energy and greenhouse footprints of wastewater treatment ... · 8/21/2009  · urban water security...

Urban Water Security Research Alliance

ENERGY AND GREENHOUSE FOOTPRINTS OF WASTEWATERFOOTPRINTS OF WASTEWATER

TREATMENT PLANTS INSOUTH-EAST QUEENSLANDSOUTH-EAST QUEENSLAND

David de Haas & Paul LantLCA project

17 or 18 August 2009

ContentsContents1. Introduction

Wastewater Treatment Energy & GHG – Why? Urban Water Security Research Alliance in SE Queensland

2. Methodology Study Area Study Area Type of WWTPs & size Data inventory Calc lation methods Calculation methods

3. Results What emissions dominated? Scopes 1, 2 & 3 Uncertainty analysis

4. Conclusions What are the trends?

IntroductionIntroduction

Why calculate WWTP greenhouse ?gas?

Use ~ 5 to 10 W/EP (~500 to 1000 kWh/ML) electricity( ) y Can be a significant part of local government total GHG

footprint (~40 to 50%) Potential emissions up to:

~0.8 kg CO2-e per day per EP as methane

~0.3 kg CO2-e per day per EP as nitrous oxide

?? kg CO2-e per day per EP as carbon dioxide (non-biogenic)

NGERS (2007) – Australian Federal govt. requires reporting (effective July 2008) when exceeding certain th h ldthresholds

NGERS ThresholdsNGERS Thresholds

WWTP ~200,000 to 300,000 EP

Life Cycle AssessmentLife Cycle Assessment

•• Research alliance objective : LCA model ofResearch alliance objective : LCA model ofResearch alliance objective : LCA model of Research alliance objective : LCA model of SEQLD water gridSEQLD water grid Dams Water Treatment & Distribution Wastewater Collection Wastewater Treatment & Disposal (effluent & biosolids)p ( ) Water recycling/ Advanced treatment Desalination

U LCA t l t i f d i i kU LCA t l t i f d i i k•• Use LCA as a tool to inform decision makersUse LCA as a tool to inform decision makers Energy/ greenhouse & secure water supply Environmental impacts / burden Environmental impacts / burden

Previous LCA studiesPrevious LCA studies

• WWTP impacts dominated by operational inputs & outputs (power, chemicals, biosolids etc.)

• Embodied energy (construction/Embodied energy (construction/ demolition) relatively minorE l L di t l 2004 f• Examples : Lundie et al. 2004 for Sydney

Lundie et al (2004) – Sydney WaterLundie et al.(2004) Sydney Water

Lundie et al (2004) – Sydney WaterLundie et al.(2004) Sydney Water

MethodologyMethodology

Inventory data - WWTPsInventory data WWTPs

• South-EastSouth East Queensland

• Approx. total 40 no.Approx. total 40 no. WWTPs

• Surveyed 35 no. yWWTPs

• Beaudesert, Esk, Brisbane

Laidley, Gatton, Kilcoy etc. not included in t dstudy area

Two types of WWTPTwo types of WWTP

TYPE 1TYPE 1 TYPE 2TYPE 2TYPE 1TYPE 1• Primary sedimentation• BNR activated sludge• Anaerobic co-digestion of (primary

• Extended aeration BNR activated sludge

• Some with aerobic digestion of Anaerobic co digestion of (primary + waste activated sludges)

• Biogas collection some with on-site co-generation (electrical

gwaste activaeted sludge

• No biogas/ collection• No co-generation

power & heat)• Most with chemical

supplementation for nutrient removal (N&P)

• Many with chemical supplementation for nutrient removal (N&P)

removal (N&P)

WWTPs surveyed – size summaryWWTPs surveyed size summary

TYPE OF PLANT & DESCRIPTION

No. of plants surveyed of this T

% of TOTAL ADWF treated

for WWTP

% of Effluent

TOTAL N LOAD

(50%ILE) f

Approximate % of effluent

TOTAL P LOAD

(50%ILE) for WWTP

Current ADWF (ML/d) Indicative EP from ADWF, assuming 200 L/EP.d

Two biggest plants – no Type WWTPin

survey

for WWTP

surveyed

WWTP surveyed

Min Max Average Min Max Average

Type 1 – No Cogen 3 8% 7% 5% 5.7 24.1 13.3 29,000 121,000 66,000

gg padvanced P removal

yp g , , ,

Type 1 – Cogen 3 38% 44% 60% 15.1 123.7 63.6 76,000 619,000 318,000

Type 2 - Small 5 0.3% 0.6% 0.4% 0.075 0.46 0.30 400 2,300 1,500

Type 2 - Medium 17 19% 20% 8% 1.5 9.8 5.5 8,000 49,000 28,000

Type 2 - Large: 7 35% 28% 27% 10 4 54 0 24 8 52 000 270 000 124 000Type 2 Large: 7 35% 28% 27% 10.4 54.0 24.8 52,000 270,000 124,000

TOTAL no. of plants surveyed

35 100% 100% 100% 0.1 123.7 14.4 400 619,000 71,000

Effluent N & PEffluent N & P

Type 1 Type 2P ti f l t i

>10

5 to 10

>10 5 to 10<3Total N

Type 1 Type 2Proportion of plants in survey

3 to 5

<3

3to5

mgN/L

>5

3 to 5

>5<1 Total P2 to 5

1t 2

<1

2

1 to 2

Total PmgP/L

1 to 22 to 5

Data collectedData collected

• Flow (dry weather average)( y g )• Influent COD (or BOD), TKN and TP concentrations• Effluent Total N and Total P concentrations • Power consumption & Power generated (on site) from biogas (if any)• Power consumption & Power generated (on site) from biogas (if any)• Chemicals consumption • Biosolids• Screenings & grit • Biogas produced (if any) & methane content (if measured)• Flow of sludge stream(s) treated through anaerobic digestiong ( ) g g• Method of effluent disposal (ocean, estuary, river, wetlands or

irrigation)• Approx. % effluent volume disposed to effluent reuse (e.g. irrigation)Approx. % effluent volume disposed to effluent reuse (e.g. irrigation)

GHG calculationsGHG calculations

• Former AGO workbook methodology inadequate (WSAA, 2007)For WWTPs

• IPCC ‘Tier 3’ or NGERS (2008) Technical Guidelines ‘higher order’• First principles (mass balance approach) • WWTP functional areas (C & N in/out)• WWTP functional areas (C & N in/out)• Fugitive emissions

Raw sewage dissolved CH4 D it ifi ti N O Denitrification N2O Anaerobic digestion CH4 (not combusted) Biosolids CH4 Effluent N2O (receiving water/ environment) Effluent N2O (receiving water/ environment)

• Non-biogenic carbon (CO2)• Biosolids carbon sequestration (?)• ALL SCOPES: 1, 2 & 3 included

Scopes 1 2 & 3Scopes 1, 2 & 3

Scope 2: IndirectpEmissions from electicity consumed butgenerated elsewhere

Scope 3Other indirect emissions resulting from business butfrom business but emission sources are not owned e.g. from supply chain of consumables or Scope 1: Direct

Emissions from fossil fuels or other sources under direct control (e.g. own production process or vehicle fleet).

of consumables or transport of goods purchased

Emission factorsEmission factors

• Sources NGERS (2008) Technical Guidelines (e.g. power, fuels) Simapro® LCA software databases (e.g. chemicals) Literature survey (WSAA, 2007) (fugitive emissions) Other: literature or estimates (e.g. non-biogenic carbon)

• Major uncertaintiesMajor uncertainties Fugitive emissions factors

DN process off-gas N2O : ~500-fold range Biosolids disposal N2O or CH4 : ~5 to 100-fold rangep 2 4 g Effluent N2O : ~up to 1000-fold range

Non-biogenic organic carbon in wastewater (1% to 50%??) Carbon sequestration in biosolids disposed (5 to 25%??)

Uncertainty analysisUncertainty analysisPlant 1

• Monte Carlo simulation (Excel®-based Add-on)

• Input values for uncertain emission factors200,000

250,000

Total GHG emissions, kg CO2-e/d

Minimum, Maximum and ‘Best estimate’ (or Median) Normal distribution simulated in the model from these estimates

• Combined probability from multiple150,000Combined probability from multiple uncertainties100,000

50,000

00% 25% 50% 75% 100%

ResultsResults

GHG emissions resultsGHG emissions breakdown for example plants

GHG emissions resultsAssumptions 8 mg/L raw influent dissolved methane

10 % non-biogenic raw influent organics1% denitrified N as nitrous oxide

124 ML/d

100,000

120,000Raw Sewage Dissolved Methane

Disposal of Effluent

124 ML/d Type 1 + Cogen

54 ML/d T 2

80,000

day

Disposal of Biosolids

Anaerobic digesters/ biogas, incl. combustionDisposal of Screenings & Grit

Type 2

40 000

60,000

kg C

O2-

e/ Disposal of Screenings & Grit

Secondary Treatment Off-gases

Chemical & Fuel Consumption24 ML/d Type 1

20,000

40,000Imported Electrical Power

NGERS (2008) single facility threshold

6 ML/d T 2

0Plant I Plant II Plant III Plant IV

6 ML/d Type 2

Emissions by Scope – Type 1Emissions by Scope Type 1

Plant I: Type 1 (with biogas + cogen.)

37%

20%

8%

Scope 1 N2O20% Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2Scope 3

17%17%

Plant II: Type 1 (with biogas but no cogen.)

25%

10% Scope 3

14%

Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2

5%47%

pScope 3125 tonnes/d CO2-e

124 ML/d ADWF

43 tonnes/d CO2-e

24 ML/d ADWF

Emissions by Scope – Type 2Emissions by Scope Type 2

Plant III: Type 2 (Extended aeration, large)

22%

13%

Scope 1 N2OPl t IV T 2 (E t d d ti di )

13%

Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2S 3

19%17%

Plant IV: Type 2 (Extended aeration, medium)

4%48%

Scope 3

17%

Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2

5%42%

Scope 394 tonnes/d CO2-e

54 ML/d ADWF

12 tonnes/d CO2-e

7.9 ML/d ADWF

Snapshot of GHG from SE QLD WWTPsSnapshot of GHG from SE QLD WWTPsGHG emissions per day

Total GHG, including lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations180)

120

140

160

(tonn

es C

O2-

e/ d

Potential biosolids C-sequestration

Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)

Secondary process off-gas N2O (0.01 to 5% of N-denitrified)

40

60

80

100

" G

HG

em

issi

ons Potential biosolids C sequestration

Total Greenhouse Gas emissions

-20

0

20

40

"Bes

t est

imat

e"

Assuming 20% biosolids C sequestered (long term)

Nambo

ur STP

Redcli

ffe W

WTP

Elanora

WW

TP

Marooc

hy S

TPOxle

y STP

Lugg

age P

oint S

TPNud

gee S

TP

Rosew

ood S

TP

Woodfo

rd STP

Karana

Dow

ns STP

Mt Cott

on S

TP

STP: prop

osed

upgra

de

Fairfie

ld STP

Carole

Park S

TPBrib

ie STP

Sunco

ast (B

li Bli)

STPWac

ol STP

Coolum

STP

Capala

ba STP

Clevela

nd S

TP

Wynnu

m STP

Victori

a Poin

t STP

Brenda

le STP

Burpen

gary

East S

TP

Thorne

side S

TP

Goodn

a (Red

bank

) STP

Caboo

lture

South

STP

Beenle

igh W

WTP

Noosa

Coa

stal S

TP

Bunda

mba STP

Murrum

ba D

owns

STP

Sandg

ate STP

Merrim

ac W

WTP

Gibson

Islan

d STP

Coomba

bah W

WTP

Cooroy

ST S B

Go C M

PLANT NAME Data not available for some plants

GHG emissions per ML

4

GHG emissions per MLTotal GHG, including lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations

3

Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)

Secondary process off-gas N2O (0.01 to 5% of N-denitrified)

Three plants with power

2

HG

em

issi

ons

ML

AD

WF)

generation from biogas

y = 1.9773x-0.093

R² = 0.3295 y = 4.177x-0.278

1

t est

imat

e" G

Hon

nes

CO

2-e/

R² = 0.8951

0"Bes

t(to

GHG emissions for Plant Type : Extended aeration BNR-Act. Slg.GHG emissions for Plant Type : PST/An. Dig./BNR-Act. Sludge. Assuming 20% biosolids C sequestered (long term)

-10.01 0.1 1 10 100

ADWF treated (ML/d)

Potential biosolids C-sequestrationg q ( g )

GHG emissions per ML

4

GHG emissions per MLTotal GHG, excluding lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations

Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:

3

s

( )Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)

Secondary process off-gas N2O (0.01 to 5% of N-denitrified)

Three plants with power

2

GH

G e

mis

sion

s/ M

L A

DW

F)

Three plants with power generation from biogas

y = 1.9127x-0.101

R² = 0.3509y = 3.3484x-0.235

R² = 0.8979

1

st e

stim

ate"

Gto

nnes

CO

2-e

1

0"Bes (t

GHG emissions for Plant Type : Extended aeration BNR-Act. Slg.

GHG emissions for Plant Type : PST/An. Dig./BNR-Act. Sludge.Potential biosolids C-sequestration

Assuming 20% biosolids C sequestered (long term)

-10.01 0.1 1 10 100

ADWF treated (ML/d) Data not available for some plants

Power consumption per ML

3000

Gross plant power consumption per ML ADWFexcluding lift pumps.

y = 1076.5x-0.167

R² = 0 3705y = 2868.2x-0.364

R² = 0 8953

2500

h/M

L)

R 0.3705 R = 0.8953

1500

2000

w-s

peci

fic (k

Wh

Three plants with power generation

860

10121000

1500

onsu

med

, flo

w

Upper 95% conf. interval

Mean 860708

500

Plan

t pow

er c

o

Plant Power per ML for Plant Type : Extended aeration BNR-Act. Slg.

Pl t P ML f Pl t T PST/A Di /BNR A t Sl d

Mean

Lower 95%

00.01 0.1 1 10 100

P

ADWF treated (ML/d)

Plant Power per ML for Plant Type : PST/An. Dig./BNR-Act. Sludge.

Power consumption vs N removalPower consumption vs. N removal4

GHG emissions per MLTotal GHG, excluding lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations

3

Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)

Secondary process off-gas N2O (0.01 to 5% of N-denitrified)

Th l t ith

2

G e

mis

sion

sL

AD

WF)

Three plants with power generation from biogas

y = 1.5678x0.0442 y = 1.1903x0.2006

1

st e

stim

ate"

GH

Gto

nnes

CO

2-e/

M

y 1.5678xR² = 0.0163 R² = 0.1191

0

"Bes (t

GHG emissions for Plant Type : Extended aeration BNR-Act. Slg.

GHG emissions for Plant Type : PST/An. Dig./BNR-Act. Sludge. A i 20% bi lid C t d (l t )

-11 10 100

Effluent Total N (mgN/L)

GHG emissions for Plant Type : PST/An. Dig./BNR Act. Sludge.Potential biosolids C-sequestration

Assuming 20% biosolids C sequestered (long term)

ConclusionsConclusions

ConclusionsConclusions

• Useful inventory of operating data from WWTPs in SEQLDy p g• Uncertainty in emission factors highlighted

Fugitive emissions of CH4 & N2O Non-biogenic organics in raw sewage Non biogenic organics in raw sewage Carbon sequestration in biosolids

• Uncertainties appear to influence results in range: ~mean (±20%)• Typical emissions flow specific basis ~1 2 5 tonnes CO e /ML• Typical emissions, flow-specific basis ~1 - 2.5 tonnes CO2-e /ML• Economies-of-scale: lower emissions/ML with increasing plant size•• Type 1Type 1 plants: lower emissions only with power recovery from

bi di ti & bianaerobic digestion & biogas• Trade off with advanced nutrient removal LCA• Need to extend study to full SEQ Water Cycle

Thank you

www.urbanwateralliance.org.au