of laboratory testing and field testing. The cartridge deck
5 L/s for the high-flow cartridges and 2.5 L/s for the contains apertures for each filter cartridge.
draindown cartridges.
4 JellyFish® filter
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System operation Gravitational forces remove coarse sediment (generally
JellyFish® filter
>50 microns), particulate-bound pollutants (nutrients,
As a tertiary treatment system, the JellyFish® filter toxic metals, hydrocarbons), free oil and floatable
is designed to be an “offline” structure, as part of a trash and debris (that may bypass upstream primary
treatment train. For effective operation, the system treatment devices). Large, heavy particles fall to the sump
requires a difference in elevation between upstream and (sedimentation) and low specific gravity pollutants rise to
downstream water levels. Typically, a minimum 455 mm the surface (floatation) behind the MAW.
of driving head is designed into the system but may
vary from 305 mm to 610 mm depending on specific Treatment begins when flow enters the system through
site requirements. the inlet pipe (standard). Below-deck inlet pipes are
offered as an option. Influent enters the MAW zone and
The JellyFish® filter uses gravity, flow rotation and passes through a large opening in the deck to the lower
membrane filtration treatment to remove pollutants chamber. The large deck opening and change in flow
from stormwater runoff. These functions are depicted in direction attenuate the influent flow velocity. Buoyant
Figure 3 below. pollutants remain on the surface in the MAW zone.
Figure 3 – JellyFish® filter functions
Inlet
Outlet
Gravitational Flow rotation
particles settling
Membrane
filtration
JellyFish® filter 5
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Flow into the lower chamber must then pass tangentially Self-cleaning functions
around the separator skirt protecting the cartridges
and increasing the flowpath length. Coarse sediment The JellyFish® filter utilises several self-cleaning
settles out of the MAW zone into the sump. As water processes to remove accumulated sediment from the
flows tangentially around the separator skirt in the external surfaces of the filtration membranes, including
lower chamber, the large opening in the bottom of the automatic backwash of the high-flow cartridges,
separator skirt and upward change in direction further vibrational pulses, and gravity. These processes have
reduces flow velocity and enhances particle separation. been confirmed by more than 12 months of full
As a result, sediment settles in the sump. scale prototype testing. Combined, these processes
significantly extend the cartridge life, maintenance
Flows pass through the cartridge in the filtration zone. interval and reduce life-cycle costs.
Each filter cartridge consists of multiple tentacles.
Hydraulic pressure across the entire membrane surface Automatic backwash occurs with the high-flow
area causes water to penetrate the filtration tentacles. cartridges at the end of each runoff event. This can occur
Water enters the membrane pores radially and deposits multiple times during a single storm event as intensity
fine particulates on the exterior membrane surface. and driving head varies. As the inflow subsides and
Filtered water flows into the centre drain tube of each driving head decreases, water in the backwash pool
tentacle, the water then flows upward and out the top. reverses flow direction and automatically backwashes
the high-flow cartridges, removing sediment from the
Water exiting the top of the tentacles combines under membrane surfaces. Water in the lower chamber (below
the lid, where the combined flow exits the cartridge deck) is displaced through the draindown cartridges.
through the orifice with a pulsating fountain effect
into the backwash pool. When the water level in the Vibrational pulses occur as a result of complex and
backwash pool exceeds the weir height it overflows to variable pressure and flow direction conditions that
the outlet pipe. arise in the deck during operation. During forward flow
a stream of filtered water exits the top of each filtration
Outside the backwash pool, the draindown cartridge cartridge and encounters resistance from the turbulent
provides treatment at a reduced flow rate (2.5 L/s) and pool of water in the backwash pool. Water is forced
allows the treated water captured in the backwash pool through the cartridge lid orifice into the backwash pool
to return through the cartridges and balance water with a pulsating fountain effect. The resulting pulses
pressure as the storm event ends. transmit vibrations through the deck to the membranes,
thereby dislodging accumulated sediment. The effect
As particles build up on the external membrane surface, is pronounced at higher flow rates, and influences
the pores progressively become smaller. This process, all cartridges.
referred to as “filter ripening”, significantly improves
the removal efficiency relative to a brand new or clean Accumulated sediment on the membranes will settle
membrane. Filter ripening accounts for the ability of under gravity both during inflow events and inter-event
the JellyFish® filter to remove particles finer than the dry periods. As fine particles form into larger masses
nominal pore size. An animation of the JellyFish® filter on the membrane surface, adhesion to the membrane
operation and maintenance is available at humes.com.au. surface lessens, and sediment sheds away from the
membrane. Chemical processes and biofilm effects also
play a role.
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System performance Inlet and outlet pipes
JellyFish® filter
The JellyFish® filter has been designed to provide tertiary An above-deck inlet pipe configuration is standard
level treatment and may be combined with a Gross for the JellyFish® filter and an optional below-deck
Pollutant Trap (GPT) as part of a treatment train to inlet configuration is available on request. Specific site
optimise overall performance. constraints generally determine the configuration that
is most favourable. In both configurations, the invert
level of the outlet pipe is identical to the cartridge
Treatment efficiency deck elevation.
Extensive research of the JellyFish® filter has proven
its performance under Australian laboratory, US field
conditions and Australian field conditions. Field testing in
the United States has received independent verification
under the stringent New Jersey Corporation for Advanced
Technology (NJCAT) protocol. The results are summarised
in Table 1 below.
Table 1 – JellyFish® filter performance summary
Pollutant Median reduction
TSS 89%
TP 65%
TN 55%
Cu 61%
Zn 91%
Total oil and grease 62%
Reference: University of Florida (2011) and West Ipswich (2014).
JellyFish® filter 7
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System options
The JellyFish® filter can be customised to suit your project
requirements. As each treatment train is unique and
units are designed to match surface levels, please contact
Humes for assistance, however, Table 2 below can be
used as a guide.
Table 2 – JellyFish® filter model range and details
Number of Nominal
JellyFish® Number of high- draindown Inlet pipe Treatment flow structure
model flow cartridges cartridges diameters (mm) (L/s) diameter (mm)
JF1200-1-1 1 7.5
1,200
JF1200-2-1 2 12.5
JF1800-3-1 3 17.5
1
JF1800-4-1 4 22.5
150 - 225 1,800
JF1800-5-1 5 27.5
JF1800-6-1 6 32.5
JF2400-6-2 6 35.0
JF2400-7-2 7 40.0
JF2400-8-2 8 2 45.0 2,400
JF2400-9-2 9 50.0
JF2400-10-2 10 55.0
JF3000-11-3 11 62.5
3
JF3000-12-3 12 225 - 300 67.5
JF3000-12-4 12 70.0
JF3000-13-4 13 75.0
JF3000-14-4 14 80.0
3,000
JF3000-15-4 15 85.0
4
JF3000-16-4 16 90.0
JF3000-17-4 17 95.0
JF3000-18-4 18 100.0
JF3000-19-4 19 105.0
JF3600-20-5 20 112.5
JF3600-21-5 21 117.5
300 - 375
JF3600-22-5 22 122.5
JF3600-23-5 23 127.5
5 3,600
JF3600-24-5 24 132.5
JF3600-25-5 25 137.5
JF3600-26-5 26 142.5
JF3600-27-5 27 147.5
Note: Designers should allow for upstream and downstream bypass pits and a bypass pipe link in a triangular configuration.
8 JellyFish® filter
Page 24
Design information For the standard above-deck inlet pipe configuration, the
JellyFish® filter
invert elevation of the inlet pipe is typically set 150 mm
The JellyFish® filter has many flexible design features to higher than the invert elevation of the outlet pipe. This
accommodate a wide range of specific site requirements generally ensures that the inlet pipe will drain completely
and constraints. Please contact Humes for assistance. after each rainfall/runoff event, while providing sufficient
volume within the MAW zone for surface accumulation
of floatables below the inlet pipe. The elevation of the
Configurations and design capacities inlet pipe can be varied as required.
Design flow capacities and pollutant capacities for The JellyFish® filter can accommodate a range of angles
standard JellyFish® filter configurations are shown in between the inlet and outlet pipes. Standard outlet pipe
Table 2 on the previous page. locations are within a 180° span about the circumference
of the structure (refer to Figure 4 below). The separation
The JellyFish® filter standard model numbers provide angle relationship of the inlet pipe to the outlet pipe can
information about the manhole inside diameter and vary from 90 to 270 degrees to provide maximum design
cartridge counts for high-flow and draindown cartridges. flexibility. Contact Humes Water Solutions for assistance
For example, the JellyFish® filter model number with angles outside this range. The JellyFish® filter can be
JF1800-5-1 is an 1,800 mm diameter structure with five installed at a range of depths of cover.
high-flow cartridges and one draindown cartridge.
With systems using an external bypass from an upstream Figure 4 – Typical JellyFish® filter outlet locations
diversion structure, the driving head is calculated as the Inlet Outlet
difference in elevation between the top of the diversion
structure weir and the invert of the JellyFish® filter outlet
pipe. A minimum of 455 mm is required to drive the flow
through the JellyFish® filter at the rates shown in Table 2.
Recommended design flows of 5 L/s for high flow
cartridges and 2.5 L/s for draindown cartridges are to
ensure longevity of the filter.
Submerged or backwater conditions may be
accommodated in some circumstances and assistance
from Humes engineers is recommended.
JellyFish® filter 9
Page 25
Bypass design The pressure relief pipe height and diameter can be
varied to accommodate the design peak flow rate and
The JellyFish® filter should be designed in an offline system driving head requirements. For these systems, the
configuration for most catchments. All stormwater filter driving head is calculated as the difference in elevation
systems will perform for a longer duration between between the top of the pressure relief pipe and the invert
required maintenance services when designed and of the outlet pipe.
applied in offline configurations. A standard offline
configuration has an external bypass that uses an When the internal bypass option is utilised, peak flow
upstream diversion structure. The elevation difference rates receive membrane filtration treatment up to the
between the top of the diversion structure and the filtration design flow rate, with the balance of the peak
JellyFish® filter outlet pipe invert should be a minimum flow receiving primary treatment only. In these cases,
of 455 mm. Excess flow that overflows the diversion, increased sump depth may be required to increase
bypasses the JellyFish® filter. sediment storage capacity and to minimise re-suspension
of previously captured sediment at peak flow rates.
For smaller sites an online configuration might be more
suitable. In these cases, an optional internal bypass It is also possible to configure this arrangement using
pressure relief pipe(s) can be placed in one or multiple a single diversion structure. Contact Humes for design
cartridge receptacles within the JellyFish® filter. assistance.
Figure 5 – JellyFish® filter offline arrangement without diversion weir
JellyFish® unit outlet I.L.
Diversion chamber inlet is 455 mm lower than
I.L. of high flow bypass
inlet
w Low flo
flo w
w
Lo
High flow bypass
Bypass inlet I.L. is 455 mm
higher than JellyFish®
unit outlet
Diversion chamber JellyFish® unit Collection chamber
Figure 6 – JellyFish® filter offline arrangement with diversion weir
JellyFish® unit outlet I.L.
is 455 mm lower than
Diversion chamber inlet top of weir wall
w Low flo
flo w
w
Lo
High flow bypass
Top of weir is 455 mm
higher than I.L. of
JellyFish® unit outlet
Diversion chamber JellyFish® unit Collection chamber
10 JellyFish® filter
Page 26
Dry sump option Figure 7 – JellyFish® filter with dry sump option
JellyFish® filter
Concrete cover Manholes
The JellyFish® filter can be supplied with a dry sump
Upper converter
option by way of filtering the water collected in the sump surround ring
Lower converter
during a rainfall event to an offline soak well. surround ring
The captured water is filtered with a horizontally Treatment
Maintenance access wall chamber
positioned filter “tentacle” that is covered by a stainless
Outlet pipe
steel shroud for protection at the sump invert. Inlet pipe
Drainage channel
The release of filtered water is controlled by using an
orifice plate at the exit of the sump, which is sized Separator skirt
to match the required hydraulic conductivity of the
Filter cartridges
receiving media.
Base unit
Submerged (tidal/tailwater) installations
The JellyFish® filter is designed to function optimally Perforated shroud
under free-surface flow conditions. For systems that Filter medium
may experience submerged or backwater conditions due
One-way valve
to tailwater or tidal effects, driving head calculations Stop valve
must account for water elevation during the backwater
uPVC pipe
condition. The JellyFish® filter treatment functions
will continue to operate despite backwater conditions.
A customised design will be required to ensure litter
capture and backwash. Contact Humes for assistance.
Series JellyFish® filter
For sites with water quality treatment flow rates that
exceed the design flow rate of the largest standard
JellyFish® filter model, custom systems can be
designed that hydraulically connect multiple JellyFish®
filters in series. For any design questions or custom
modifications, contact Humes for assistance.
JellyFish® filter 11
Page 27
System installation
The JellyFish® filter can be installed by a civil or plumbing
contractor in much the same way as a manhole or other
stormwater drainage structures. It is supplied on-site in
separate, easily identifiable components. The upstream
bypass pit and downstream junction pit are supplied
separately.
The JellyFish® filter is lowered into place by a crane
or excavator, the components interlock and a tapered
surround can be supplied and rotated to match a surface
grade (if the system is located in road pavement). The
system arrives to the site with the internal parts fully
assembled.
Inspection and maintenance
For inspection and maintenance information, please refer
to the JellyFish® filter owner’s manual.
An animation of the JellyFish® filter operation and
maintenance is available at humes.com.au.
Replacement parts
Replacement parts for the JellyFish® filter can be
ordered by contacting Humes at ph 1300 361 601 or
[email protected] JellyFish® filter
Page 28
Appendices
JellyFish® filter
JellyFish® filter standard drawings
JellyFish® filter inspection and maintenance log
JellyFish® filter 13
Page 29
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Page 34
Humes
Holcim (Australia) Pty Ltd
ABN 87 099 732 297
18 Little Cribb st
Milton QLD 4064
Australia
Phone (07) 3364 2800
Fax (07) 3364 2835
JellyFish® filter inspection and maintenance log www.humes.com.au
Owner:
Location: GPS coordinates:
JellyFish® filter model number:
Land use: Commercial: Industrial: Service station:
Road/highway: Mine site: Residential: Other:
Date:
Time:
Inspector
Maintenance contractor
Visible oil present (Y/N)
Oil quantity removed (Y/N)
Floatable debris present (Y/N)
Floatable debris removed (Y/N)
Water depth in backwash pool
Draindown cartridges backflushed (Y/N)
Draindown cartridges externally rinsed
and re-commissioned (Y/N)
New tentacles put on draindown
cartridges (Y/N)
High-flow cartridges backflushed (Y/N)
High-flow cartridges externally rinsed
and re-commissioned (Y/N)
New tentacles put on high-flow
cartridges (Y/N)
Sediment depth
Sediment removed (Y/N)
Cartridge lids intact (Y/N)
Other comments:
(Deck condition, blockages of upstream/
downstream junctions, skirts, etc..)
Page 35
Precast solutions
Top: Stormwater
StormTrap® system
Stormwater treatment
Middle:
RainVault® system Primary treatment
HumeGard® Gross Pollutant Trap
Bottom:
Segmental shaft Secondary treatment
HumeCeptor® hydrodynamic separator
Tertiary treatment
JellyFish® filter
Detention and infiltration
StormTrap® system
Soakwells
Harvesting and reuse
RainVault® system
ReserVault® system
RainVault® Mini system
Precast concrete cubes
Segmental shafts
Stormwater drainage
Steel reinforced concrete pipes – trench
Steel reinforced concrete pipes – salt water cover
Steel reinforced concrete pipes – jacking
Corrugated Metal Pipe (CMP)
Box culverts
Uniculvert® modules
Headwalls
Stormwater pits
Access chambers/Manholes
Kerb inlet systems
Floodgates
Geosynthetics
Sewage transfer and storage
Bridge and platform
Tunnel and shaft
Walling
Potable water supply
Irrigation and rural
Traffic management
Cable and power management
Rail
20 JellyFish® filter
Page 36
Contact information
National sales 1300 361 601
humes.com.au
[email protected]
Head Office New South Wales Tasmania
18 Little Cribb St Grafton Launceston
Milton 4064 QLD Ph: (02) 6644 7666 Ph: (03) 6335 6300
Ph: (07) 3364 2800 Fax: (02) 6644 7313 Fax: (03) 6335 6330
Fax: (07) 3364 2963
Newcastle
Ph: (02) 4032 6800 South Australia
Queensland Fax: (02) 4032 6822
Adelaide
Ipswich/Brisbane Sydney Ph: (08) 8168 4544
Ph: (07) 3814 9000 Ph: (02) 9832 5555 Fax: (08) 8168 4549
Fax: (07) 3814 9014 Fax: (02) 9625 5200
Rockhampton Tamworth Western Australia
Ph: (07) 4924 7900 Ph: (02) 6763 7300
Fax: (07) 4924 7901 Fax: (02) 6763 7301 Gnangara
Ph: (08) 9302 8000
Townsville Fax: (08) 9309 1625
Ph: (07) 4758 6000 Victoria
Fax: (07) 4758 6001 Perth
Echuca Ph: (08) 9351 6999
Ph: (03) 5480 2371 Fax: (08) 9351 6977
Fax: (03) 5482 3090
Melbourne Northern Territory
Ph: (03) 9360 3888
Fax: (03) 9360 3887 Darwin
Ph: (08) 8984 1600
Fax: (08) 8984 1614
Page 37
National sales 1300 361 601 humes.com.au [email protected] A Division of Holcim Australia This brochure supersedes all previous literature on this subject. As the specifications and details contained in this publication may change please check with Humes Customer Service for confirmation of current issue. This document is provided for information only. Users are advised to make their own determination as to the suitability of this information or any Humes product for their own specific circumstances. We accept no responsibility for any loss or damage resulting from any person acting on this information. Humes is a registered business name of Holcim (Australia) Pty Ltd. The JellyFish filter is manufactured, marketed, and sold by Humes under licence from Imbrium Systems Inc, Canada. JellyFish is a registered trademark of Imbrium Systems Inc, Canada and is used in Australia under license. Humes is a registered trademark of Holcim Australia Pty Limited. JellyFish is registered under Australian Patent No. 2008286748, US Patent Application No. 11/839,303 and 12/014,888 and International Publication No. WO 2009/023832 A1. © April 2015 Holcim (Australia) Pty Ltd ABN 87 099 732 297. All rights reserved. This guide or any part of it may not be reproduced without prior written consent of Holcim.
Page 38
Page | 14 [OCR of page images] Appendix B: Published Papers Page | 14
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Australian Journal of Water Research
ISSN: 2203-9482 (Print) ISSN: 2203-9490 (Online)
Evaluation of Treatment Performance of a Stormwater
Treatment Membrane Filter under Australian Conditions
Dr Charles Kelly (Corresponding author)
Commercial Manager – Humes Water Solutions
18 Little Cribb Street, Milton 4064, Australia
Tel: 61-7-3364 2957 E-mail: [email protected]
Anton Bardak
Water Solutions Engineer – Humes Water Solutions
18 Little Cribb Street, Milton 4064, Australia
Tel: 61-7-3364 2956 E-mail: [email protected]
The research was conducted by Queensland University of Technology and is financed by Holcim
Australia
Abstract
Stormwater runoff transports a range of pollutants and is a significant non-point source of urban water
pollution. Effective removal of stormwater pollutants requires treatment technologies that are
appropriate to site constraints, as well as rainfall and pollutant load characteristics. A range of
engineered stormwater treatment systems are marketed by various commercial vendors. Assessing
their performance requires quantification of inputs and outputs to receiving environments under diverse
event durations, flows, and loads, Herr and Sansalone (2015).
In line with these recommendations the treatment performance of a device has been assessed with a
field monitoring program centered on a device installed at Ipswich in South East Queensland. The
findings to date of the field monitoring are as follows:
• For the seven qualifying events monitored, median concentration reduction efficiencies for
TSS, TN and TP are 89%, 55% and 65%, respectively.
• Comparison of monitored rainfall events suggests that these are within the envelope of typical
rainfall events for Brisbane and are equivalent to events monitored in Florida.
• Monitored stormwater quality in terms of TSS, TN and TP are well within the expected
stormwater treatment quality for the Brisbane region.
• Performance characteristics observed in this field monitoring study are comparable with the
performance reported in the previous Florida study.
Keywords: Performance comparison; Performance evaluation; Stormwater quality; Stormwater
treatment
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1. Introduction
Stormwater runoff transports a range of pollutants and is a significant non-point source of urban water
pollution. Effective removal of stormwater pollutants requires treatment technologies that are
appropriate to site constraints, as well as rainfall and pollutant load characteristics. A range of
engineered stormwater treatment systems are marketed by various commercial vendors. Assessing
their performance requires quantification of inputs and outputs to receiving environments under diverse
event durations, flows, and loads, Herr and Sansalone (2015).
The Jellyfish® Filter is an engineered stormwater quality treatment technology featuring membrane
filtration in a compact stand-alone treatment system that is designed to remove a wide variety of
stormwater pollutants. The Jellyfish® Filter integrates pre-treatment and filtration with a passive self-
cleaning mechanism. The system utilizes membrane filtration cartridges with high filtration surface
area and flow capacity and designed to operate under relatively low driving head compared to
conventional filter systems. Performance of the Jellyfish® Filter as a stormwater treatment device had
been tested and deemed appropriate for the US market (UoF, 2011).
Extensive field testing of the Jellyfish® Filter treatment device had been undertaken in the US by
University of Florida. However, further performance testing under Australian climatic conditions was
considered essential for the introduction of the Jellyfish® Filter to the local market. Accordingly,
Queensland University of Technology (QUT) was requested by Humes Australia to undertake a
comprehensive field based monitoring study to verify the performance of the treatment device under
South East Queensland (SEQ) climatic conditions.
A two phase approach was adopted by QUT to assess the treatment performance of the Jellyfish® Filter.
The first phase consisted of assessing the compatibility of performance characteristics reported in UoF
(2011) to South East Queensland (SEQ) climatic conditions using advanced statistical approaches. The
second phase consisted of evaluating the treatment performance under SEQ climatic condition by
undertaking a field monitoring program centered on a device installed at Ipswich, South East
Queensland.
Phase 1 of the study has been completed and reported in August 2014 (Egodawatta et al. 2014). This
paper follows on from Phase 1 and presents the findings to date of the field monitoring program
currently being undertaken as Phase 2.
2. Monitoring System
2.1 Site Description
The site is a recently developed commercial facility located at 292 Brisbane Street, West Ipswich 4305,
QLD. The site has a total area of 1678m2 with approximately 550m2 of roof area and 1128m2 of
impervious driveways and parking lots. Figure 1 shows the layout of the development, including the
stormwater drainage network.
(a) (b)
Figure 1. Study Site: (a) Aerial View; (b) Drainage Network
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The stormwater drainage at the site is handled by a piped, subsurface network. Driveway runoff from
the site enters the drainage network via grated field inlet pits while downpipes from the roof are
directed to rainwater tanks, and the overflow pipe to the closest field inlet. The treatment device is
fitted as a part of the drainage network after the last grated manhole within the site as shown in Figure
2.
Figure 2. Monitoring System Layout
2.2 Instrumentation and Sampling Methodology
Automatic sampling stations are installed at two sampling points as shown in Figure 2. The upstream
sampling point is equipped with a flow measuring device and an automatic sample collection system
while the downstream sampling point is equipped with an automatic sample collection system. A
tipping bucket rain gauge is installed as part of the field monitoring system. A schematic of the field
monitoring system is shown in Figure 3.
Figure 3. Schematic of the Two Sampling Points
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The flow measuring device installed at the upstream sampling point consists of a high accuracy Control
Logic LMP307 pressure transducer with accuracy of 0.1% and a pipe insert Palmer-Bowlus flume (8
inch). The pressure transducer is mounted on the wall of the field inlet pit just upstream of the flume so
that depth measurements can be transformed to flow using the rating table associated with the flume.
The flume device and the pressure transducer operation were calibrated over a range of flow rates prior
to the commencement of the monitoring program.
Two ISCO 6712 automatic samplers with capacity to hold 24 samples are installed to collect runoff
samples from upstream and downstream sampling points as shown in Figure 3. The samplers are
housed in weatherproof security cabinets close to each sampling point. The suction hoses are kept as
short as possible to reduce head loss. Sampling stations are equipped with data loggers, battery and
solar chargers to ensure independent operation. The RIMCO RIM7499 tipping bucket rain gauge
installed to measure rainfall has a resolution of 0.2mm. The rain gauge is mounted on a three meter
pole as shown in Figure 3 to reduce the impact of the rain shadow created by any neighboring
buildings. A Bureau of Meteorology operated rain gauge is situated approximately 600m away from the
study site.
2.3 Sample Handling and Testing
A carefully formulated methodology was adopted for the selection of appropriate rainfall events for
evaluation, runoff sample collection and sample handling and testing. Only runoff samples originating
from rainfall events with a minimum of three antecedent dry days and more than 2.6mm rainfall depth
were tested. These rainfall events were classified as qualifying events. This was to ensure that there are
representative contributions from both driveways/parking lots and roof runoff as well as to ensure that
there was appreciable accumulation of pollutants on the impervious surfaces. The automatic samplers
were programmed to collect discrete samples.
The samplers were programmed to trigger based on the occurrence of rainfall. Sampling intervals were
programmed to vary based on the intensity of the rainfall received. Runoff samples collected from a
qualifying rainfall event are combined to form a composite sample (Event Mean Concentration –
EMC). Representative aliquots of samples were extracted using a churn sample splitter (Bel-Art
Products) and submitted to a NATA registered laboratory (Advanced Analytical Laboratory) for
analysis. The parameters tested and the test methods adopted are listed in Table 1. Sample collection,
handling and transport were undertaken in accordance with AS/NZS 5667.1:1998.
Table 1. List of Parameters and Test Methods
Parameter Method
pH APHA (2012) Method 4500
Electrical conductivity APHA (2012) Method 2510B
Total metals – Fe , Al, Mn , Cu, Cr, US EPA 200.8/3050/6010B by ICPMS US
Pb, Ni, Zn, Cd EPA (1994), US EPA (1996)
APHA (2012) Method 4500 by discrete
Total nitrogen, NO3, TKN
analyser with persulphate digestion
Total phosphorus, FRP APHA (2012) Method 4500 by discrete
(orthophosphate P) analyser with persulphate digestion
Total suspended solids (103oC) APHA (2012) Method 2540 D
APHA (2012) Method 5310 by TOC
Total organic carbon
analyser
Total petroleum hydrocarbon (C10-
NEPM 2011 (draft)
C40), silica gel
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3. Results and Discussion
The outcomes from the evaluation of seven qualifying rainfall events that occurred over a 5 month
period from 28th June 2014 to 7th November 2014 are presented in this paper. Rainfall and runoff data
for the monitored events are presented in Table 2.
Table 2. Characteristics of Monitored Events
Date Sampled Rainfall Depth Rainfall Duration Peak Intensity Number of
(mm) (minutes) (mm/hr) Samples
collected
28/06/2014 5.2 19 57 3
16/08/2014 33 1222 27.4 24
27/08/2014 7.2 128 13.7 14
25/09/2014 8.6 214 18.9 4
13/10/2014 7 597 15.4 13
27/10/2014 3 36 10.3 7
3.1 Parameter Concentration Values
The laboratory test results for the monitored events are given in Table 3 and Table 4.
Table 3. Water Quality Parameters: Solids, Nutrients and Organic Carbon
Date
Sampling pH EC TSS Nitrate TKN TN Phosphate TP TOC
sampled
Point Units pH µS/cm mg/L mg/L mg/L mg/L mg/L mg/L mg/L
PQL 1.0 1 2 0.01 0.1 0.1 0.01 0.02 1.0
Inflow 6.4 40 180 0.19 1.1 1.3 <0.01 0.24 5
28/06/2014
Outflow 6.8 120 13 0.11 1.2 1.3 0.03 0.24 5
Inflow 7.3 70 170 0.33 2.4 2.8 0.18 0.41 9
16/08/2014
Outflow 7.1 50 2 0.28 0.2 0.5 0.02 0.03 4
Inflow 7 120 19 0.42 0.6 1.1 0.06 0.093 4
27/08/2014
Outflow 6.9 70 2 0.17 0.3 0.5 0.02 0.05 2
Inflow 6.9 150 26 1.9 1.7 3.1 0.02 0.17 9
25/09/2014
Outflow 6.6 55 4 0.28 0.8 1.1 <0.01 0.06 6
Inflow 7 125 25 0.79 1 1.9 0.05 0.17 7
13/10/2014
Outflow 7 140 4 0.31 1.5 1.9 0.03 0.12 6
Inflow 6.7 145 51 0.73 5.2 6 0.27 0.53 32
27/10/2014
Outflow 6.9 130 10 0.82 1.5 2.3 0.07 0.17 18
Inflow 6.8 105 74 0.58 2.6 3.2 0.36 0.31 11
06/11/2014
Outflow 6.8 80 8 0.61 1 1.6 0.06 0.08 8
Note: PQL – Practical Quantification Limit
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Table 4. Water Quality Parameters: Total Petroleum Hydrocarbons and Metals
Date TPH TPH TPH TPH
Sampling Al Cd Cr Cu Fe Mg Ni Pb Zn
sampled C6-C9 C10-14 C15-28 C29-36
Point Units µg/L µg/L µg/L µg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
PQL 25 50 100 100 0.05 0.001 0.002 0.002 0.05 0.005 0.003 0.006 0.005
Inflow <25 <50 <100 <100 3 <0.001 0.012 0.039 3.8 0.12 0.005 0.01 0.3
Outflow 28/06/2014 <25 150 200 <100 0.2 <0.001 0.002 0.015 1.2 0.13 <0.003 <0.006 0.041
Inflow <25 <50 <100 <100 4 <0.001 0.018 0.069 4.6 0.16 0.012 0.012 0.75
Outflow 16/08/2014 <25 <50 <100 <100 0.05 <0.001 0.006 0.012 <0.05 0.012 <0.003 <0.006 0.017
Inflow <25 <50 <100 <100 0.74 <0.001 0.004 0.01 0.68 0.023 <0.003 <0.006 0.11
Outflow 27/08/2014 <25 <50 <100 <100 0.08 <0.001 <0.002 0.005 0.17 0.023 <0.003 <0.006 0.024
Inflow <25 <50 140 <100 0.94 <0.001 0.008 0.026 0.96 0.034 <0.003 <0.006 0.18
Outflow 25/09/2014 <25 <50 120 <100 0.08 <0.001 0.007 0.01 0.11 0.013 <0.003 <0.006 0.027
Inflow <25 <50 160 110 0.75 <0.001 0.004 0.016 0.86 0.037 <0.003 <0.006 0.15
Outflow 13/10/2014 <25 <50 180 110 0.093 <0.001 0.002 0.005 0.95 0.099 <0.003 <0.006 0.032
Inflow <25 100 570 240 1.8 <0.001 0.009 0.038 2.1 0.13 0.005 0.01 0.43
Outflow 27/10/2014 <25 98 450 200 0.19 <0.001 0.002 0.012 1.1 0.087 <0.003 <0.006 0.06
Inflow <25 <50 160 <100 1.9 <0.001 0.007 0.025 2.3 0.11 0.004 0.007 0.29
Outflow 06/11/2014 <25 <50 160 <100 0.19 <0.001 <0.002 0.007 0.34 0.032 <0.003 <0.006 0.035
Note: PQL – Practical Quantification Limit
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Table 4. Water Quality Parameters: Total Petroleum Hydrocarbons and Metals
Date TPH | TPH | TPH | TPH ,
Sampling | sampled c6-c9 | c10-14 | c15-28 | c29-36 | “I Cd Cr Cu Fe Mg Ni Pb Zn
Point Units pg/L pg/L pg/L pg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
PQL 5 50 100 100 0.05 | 0.001 | 0.002 | 0.002 | 0.05 | 0.005 | 0.003 | 0.006 | 0.005
Inflow <25 <50 <100 | <100 3 <0.001 | 0.012 | 0.039 3.8 0.12 | 0.005 | 0.01 03
Outflow | 28/06/2014 |~~Z55 150 200 <100 02 | <0.001 | 0.002 | 0.015 12 0.13 | <0.003 | <0.006 | 0.041
Inflow <25 <50 <100 | <100 4 <0.001 | 0.018 | 0.069 4.6 0.16 | 0.012 | 0.012 | 0.75
Outflow | 16/08/2014 [~~ 255 <50 <100 | <100 | 0.05 | <0.001 | 0.006 | 0.012 | <0.05 | 0.012 | <0.003 | <0.006 | 0.017
Inflow <25 <50 <i00 | <100 | 0.74 | <0.001 | 0.004 | 0.01 0.68 | 0.023 | <0.003 | <0.006 | 0.11
Outflow | 27/08/2014 |~Z55 <50 <i00 | <100 | 0.08 | <0.001 | <0.002 | 0.005 | 0.17 | 0.023 | <0.003 | <0.006 | 0.024
Inflow <25 <50 140 <i00 | 0.94 | <0.001 | 0.008 | 0.026 | 0.96 | 0.034 | <0.003 | <0.006 | 0.18
Outflow | 29/09/2014 |~Z55 <50 120 <100 0.08 | <0.001 | 0.007 | 0.01 0.11 0.013 | <0.003 | <0.006 | 0.027
Inflow <25 <50 160 110 0.75 | <0.001 | 0.004 | 0.016 | 0.86 | 0.037 | <0.003 | <0.006 | 0.15
Outflow | 13/10/2014 |~Z55 <50 180 110 0.093 | <0.001 | 0.002 | 0.005 | 0.95 | 0.099 | <0.003 | <0.006 | 0.032
Inflow <25 100 570 240 18 | <0.001 | 0.009 | 0.038 21 0.13 | 0.005 | 0.01 0.43
Outflow | 27/10/2014 | 255 98 450 200 0.19 | <0.001 | 0.002 | 0.012 11 0.087 | <0.003 | <0.006 | 0.06
Inflow <25 <50 160 <100 19 | <0.001 | 0.007 | 0.025 23 0.11 | 0.004 | 0.007 | 0.29
Outflow | 99/11/2014 [~~ 255 <50 160 <100 | 0.19 | <0.001 | <0.002 | 0.007 | 0.34 | 0.032 | <0.003 | <0.006 | 0.035
Note: PQL — Practical Quantification Limit
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3.2 Treatment Performance
Measured concentrations representing inflows to the treatment device and outflows from the device
was used for determining the treatment performance. Treatment performance was evaluated for TSS,
TN and TP as these are the most common stormwater quality parameters. The estimated treatment
performance is shown in Table 5.
Table 5. Concentration Reduction Efficiency and Efficiency Ratio
Date TSS (mg/L) TN (mg/L) TP (mg/L)
sampled
Inflow Outflow CRE Inflow Outflow CRE Inflow Outflow CRE
28/06/2014 180 13 92.8 1.3 1.3 0.0 0.24 0.24 0.0
16/08/2014 170 2 98.8 2.8 0.5 82.1 0.41 0.03 92.7
27/08/2014 19 2 89.5 1.1 0.5 54.5 0.093 0.05 46.2
25/09/2014 26 4 84.6 3.1 1.1 64.5 0.17 0.06 64.7
13/10/2014 25 4 84.0 1.9 1.9 0.0 0.17 0.12 29.4
27/10/2014 51 10 80.4 6 2.3 61.7 0.53 0.17 67.9
06/11/2014 74 8 89.2 3.2 1.6 50.0 0.31 0.08 74.2
Average 78 6 2.8 1.3 0.27 0.11
Average CRE 88.5 44.7 53.6
Median CRE 89.2 54.5 64.7
Note:
1. CRE – Concentration reduction efficiency. CRE is the percentage reduction in concentration
with respect to inflow concentration for individual events.
2. Concentrations less than Practical Quantification Limit (PQL) was replaced by 50% of
detection limit for performance calculations.
It is commonly known that the average of a small sample set can be far from the true mean of a
population. In this respect, median values could be a true representative of performance indicators. This
is particularly true when a data set is log-normally distributed rather than normally distributed. As
evident in Table 5, median of the percentage concentration reduction efficiencies for TSS, TN and TP
are 89.2%, 54.5% and 64.7%, respectively. Average values (mean) for the same parameters are less
than 10% difference from the median except for TP. This indicates consistency in treatment
performance of the Jellyfish® Filter for all the events investigated.
4. Comparison of Treatment Performance with Previous Studies
Demonstrating the validity of the data obtained and estimated performance is critical in field
monitoring. Accordingly, the data obtained and estimated performance was compared with treatment
performance reported in previous studies.
4.1 Comparison of Rainfall Data
It was considered important to demonstrate that the range of rainfall events monitored is within the
range of rainfall typical to local conditions. At the same time, it was also important to compare the
events monitored with the events monitored by the UoF (2011) study. Accordingly, the events
monitored in this study are plotted with typical Brisbane events from 2004 (selected as a representative
year) and events monitored in UoF (2011) study as shown in Figure 4. Phase 1 of the study undertaken
confirmed that 2004 represents a rainfall year with average annual rainfall depth and hence can be
considered as a representative year (Egodawatta et al. 2014). As evident in Figure 4, events monitored
in this study are well within the envelope created by the other two sets of rainfall data, thus confirming
the representativeness of the monitored events in this study.
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Figure 4. Comparison of Rainfall Events
4.2 Comparison of Inflow Water Quality Data
Additionally, the stormwater inflow concentrations were evaluated with respect to typical values from
urban areas in SEQ and Florida as shown in Figure 5 and Figure 6. Typical inflow stormwater quality
was considered equivalent to MUSIC concentration parameter values for the Brisbane region. Two
monitoring programs undertaken by QUT in the SEQ region are also included in the comparison.
Inflow concentrations for Florida were obtained from UoF (2011).
MUSIC parameters are typically presented as mean and standard deviation of a long-term data set.
Since the requirement of this comparison was to justify the fact that measured data are within those
statistical limits, mean and 1.96 times the standard deviation is presented as positive and negative error
bars in Figures 5 and 6. It is typically considered that 1.96 times the standard deviation as positive and
negative error bars represent 95% of the data ranges of the assessed population. Means and 95% error
bars are presented for urban residential, industrial, commercial and rural residential areas for
comparison. Similar statistical measures of Florida and Ipswich water quality data are presented for
direct comparison. Original Florida and Ipswich water quality data are also presented as scattered data
sets (x axis is not relevant) in Figure 5 and Figure 6.
As evident from Figure 5 and Figure 6, stormwater quality at the monitored site is well within the
envelope of expected Brisbane water quality characteristics. Particularly, TP concentrations are within
the typical concentrations for Brisbane, though they show significant differences with the Florida data.
It is hypothesised that specific soil characteristics and landscape management practices would have
influenced phosphorus levels reported in UoF (2011) study.
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3.5
3
2.5
Log 10 TSS (mg/L)
2
1.5
1
Brisbane MUSIC Parameters
Florida Data
0.5 Florida Statistics
Ipswich Data
Ipswich Statistics
0 0 1 2 3 4 5 6
Industrial
Urban
Residential
Commercial
Ipswich
Statistics
Florida
Statistics
Rural
Residential
TSS
Figure 5. Comparison of Ipswich Inflow TSS Concentrations with Typical Brisbane and Florida Data
1.5 2.5
Brisbane MUSIC Parameters
1 Florida Data
2 Florida Statistics
0.5 Ipswich Data
Ipswich Statistics
1.5
0 0 1 2 3 4 5 6
Log 10 TN (mg/L)
Log 10 TP (mg/L)
-0.5 1
-1
0.5
-1.5 Brisbane MUSIC Parameters
Florida Data
Florida Statistics 0 0 1 2 3 4 5 6
-2
Ipswich Data
Ipswich Statictics
-2.5 -0.5
Urban
Urban
Residential
Residential
Commercial
Industrial
Industrial
Rural
Residential
Ipswich
Statistics
Commercial
Rural
Residential
Ipswich
Statistics
Florida
Florida
Statistics
Statistics
TP TN
Figure 6. Comparison of the Ipswich Inflow TP and TN Concentrations with Brisbane and Florida Data
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4.3 Comparison of Treatment Performance
Event based performance of a treatment device can be influenced by a range of factors including inflow
concentration, flow rate and the characteristics of the events that the device has treated. This paper
compares the field performance of the Jellyfish® Filter under Southeast Queensland climatic
conditions with the performance shown in the Florida study. As the study is still ongoing, it is expected
that further detailed analysis of treatment performance will be undertaken in the future.
Table 6. Comparison of Pollutant Removal Performance
Median Removal Efficiency (%)
Monitoring Program TSS TN TP
Florida 89 51 59
Ipswich (SEQ) 89 55 65
Table 6 presents the median pollutant removal efficiencies in relation to TSS, TN and TP. It is evident
that all three removal efficiencies are of a similar order for both monitoring programs. TSS and TN
removal efficiencies are closely comparable and can be primarily attributed to having similar inflow
quality as shown in Figures 5 and 6. TP removal efficiency does not show a significant difference even
though inflow concentrations of phosphorous in Florida are significantly higher compared to the
Ipswich values.
5. Summary
The treatment performance under SEQ climatic conditions are being assessed by undertaking a field
monitoring program centered on a device installed at Ipswich in South East Queensland. The findings
to date of the field monitoring are as follows:
• For the seven qualifying events monitored, median concentration reduction efficiencies for
TSS, TN and TP are 89%, 55% and 65%, respectively.
• Comparison of monitored rainfall events suggests that these are within the envelope of typical
rainfall events for Brisbane and are equivalent to events monitored in Florida where a
comprehensive field monitoring program was undertaken.
• Monitored stormwater quality in terms of TSS, TN and TP are well within the expected
stormwater treatment quality for the Brisbane region.
• Performance characteristics observed in this monitoring study are comparable with the
performance reported in the previous Florida study.
MUSIC nodes have been created for the Jellyfish Filter treatment device based on all of the field test
data, which can be found at the following location.
http://www.humes.com.au/precast-solutions/stormwater/stormwater-treatment/tertiary.html
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6. References
APHA, 2012, Standard methods for the examination of water & wastewater, Washington DC, American
Public Health Association.
AR&R, 1998, Australian rainfall and runoff - A Guide to Flood Estimation, Vol 1, Engineers, Australia,
Barton, ACT.
AS/NZS 1998, Australian New Zealand Standards, Water quality Sampling, Part 1: Guideline of the
Design of Sampling Programmes, Sampling Techniques and the Preservations and Handling of
Samples.
Egodawatta, P. and McGree, J., Wijesiri, B., Goonetilleke, A., 2014, Compatibility of Stormwater
Treatment Performance Data between Different Geographical Areas, Water, Vol 41, No 5
Herr, C. and Sansalone, J. 2015, In Situ Volumetric Filtration Physical Model to Separate Particulate
Matter from Stormwater. J. Environ. Eng., 10.1061/(ASCE)EE.1943-7870.0000946, 04015017.
Published on line 24/02/2015
UoF, 2011, TARP field test performance monitoring of a Jellyfish® Filter JF4-2-1, Engineering School
of Sustainable Infrastructure and Environment (ESSIE), University of Florida, Gainesville, Florida.
US EPA, 1994, Method 200.8: Trace elements in waters and wastes by inductively coupled plasma-
mass spectrometry, U.S. Environmental Protection Agency, Ohio.
US EPA, 1999, Method 3050B: Acid digestion of sediments, sludges, and soils, U.S. Environmental
Protection Agency, Ohio.
Australian Society for Commerce Industry & Engineering
www.scie.org.au
11
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A STATISTICAL APPROACH TO
ASSESS STORMWATER TREATMENT
DEVICE PERFORMANCE DATA
A demonstration of how statistical methods can be
used to gauge whether or not presented data sets
are sufficient to draw reliable conclusions
C Kelly, A Bardak
ABSTRACT There is a range of engineered analysis to be conducted, such as the
stormwater treatment systems to paired t-test, and calculations involving
It has long been established that the pre-
consider. Assessing the performance confidence intervals, and the number of
treatment of stormwater runoff prior to
of hard engineered systems requires paired samples required to achieve a given
reaching its final destination is essential
quantification of inputs and outputs to confidence level. Table 1 displays a data set
for river and catchment health. There is a
receiving environments under a diverse of 10 qualifying events (Kelly and Bardak,
plethora of stormwater runoff treatment
range of event durations, flow rates and 2015), which is used to demonstrate this
devices either available or about to
loads (Herr and Sansalone, 2015). statistical approach for three pollutants:
enter the market. Stormwater Australia
Total Suspended Solids (TSS), Total
has released a consultation draft of its Stormwater Australia has released
Phosphorus (TP) and Total Nitrogen (TN).
Stormwater Quality Improvement Device a consultation draft of its Stormwater
Evaluation Protocol (SQIDEP), which Quality Improvement Device Evaluation The Event Mean Concentration (EMC) is
includes a requirement to undertake Protocol, or SQIDEP (2014), which a flow-weighted concentration parameter
statistical assessment as part of the includes a requirement for statistical derived for each storm. It can be calculated
validation of performance data. assessment to validate the performance as follows (Geosyntec Consultants &
data. There remains an important step Wright Water Engineers, 2009):
This paper uses data from a field- in the further development of SQIDEP,
testing case study of an engineered which is to engage with academia and
treatment technology to demonstrate regulators to reach a consensus on the
how statistical methods can be used to robustness of a statistical approach.
where:
determine whether or not the data sets
being presented are actually sufficient Field-testing data are used in this
paper to illustrate a statistical assessment
to be able to draw reliable conclusions.
process to determine whether or not the
The assessment is conducted for Total
data sets being presented are actually The Concentration Removal Efficiency
Suspended Solids, Total Phosphorus and
sufficient for statistically valid conclusions (CRE) method was used to calculate the
Total Nitrogen data, and shows that the
to be drawn. The paper presents a per cent removal in Kelly and Bardak
required number of paired samples is
worked example of the proposed analysis (2015). It is also used in this paper to
different for each of these parameters
technique using a data set reported in demonstrate the statistical methodology.
and depends on the variability of the
Kelly and Bardak (2015) for a proprietory CRE% was calculated for each event as
data sets. It is necessary to engage
stormwater treatment device (the follows (Geosyntec Consultants & Wright
with academia and regulators to reach
Jellyfish Filter). Details on experimental Water Engineers, 2009):
a consensus on the robustness of a set-up, sampling methods used and
statistical approach, and this paper definitions of qualifying events are
provides an example. reported in Kelly and Bardak (2015).
INTRODUCTION METHOD DETAIL It should be noted that there are
Stormwater runoff transports a range AND RESULTS several recommended methods that can
of pollutants and is a significant non- Initially, potential outliers are identified, be used to calculate the contaminant
point source of urban water pollution. examined and removed (if necessary). removal efficacy, such as CRE, Efficiency
It has long been established that the The data is then tested for normality to Ratio, Mass Reduction Efficiency, etc.
pre-treatment of stormwater runoff identify which statistical methods can Selection of a relevant percentage
prior to reaching its final destination is be used on that set. For cases where removal calculation method is discussed
essential for river and catchment health the raw data is not normally distributed, in SQIDEP (Stormwater Australia, 2014)
(Herr and Sansalone, 2015). Effective but appears to be positively skewed, and Geosyntec Consultants and Wright
treatment requires technologies that are a lognormal transformation may be Water Engineers (2009). The following
appropriate to site constraints, as well as applicable (Singh et al., 1997). Normally statistical assessment approach can be
rainfall and pollutant load characteristics. distributed data allows standard statistical applied to all of these methods.
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Table 1. Influent and effluent data for TSS, TP and TN.
Event TSS EMCi TSS EMCe TSS CRE TP EMCi TP EMCe TN EMCi TN EMCe
TP CRE (%) TN CRE (%)
Number (mg/L) (mg/L) (%) (mg/L) (mg/L) (mg/L) (mg/L)
1 180.0 13.0 93 0.2 0.2 0 1.3 1.3 0
2 170.0 2.0 99 0.4 0.0 93 2.8 0.5 82
3 19.0 2.0 89 0.1 0.1 44 1.1 0.5 55
4 26.0 4.0 85 0.2 0.1 65 3.1 1.1 65
5 25.0 4.0 84 0.2 0.1 29 1.9 1.9 0
6 51.0 10.0 80 0.5 0.2 68 6.0 2.3 62
7 74.0 8.0 89 0.3 0.1 74 3.2 1.6 50
8 21.0 5.0 76 0.5 0.1 80 5.7 2.5 56
9 13.0 1.0 92 0.2 0.1 63 2.0 1.3 35
10 18.0 3.0 83 0.14 0.09 36 1.3 1.0 23
Notes: EMCi: Event mean concentration of influent sample; EMCe: Event mean concentration of effluent sample; CRE: Concentration removal efficiency.
OUTLIER IDENTIFICATION Outlier
An outlier is defined as a data point that Max Observation Below
is distinctly separate from and inconsistent Upper Inner Fence
with the majority of the data points
(Walfish, 2006). Outliers can have negative
Q3
effects on data analyses as they skew and
distort the data set or, conversely, can
provide useful information about abnormal
characteristics of the data (Seo, 2006). Median
Therefore, it is a critical step to identify
Q1
and detect outliers.
A basic approach for identifying outliers Min Observation Above
is outlined in Tukey (1977), and involves Lower Inner Fence
simple numerical and graphical methods Figure 1. Definition of features in a Box
that are used when constructing box plots and Whisker Plot.
of the data (Walfish, 2006). It identifies a As seen in the Box and Whisker Plots in
potential outlier as any data point more Figure 2, a number of influent and effluent
than 1.5 interquartile ranges (IQRs) below
data points fell outside the inner fences,
the first quartile, or above the third
but were within the outer fences (see
quartile; that range is referred to as the
Tukey (1977) for calculation method of the
inner fences. An extreme outlier is a value
inner and outer fences). However, the data
that is outside the outer fences, and can be
between the inner and outer fences all
defined as three interquartile ranges below
fell within referenced parameters, which
the first quartile or above the third quartile
suggest typical concentration occurrences
(Tukey, 1977) and should be thoroughly
in Australia (Duncan, 1999); therefore, there
investigated. The box and whisker plots
is no justifiable reason to exclude any data
graphically display outliers, and examples
points from this data set. The CRE (%) data
can be seen in Figure 2 for the three
was also analysed for outliers, but all CRE
parameters of interest. Figure 1 describes
percentages fell within the inner fences.
the features of the box and whisker plot,
Therefore, all data points have been used.
including all relevant statistical measures.
NORMAL DISTRIBUTION
There is no rigid mathematical
VALIDATION
definition of what constitutes an outlier in
the literature; it is a subjective decision. An Following outlier evaluation, the next
analysis should be conducted to determine step in this approach is to test for data
why the outlier may have occurred, and if a normality using graphical methods
justifiable reason can be identified then the and parametric statistical analysis.
data point may be legitimately excluded The Stormwater Australia protocol calls
(Walfish, 2006). Most importantly, data for log-transformed inlet and outlet-
that have been excluded as outliers must paired samples (Stormwater Australia,
still be reported along with the reasons 2014), however if the baseline data
Figure 2. Box and Whisker Plots for TSS,
for their exclusion, as they still form part is already normally distributed then TP & TN, with outliers labelled with their
of the evaluation process. there is no requirement to conduct a event number.
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Figure 3. Q-Q plots for the Concentration removal efficiency (CRE) % distribution for TSS, TP and TN against Z-Score
(distance from the mean).
Figure 4. Q-Q Plots of log-transformed inlet and outlet concentration paired samples for TSS, TP and TN. The linear plots
demonstrate the data is now normally distributed.
log-transformation on the data. If the “bell” shape if it is normally distributed. Table 2 presents the results of the
baseline data is not normally distributed, It should be noted that skewed data parametric tests on the CRE% removal
a log-transformation may be required, and data with large kurtosis (high and data from Table 1. The test statistic, W,
which will result in some minor variations wide peak), even though it may have is calculated using the following formula:
to the statistical processes, as described the appearance of a “bell” shape, is
in Olsson (2005) regarding confidence not considered normally distributed.
levels, and Singh et al. (1997) regarding The Q-Q plot is the most common and
transformation back to original data. effective method to graphically identify
where:
if data is normally distributed (Razali and
LOG TRANSFORMATION OF DATA
Wah, 2011). Using this method, normally
Lognormal distributions tend to be distributed data will plot as a straight
common in environmental data sets, as The per cent removal data is
line, with minimal variance.
results from contaminant concentration normally distributed as the p-value
data sets tend to follow a skewed Figures 3 and 4 display how the data from the Shapiro-Wilk Test in Table 2
probability distribution. In the case that in Table 1 are distributed. The per cent is greater than 0.05. This numeric
the raw data is not normally distributed, removals of TSS, TP and TN can be seen result supports the graphical analysis
but appears to be positively skewed, to be normally distributed according to of Figure 2. The influent and effluent
then a lognormal transformation may the linear Q-Q plot (Figure 3). The inlet results were also tested for normality
be applicable (Singh et al., 1997). and outlet Q-Q plots in Figure 4 were after being log transformed, and the
The log-transformed data is then run constructed using a log transformation results shown in Table 3.
through parametric tests as per normally of the inlet and outlet data. These results
The p values in Table 3 show that
distributed data, albeit with slight indicate that the percentage removal is
the influent and effluent data for all three
variations (Olsson, 2005; Singh et al., normally distributed, while the inlet and water quality parameters are lognormally
1997). However, it should be noted that, outlet EMCs are log normally distributed. distributed. This is a critical step in
if this log-transformed dataset still fails Visual inspection alone, however, usually preparation for the subsequent paired
the normality test (graphical method and does not provide conclusive evidence t-test, as a valid test requires that data
Shapiro-Wilk Test (1965)), then a lognormal that the data is normally distributed, and be normally distributed (Shapiro and
distribution is not suitable and another a numerical parametric analysis should be Wilk, 1965).
transformation should be adopted conducted (Razali and Wah, 2011).
(Limpert et al., 2001). It should be noted that, while it
PARAMETRIC ANALYSIS is crucial to prove that the influent
GRAPHICAL METHODS TO OF NORMALITY and effluent data are lognormally
IDENTIFY NORMALITY A parametric statistical analysis involves distributed in preparation for the
Graphical methods involve plotting data using numerical statistical methods to paired t-test, it is also important to
frequency on a histogram, a box-plot, a test data against the null hypothesis prove that the percentage removal
stem-and-leaf plot, or a quantile-quantile that the data is normally distributed. rates are also normally distributed.
plot (Q-Q plot), then visually inspecting There are many such statistical methods This is critical, as the confidence interval
it in order to conclude if the data is available, however the Shapiro-Wilk is constructed around the percentage
normally distributed. For histogram Test (1965) is considered to be the removal, which requires the data to
plots, the data will display a uniform most powerful (Razali and Wah, 2011). be normally distributed.
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Table 2. Results of the Shapiro-Wilk Table 3. Results of the Shapiro-Wilk Parametric Test for log-transformed influent
Parametric Test for % removal to test and effluent data.
for normality of the data distribution.
TSS TP TN
%
% TP % TN Influent Effluent Influent Effluent Influent Effluent
TSS
0.039 0.511 0.449 7.012 5.574 2.893 3.215 3.208 2.589
0.040 0.551 0.516 8.054 5.724 3.032 3.234 2.979 2.835
W (test statistic) 0.982 0.928 0.872 W (test statistic) 0.871 0.974 0.954 0.994 0.929 0.913
p-value (>0.05) 0.974 0.441 0.116 p-value (>0.05) 0.111 0.708 0.693 0.996 0.448 0.357
NON-PARAMETRIC TESTING 3. Calculate the mean of the differences CONFIDENCE INTERVAL
If there are no transformations that can ( ) After the data has been proven to be
be utilised to transform the raw data 4. Calculate the standard deviation of the statistically significant, a 95% confidence
into a normal distribution, then non- differences ( ) interval can be constructed. The 95%
parametric tests need to be used to confidence interval is defined in Neyman
test for differences in populations. An 5. Calculate the Standard Error (SE) of the
(1937) as follows:
example is the Wilcoxon Signed-Rank Test mean difference:
instead of the paired t-test (McDonald, “There is a 95% probability that
2014). Confidence intervals can also be the calculated confidence interval
calculated non-parametrically, as described encompasses the true value of the
in Geyer (2003), or utilising methods such population mean.”
6. Calculate the t-statistic, using a
as bootstrapping (Wang, 2001). However, t-distribution with n – 1 degrees of This involves calculating the standard
these methods can sometimes be complex freedom: error, then adding/subtracting the
in nature and require specialised statistical standard error to/from the mean to create
software to conduct statistical modelling. the upper and lower bounds. There is a
A less mathematically intensive method 7. t-distribution tables can be referenced
95% probability that the true population
would be to increase the sample size to to find the corresponding p-value mean exists within these bounds (Tan
larger than 30, as the Central Limit Theorem (probability) with the t-statistic (T) and Tan, 2010; Neyman, 1937).
states that, as sample size increases, the and n – 1 degrees of freedom.
distribution of sample means approaches
a normal distribution, regardless of initial For a 95% confidence level, if the p-value
distribution (Bonacci, 2012). is less than 0.05, the null hypothesis (there where:
is no difference between the sample set
STATISTICAL SIGNIFICANCE – means) is rejected, and the data sets are
PAIRED T-TEST considered statistically different.
The paired t-test is used to assess the
The paired t-test was conducted on the
statistical difference (or significance)
log transform of the influent and effluent
between two normally distributed data
test data, and results are presented in For the 95% confidence level, the Z
sets. In this case, it is used to compare the
Table 4. The paired t-test results for TSS, score equates to 1.96 for a two-tailed test.
influent and effluent data sets to establish
TP and TN are significantly beyond the 95%
if their population means are statistically In this case, as the CRE percentage
confidence level (p<0.05), therefore we can
different. If it can be confirmed that the defines the removal efficiency of the
reject the null hypothesis and conclude that
two sets of data do not have the same stormwater treatment device, confidence
the influent and effluent concentration data
population mean, then it can be concluded intervals were constructed around the
sets are statistically different.
that there is a statistical significance
between them (Cramér, 1946). Table 4. Paired t-test results for the field test data reported in Table 1. The
log-transformed influent and effluent concentrations are being compared for
The following steps describe the statistical difference.
methodology used in conducting a paired
H0 = there is no difference between the
t-test (Shier, 2004):
concentration means of the influent & TSS TP TN
1. Construct a null hypothesis, i.e. effluent populations
that there is no statistical difference
Mean of Differences (µd) Loge(mg/L) 2.267 1.011 0.672
between the population means.
Std. dev. of Differences(σd) 0.858 0.746 0.525
2. Calculate the difference for each pair.
This is done by subtracting the effluent Std Error (SE) 0.271 0.746 0.166
concentration from the influent
T-Statistic 8.357 4.288 4.042
concentration for each event (i.e.
for each Degrees of Freedom (n-1) 9 9 9
event pair of data, where i varies from
Paired t-test Result (Must be < 0.05) 1.6x10 -5
0.002 0.0029
1 to n).
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mean CRE percentage for each pollutant,
Table 5. Confidence Levels for TSS, TP Table 6. Minimum number of paired
rather than the influent and effluent and TN about CRE % Removal. samples required to establish CRE%
concentration data. This is due to the at the 95% Confidence Level.
TSS TP TN
fact that justification is required by the TSS TP TN
approving authority for the percentage Sample Set Mean (µ) 91% 63% 51%
removal of the device, rather than the Z-Score (Zα/2) 1.96 1.96 1.96
Z-Score (Zα/2) 1.96 1.96 1.96
justification of influent and effluent COV 0.07 0.44 0.54
concentrations at the testing site, which Std. dev. (σ) 0.07 0.28 0.28
% Removal 91% 63% 51%
can be justified by aligning with referenced Number of Samples (n) 10 10 10
parameters for typical concentration Number of Paired
occurrences in Australia as described Std Error of the Mean Samples Required
4% 17% 17% 0.03 3.46 9.87
in Duncan (1999). Table 5 presents (SE) to Meet 95%
the confidence intervals for the data. Lower Confidence Confidence Interval
87% 46% 34%
Bound
It can be observed that TSS has a much POPULATION VARIABILITY
lower range than TP and TN. This can be Mean 91% 63% 51%
& SAMPLE NUMBER
attributed to the fact that the TSS data is Upper Confidence
more closely correlated and robust than 95% 80% 68% It is clear from Table 6 that the number
Bound
the other two data sets, as a narrower of paired samples required to achieve
confidence interval will indicate a more where the 95% confidence level can vary greatly
precise estimate, while a wider confidence depending on the target pollutant and
interval indicates a less precise estimate the variability of the particular data set.
of the true population mean (Tan and Tan, Next, setting the lower boundary Also, it might appear strange that there
2010). From Table 5 it can be concluded of the influent confidence interval to is less than one paired sample required
that we are 95% confident that the true the upper boundary of the effluent to satisfy the 95% confidence level for
population mean of the per cent removal confidence interval gives: the mean TSS removal figure (91%).
is between the lower and upper limit for Revisiting the TSS baseline data in
each pollutant type. Table 1, however, shows a very consistent
per cent removal rate for each event
MINIMUM NUMBER
regardless of influent concentration.
OF PAIRED SAMPLES The Coefficient of Variation (COV) is
Ensuring sufficient samples are collected then substituted for in the above Contrast this, however, with a different
for any type of field research should be equation using the standard relationship: TSS data set, using more paired samples
considered imperative to ensure that than shown in Table 1, where the results
statistically relevant conclusions can be will be very different. Table 7 is a set of
derived from the results. However, in It is more accurate to make the TSS data from a different site, using a
most stormwater data sets, no statistical assumption that as it is different treatment device to that used
processes are utilised to guide the highly likely that . Substituting for Table 1 data.
minimum sample size, and Burton and and rearranging the above equations and
Pitt (2002) suggest that most follow Table 7. Alternative example
solving for ‘n’ eventually yields: influent and effluent data for TSS for
subjective professional judgement. a different site and different treatment
device to that used in Table 1.
In order to numerically and statistically
show that there are sufficient data Event TSS EMCi TSS EMCe TSS
collected to ensure the required confidence Number (mg/L) (mg/L) CRE (%)
This formula will then calculate the
level, Burton and Pitt (2002) proposed minimum required number of paired 1 247 28 89
the use of a variety of basic equations, samples for a specific confidence level. 2 300 280 7
based on coefficients of variation, Z-scores, The results of this equation for the CRE
sample set means, and standard deviations. 3 233 113 52
test data can be seen in Table 6.
4 21 16 24
The Urban Stormwater BMP It is shown in Table 6 that TSS requires
Performance Monitoring Manual 5 32 23 28
less than one paired sample, TP requires
(Geosyntech, 2009), however, describes just over three paired samples, and TN 6 19 14 26
an equation specifically for sampling requires almost 10 paired samples to 7 55 40 27
stormwater treatment removal rates ensure a 95% confidence level of the
8 27 8 70
around a stormwater treatment device, mean % reductions in concentrations
based on similar methods described from Table 5. Given that there are 9 67 10 85
in Burton and Pitt (2002). The BMP 10 samples in the data set, and the 10 28 20 29
Performance Monitoring Manual minimum number of paired samples
11 70 35 50
(2009) begins with the formula defining required for each pollutant is all less
percentage removal (as a fraction than 10, we conclude this data set will 12 45 14 69
rather than a per cent): achieve statistical significance with a 95% 13 208 156 25
confidence level. Table 6 is a calculation 14 121 114 6
used to inform required sample numbers,
15 38 28 26
rather than an a posteriori calculation.
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because both false negatives and false Z score, should be chosen Z scores
Table 8. Minimum number of paired
influent/effluent samples required positives are important (see section on from the one-tailed distribution Z-scores
to establish the mean CRE% for TSS errors). In many field monitoring programs, (Teixeira et al., 2009). Given that,
removal at the 95% Confidence Level. power issues (such as false negative confidence level and statistical power
TSS problems) may actually be more critical should be balanced to ensure further
than confidence (Burton and Pitt, 2002). confidence in the results, but without
Z-Score (Zα/2) 1.96
TYPE I AND TYPE II ERRORS making the test too exclusive. Therefore,
COV 0.645 the confidence level can be reduced to
A Type I error can be defined as the
% Removal 41% 90% and the power increased to 80%.
probability of rejecting a null hypothesis
Z-scores for these values are:
Number of paired samples that is actually true (a false positive) and
required to meet 95% 24.32 is typically associated with the confidence
Confidence Interval level, or α value. The confidence level chosen
is the largest probability of committing an
allowable Type I error and still rejecting the Table 10 presents the results for the
It can be seen in Table 7 that there is
null hypothesis (Park, 2008). number of paired samples required if the
much greater variability around the TSS
treatment removal performance, with per Type II error is included with an 80% power
A Type II error can be defined as the level and balanced with a 90% confidence
cent removal varying from 6% to 89%.
probability of incorrectly retaining the null level requirement (using Table 1 data).
It is important to note that the SQIDEP
hypothesis (a false negative), and is typically
protocol (2014) calls for 15 qualifying When compared to Table 6, it can be
associated with statistical power, or β value.
events, a criterion that this data would seen in Table 10 that when the power is
The power of the test will determine the
satisfy. However, if we apply the minimum included, the number of paired samples
likelihood of selecting an incorrect null
number of paired samples calculations as required almost doubles, even though the
hypothesis, and is often used to determine
per the above, we can see in Table 8 that confidence level is reduced from 95% to
the required sample size (Park, 2008).
the data set falls well short of the required 90%. The importance of setting the power
number, at 24 paired samples required to In many environmental programs, the
to 80% cannot be underestimated, and
establish the mean CRE% for TSS (41%) importance of a Type II error (β) can be very
can be a critical element to increasing the
at the 95% confidence level. high. This would be equivalent to having
statistical quality of data presented.
a null hypothesis of “the water sample
The importance of calculating a
is not contaminated”, then retaining the LIMIT ON MINIMUM NUMBER
minimum number of paired samples
null hypothesis, when it was actually false. OF PAIRED SAMPLES
has been clearly established and it will be
This could lead to environmental damage,
necessary to explore this in great detail for This approach for assessing proprietory
health risks to the public, and possibly
the statistical validation of performance device performance claims recommends
fines imposed by regulatory agencies, as
data under SQIDEP. Burton and Pitt (2002), that field-testing is continued until the
a contaminated discharge of water would
however, take this calculation one step required number of paired samples is
occur (Burton and Pitt, 2002). A higher
further as we discuss next. met as per the criteria shown for Table
statistical power (1-β) can be used to
6 or 10. However, SQIDEP (Stormwater
CONFIDENCE LEVEL AND minimise this type of error, and should be
Australia, 2014) calls for a minimum
STATISTICAL POWER (α carefully selected. Typically, a power of
number of monitoring events that are
AND β VALUES) 80% (β = 0.2) is recommended, whereas
if the Type II error is to be ignored, then a statistically relevant.
Most statistical tests convey a certain
power of 50% (β = 0.5) is adopted. Given the variable nature of field-
required level of confidence (α). This
indicates and sets the criteria for a Therefore, if statistical power were to testing stormwater treatment devices, the
decision about the null hypothesis for the be included in the above equation used to number of paired samples required can
test (Privitera, 2011). Statistical power (β), calculate the minimum number of paired be very high if the data is not particularly
on the other hand, is frequently ignored, samples required, the equation becomes: consistent, as shown by the TSS removal
even though it can sometimes be more data in Table 8. One solution could be
important than confidence level (Burton that, for the full performance claims to be
and Pitt, 2002). verified, the 90% confidence interval and
where 80% power requirements must be met.
Confidence levels are defined as:
However, if the required number of paired
samples has not been achieved under
these statistical criteria, then perhaps the
Whereas statistical power can be
It should be noted that while overall performance of the device could
defined as:
should be chosen from the two-tailed be reduced by a scaling factor. Further
Table 9. Summary of the available choices with Type I and Type II Errors when
When evaluating data using a statistical assessing the null hypothesis.
test, power is the sensitivity of the test
Do Not Reject H0 Reject H0
for rejecting the hypothesis. It is important
that power and confidence be balanced Correct Decision Type I Error
H0 is True
for an effective monitoring program. Most 1 – α: Confidence Level α: Size of Test (Significance Level)
studies ignore power, while providing Type II Error Correct Decision
a high value (typically 95%) for the level H0 is False
β: Size of Test (Power Level) 1 – β: Power of Test
of confidence. This is a flawed approach
WATER MAY 2015
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Table 10. Number of paired samples THE AUTHORS McDonald J (2014): Paired t-test, Log Normal
required to establish CRE% at the Transformations and Wilcoxon Ranked Sign Test,
Charles Kelly (email: charles.
90% Confidence level and 80% Handbook of Biological Statistics, 3rd ed., Sparky
Power level. [email protected]) has over House Publishing, Baltimore, Maryland.
16 years’ experience in the
TSS TP TN construction, consulting and Neyman J (1937): Outline of a Theory of Statistical
Estimation Based on the Classical Theory of
Z-Score (Zα/2) 1.64 1.64 1.64 manufacturing industries.
Probability, University College, London.
He is currently Commercial
Z-Score (Zβ) 0.84 0.84 0.84
Manager – Humes Water Solutions and is Olsson U (2005): Confidence Intervals for the Mean
COV 0.07 0.44 0.54 undertaking research and development of of a Log-Normal Distribution, Swedish University
existing and new stormwater treatment of Agricultural Sciences, Journal of Statistics
% Removal 91% 63% 51% Education, 13, No 1.
products for the Australian market.
Number of paired Charles holds a Bachelor’s degree in Park H (2008): Hypothesis Testing and Statistical
samples required Environmental Engineering, and a PhD in
0.05 5.57 15.9 Power of a Test, The University Information
to meet 90%
Confidence Interval
Mechanical/Environmental Engineering. Technology Services (UITS) Center for Statistical
and Mathematical Computing, Indiana
Anton Bardak is a Civil University, US.
engagement and consultation is required
Engineer with three years’
to determine the most appropriate Privitera G (2011): Chapter 8: Introduction to
experience in the water
approach for calculating the minimum Hypothesis Testing, Statistics for the Behavioral
engineering industry.
number of required paired samples. Sciences, 225, Sage Publications.
After two years with KBR
CONCLUSION as a graduate water and Razali M & Wah Y (2011): Power Comparisons of
wastewater engineer, he joined Humes Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and
The draft Stormwater Australia
as a Water Solutions Engineer and now Anderson-Darling Tests, Journal of Statistical
Stormwater Quality Improvement Device Modelling and Analytics, 2, pp 21–33.
Evaluation Protocol (SQIDEP) requires focuses solely in the area of stormwater
inter alia, statistical assessment of the management. Anton provides technical Seo S (2002): A Review and Comparison of
data presented. It is necessary to engage support and designs for the national Methods for Detecting Outliers in Univariate
with academia and regulators to reach Humes business and is also heavily involved Data Sets, Graduate School of Public Health,
in ongoing research and development. Pittsburgh University, US.
a consensus on the robustness of a
statistical approach, and this paper is Shapiro S & Wilk M (1965): An Analysis of
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Page 57
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[OCR of page images]
Appendix C: MUSIC Transform Functions
Properbes of Huses Jellyfish JF-420024 E20) ([properies of tumes Jewynsn Iraz0024
Location Humes Jelfish JF-1200-2-1 Location Humes delish JF-1200-2-1
~ Inlet Properties — ——_ y-hlet Properties
Low ow By-pass (cubicmetres persec) 0.000 Low ow By-pass (cubic metres persec) 0.000
High Flow Bypass (cubiometres persec) 0.013 High Flow By-pass (cubicmetres perses) 0.013
Transfer Functions | Transfer Functions
Flow (cubic metres per séc) © Total Ntrogen (ng) © Flow (cubic metres per sec) (© Total Ntrogen img/L)
(@ Total Suspended Solids ng/L) © Gross Polutarts (cg/ML) (© Total Suspended Sotds (mg/L) Gross Polktants (kg/ML)
© Total Phosporus (mg/L) Total Phosporus (mg/L)
p Taal Solids (ng/t) Teta Phosporus (ng/L)
Transfer Functions ————__—_—_—_——
[Rares aierere (© Flow Based Capture Eiciency | ! (© Flow Based Capture Eficiency
Percentage Capture Concentration Eficeney Tender Function Percentage Capture
| Inflow (m*3/s)_|% Capture: | nftow (3/5) | Capture |
} ; aL Boe | ‘Total Phosponus (ng/L) 0.0000 00,0000
1.0000 100.0000 1.0000 100.0000
° o4 02 03 os
input |
Drag points onthe graph to mody the transfer function rag points on the graph to madiy the transfer function
i
I | iows.. || netes.. |
(Miwa ee _|lvinen ieen)| +e |ovael
Properties of Humes Jellyfish JF-1200-2-4
J) [Properties of Humes Jelyfish JF-4200-24 (ee)
Location Humes Jeliish JF-1200241 Location Humes Jelyfish JF-1200241
(Wet Properties (let Proper
Low Faw By-pass (cubiemeres persec) 0.000 Low Flow Bypass (cubiemees persec) 0.000
‘High Flow By-pass (cubic metres per sec) 0.013 High Flow By-pass (cubic metres per sec) 0.013
[Transfer Functions Tensor inetions -
(© Flow (cubic metres per sec) (© (Total Htrogen (ng) Fw (cubie metres per sec) (O Total Nirogen (ng/t)
© Total Suspended Solds g/L) © Gross Potants (9/1) Total Suspended Soids (mg/l) Gross Polutans (g/t)
© Total Phosporue (mg/l) Total Phosponus mg/L)
et "Gross Poltarts k/ML)
Transfer Functions Transfer Functions
(© Concentration Based Capture Eicienoy (© Flow Based Captue Eficienay (© Concentration Based Capture Eiciency (© Flow Based Capture Eficiency
© Bath © Both
Concentration Efciency Transfer Function Percentage Capture Concentration Efciency Transfer Function Percentage Capture
Inflow (m*3/s)_|% Capture Inflow (m*3/s)_|% Capture
| Total Nirogen (mg/1) ara sine Gross Potutats f/ML) 0.0000 100.0000
1.0000 00,0000 1.0000 190.0000
Dutput
°
10 20 30 40 50 60 70 80 90 100
Input
Drag points on the graph to modify the transfer function
‘Drag points on the graph to modify the transfer function
[Pies || Notes. | Fluxes, Notes.
Hicorce | <a || A Bosh | HK concet |] <P Beck || wo Bish
Page | 15
Page 58
National sales 1300 361 601 humes.com.au [email protected] A Division of Holcim Australia [OCR of page images] National sales 1300 361 601 humes.com.au [email protected] PST mate rec)
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