assessing decentralised wastewater treatment technologies

International Conference on Integrated Water Management, Perth 2011 1

Assessing decentralised wastewater treatment technologies:

Correlating technology selection to system robustness, energy

consumption and fugitive GHG emission

Meng Nan Chong1*, Angel Ho2, Ted Gardner1,3, Ashok Sharma4, Barry Hood3

1 CSIRO Land and Water, Ecosciences Precinct, Dutton Park Queensland 4102 2 Macao Environmental Protection Department, Taipa Macao SAR 3 Departments of Environment and Resource Management, Ecosciences Precinct, Dutton Park Queensland 4102 4 CSIRO Land and Water, Highett Victoria 3109

*Corresponding author: Dr Meng Nan Chong

CSIRO Land and Water, Ecosciences Precinct, Dutton Park Queensland 4102

Tel: +61 7 3833 5593, Fax: +61 7 3833 5501, Email: [email protected]

Key words: Decentralised wastewater, Energy consumption, Water-energy nexus, Greenhouse gas emissions,

Membrane bioreactor

Abstract

The projected population growth of 1.5 million in South-East Queensland (SEQ) by

2031 is expected to pose a serious challenge to the treatment capacities of existing

sewage treatment plants. New sewage infrastructures need to be planned and

implemented to accommodate the future treatment demands, and will include either

conventional centralised systems, or a resilient suite of decentralised technologies. In

the past, decentralised technologies have been largely viewed as an alternative for

remote or specialised boutique developments such as ecovillages. However, there is

an emerging demand for the adoption of decentralised technologies in SEQ based on

technical and cost minimisation criteria, as well as sustainability grounds. A major

limitation to the wider uptake of decentralised technologies is the lack of both

technical and scientifically-credible information on process selection using criteria

such as system stability, energy consumption and environmental sustainability. In this

study, we compared two different types of decentralised systems in SEQ, and assessed

their system robustness to shock loads (for MBR system only), energy consumption

and fugitive greenhouse gas (GHG) emissions. Both systems were designed to

produce Class A+ recycled water suitable for toilet flushing and external irrigation

water use. These systems are: (i) a holding wetwell, immersed membrane bioreactor

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(MBR) with anoxic and aerobic zones, ultraviolet irradiation (UV) and chlorination

and; (ii) communal septic tanks, anoxic and aerobic bio-filtration, microfiltration, UV

and chlorination. To evaluate the stability of MBR to different shock loads, we used a

modelling approach whereby an industry standard activated sludge BioWin® model

was calibrated and validated before conducting a series of “virtual” dynamic shock

loads experiments. We found that the MBR system was relatively robust to hydraulic

shock loads with tolerance up to 1.5 times of the design dry weather daily flow

without violating the plant’s licence requirements. However, the stability of

nitrification process in MBR was significantly affected when the total nitrogen load in

the influent increased by 30% whilst maintaining the constant inlet wastewater flow

rate. Once upset, it took approximately 12 hr for nitrification behaviour to recover.

Such sensitivity did not occur with carbonaceous (COD) shock loads. For the energy

consumption study, we found that the specific energy requirement (kWh/kL of treated

sewage) for the MBR system was 6.1 kWh/kL, which was substantially higher than

that for the other decentralised aerobic bio-filtration system (1.9 kWh/kL). We also

used a mass balance approach to estimate the fugitive GHG emissions (CH4, N2O)

and concluded that electrical energy consumption data alone could substantially

underestimate the overall GHG footprints for the decentralised systems. When the

estimated CH4 fluxes were added to the initial low electrical energy consumption, the

communal septic tanks with aerobic bio-filtration system generated a carbon dioxide

equivalent footprint similar to that of the MBR system.

1. Introduction

Significant and continued population growth in most of the major urban regions in

Australia, and in particular, the South-East Queensland (SEQ) region has placed an

increasing pressure on wastewater service providers to develop a response plan to

cope with their obligations. By 2031, it is expected that the urban population in SEQ

will grow by more than 1.5 million people (DIP, 2009). In the conventional “end-of-

pipe” paradigm for wastewater services, it is expected that all these urban residential

developments spread over about 32,767 ha of new urban areas can be connected to the

existing major wastewater treatment infrastructures by expanding the existing

centralised sewage collection, conveyance and treatment systems. If we utilise the

“business-as-usual” solutions for new urban growth areas, there will be a high capital

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cost and pumping energy penalties associated with long conveyance distance, as well

as operational difficulties due to the age and capacity of the current sewage treatment

plants.

In view of this, decentralised technologies covering a range of wastewater treatment

technologies could be an “ideal” solution to accommodate urban growth by providing

location specific sewage treatment options that can outweigh most of the negative

aspects associated with centralised systems (Tjandraatmadja et al., 2009). In the past,

decentralised technologies have been largely viewed as an alternative for remote or

specialised boutique developments such as ecovillages. With the emerging suite of

decentralised technologies, however, conventional technology such as septic tank can

be combined with advanced decentralised wastewater treatment technologies to

deliver treated sewage quality of up to Class A+ recycled water. The advanced suite of

decentralised treatment technologies include attached biological media (biological

activated carbon, biofilters), adsorption processes using sand or clay materials,

membrane technologies such as membrane bioreactor (MBR), microfiltration (MF),

ultrafiltration (UF) and reverse osmosis (RO) and tertiary disinfection treatments such

as UV sterilisation and chlorination. The availability of such an advanced suite of

wastewater technologies has certainly enhanced the wider uptake of decentralised

systems, as it allows for “fit-for-purpose” application with greater flexibility in

process selection and matching specific end-uses. However, a major limitation to the

wider uptake of decentralised technologies is lack of both technical and scientifically-

credible information on process selection using criteria such as system stability,

energy consumption and environmental sustainable (i.e. the total associated GHG

emissions).

In this study, we compared two different decentralised systems in SEQ and correlated

their system behaviour to shock loads (for MBR system only), energy consumption,

and fugitive greenhouse gas (GHG) emissions. These decentralised systems are

located at Capo di Monte (Mount Tamborine) and Currumbin EcoVillage (Currumbin

Valley) and were designed to produce Class A+ recycled water for toilet flushing and

external irrigation use. A 6-day diurnal wastewater quality sampling was conducted

for the MBR plant at Capo di Monte (CDM) in order to calibrate a commercially

available activated sludge BioWin® model. Using the calibrated model, the impacts

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of various shock loads including hydraulic, nitrogen and carbonaceous COD on the

decentralised MBR operation were evaluated. In addition, we also measured the

specific energy consumption for process equipments using data-logged smart water

and energy meters. We complemented this energy data with a mass balance approach

to estimate the fugitive GHG emissions from certain treatment components such as

communal septic tanks, MBR and aerobic bio-filtration processes.

2. Site, Process Description and Monitoring Equipments Setup

2.1. Capo di Monte Sewage Treatment Plant (CDM-STP)

Capo di Monte (CDM) decentralised sewage treatment plant, located at Mount

Tamborine, was built within a 4.3 ha urban residential development that comprises of

46 detached and semi-detached residential dwellings and a large community centre.

This urban development was built as a “retirement village” theme that caters for

“over-50s” individuals, and each dwelling was designed with one to two bedrooms.

The main reason for the adoption of decentralised technologies was the absence of a

centralised sewer collection system. The CDM-STP was designed for a hydraulic

capacity of 11,000 L/day based on estimated peak sewage flows. Fig. 1 shows a

schematic of decentralised wastewater system. The CDM-STP involves a raw sewage

holding wet-well followed by submerged flat sheet MBR (Kubota) that incorporates a

raked screen, anoxic/aerobic treatment zones, alum dosing in aerobic zones (for

phosphorus removal) and tertiary disinfection that includes UV disinfection and

chlorination. A submersible pump in the aerobic MBR zone allows for a return

activated sludge (RAS) stream back to the anoxic zone. Excess activated sludge from

the anoxic zone is pumped out on a fortnightly basis to a Gold Coast regional sewage

treatment plant for further treatment. Class A+ recycled water is produced from this

STP, and used for household toilet flushing and external irrigation via a dual

reticulation system. A vegetated buffer zone of 6,000 m2 is available for land

application of excess treated wastewater to prevent direct discharge into the local

waterway.

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Fig.1: Schematic of decentralised wastewater system at Capo di Monte. RAS flow is

the return activated sludge stream.

2.2. Currumbin Ecovillage STP

Currumbin Ecovillage (CEV) STP is situated at the Currumbin Valley, Gold Coast

and comprises 110 residential lots that range from 400 to 1,600 m2 with an extensive

proportion for communal open areas (80:20 of open-to-living space). The main reason

for the uptake of decentralised technologies was due to the unavailability of access to

a centralised sewer network. The CEV-STP has a design capacity of 51,000 L/day for

raw sewage treatment. Fig. 2 shows the schematic for the treatment processes, as well

as its wastewater flow lines. The wastewater is collected at each household and

conveyed to the STP using a combination of gravity and sewer pumping. The initial

anaerobic treatment is performed by three in-series septic tanks with a BioTube® filter

installed in the last tank to remove carry-over solids. The sewage effluent is then

treated in a secondary process of aerobic bio-filtration and denitrification. An Orenco

Advantex® Textile Filter (AdvanTex AX100) is used for the simultaneous aerobic

degradation and nitrification of carbonaceous and nitrogen compounds in the primary

treated effluent. A proportion of the treated effluent from the textile bio-filters is

recycled back to an anoxic/recirculation tank to allow a denitrification process to

occur. This recycling ratio is a crucial process parameter and is currently set at a 5:1

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ratio (Xavier, 2008). This recycling ratio means that only one sixth of the wastewater

flow in a full pumping cycle is diverted for subsequent downstream treatment, whilst

the remaining flow fraction is recycled continuously to ensure sufficient BOD

reduction is achieved. The diverted effluent is treated to a Class A+ recycled water via

microfiltration (with an effective pore size of 0.2 µm) follows by UV disinfection and

chlorination. The Class A+ recycled water produced from the CEV-STP is stored in a

large storage tank before being reticulated to the households for toilet flushing and

external irrigation use.

Fig. 2: Schematic of decentralised wastewater system at Currumbin EcoVillage.

3. Results and Discussion

3.1. Influence of various shock loads to MBR operational

Membrane bioreactor (MBR) is an emerging technology for wastewater treatment that

is capable of transforming various types of wastewater into high quality treated

effluent, equal or exceeding almost every discharge requirement. Unlike conventional

activated sludge process, MBR usually comes in a small physical size, produces less

activated sludge, and achieves higher biomass concentration for organic

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mineralisation (Gander et al., 2000). All these characteristics made MBR an attractive

technology option for decentralised wastewater treatment. To date, there are only a

few published studies that discuss on the potential application of MBR for small-scale

decentralised wastewater treatment (Gander et al., 2000). Most of the current design

knowledge and guidelines on MBR plants are applicable to large scale centralised

WWTPs. Thus, there exists an imperative to close the knowledge gaps on design and

implementation for small-scale MBR plants.

Abbeglen et al. (2008) also discussed that the conventional MBR design cannot be

applied directly to a decentralised system before detailed considerations are resolved.

This is due to the wastewater flows being subjected to high fluctuation in volume and

composition, and shorter residence times. The decentralised MBR needs to be

designed with a certain “buffering capacity” for wastewater flow rates and other

potential nutrient and pollutant perturbations. Such fluctuations in influent wastewater

characteristics, however, can be dampened by the provision of flow equalisation or

buffer tank prior to treatment in the decentralised MBR system (Sipma et al., 2010).

Membrane bioreactorInfluent Effluent

WAS

Anoxic

Alum addition

Fig.3: Simplified process schematic in BioWin® simulation model. WAS represents

the waste activated sludge stream.

To understand and evaluate the impacts of various shock loads to the decentralised

MBR operation directly (neglecting the upstream buffer in this instance) at CDM, we

set-up and calibrated the dynamic activated sludge system model in BioWin®

(EnviroSim Associated Ltd, USA) based on the simplified process schematic in Fig.

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3. The bio-kinetic model used was the ASM1 (Henze et al., 1987) for organic matter

and nitrogen, with the parameter set from Vanrolleghem et al. (1999). As phosphorus

is removed via alum dosing at CDM, the biological P-removal process is not an

important part of our simulations. Model calibration included both the steady and

dynamic state simulations, which involved the matching of sludge production from

measured plant data with the modelled data sets (dynamic simulation was achieved

via matching of COD values).

Table 1 shows the summary of license requirements, measured influent wastewater

quality at CDM-STP, and its comparison with the common values from centralised

WWTPs. It is evident that the wastewater composition for the decentralised systems

exhibited wide variations in COD, BOD and total nitrogen concentrations compared

to those of the centralised treatment plants. This is almost certainty due to the small

connected population whereby variations from individual households are not buffered

very well compared with the large connected population in a centralised sewage

system.

Table 1: Summary of license requirements, measured influent wastewater qualities at

CDM-STP and its comparison with common values from centralised WWTPs.

Wastewater Parameters Units

CDM-STP License Limits

CDM-STP Influent

Values Range

CDM-STP Average Values

Common Values Range at

centralised WWTPs

CODtotal mg/L - 590 - 1060 825 314 - 438*

BODtotal mg/L 10 240 - 430 335 120 - 190#

Ntotal mg/L 10 69 - 140 105 87 - 94*

Ptotal mg/L 5 14 - 27 21 - Suspended solids (TSS) mg/L 10 120 - 260 190 144 - 207* Volatile Suspended solids (VSS) mg/L - 120 - 180 150 125 - 168* From *Pollice et al. (2004) & #Freeman et al., (2009)

From the BioWin® simulation result, we found that the MBR system was relatively

robust to hydraulic shock loads with tolerance up to 1.5 times of the design dry

weather daily flow without violating the licence requirements at CDM. In this

instance, the susceptibility of the MBR to different hydraulic shock loads was found

to be highly dependent on its effective “working” volume, hydraulic retention time

(HRT) and solid biomass concentration (a function of sludge retention time, SRT).

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Since the “working” volume for the MBR volume is constant, the MBR robustness

was found to be highly dependent on both the HRT (1-2 hr) and SRT (approximately

200 d) used. Wastewater flows fluctuation of more than 50% is uncommon (as

observed from the measured diurnal flow patterns) and thus the dynamic simulation

has been constrained at this threshold limit.

When a stepwise increase in nitrogen shock loadings was applied to the model, we

found the MBR operational stability was impacted at N-loads greater than 30% from

its average value listed in Table 1. This was simulated assuming the inlet wastewater

flows remained within normal operational range. From the model simulation, it was

found the high susceptibility to increasing N-loads is due to the low biomass

concentration and substrate utilisation rate as estimated using the default ASM1 bio-

kinetic parameters that affects both the autotrophic and heterotrophic growth

processes. Further work needs to be carried out to determine whether the current

default bio-kinetic parameters can be applied to the specific MBR system at CDM.

Once the nitrification process was upset, the overall MBR system took approximately

12 hr to re-establish steady-state operation.

In contrast, it was identified that there are no net impacts of carbonaceous COD shock

loads (590 – 1060 mg/L) on the MBR operation. This is probably due to the high

concentration of mixed liquor suspended solids (MLSS) of 24,000 mg/L within the

current MBR system that can cope with the COD load variations. However, high

concentrations of non-biodegradable COD can accumulate within the MBR and

subsequently impair its operation. Thus, a further analysis on the COD fractionation

(dissolved, particulate and non-biodegradable components) in the local wastewater

needs to be carried out in order to understand its effects on the subsequent

decentralised MBR operation.

Currently we are also simulating both the steady-state and dynamic-state wastewater

treatment conditions for the decentralised system at CEV. The CEV-STP presents a

challenge as BioWin® cannot be used for process modelling and as we are using the

empirical National Research Council (NRC) and first-order formulation methods to

simulate the impacts of shock loads on its process operation (Crites and

Tchobanoglous, 1998).

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3.2. Energy requirements of decentralised technology

Decentralised technology is regarded as being less resource intensive, more

environmentally benign and more precautionary in terms of its ability for (1)meeting

location specific solutions; (2)targeting costly augmentations to centralised systems

and (3)avoiding the financial risks inherent for new large wastewater infrastructures

(Fane et al., 2006). However, there is still a lack of credible scientific data on

sustainability issues such as energy use and carbon footprints which are needed to

facilitate its wider adoption. In this study, we monitored the energy use of various

components of the decentralised wastewater technologies for energy-related carbon

emission (Section 3.2) and also estimated fugitive GHG emissions for direct carbon

emission (in Section 3.3).

Fig. 4: Comparison on specific energy use for wastewater pumping and treatment at

our studied sites to other recycled water schemes in Australia.

Fig. 4 shows the specific energy use (kWh/kL) for our monitored decentralised

wastewater systems that produced Class A+ recycled water. The CDM-STP was found

to consume 6.1 kWh/kL whereas, the CEV-STP has a much lower total specific

energy requirement of 1.9 kWh/kL. Result in Fig. 4 also shows the specific energy

requirements for both decentralised systems are higher than the centralised

wastewater treatment facilities in Pimpama-Coomera (Gold Coast) , but are similar to

the energy requirement of the Western Corridor purified recycled water (PRW),

Tugun RO desalination plant and Sydney desalination plant (Hall et al., 2009;

Kenway et al., 2008; Australia Institute Ltd., 2005). Such a comparison suggests that

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decentralised systems have a real prospect to deliver alternative urban water resources

at a better energy cost, if the decentralised systems can be properly selected,

configured and operated.

3.3. Estimation on GHG emissions from decentralised technology

As indicated earlier, there is a deficiency in the current national carbon accounting

methodology for GHG emissions from decentralised wastewater systems. To date,

virtually no information is available on the GHG emissions from different

decentralised wastewater systems. Energy-related GHG emissions are the

predominant source of GHG emissions from wastewater treatment (Kenway et al.,

2008). However, the non-energy related GHG, often referred to as fugitive GHG

emissions such as methane (CH4) and nitrous oxide (N2O), are also of significance

owing to their high global warming potential (GWP). Both CH4 and N2O are reported

to have a GWP of 25 and 298 times of carbon dioxide equivalent (CO2-e)

respectively, over a 100 years period (Foley et al., 2009). Due to uncertainties in their

magnitude and the lack of standard measurement protocols to quantify the fugitive

GHG emissions, there is limited information available to use as an informed selection

guide for sustainable decentralised technologies.

Previous studies by Kinnicutt et al. (1910) and Winneberger (1984) found a high CH4

emission potential from decentralised septic systems. However, conversion of their

data into a statistical distribution that can be used to predict CH4 emission was of

limited value due to the low sample numbers. Consequently, other CH4 predictive

models were investigated to predict CH4 emission rates, such as the Inter-govermental

Panel on Climate Change, IPCC models (1996, 2007), the Sasse model (1998) and the

Foley model (2009). We found that the estimated CH4 emission rates using these

proposed models are highly variable owing to the different underlying assumptions

made. Table 2 summarises many of the relevant studies and the assumptions for their

CH4 models, along with the magnitude of CH4 emission rates that were estimated

using the respective models. Summary results show that the current IPCC model

yields the highest estimate on CH4 emission rate compared with Sasse (1998) and

Foley models (2009).

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Table 2: Summary of key assumptions of each CH4 emission models from sewage,

along with some field measurement values from the literature.

Reference Model assumption CH4 estimate

(g CH4/capita.d)

Kinnicutt et al. (1910) *Measured value 10.1

Winneberger (1984) *Measured value 14 – 18

Sasse (1988) *Estimated value assuming 25% CH4 dissolved 18

IPCC (2007) *Estimated value based on 50% BOD is converted anaerobically 25.5

Foley et al. (2009) *Estimated value based on that 40% of solids are removed as septage 11

Leverenz et al. (2010) *Measured value 11

Similarly, there is also limited information available on nitrous oxide emission from

wastewater treatment plants. Only recently have Foley and Lant (2009) reported on an

off-gas measurement method to quantify the direct GHG fluxes from seven full-scale

wastewater treatment plants (WWTPs) across Australia. In their study, they found that

the magnitude of N2O emissions from WWTPs ranged from 0.006 – 0.253 kg N2O-N

per kg N denitrified. Kampschreur et al. (2009) also reviewed N2O emission during

biological nutrient removal processes and found that the nominal direct N2O

emissions lay between 0-4% of the total nitrogen loads in the influent wastewater.

The N2O emission rate during wastewater treatment can be estimated using a mass

transfer kinetic expression (Eq. 1), with an empirical volumetric mass transfer

coefficient kLa that can be tailored to the process conditions studied (Eq.2) (Foley et

al., 2009).

[ ] }N]O[NNON{akVTrTr *

S2R2LRRN,N2OWWTPN2O, ∑ −−∑ −×== −

(Eq. 1)

0.86

g

0.49

L

R

L34,500υ

D

Dak ×

=

−

(Eq.2)

where TrN2O-N,R is the N2O emission rate from individual treatment vessels; VR is the

volume of reactor zone, [N2O-N]R is the dissolved N2O-N concentration in the reactor,

[N2O-N]S is the saturation N2O-N concentration in water at atmospheric conditions

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(2.57x10-4 g/m3), kLa is the volumetric mass transfer coefficient, DR is the depth of the

reactor, DL is the depth of the lab stripping column (0.815 m), and υg is the superficial

gas velocity of the reactor.

Based on the literature, we have assumed that the major fugitive GHG emissions in

our study are sourced from the anoxic/aerobic zones in the MBR system at CDM STP,

and the communal septic tanks, aerobic bio-filtration and denitrification processes at

CEV STP. Both the CH4 and N2O emission rates are preliminary estimates based on

the Sasse (1998) and Foley (2009) models respectively. Table 3 shows the overall

GHG emissions from the two decentralised wastewater systems that include the

measured energy-related GHG and the estimated fugitive GHG emission values. The

GHG emissions from other small components (e.g. landfill, irrigation, etc) were

estimated based on the first-principle solid and mass balance approach. Results from

Table 3 indicate that the potential of fugitive GHG emissions from CEV can

significantly exceed the high energy-related GHG measured for CDM. The overall

GHG emissions from CEV is estimated at 7.06 kg CO2-e per kL of treated wastewater

compared with 5.96 kg CO2-e per kL for CDM (i.e. a reversal of magnitude when

only energy-related GHG was considered).

If the current IPCC protocol were utilised, the estimation uncertainty associated with

CH4 emission at CEV can be increased by an additional 40% from the current values.

Fig. 5 shows a stochastic log-normal distribution of the estimated CH4 emission from

the communal septic tanks at CEV-STP using the IPCC model. In this instance, a log-

normal distribution is used because of the assumption that CH4 emission from septic

tanks always exist, and have unlimited positive emission potential. It was estimated

that in the 90% probability distribution range, the communal septic tanks have a

methane emission potential of 6.69 to 15.18 kg CO2-e/kL. When these estimated

values are added to the energy-related GHG, the total GHG emissions for CEV-STP

increases to 8.83 to 17.32kgCO2e/kL.

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Table 3: Overall GHG emissions from the decentralised wastewater systems.

Estimated GHG emissions

(kg CO2-e per kL)

Components

CDM CEV

Current average daily wastewater flows (kL/d) 9.3 50.5

Energy related GHG emissions from imported electrical power 5.59 1.81

CH4 emissions from identified decentralised process 0 4.92

N2O emissions from identified decentralised process 0.23 0.22

Landfill disposal of screens, grit and bio-solids 0.01 0

Effluent disposal for irrigation 0.02 0.03

Dissolved CH4 in raw sewage 0.08 0.08

Chemical and fuel consumption 0.03 0

Total GHG emissions 5.96 7.06

Fig. 5: Stochastic log-normal distribution of estimated CH4 emission from the

communal septic tanks at CEV STP using IPCC model

It should be noted that our estimates of fugitive GHG emissions from both

decentralised systems are based largely on assumptions, predictive models and

literature values from centralised WWTPs. The “true” behaviour on fugitive GHG

emissions from wastewater treatment facilities might be different across treatment

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capacities and scales, and might well be affected by the influent wastewater

characteristics and local conditions at the decentralised systems. Hence, our results

should be used only as a preliminary guide for the selection of sustainable

decentralised technology for wastewater treatment. Further direct measurement of

CH4 and N2O is needed in order to validate the modelled fugitive GHG values for

their use in assessing the sustainability of decentralised wastewater technologies.

4. Conclusion

This work has provided a new insight into understanding the operational stability and

GHG impacts of various decentralised wastewater technologies. From the outcomes

of this study, it can be concluded that MBR operated at a decentralised scale offers an

excellent treatment option in terms of final treated effluent qualities (i.e. meeting the

license requirements), system robustness, and resistance to various hydraulic and

pollutant shock loadings. When the MBR is coupled with an upstream primary

holding tank, the susceptibility to various shock loads was enhanced owing to its

buffering capacity. However, the utilisation of MBR at CDM comes at the expense of

high specific energy use (kWh per kL of treated sewage) which disadvantages the

MBR in terms of energy-related GHG emissions. In comparison, the decentralised

CEV-STP provides an effective solution to treat the sewage effluent to Class A+

recycled water with a much lower specific energy-related GHG emissions. However,

when the fugitive GHG emissions from the communal septic tanks at CEV are

included, the high CH4 emission potential along with its uncertainties makes the CEV

system as operated relatively unattractive. Taken in total, our findings have provided

some useful technical insights into the decentralised technologies selection to achieve

sustainable operations in future urban developments. Further studies are currently

under way to model and optimise the wastewater treatment efficiency and energy use

in order to reduce the total GHG emission. Significant efforts will also be directed to

measuring and validating the fugitive GHG emissions from our studied sites.

Acknowledgement

The authors would like to thank the management and staff of Capo di Monte

(especially Mr Gary McOrmish) and Currumbin Ecovillage for their valuable

feedback, information, cooperation and assistance. Special thanks also due to Mr

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Peter Tardrew (CEV), Mr Dominic Xavier and Mr Ivan Bragg (Sustainable Solutions

International P/L, Brisbane) in providing technical assistance and information on the

decentralised system operation at CEV.

This work was funded by the Urban Water Security Research Alliance under the SEQ

Decentralised Systems project.

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