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Comprehensive life cycle inventories of alternativewastewater treatment systems

Jeffrey Foley a, David de Haas a, Ken Hartley b, Paul Lant a,*a Advanced Water Management Centre, The University of Queensland, St Lucia 4072, Australiab Ken Hartley Pty Ltd, Unit F1c, 235 Forest Lake Boulevard, Forest Lake 4078, Australia

a r t i c l e i n f o

Article history:

Received 29 July 2009

Received in revised form

29 July 2009

Accepted 14 November 2009

Available online 2 December 2009

Keywords:

Life cycle inventory

Biological nutrient removal

Energy

Nutrient recovery

Global environmental impacts

Effluent standards

Greenhouse gas

* Corresponding author. Tel.: þ61 7 3365 472E-mail address: paul.lant@uq.edu.au (P. L

0043-1354/$ – see front matter ª 2009 Elsevidoi:10.1016/j.watres.2009.11.031

a b s t r a c t

Over recent decades, the environmental regulations on wastewater treatment plants

(WWTP) have trended towards increasingly stringent nutrient removal requirements for

the protection of local waterways. However, such regulations typically ignore other envi-

ronmental impacts that might accompany apparent improvements to the WWTP. This

paper quantitatively defines the life cycle inventory of resources consumed and emissions

produced in ten different wastewater treatment scenarios (covering six process configu-

rations and nine treatment standards). The inventory results indicate that infrastructure

resources, operational energy, direct greenhouse gas (GHG) emissions and chemical

consumption generally increase with increasing nitrogen removal, especially at discharge

standards of total nitrogen <5 mgN L�1. Similarly, infrastructure resources and chemical

consumption increase sharply with increasing phosphorus removal, but operational

energy and direct GHG emissions are largely unaffected. These trends represent a trade-off

of negative environmental impacts against improved local receiving water quality.

However, increased phosphorus removal in WWTPs also represents an opportunity for

increased resource recovery and reuse via biosolids applied to agricultural land. This study

highlights that where biosolids displace synthetic fertilisers, a negative environmental

trade-off may also occur by increasing the heavy metals discharged to soil. Proper analysis

of these positive and negative environmental trade-offs requires further life cycle impact

assessment and an inherently subjective weighting of competing environmental costs and

benefits.

ª 2009 Elsevier Ltd. All rights reserved.

1. Introduction come at a cost of higher resource consumption (e.g. energy,

Since the mid-19th century, modern societies have raised the

public health standard by the collection and treatment of

domestic sewage. In more recent decades, regulatory authori-

ties in industrialised regions have also endeavoured to improve

local receiving water quality by more advanced forms of

wastewater treatment, such as biological nutrient removal

(BNR). However, increasingly sophisticated means of treatment

8; fax: þ61 7 3365 4726.ant).er Ltd. All rights reserved

chemicals, infrastructure) and elevated environmental emis-

sions (e.g. greenhouse gases to atmosphere, biosolids to land-

fill). To date, these additional environmental burdens have been

largely ignored in the regulatory push for cleaner local water-

ways. Hence, there is a need for a detailed life cycle assessment

(LCA) of a range of wastewater treatment options (with varying

nitrogen and phosphorus removal capacities), which also

includes the broader environmental consequences and impacts

.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 6 1655

of their construction and operation. This paper uses the inter-

nationally standardised LCA framework (ISO, 2006) to quanti-

tatively define the inventory of resources consumed and

emissions produced in the typical life cycle of different cen-

tralised wastewater technologies, at varying degrees of

treatment.

There are several existing LCA studies of wastewater

treatment systems. Some of these have examined competing

technology configurations, and consistently identified the

strong influence of energy consumption on the overall envi-

ronmental impact (Emmerson et al., 1995; Vidal et al., 2002;

Gallego et al., 2008). However, these studies have often been

limited in scope, either in terms of the small number of

alternative process configurations considered, the size of

facility, or the exclusion of significant parts of the wastewater

treatment system. In particular, the exclusion of solids

handling and disposal was a notable weakness in some

studies (Dixon et al., 2003; Gaterell et al., 2005). Other authors

have shown these processes represent a major fraction of the

environmental footprint of wastewater treatment systems,

especially when considering the toxicological effects of heavy

metals in biosolids (Hospido et al., 2004; Houillon and Jolliet,

2005; Pasqualino et al., 2009). Therefore, it was important that

the LCA system boundary of this study included sludge

handling and disposal processes, along with any potential

benefits that may arise due to displacement of synthetic fer-

tilisers by biosolids.

Other studies have focused more upon small and decen-

tralised wastewater systems (e.g. Machado et al., 2007), which

consider different issues and scales than those investigated in

this study.

A limited number of studies have examined the relative

environmental impacts of different treatment standards.

These studies have highlighted the important role of WWTPs

in protecting receiving waters from eutrophication, and hence

increased levels of nutrient removal are generally considered

highly beneficial (Gaterell et al., 2005; Lassaux et al., 2007).

0

2

4

6

8

10

12

0 10 20Effluent Total N

L.gm( surohpsohP latoT tneulffE

1-)

Case 0: Raw SewageCase 1: Primary Sedimentation + Anaerobic Digestion Case 2: Primary Sedimentation + Activated Sludge + ACase 3: Primary Sedimentation + Nitrifying Activated SCases 4, 5 and 6: Primary Sedimentation + MLE BNR Cases 7, 8 and 9: Bardenpho (5 Stage) BNR Activated

Case 4

Case 5Case 6Case 7

Case 8Case 9

Fig. 1 – Wastewater treatment system scenarios defined by typ

quality (refer to x and y axes).

However, these studies only considered BNR effluents with

approximately 10–20 mgN L�1 as total nitrogen (TN), and did

not specify the nutrient limitations of the receiving water

body. There are few studies that have examined the envi-

ronmental impacts associated with stringent nitrogen and

phosphorus removal conditions, such as those often

mandated in North America (Oleszkiewicz and Barnard, 2006)

and south-east Queensland, Australia (e.g. TN < 3 mgN L�1,

total phosphorus, TP < 1 mgP L�1). Frequently, advanced

nutrient removal requires supplementary chemical addition.

This adds a negative environmental impact associated with

manufacture and transport of the chemicals, which is often

overlooked.

The quantification of greenhouse gas (GHG) emissions

from BNR wastewater treatment systems is also a substantial

area of uncertainty. Only very basic estimation methodologies

for methane and nitrous oxide emissions have been published

by the Intergovernmental Panel on Climate Change (IPCC,

2006a). In the past, these questions of GHG uncertainty have

been largely overlooked. However, rapid changes to interna-

tional and national regulatory landscapes (e.g. National

Greenhouse and Energy Reporting System in Australia, Euro-

pean Union emissions trading scheme, Kyoto Protocol Clean

Development Mechanism), combined with increasing volun-

tary organisational commitments to ‘‘carbon neutrality’’,

mean that this level of uncertainty in the environmental cost-

benefit ratio of wastewater treatment now represents an

unacceptable business risk to many water utilities.

2. Goal and scope definition

The goal of this study was to quantitatively model and eval-

uate the life cycle inventories of a range of wastewater treat-

ment scenarios, including BNR. The ten scenarios investigated

in this paper are introduced in Fig. 1. A further 40 scenarios are

30 40 50itrogen (mg.L-1)

+ Energy Recoverynaerobic Digestion + Energy Recoveryludge + Anaerobic Digestion + Energy RecoveryActivated Sludge + Anaerobic Digestion + Energy Recovery Sludge + Sludge Stabilisation Lagoon

Case 0

Case 1

Case 2Case 3

e of process configuration (refer to Legend) and effluent

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 61656

reported in the Supporting Information, but are not discussed

in this paper.

The ten scenarios covered six wastewater treatment

system configurations and a wide range of effluent qualities –

from ‘‘do nothing’’ in Case 0 (TN 50 mgN L�1, TP 12 mgP L�1),

through to best practice advanced nutrient removal in Case 9

(TN 3 mgN L�1, TP 1 mgP L�1). Case 0 represented no treatment

(i.e. disposal of raw sewage to an estuarine environment),

which still occurs in many countries. Case 1 represented basic

primary sedimentation treatment only, coupled with meso-

philic anaerobic digestion for solids stabilisation and energy

recovery through biogas combustion. This practice occurs at

large-scale in many regions, including industrialised cities

(e.g. Sydney, Australia; refer to Lundie et al., 2005). Case 2

represented primary treatment plus basic activated sludge

secondary treatment for organics removal, but no deliberate

nutrient removal. This type of basic treatment still exists in

many parts of the world, including Europe and North America.

Case 3 represented primary treatment plus the addition of

nitrification to the activated sludge process, to protect

receiving waters from high ammonia concentrations. The

progression to BNR was represented by Cases 4–6, which

adopted primary treatment plus anoxic-aerobic Modified

Ludzack-Ettinger (MLE) process configurations. The MLE

configuration is widely used, and is generally capable of

achieving biological nitrogen removal to effluent TN concen-

trations <10 mgN L�1. However, little or no excess biological

phosphorus removal (EBPR) can be achieved with the MLE

configuration, and hence it relies upon chemically-assisted

precipitation to achieve low effluent TP concentrations (i.e.

Cases 5 and 6). In Cases 1–6, mesophilic anaerobic digestion

was adopted for solids stabilisation and energy recovery (heat

and electricity) through biogas combustion.

Cases 7–9 represented ‘‘advanced’’ nutrient removal,

through the use of the 5-stage (anaerobic, primary anoxic,

primary aerobic, secondary anoxic, secondary aerobic) Bar-

denpho process configuration. These cases represented best

practice for nutrient removal, being capable of achieving

effluent TN < 3 mgN L�1 and TP < 1 mgP L�1, with EBPR and

chemically-assisted precipitation. The Bardenpho process has

been implemented in many developed countries for advanced

nutrient removal. In Cases 7–9, solids stabilisation by anaer-

obic digestion was replaced by extended aeration in the

secondary treatment bioreactors. This was reflective of recent

trends in BNR plants to avoid primary sedimentation and the

associated loss of chemical oxygen demand (COD) for deni-

trification in the secondary treatment process. However even

in these extended aeration scenarios, waste activated sludge

storage for 180 days (in an uncovered lagoon) was required to

satisfy biosolids stabilisation requirements for agricultural

land application (NRMMC, 2004).

The functional unit for this study was defined as: ‘‘The

treatment of 10 ML d�1 of raw domestic wastewater

(5000 kgCOD d�1, 500 kgN d�1, 120 kgP d�1) over 20 years. The

resulting biosolids must also be in compliance with the

Australian national guidelines for agricultural land

application’’.

The system boundary was drawn at the raw sewage

arriving at the WWTP and included all discharges to the

receiving environments (Fig. 2). No consideration was given to

upstream infrastructure (e.g. sewers, pumping stations),

consumables (e.g. oxygen for odour control) or emissions (e.g.

methane from rising mains – refer to Guisasola et al., 2008;

Guisasola et al., 2009). For consistency with IPCC accounting

guidelines (IPCC, 2006a), it was assumed that 100% of the

organic carbon in the raw sewage was biogenic. However,

recent evidence suggests that there may a substantial fossil-

carbon signature in domestic wastewater from the disposal of

such items as detergents and soaps (Griffith et al., 2009). For

the aquatic receiving environment, it was assumed that 100%

of the treated effluent was disposed to an environmentally

sensitive estuary. All stabilised, dewatered biosolids were

assumed to be transported by road to agricultural land for use

as organic fertiliser, in compliance with Stabilisation/Path-

ogen Grade P3 of the Australian biosolids management

guidelines for agricultural land application (NRMMC, 2004),

which are largely based on United States EPA regulations

(USEPA, 1992, 1999).

The system boundary included first-order processes

(e.g. direct atmospheric emissions, effluent discharges) and

second-order processes (e.g. purchased electricity generation,

chemicals manufacture) for the construction and operating

phases only. Processes associated with the end-of-life phase

were ignored since they are generally negligible, when

compared with the operating and construction phases

(Emmerson et al., 1995; Zhang and Wilson, 2000). Since

biosolids were assumed to be land-applied as organic fertil-

iser, it was assumed that the synthetic fertiliser, diammonium

phosphate (DAP) was displaced. Processes associated with the

avoided DAP were included as a credit to the scenarios, which

was consistent with the approach of earlier authors (e.g.

Lundin et al., 2000). Similarly, where electricity was produced

from biogas, the avoided impacts of the displaced electricity

from the east Australian grid (90.8% coal-fired, 5.0% natural

gas-fired, and 4.2% renewables) were credited to the scenario

(Grant, 2007).

The construction of this study was based on the specific

Australian experience of the authors, local regulatory condi-

tions and environmental constraints. However, the process

configurations, treatment standards, broad regulatory

constraints and environmental drivers are representative of

those in most developed countries. Therefore, the construc-

tion of these WWTP scenarios and the resultant conclusions

are globally relevant in many respects.

3. Modelling and design approach

3.1. Operating phase inventory

All ten scenarios were constructed using the BioWin� simu-

lation package (v.3.0.1.802), common engineering design

methods and the collective experience of the co-authors.

BioWin is a widely-used Windows-based simulator for the

design of wastewater treatment processes. It uses an inte-

grated kinetic model and mass balance approach, incorpo-

rating pH/alkalinity and general Activated Sludge/Anaerobic

Digestion Models that tracks over 50 components through

more than 80 processes (Envirosim, 2007). Only steady-state

simulations at average conditions were conducted. The

Table 1 – Influent characteristics and general plantparameters.

Parameter Value

Average dry weather flow (ADWF) 10 ML d�1

Peak wet weather flow (PWWF) ratio 3.0 � ADWF

Ambient water and air temperature 20 �C

Winter air temp. for digester

heating calculations

15 �C

Plant altitude 20 m

Influent Chemical

Oxygen Demand (COD)

500 mgCOD L�1

Influent Total

Kjeldahl Nitrogen (TKN)

50 mgN L�1

Influent Total Phosphorus (TP) 12 mgP L�1

Influent pH and alkalinity 7.2, 5 mmol L�1

(250 mg L�1 as CaCO3)

Influent inorganic suspended solids 30 mg L�1

Influent calcium and magnesium 50 mgCa L�1, 15 mgMg L�1

Fraction of readily biodegradable

COD

0.2 gCOD gCODtotal�1

(default ¼ 0.16)

Fraction of unbiodegradable

particulate COD

0.2 gCOD gCODtotal�1

(default ¼ 0.13)

Fraction of soluble

unbiodegradable TKN

0.01 gN gTKN�1

(default ¼ 0.02)

All other influent COD, TKN and TP fractionation parameters in

BioWin were left at default values, except for those listed above.

Fig. 2 – System boundary for life cycle inventory of WWTP scenarios.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 6 1657

steady-state raw wastewater characteristics and general plant

parameters are shown in Table 1. Tables 2–4 summarise the

basic design parameters adopted for the construction of each

scenario. Table 4 also shows the uncertainty ranges of the

design assumptions for GHG calculations and biosolids

nutrient availability.

In general, the model parameters of the BioWin simulator

were left at default values. However, the ammonium-oxidis-

ing bacteria (AOB) and nitrite-oxidising bacteria (NOB)

substrate half-saturation constants were lowered for Case 9

only (0.35 mgN L�1 cf. default 0.70 mgN L�1; 0.02 mgN L�1 cf.

default 0.10 mgN L�1, respectively), guided by the work of

Ciudad et al. (2006) on the kinetics of AOB and NOB at low

concentrations of ammonium and nitrite.

The BioWin aeration model parameters were also adjusted

to achieve a standard oxygen transfer efficiency (SOTE) of

approximately 6.5% per mreactor depth, based on values typically

stated by suppliers of fine bubble aeration diffusers. Average

aeration blower power was calculated based on ambient

temperature (20 �C), airflow, diffuser face pressure (which

included 5 kPa losses for fouling and 40% additional minor

losses in the aeration pipework), and an overall mechanical-

electrical efficiency of 55% (Tchobanoglous et al., 2003). The

blowers were sized for a diurnal aeration peaking factor of 1.5.

Power consumption for all pumps was calculated, based on

flowrate and assumed pumping head. Hydraulic efficiencies

were estimated from standard curves (Sinnott, 2000) and

motor efficiency was assumed to be 90% in all cases. The

Table 2 – Summary of design parameters for wastewater treatment scenarios.

Process unit Design parameter Value

Primary Sedimentation

Tanks Cases 1–6

Hydraulic Retention Time (HRT)

at ADWF

3 h

Underflow 0.10 ML d�1 at 1.5% dry solids ((d.s.);

80 h wk�1 operation

Scraper drive 5 kW continuous operation

Activated Sludge Bioreactor

Cases 2 and 3

HRT at ADWF 1.5 h

Solids Retention Time (SRT) 1.3 d for Case 2 (organics removal only);

10 d for Case 3 (nitrification)

Mixed Liquor Suspended Solids

(MLSS) concentration

2700 mg L�1 for Case 3; 3500 mg L�1 for Case 3

Dissolved Oxygen (DO) concentration 2.0 mg L�1

MLE Nitrogen Removal Bioreactor

Cases 4–6

SRT 13 d for Cases 4 and 5; 15 d for Case 6

MLSS concentration 2500 mg L�1 for Case 4; 3500 mg L�1 for Cases 5 and 6

DO concentration 2.0 mg L�1

a-recycle ratio 0.7 � ADWF for Cases 4 and 5; 4.0 � ADWF for Case 6

Anoxic mass fraction 23% for Cases 4 and 5; 50% for Case 6

Ferric chloride (FeCl3) dosing

(43 wt% solution)

0 mgFe L�1 for Case 4; 13 mgFe L�1 for Cases 5 and 6

Methanol dosing (100 wt%) 100 L d�1 for Case 6 only

(equivalent to 12 mgCOD L�1)

Bardenpho BNR Bioreactor

Cases 7–9

Anaerobic zone HRT at ADWF 1.5 h

SRT 20 d for Cases 7 and 8; 25 d for Case 9

MLSS concentration 3500 mg L�1 for Case 7, 4100 mg L�1 for Cases 8 and 9

a-recycle ratio 3.4 � ADWF for Cases 7 and 8; 6.0 � ADWF for Case 9

Anoxic mass fraction 55%

DO concentration 1.2 mg L�1 in primary aerobic zone;

1.5 mg L�1 in secondary aerobic zone

Ferric chloride (FeCl3) dosing

(43 wt% solution)

11 mgFe L�1 for Case 7; 24 mgFe L�1 for

Cases 8 and 9

Methanol dosing (100 wt%) 180 L d�1 for Case 9 only

(equivalent to 21 mgCOD L�1)

All Bioreactors Cases 3–9 Depth 4.5 m

Aspect ratio (length:width) 10:1

Anaerobic and anoxic

zone mixing – velocity gradient

1900 s�1 (or 4 W m�3)

Lime solution

dosing (19.8 wt% Ca(OH)2)

4000 L d�1 (equivalent to 120 mgCaCO3 L�1)

Secondary Sedimentation

Tanks Cases 2–9

Solids loading 1.2 � average MLSS at PWWF

RAS Ratio 0.7 � ADWF; 0.6 � PWWF

Scraper drive 2 kW continuous operation

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 61658

secondary sedimentation tanks (SSTs) were modelled using

the modified flux engineering design procedure of Ekama et al.

(1997).

Biosolids were transported in 20 tonne articulated trucks

for 200 km to agricultural land application sites, where they

were assumed to replace DAP fertiliser (18 wt% N, 20 wt% P) on

a limiting nutrient basis. The biosolids were mechanically

spread onto the land, using 0.325 L diesel per wet tonne

(Johansson et al., 2008). Heavy metals in the biosolids were

calculated using data from 17 BNR plants across Queensland,

Australia (refer to Table 5). Avoided heavy metals in the dis-

placed DAP fertiliser were calculated from data on 15 DAP

fertilisers from several literature references (Charter et al.,

1993; McLaughlin et al., 1996; de Lopez Camelo et al., 1997;

Batelle Memorial Institute, 1999; Nicholson et al., 2003; Saltali

et al., 2005; Washington State Department of Agriculture,

2008) (refer also to Table 5 for 10th and 90th percentiles of

heavy metal reference data).

Emissions of CH4, H2, N2 and NH3 from the bioreactors were

calculated using the BioWin mass balance and mass transfer

models. BioWin did not calculate N2O emissions, but these

were estimated using the emission factors assumed in Table 4.

Carbon dioxide emissions from the oxidation of sewage

organics are not counted under current protocols, because they

are assumed to be 100% biogenic (IPCC, 2006a). However, in

scenarios that include methanol dosing (made from non-

renewable natural gas), CO2 emissions were calculated based

on COD concentration (1.18 kg L�1), total organic carbon to COD

ratio (0.25 kgC kgCOD�1) and assuming that ultimately 100% of

the methanol was oxidised.

3.2. Construction phase inventory

Based on the engineering design of each scenario, the volume

of reinforced concrete in the main civil structures was calcu-

lated for each scenario (i.e. Cases 1–9). The concrete volume

Table 3 – Summary of design parameters for sludge handling scenarios.

Process unit Design parameter Value

Anaerobic Digestion Cases 1–6 HRT at ADWF 22 d

Mechanical mixing 8 W m�3

Heat transfer coefficients Above-ground 300 mm-thick, un-insulated concrete

walls: 5.0 W m�2 K�1; 300 mm-thick concrete floor

in dry earth: 1.7 W m�2 K�1; 35 mm wood-deck floating

cover with no insulation: 2.0 W m�2 K�1

(Tchobanoglous et al., 2003) Additional allowance

of 10% of the total heating demand was made for

heat losses through digester pipework

Sludge recirculation 24 h turnover

Water bath heat exchanger 85% thermal efficiency; Heat supplied from co-generation

gas engines

Co-generation Gas Engines Combustion efficiency 99%

Thermal efficiency 38% (Winnick, 1997)

Sludge Stabilisation Lagoon Cases 7–9 HRT at ADWF 180 d

Primary Sludge And Waste Activated

Sludge – Gravity Belt Thickener (GBT)

and Stabilised Sludge Dewatering – Belt

Filter Press (BFP) All cases

Polymer dosing 7 kg per tonne d.s.

Solids capture 95%

Power 15 kW, operating 80 h wk�1

Biosolids solids content 20% d.s.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 6 1659

for each scenario was then used as a multiplier for the

consumption of other materials and processes in the

construction phase of each scenario (refer to Table 6), as

defined by previously catalogued construction inventory data

from Swiss WWTPs (Doka, 2003).

Each aeration diffuser was assumed to consist of 0.5 kg of

ethylene propylene diene M-class (EPDM) perforated

membrane material, plus a 1 kg polypropylene support frame.

Diffusers were assumed to be transported 1000 km by road to

the WWTP, with an operating life of five years before

replacement.

The type and mass of materials in each electric motor and

pump was calculated using parameterisation expressions,

based on rated kW for motors (Mueller et al., 2004; de Almeida

et al., 2007), and hydraulic flowrate for pumps (Falkner and

Dollard, 2007).

4. Life cycle inventory results

The results of the engineering design exercise for the treat-

ment plant scenarios are summarised in the process flow

diagrams (PFDs) of Fig. 3. Based on these engineering designs,

full inventories of the resources and environmentally-rele-

vant emissions in the construction and operating phases of

each scenario were developed. This comprehensive data set

and more detailed PFDs are attached in the Supporting

Information for all 49 scenarios.

Shown in Figs. 4–6 are comparisons of selected inventory

data for the ten treatment scenarios.

Whilst being instructive in their own right, these inventory

data could also be used for life cycle impact assessment

(LCIA), in a full LCA. Analysis of the scenarios, using the

variously available mid-point and end-point LCIA methodol-

ogies (e.g. IMPACT, 2002þ, refer to Jolliet et al., 2003) would

better establish the relative environmental burdens caused by

different process configurations and levels of treatment.

However, this paper presents the life cycle inventory results

only.

5. Discussion

5.1. Infrastructure resources

The tonnage of concrete used in each scenario was a useful

proxy indicator of resource intensity in the construction

phase. From Fig. 4, it is clear that the demand for infrastruc-

ture resources generally increased with higher levels of

nutrient removal. The largest increases in infrastructure

requirements occurred in moving from Case 0 (‘‘do nothing’’)

to Case 1 (primary treatment), and then to Case 2 (activated

sludge). From Case 2 to Case 7, there were further incremental

increases in the infrastructure requirements, as the size of

bioreactors increased with longer SRTs and additional FeCl3dosing. Cases 8 and 9 were the most resource-intensive of all

scenarios, due to the very low effluent TP required of these

scenarios (TP < 1 mg L�1). Whilst the Bardenpho process

configurations did achieve EBPR, FeCl3 dosing up to

24 mgFe L�1 was required for enhanced chemical precipita-

tion. The additional solids loading was accommodated using

larger SSTs (i.e. more infrastructure), for it was assumed that

the settling rate was unaffected by the added FeCl3.

From this analysis it was evident that improved levels of

wastewater treatment and nutrient removal caused an

increased environmental burden in terms of resources

required for the physical infrastructure of the plant.

5.2. Chemical use

Chemicals consumption in Cases 1 and 2 were negligible,

because only primary and secondary (organics removal)

treatment were necessary. From Case 3 onwards, lime addi-

tion was necessary for alkalinity correction in the nitrification

process. However, the large increases in overall chemical

Table 4 – Summary of design parameters for GHG emissions and biosolids land application.

Parameter Units Low-rangevalue

Mid-rangevalue

High-rangevalue

Reference

CH4 from PSTs kg CH4 per kg COD

removed

0 0.0125 0.025 (IPCC, 2006a; Table 6.3)

N2O from

secondary treatment

kg N2O–N per kg N

denitrified

0.0003 0.01 0.03 (Foley et al., 2008)

CH4 from effluent

discharge to estuary

kg CH4 per kg COD

discharged

0 0.025 0.05 (IPCC, 2006a; Table 6.3)

N2O from

effluent discharge

to estuary

kg N2O–N per kg N

discharged

0.0005 0.0025 0.005 (IPCC, 1997; Tables 4–23; 2006b; Table

11.3)

CH4 from biogas

combustion

g CH4 per Nm3 biogas – 16.02 – (Doka, 2003)

N2O from

biogas combustion

g N2O per Nm3 biogas – 0.73 – (Doka, 2003)

Direct N2O volatilisation

from biosolids and DAP

kg N2O–N per kg N

biosolids

0.003 0.01 0.03 (Doka, 2003; IPCC, 2006b; Table 11.1)

NH3 volatilisation from

biosolids

kg NH3–N per kg N

biosolids

0.05 0.20 0.50 (Lundin et al., 2000; Doka, 2003; IPCC,

2006b; Table 11.3)

NH3 volatilisation

from DAP

kg NH3–N per kg N

biosolids

0.03 0.10 0.30 (IPCC, 2006b; Table 11.3)

Indirect N2O via NH3

volatilisation from

biosolids and DAP

kg N2O–N per kg NH3–N

volatilised

0.002 0.01 0.05 (IPCC, 2006b; Table 11.3)

Indirect N2O via N

leaching from

biosolids and DAP

kg N2O–N per kg N

leached

0 0 0 Assumed dryland

region (precipitation < evapo-

transpiration)

(IPCC, 2006b; section 11.2.2.2)

Carbon sequestration

in soil via biosolids

application

kg C per kg C applied to

soil

0 0.1 0.2 (Gibson et al., 2002; Li and Feng, 2002)

Assumed 0.37 kg C per kg COD for

biosolids

(Ekama et al., 1984)

Bio-availability of N in

biosolids

– 25% 50% 75% (USEPA, 1995; O’Connor et al., 2002;

Lundin et al., 2004; Houillon and

Jolliet, 2005; Barry and Bell, 2006;

Johansson et al., 2008)

Bio-availability of P

in biosolids

– 25% 50% 75%

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 61660

consumption coincided with increased P removal require-

ments and hence FeCl3 dosing. The chemical consumption

jumped substantially from Case 4 (TP < 9 mg L�1) to Case 5

(TP < 5 mg L�1), and then again from Case 7 (TP < 5 mg L�1) to

Case 8 (TP < 1 mg L�1).

A small decrease in chemical consumption was seen in the

transition from the MLE process in Case 6 to the 5-stage Bar-

denpho process in Case 7. To achieve TN< 10 mg L�1 in Case 6,

the MLE process required some methanol dosing

(12 mgCOD L�1), as there was insufficient COD in the primary

effluent for denitrification. In the Bardenpho configuration of

Case 7 however, there was sufficient COD in the raw waste-

water to achieve TN < 5 mg L�1, without methanol dosing.

This represented a small positive environmental outcome for

the more advanced level of nutrient removal. However, to

achieve an even lower effluent TN in Case 9 (TN < 3 mg L�1),

methanol dosing was required at 21 mgCOD L�1.

Overall, it was evident that improved levels of wastewater

treatment and nutrient removal generally caused an

increased environmental burden in terms of consumption of

synthetic chemicals. These chemicals require additional

resources and energy for manufacture, and further resources

and energy for transportation to the WWTP. Whilst not

captured at this inventory stage of the LCA, further charac-

terisation and impact assessment would determine the

additional embodied resources and emissions represented by

the increased use of chemicals. This should be the subject of

a full LCA investigation.

5.3. Operational energy

The best scenario from an energy perspective was Case 1 –

basic primary treatment, anaerobic sludge digestion and

energy recovery from biogas. This configuration had a positive

energy balance and was able to export a small amount of

electricity. The transition to activated sludge secondary

treatment (Case 2) required substantial importation of elec-

trical energy, and even more so to achieve nitrification in Case

3. For Cases 3–6 however, increased nitrogen removal required

no additional energy. The increase in aeration energy for

Table 5 – Heavy metal concentrations (mg kgL1) in biosolids and DAP fertiliser.

Heavy metal Biosolids Diammonium phosphate (DAP)

10th %ile 50th %ile 90th %ile No. of Plants 10th %ile 50th %ile 90th %ile No. of Refs

Arsenic 2.7 4.5 9.4 17 10.0 16.0 22.6 7

Cadmium 1.1 2.0 2.4 17 3.8 20.0 93.4 15

Chromium 10.8 23.2 39.2 17 63.5 133.5 402.0 6

Copper 210.2 280.0 459.4 17 1.4 2.9 34.5 8

Lead 12.2 37.0 62.0 17 4.9 8.0 16.3 16

Nickel 9.3 16.4 21.7 16 3.5 24.5 153.1 14

Zinc 212.7 492.8 775.5 17 20.5 135.0 1319.0 14

Mercury 0.4 1.3 3.6 17 0.0 0.1 1.1 6

Selenium 2.9 3.7 5.5 15 0.8 1.0 10.0 6

Molybdenum 3.4 6.8 7.4 3 2.6 13.0 21.0 6

Italic items indicate the average DAP metals concentration is significantly greater than the average biosolids metals concentration

(t-dist., a ¼ 0.05)..

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 6 1661

larger biomass inventories and higher a-recycle rates appears

to have been offset by the savings garnered from increased

denitrification. This represents a positive environmental

outcome in that incremental nitrogen removal from

TN < 40 mg L�1 up to TN < 10 mg L�1 can be achieved with

minimal overall additional energy input, within a basic

anoxic-aerobic MLE process configuration.

However, there was a distinct increase in the energy

demand of the advanced Bardenpho configurations,

compared to the MLE/anaerobic digestion configuration. This

was due mainly to the energy recovery possible from the

combustion of biogas in the MLE Cases 4–6, but also to the

longer SRTs and larger bioreactors required for extended

aeration in Cases 7–9.

Fig. 6 also demonstrates that the increase in operational

power consumption for additional phosphorus removal was

Table 6 – WWTP construction materials and processes.

Material/Construction Process Value (per m3 concretein civil structures)

Excavation by hydraulic digger 3.48 m3

Material transportation

by 28 tonne lorry

49.29 t km

Material transportation by rail 58.30 t km

Electricity consumption

for construction

0.04 kWh

Reinforcing steel 77.58 kg

Water consumption 121.98 kg

Aluminium 0.87 kg

Limestone 21.45 kg

Chromium steel (stainless steel) 6.23 kg

Fibreglass 1.96 kg

Copper 0.92 kg

Synthetic rubber (EPDM) 0.88 kg

Rock wool (insulation material) 0.87 kg

Organic chemicals 4.05 kg

Bitumen 0.50 kg

Inorganic chemicals 0.50 kg

Low density polyethylene (LDPE) 0.02 kg

High density polyethylene (HDPE) 2.44 kg

Polyethylene terephthalate (PET) 2.46 kg

minimal. This was seen in the transition from Case 4

(TP < 9 mg L�1) to Case 5 (TP < 5 mg L�1), which required no

additional energy; and in the transition from Case 7

(TP < 5 mg L�1) to Case 8 (TP < 1 mg L�1), which required

minimal additional energy. The additional P removal was

achieved via increased chemical dosing, rather than any

increased operational energy input.

Overall, it was evident that primary treatment and basic

activated sludge treatment were the most favourable options

from an energy consumption perspective. The net energy

input tripled from Case 2 to Case 3 in achieving nitrification.

This represents a major negative environmental outcome.

However, once nitrification had been achieved, then effluent

nitrogen was reduced to TN < 10 mg L�1 by improved deni-

trification, for minimal additional energy input. This repre-

sented a positive environmental outcome. It was only in

pursuing lower effluent TN levels that marginally increased

energy may have been required. Therefore, in an environ-

mental trade-off between energy consumption and level of

nutrient removal, these results suggest there is likely to be

some optimum which minimises the combined environ-

mental burden of eutrophication from effluent discharge and

fossil-energy resource consumption.

5.4. Direct greenhouse gas emissions

In Fig. 5, direct GHG emissions are reported by gas type (CO2,

CH4, N2O) and in total. These emissions were directly from the

process units of the treatment plants, the effluent receiving

environment and the biosolids receiving environment. They

do not include the embodied GHG emissions associated with

plant infrastructure, chemical consumption or operational

energy use. However, it is worth noting that these embodied

emissions, especially for fossil-dependent energy consump-

tion, can dominate the life cycle GHG emissions profile of

a WWTP (Gallego et al., 2008). For example, 1 kWh of Austra-

lian electricity embodies approximately 0.9–1.1 kg CO2-e

(Grant, 2007).

In this analysis, the CO2 emissions were associated with

the oxidation of non-renewable methanol (Cases 6 and 9 only),

and the soil carbon sequestration potential from biosolids

land application. At the assumed sequestration rates

Fig. 3 – Process flow diagrams (A: Cases 1–6; B: Cases 7–9) and design summary.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 61662

-1,000

1,000

3,000

5,000

7,000

TN50,TP12

TN46,TP10

TN40,TP9

TN40,TP9

TN20,TP9

TN20,TP5

TN10,TP5

TN5,TP5

TN5,TP1

TN3,TP1

Effluent Quality

dna )sennot( noitcurtsnoC ni etercno

Cd.gk( es

U lacimeh

C latoT1-)

-1,000

1,000

3,000

5,000

7,000

0 esaCC

1 esaC

2 esaCesa

3 C

sa4 e5 esaC

6 esaC

7 esaC

8 esaCC

9 esa

d.hWk( noitp

musnoC ygrenE te

N1-)Concrete

Chemicals

Energy

Fig. 4 – Resource consumption inventory results – concrete

used in construction, daily chemical consumption and

daily net electricity consumption.

0

100

200

300

d.gk(PA

Ddecalpsi

D1-)

Csae 0

Csa

1eC

sa2e3esaCC

sae 4

Csa

5eC

sa6e

Csa

7e8esaCCsa

9e

-1,000

-500

0

500

1,000

1,500

2,000

TN50,TP12

TN46,TP10

TN40,TP9

TN40,TP9

TN20,TP9

TN20,TP5

TN10,TP5

TN5,TP5

TN5,TP1

TN3,TP1

Effluent Quality

d.g(lioSot

slateM

yvaeH

1-) Biosolids

DAP

Fig. 6 – Daily displacement of DAP fertiliser by biosolids

application to agricultural land; Daily discharge of heavy

metals to agricultural soil by biosolids and displaced DAP.

Error bars represent the 10th to 90th percentile of the

uncertainty range, due to low-range and high-range

assumptions of bio-availability in Table 4, and the 10th

and 90th percentile heavy metal concentrations in Table 5.

Uncertainty analysis conducted using a 1000-run Monte

Carlo analysis in MS Excel.

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 6 1663

(0–0.2 kg C sequestered per kg C applied – refer to Table 4), it is

clear that the carbon sequestration potential of biosolids was

fairly limited for these scenarios. This is in contrast to claims

by other authors that carbon sequestration via basic activated

sludge offers a large-scale opportunity for mitigation of GHG

emissions (Rosso and Stenstrom, 2008; Peters and Rowley,

2009).

Methane emissions were associated with effluent

discharge, and direct emissions from the process units. The

BioWin model predicted small emissions of methane and

hydrogen from the secondary treatment process (<10% of

influent COD), mainly by being stripped from solution in the

highly turbulent, aerated reactors. The emitted methane was

caused, in part, by recycling from the anaerobic sludge

lagoons/digesters, but also by limited fermentation in the

activated sludge reactors. In Cases 0 and 1, the large COD load

in the effluent was estimated to cause substantial methane

emissions by inducing methanogenic conditions in the

receiving waters. Clearly, this result will be site-specific, as

some deep-ocean outfalls may be sufficiently aerated to

assimilate high COD loads without significant methane

generation. However, this analysis clearly highlights

-0.1

0.1

0.3

0.5

0.7

0.9

TN50,TP12

TN46,TP10

TN40,TP9

TN40,TP9

TN20,TP9

TN20,TP5

TN10,TP5

TN5,TP5

TN5,TP1

TN3,TP1

Effluent Quality

OCt(

snoissimE

GH

GtceriD

2L

M.e-1-)

Csa

0eC

sa1e2esaC

3esaC

4esaC

5esaCC

6esaC

7esaCesa

8

Csa

9e

CO2 CH4N2O Total

Fig. 5 – Daily greenhouse gas emissions by gas type (CO2,

CH4 and N2O) and total. Error bars represent the 10th to

90th percentile of the uncertainty range, due to low-range

and high-range assumptions in Table 4. Uncertainty

analysis conducted using a 1000-run Monte Carlo analysis

in MS Excel.

a significant GHG risk associated with low levels of waste-

water treatment. The transition to activated sludge secondary

treatment with anaerobic digestion (Cases 2–6) significantly

lowered the methane emissions. Most of the organic load was

aerobically degraded to CO2, which was considered GHG-

neutral from an IPCC accounting perspective. The majority of

methane generated anaerobically in the digesters was

captured for useful purposes. In the transition to advanced

nutrient removal in Cases 7–9, methane emissions rose

sharply. This was due to the assumed use of open sludge

stabilisation lagoons for these cases. It represented a negative

environmental outcome for the more advanced nutrient

removal cases modelled here. This study highlights the risk of

methane emissions from the use anaerobic lagoons for sludge

treatment. For advanced BNR, process designs have generally

moved away from anaerobic digestion for sludge stabilisation.

At a basic level, nitrous oxide emissions were seen to

increase with the level of nitrogen removal. However from

Fig. 5, it is clear that much uncertainty remains in the quan-

tification of N2O emissions from BNR processes. Recent

evidence suggests that plants with greater levels of nitrogen

removal (e.g. Cases 7–9) have lower N2O emission factors than

plants that achieve intermediate levels of nitrogen removal

(e.g. Cases 3–6) (Foley et al., in press). This issue requires

further detailed investigation because Fig. 5 demonstrates

that the N2O emissions dominated the overall GHG profiles of

the different scenarios.

Overall, it was evident that from a direct GHG emissions

perspective, basic secondary wastewater treatment appeared

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 61664

to be the most favourable option. ‘‘Do nothing’’ and primary

treatment caused large CH4 emissions in the receiving envi-

ronment, and nitrogen removal leads to the risk of increased

N2O emissions. It was also evident that significant GHG

benefits can be realised from anaerobic digestion and energy

recovery from biogas combustion.

5.5. Biosolids and heavy metals

A key element of this study was the expansion of the system

to include the environmental impacts of agricultural land-

applied biosolids, and the potential for displacing synthetic

fertiliser (e.g. DAP). In Fig. 6A, it can be seen that increased

phosphorus removal at the WWTP resulted in the displace-

ment of more DAP in agriculture, particularly in moving from

Case 4 (TP < 9 mg L�1) to Case 5 (TP < 5 mg L�1), due to the

higher biosolids P content. There was negligible change in

displaced DAP due to improved nitrogen removal, since this

was achieved through denitrification to N2 (or N2O) gas. In

general, phosphorus was the limiting nutrient in the calcula-

tion of DAP displacement by biosolids.

This analysis highlights the potential value of WWTPs for

phosphorus recovery and reuse, rather than phosphorus

removal simply for the sake of receiving water quality.

Whether the overall impacts of land-applied biosolids are

better or worse than those of the displaced synthetic fertiliser

requires further analysis at an impact assessment level.

However, these inventory data clearly indicate the potential

for phosphorus recovery from sewage via biosolids, to achieve

increased displacement of synthetic non-renewable products.

Fig. 6B illustrates the flows of heavy metals associated with

the biosolids, as compared with that of the potentially dis-

placed DAP. There were substantially larger heavy metal loads

associated with biosolids, compared to synthetic fertiliser

application. Whilst the concentration of some heavy metals in

synthetic fertilisers can be higher than in biosolids (i.e.

arsenic, cadmium, chromium, nickel – refer to Table 5), the

tonnage of biosolids required to satisfy the same nutrient (P)

application rate as a concentrated synthetic fertiliser gave

much higher effective metals loading rates to land. Therefore

it must be concluded that, from a heavy metals inventory

perspective, the application of biosolids to agricultural land

had negative environmental outcomes, compared to the

equivalent application of synthetic fertilisers. It should be

noted however that not all the metals in the biosolids and

fertilisers will be bio-available to crops (Peters and Rowley,

2009). The quantity of heavy metals in biosolids was fixed by

the quantity of heavy metals in the influent raw wastewater.

Therefore, there exists an opportunity to address this issue by

strong source control.

5.6. Positive and negative environmental trade-offs ofwastewater treatment

Overall, Figs. 4–6 provide useful proxy indicators of the

increased intensity in resource consumption and environ-

mental emissions that occur with a societal push towards

higher effluent quality standards for WWTPs. A key negative

environmental trade-off is highlighted, namely, improved

local receiving water quality (in terms of eutrophication

status) may come at the expense of higher resources for

WWTP construction, higher electricity and chemicals

consumption for operation, and higher direct GHG emissions.

These additional environmental burdens, albeit more widely

dissipated, may be carried by a much larger population of

people than those that benefit directly from the improved

receiving water quality. Importantly, Fig. 6 shows the poten-

tial for increased phosphorus nutrient recovery (and hence

lower discharge to receiving waters), but at the cost of higher

export of heavy metals discharged to agricultural soil,

compared to an equivalent application of synthetic DAP

fertiliser.

To date, there has been insufficient data in the public

domain for the water industry and environmental regulators

to consider the negative and positive environmental trade-

offs that arise from improved levels of wastewater treat-

ment. As a starting point, this paper provides the inventory

data needed to identify the basis for these trade-offs, but can

make only limited comparisons. To undertake further

comparisons requires environmental life cycle impact

assessment modelling. By means of normalisation against

the total environmental burdens imposed by the wider

population, life cycle assessment enables an analysis of the

relative size of different environmental impacts. Ultimately

such an analysis allows inherently subjective conclusions to

be drawn on damage in areas such as ecosystem quality,

human health, climate change and resource depletion. In

this way, it would be possible to assess whether the general

increase in consumption of non-renewable resources and

environmentally-relevant emissions caused by more

sophisticated wastewater treatment is justified. Such justifi-

cation would test the basis of environmental protection

legislation whereby improved local water quality is traded off

against impacts elsewhere (e.g. greenhouse gas emissions or

impacts associated with manufacture, transport and use of

chemicals).

6. Conclusions

This paper has presented a comprehensive desktop life cycle

inventory analysis of ten different wastewater treatment

scenarios, covering six process configurations and treatment

standards ranging from raw sewage to advanced nutrient

removal. The inventory data provided indicates that infra-

structure resource consumption increases with lower

effluent nitrogen and phosphorus targets for wastewater

treatment. As expected, chemical consumption increases

sharply with phosphorus removal, where the wastewater

composition poses limitations on the extent of biological

phosphorus removal that can be achieved. Similarly, with

nitrogen removal where supplementation of biological

carbon (energy) sources is necessary, chemical dosing

requirements increase. In terms of operational energy

consumption, basic primary and secondary treatment are

the most favourable. However, if BNR is to be employed,

achievement of TN 10 mg L�1 can be done at the same

energy consumption as TN 40 mg L�1. Targets below TN

10 mg L�1 require additional operational energy. Similarly,

direct GHG emissions might be minimised at basic secondary

w a t e r r e s e a r c h 4 4 ( 2 0 1 0 ) 1 6 5 4 – 1 6 6 6 1665

treatment. ‘‘Do nothing’’ and primary treatment cause large

CH4 emissions in the receiving environment, and nitrogen

removal leads to increased risk of N2O emissions. These

trends represent significant negative environmental trade-

offs for improved nutrient removal and hence better local

receiving water quality.

Increased phosphorus removal in WWTPs should also be

viewed as an opportunity for increased phosphorus recovery,

where biosolids are applied to agricultural land. This positive

trade-off is not apparent for nitrogen removal, since higher air

emissions (including nitrous oxide) usually result, rather than

improved recovery of nitrogen in biosolids. However, inno-

vative nitrogen recovery processes (e.g. struvite precipitation)

could be designed to realise similar advantages in some

WWTP configurations.

Further analysis of these positive and negative environ-

mental trade-offs requires life cycle impact assessment and

an inherently subjective weighting of the competing envi-

ronmental costs and benefits.

Acknowledgements

The authors thank the Queensland State Government’s

Growing the Smart State PhD Funding Program for funding part of

this research.

Supporting information available

Scenario descriptions, and life cycle inventory data for 49

WWTP scenarios.

Appendix.Supplementary data

Supplementary data associated with this article can be found,

in the online version, at doi:10.1016/j.watres.2009.11.031.

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