Comprehensive life cycle inventories of alternative wastewater treatment systems

water research 44 (2010) 1654–1666

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Comprehensive life cycle inventories of alternative wastewater treatment systems Jeffrey Foley a, David de Haas a, Ken Hartley b, Paul Lant a,* a b

Advanced Water Management Centre, The University of Queensland, St Lucia 4072, Australia Ken Hartley Pty Ltd, Unit F1c, 235 Forest Lake Boulevard, Forest Lake 4078, Australia

article info

abstract

Article history:

Over recent decades, the environmental regulations on wastewater treatment plants

Received 29 July 2009

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

Received in revised form

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

29 July 2009

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

Accepted 14 November 2009

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

Available online 2 December 2009

produced in ten different wastewater treatment scenarios (covering six process configurations and nine treatment standards). The inventory results indicate that infrastructure

Keywords:

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

Life cycle inventory

consumption generally increase with increasing nitrogen removal, especially at discharge

Biological nutrient removal

standards of total nitrogen <5 mgN L1. Similarly, infrastructure resources and chemical

Energy

consumption increase sharply with increasing phosphorus removal, but operational

Nutrient recovery

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

Global environmental impacts

of negative environmental impacts against improved local receiving water quality.

Effluent standards

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

Greenhouse gas

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

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 authorities 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

come at a cost of higher resource consumption (e.g. energy, chemicals, infrastructure) and elevated environmental emissions (e.g. greenhouse gases to atmosphere, biosolids to landfill). To date, these additional environmental burdens have been largely ignored in the regulatory push for cleaner local waterways. 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

* Corresponding author. Tel.: þ61 7 3365 4728; fax: þ61 7 3365 4726. E-mail address: [email protected] (P. Lant). 0043-1354/$ – see front matter ª 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2009.11.031

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-1

Effluent Total Phosphorus (mg.L )

of their construction and operation. This paper uses the internationally standardised LCA framework (ISO, 2006) to quantitatively define the inventory of resources consumed and emissions produced in the typical life cycle of different centralised 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 environmental 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 fertilisers by biosolids. Other studies have focused more upon small and decentralised 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).

However, these studies only considered BNR effluents with approximately 10–20 mgN L1 as total nitrogen (TN), and did not specify the nutrient limitations of the receiving water body. There are few studies that have examined the environmental 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 L1, total phosphorus, TP < 1 mgP L1). 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 international and national regulatory landscapes (e.g. National Greenhouse and Energy Reporting System in Australia, European Union emissions trading scheme, Kyoto Protocol Clean Development Mechanism), combined with increasing voluntary organisational commitments to ‘‘carbon neutrality’’, mean that this level of uncertainty in the environmental costbenefit 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 evaluate the life cycle inventories of a range of wastewater treatment scenarios, including BNR. The ten scenarios investigated in this paper are introduced in Fig. 1. A further 40 scenarios are

Case 0

12 Case 1

10 Case 4

Case 3

Case 2

8 6 Case 7

Case 6

Case 5

4 2

Case 9

Case 8

0 0

10

20

30

40

50

-1

Effluent Total Nitrogen (mg.L ) Case 0: Raw Sewage Case 1: Primary Sedimentation + Anaerobic Digestion + Energy Recovery Case 2: Primary Sedimentation + Activated Sludge + Anaerobic Digestion + Energy Recovery Case 3: Primary Sedimentation + Nitrifying Activated Sludge + Anaerobic Digestion + Energy Recovery Cases 4, 5 and 6: Primary Sedimentation + MLE BNR Activated Sludge + Anaerobic Digestion + Energy Recovery Cases 7, 8 and 9: Bardenpho (5 Stage) BNR Activated Sludge + Sludge Stabilisation Lagoon

Fig. 1 – Wastewater treatment system scenarios defined by type of process configuration (refer to Legend) and effluent quality (refer to x and y axes).

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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 L1, TP 12 mgP L1), through to best practice advanced nutrient removal in Case 9 (TN 3 mgN L1, TP 1 mgP L1). 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 mesophilic 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 concentrations <10 mgN L1. 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) Bardenpho process configuration. These cases represented best practice for nutrient removal, being capable of achieving effluent TN < 3 mgN L1 and TP < 1 mgP L1, 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 anaerobic 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 denitrification 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 d1 of raw domestic wastewater (5000 kgCOD d1, 500 kgN d1, 120 kgP d1) 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 fossilcarbon 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/Pathogen 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 fertiliser, 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 conditions 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 construction 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 simulation 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 integrated kinetic model and mass balance approach, incorporating 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

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

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-oxidising bacteria (AOB) and nitrite-oxidising bacteria (NOB) substrate half-saturation constants were lowered for Case 9 only (0.35 mgN L1 cf. default 0.70 mgN L1; 0.02 mgN L1 cf. default 0.10 mgN L1, 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 mechanicalelectrical 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 1 – Influent characteristics and general plant parameters. Parameter Average dry weather flow (ADWF) Peak wet weather flow (PWWF) ratio Ambient water and air temperature Winter air temp. for digester heating calculations Plant altitude Influent Chemical Oxygen Demand (COD) Influent Total Kjeldahl Nitrogen (TKN) Influent Total Phosphorus (TP) Influent pH and alkalinity Influent inorganic suspended solids Influent calcium and magnesium Fraction of readily biodegradable COD Fraction of unbiodegradable particulate COD Fraction of soluble unbiodegradable TKN

Value 1

10 ML d 3.0  ADWF 20  C 15  C 20 m 500 mgCOD L1 50 mgN L1 12 mgP L1 7.2, 5 mmol L1 (250 mg L1 as CaCO3) 30 mg L1 50 mgCa L1, 15 mgMg L1 0.2 gCOD gCOD1 total (default ¼ 0.16) 0.2 gCOD gCOD1 total (default ¼ 0.13) 0.01 gN gTKN1 (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.

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Table 2 – Summary of design parameters for wastewater treatment scenarios. Process unit Primary Sedimentation Tanks Cases 1–6

Activated Sludge Bioreactor Cases 2 and 3

MLE Nitrogen Removal Bioreactor Cases 4–6

Bardenpho BNR Bioreactor Cases 7–9

Design parameter

Value

Hydraulic Retention Time (HRT) at ADWF Underflow Scraper drive HRT at ADWF Solids Retention Time (SRT) Mixed Liquor Suspended Solids (MLSS) concentration Dissolved Oxygen (DO) concentration SRT MLSS concentration DO concentration a-recycle ratio Anoxic mass fraction Ferric chloride (FeCl3) dosing (43 wt% solution) Methanol dosing (100 wt%) Anaerobic zone HRT at ADWF SRT MLSS concentration a-recycle ratio Anoxic mass fraction DO concentration Ferric chloride (FeCl3) dosing (43 wt% solution) Methanol dosing (100 wt%)

All Bioreactors Cases 3–9

Secondary Sedimentation Tanks Cases 2–9

Depth Aspect ratio (length:width) Anaerobic and anoxic zone mixing – velocity gradient Lime solution dosing (19.8 wt% Ca(OH)2) Solids loading RAS Ratio Scraper drive

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 displaced 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).

3h 0.10 ML d1 at 1.5% dry solids ((d.s.); 80 h wk1 operation 5 kW continuous operation 1.5 h 1.3 d for Case 2 (organics removal only); 10 d for Case 3 (nitrification) 2700 mg L1 for Case 3; 3500 mg L1 for Case 3 2.0 mg L1 13 d for Cases 4 and 5; 15 d for Case 6 2500 mg L1 for Case 4; 3500 mg L1 for Cases 5 and 6 2.0 mg L1 0.7  ADWF for Cases 4 and 5; 4.0  ADWF for Case 6 23% for Cases 4 and 5; 50% for Case 6 0 mgFe L1 for Case 4; 13 mgFe L1 for Cases 5 and 6 100 L d1 for Case 6 only (equivalent to 12 mgCOD L1) 1.5 h 20 d for Cases 7 and 8; 25 d for Case 9 3500 mg L1 for Case 7, 4100 mg L1 for Cases 8 and 9 3.4  ADWF for Cases 7 and 8; 6.0  ADWF for Case 9 55% 1.2 mg L1 in primary aerobic zone; 1.5 mg L1 in secondary aerobic zone 11 mgFe L1 for Case 7; 24 mgFe L1 for Cases 8 and 9 180 L d1 for Case 9 only (equivalent to 21 mgCOD L1) 4.5 m 10:1 1900 s1 (or 4 W m3) 4000 L d1 (equivalent to 120 mgCaCO3 L1) 1.2  average MLSS at PWWF 0.7  ADWF; 0.6  PWWF 2 kW continuous operation

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 nonrenewable natural gas), CO2 emissions were calculated based on COD concentration (1.18 kg L1), total organic carbon to COD ratio (0.25 kgC kgCOD1) 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 calculated for each scenario (i.e. Cases 1–9). The concrete volume

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Table 3 – Summary of design parameters for sludge handling scenarios. Process unit Anaerobic Digestion Cases 1–6

Design parameter

Value

HRT at ADWF Mechanical mixing Heat transfer coefficients

Sludge recirculation Water bath heat exchanger Co-generation Gas Engines Sludge Stabilisation Lagoon Cases 7–9 Primary Sludge And Waste Activated Sludge – Gravity Belt Thickener (GBT) and Stabilised Sludge Dewatering – Belt Filter Press (BFP) All cases

Combustion efficiency Thermal efficiency HRT at ADWF Polymer dosing Solids capture Power Biosolids solids content

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 treatment 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-relevant 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 methodologies (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.

22 d 8 W m3 Above-ground 300 mm-thick, un-insulated concrete walls: 5.0 W m2 K1; 300 mm-thick concrete floor in dry earth: 1.7 W m2 K1; 35 mm wood-deck floating cover with no insulation: 2.0 W m2 K1 (Tchobanoglous et al., 2003) Additional allowance of 10% of the total heating demand was made for heat losses through digester pipework 24 h turnover 85% thermal efficiency; Heat supplied from co-generation gas engines 99% 38% (Winnick, 1997) 180 d 7 kg per tonne d.s. 95% 15 kW, operating 80 h wk1 20% d.s.

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 infrastructure 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 FeCl3 dosing. 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 L1). Whilst the Bardenpho process configurations did achieve EBPR, FeCl3 dosing up to 24 mgFe L1 was required for enhanced chemical precipitation. 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 addition was necessary for alkalinity correction in the nitrification process. However, the large increases in overall chemical

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Table 4 – Summary of design parameters for GHG emissions and biosolids land application. Parameter CH4 from PSTs

Units

Low-range value

Mid-range value

High-range value

Reference

kg CH4 per kg COD removed kg N2O–N per kg N denitrified kg CH4 per kg COD discharged kg N2O–N per kg N discharged

0

0.0125

0.025

(IPCC, 2006a; Table 6.3)

0.0003

0.01

0.03

(Foley et al., 2008)

0

0.025

0.05

(IPCC, 2006a; Table 6.3)

0.0005

0.0025

0.005

(IPCC, 1997; Tables 4–23; 2006b; Table 11.3)

g CH4 per Nm3 biogas

–

16.02

–

(Doka, 2003)

g N2O per Nm3 biogas

–

0.73

–

(Doka, 2003)

kg N2O–N per kg N biosolids

0.003

0.01

0.03

(Doka, 2003; IPCC, 2006b; Table 11.1)

NH3 volatilisation from biosolids NH3 volatilisation from DAP Indirect N2O via NH3 volatilisation from biosolids and DAP Indirect N2O via N leaching from biosolids and DAP

kg NH3–N per kg N biosolids kg NH3–N per kg N biosolids kg N2O–N per kg NH3–N volatilised

0.05

0.20

0.50

0.03

0.10

0.30

(Lundin et al., 2000; Doka, 2003; IPCC, 2006b; Table 11.3) (IPCC, 2006b; Table 11.3)

0.002

0.01

0.05

(IPCC, 2006b; Table 11.3)

kg N2O–N per kg N leached

0

0

0

Carbon sequestration in soil via biosolids application

kg C per kg C applied to soil

0

0.1

0.2

Bio-availability of N in biosolids

–

25%

50%

75%

Assumed dryland region (precipitation < evapotranspiration) (IPCC, 2006b; section 11.2.2.2) (Gibson et al., 2002; Li and Feng, 2002) Assumed 0.37 kg C per kg COD for biosolids (Ekama et al., 1984) (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%

N2O from secondary treatment CH4 from effluent discharge to estuary N2O from effluent discharge to estuary CH4 from biogas combustion N2O from biogas combustion Direct N2O volatilisation from biosolids and DAP

consumption coincided with increased P removal requirements and hence FeCl3 dosing. The chemical consumption jumped substantially from Case 4 (TP < 9 mg L1) to Case 5 (TP < 5 mg L1), and then again from Case 7 (TP < 5 mg L1) to Case 8 (TP < 1 mg L1). A small decrease in chemical consumption was seen in the transition from the MLE process in Case 6 to the 5-stage Bardenpho process in Case 7. To achieve TN < 10 mg L1 in Case 6, the MLE process required some methanol dosing (12 mgCOD L1), 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 wastewater to achieve TN < 5 mg L1, 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 L1), methanol dosing was required at 21 mgCOD L1. 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 characterisation 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 electrical 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

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

Arsenic Cadmium Chromium Copper Lead Nickel Zinc Mercury Selenium Molybdenum

Biosolids

Diammonium phosphate (DAP)

10th %ile

50th %ile

90th %ile

No. of Plants

10th %ile

50th %ile

90th %ile

No. of Refs

2.7 1.1 10.8 210.2 12.2 9.3 212.7 0.4 2.9 3.4

4.5 2.0 23.2 280.0 37.0 16.4 492.8 1.3 3.7 6.8

9.4 2.4 39.2 459.4 62.0 21.7 775.5 3.6 5.5 7.4

17 17 17 17 17 16 17 17 15 3

10.0 3.8 63.5 1.4 4.9 3.5 20.5 0.0 0.8 2.6

16.0 20.0 133.5 2.9 8.0 24.5 135.0 0.1 1.0 13.0

22.6 93.4 402.0 34.5 16.3 153.1 1319.0 1.1 10.0 21.0

7 15 6 8 16 14 14 6 6 6

Italic items indicate the average DAP metals concentration is significantly greater than the average biosolids metals concentration (t-dist., a ¼ 0.05)..

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 L1 up to TN < 10 mg L1 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 concrete in civil structures)

Excavation by hydraulic digger Material transportation by 28 tonne lorry Material transportation by rail Electricity consumption for construction Reinforcing steel Water consumption Aluminium Limestone Chromium steel (stainless steel) Fibreglass Copper Synthetic rubber (EPDM) Rock wool (insulation material) Organic chemicals Bitumen Inorganic chemicals Low density polyethylene (LDPE) High density polyethylene (HDPE) Polyethylene terephthalate (PET)

3.48 m3 49.29 t km 58.30 t km 0.04 kWh 77.58 kg 121.98 kg 0.87 kg 21.45 kg 6.23 kg 1.96 kg 0.92 kg 0.88 kg 0.87 kg 4.05 kg 0.50 kg 0.50 kg 0.02 kg 2.44 kg 2.46 kg

minimal. This was seen in the transition from Case 4 (TP < 9 mg L1) to Case 5 (TP < 5 mg L1), which required no additional energy; and in the transition from Case 7 (TP < 5 mg L1) to Case 8 (TP < 1 mg L1), 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 L1 by improved denitrification, for minimal additional energy input. This represented a positive environmental outcome. It was only in pursuing lower effluent TN levels that marginally increased energy may have been required. Therefore, in an environmental trade-off between energy consumption and level of nutrient removal, these results suggest there is likely to be some optimum which minimises the combined environmental 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 consumption, can dominate the life cycle GHG emissions profile of a WWTP (Gallego et al., 2008). For example, 1 kWh of Australian 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

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

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3,000

1,000

1,000

-1,000

-1,000

as e

0

1 as e

2

C

C

as e C

C

as e

3

4

5

as e C

as e C

as e

6

7 C

as e C

as e

-1

200

100

0

2,000 Biosolids

TN5, TN10, TN20, TN20, TN40, TN40, TN46, TN50, TP5 TP5 TP5 TP9 TP9 TP9 TP10 TP12

-1

TN5, TP1

Displaced DAP (kg.d )

3,000

Heavy Metals to Soil (g.d )

5,000

Energy

TN3, TP1

300 -1

Chemicals 5,000

8

9 C

as e

C as e

7,000 Concrete

Net Energy Consumption (kWh.d )

Concrete in Construction (tonnes) and -1 Total Chemical Use (kg.d )

7,000

C

1

2

as e C

3

as e C

4 C

C

as e

as e

5

6 C as e

C as e

7

8

C as e

9

C as e

C as e

0

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Effluent Quality

Fig. 4 – Resource consumption inventory results – concrete used in construction, daily chemical consumption and daily net electricity consumption.

1,500

DAP

1,000 500 0 -500 -1,000 TN3, TP1

0

1

as e C

as e C

C

as e

2

3 C as e

4 C

as e

5 C as e

C as e

7 C

as e

8

9

C as e

as e C

6

(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

Direct GHG Emissions (tCO2-e.ML-1)

0.9

CO2

CH4

N2O

Total

TN46, TP10

TN50, TP12

0.7

0.5

0.3

0.1

-0.1 TN3, TP1

TN5, TP1

TN5, TP5

TN10, TP5

TN20, TP5

TN20, TP9

TN40, TP9

TN40, TP9

Effluent Quality

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.

TN5, TP1

TN5, TP5

TN10, TP5

TN20, TP5

TN20, TP9

TN40, TP9

TN40, TP9

TN46, TP10

TN50, TP12

Effluent Quality

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.

a significant GHG risk associated with low levels of wastewater 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 GHGneutral 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 quantification 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

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to be the most favourable option. ‘‘Do nothing’’ and primary treatment caused large CH4 emissions in the receiving environment, 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 landapplied 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 displacement of more DAP in agriculture, particularly in moving from Case 4 (TP < 9 mg L1) to Case 5 (TP < 5 mg L1), 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 calculation 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 displaced 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 of wastewater treatment Overall, Figs. 4–6 provide useful proxy indicators of the increased intensity in resource consumption and environmental 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 potential 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 tradeoffs that arise from improved levels of wastewater treatment. 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 justification 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 infrastructure 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 L1 can be done at the same energy consumption as TN 40 mg L1. Targets below TN 10 mg L1 require additional operational energy. Similarly, direct GHG emissions might be minimised at basic secondary

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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 tradeoffs 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, innovative 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 environmental trade-offs requires life cycle impact assessment and an inherently subjective weighting of the competing environmental 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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