Modelling and evaluating the aquatic fate of detergents

211 Pages·2001·1.66 MB·English

Doctoral thesis
Modelling and evaluating the
aquatic fate of detergents
Carsten Schulze
Institute of Environmental Systems Research
Department of Mathematics and Computer Science
University of Osnabru¨ck
January 3(cid:0)(cid:2)(cid:1) , 2001
Supervisor:
Prof. Dr. M.Matthies, DepartmentofMathematicsand ComputerScience,
University of Osnabru¨ck, D-49069 Osnabru¨ck, Germany.
Examiner:
Prof. Dr. O. Jolliet, Department of Rural Engineering, Swiss Federal In-
stitute of Technology Lausanne, CH-1015 Lausanne, Switzerland.
Abstract
Within this thesis an environmental assessment and evaluation method for analysing
aquatic ecotoxicological impacts of household laundry is developed. The methodology
allows comparative assessments of different product alternatives, washing habits, and
wastewater treatment techniques in order to identify their relevance with respect to wa-
terborne discharges. Elements from both analytical tools Life Cycle Assessment (LCA)
andEnvironmentalRisk Assessmentofchemicals (ERA)arecombined inthismethodol-
ogy. The coreconsistsof theGeography-referencedRegionalExposureAssessment Tool
for European Rivers (GREAT-ER), whichcalculates concentrationsof ‘down-the-drain’
chemicals in surface waters due to point releases. In order tosimulate the aquatic fateof
detergents,anew GREAT-ER emissionmodelisdeveloped,called GREAT-ER product
mode,whichcalculatescalculatesconcentrationincreasesofdetergentingredientsinsur-
face waters based on product formulations and assumptions concerning washing habits.
Two evaluation methods, the Critical Length (CL) and the Product Risk Ratio (PRR ),
(cid:3)
are definedfor evaluating theresults. CL is the sum of mean concentration increases, di-
vided by substance-specific no effect concentrations (NECs), over all river stretches and
all ingredients weightedby the lengths of the stretches. PRR is the (percentual)number
(cid:3)
of river stretches in a region, in which the x-percentiles of the predicted concentration
increasesofatleastoneingredientexceedasubstance-specificNEC.Theemissionmodel
requires input data that can be derived from the functional unitof an LCA, which allows
anassessment of other impactcategories byusingany existing LCAmethod.
Themethodologyisappliedtoacasestudywhichisbasedonscenariosgiveninthecom-
prehensive product assessment ‘Washing and washing agents’ (‘Produktlinienanalyse’,
PLA). In order to apply the GREAT-ER product mode, the Rur river basin in Western
North-Rhine Westphalia is chosen as study area. The catchment integration includes the
development of a simplehydrological modelthatcombines a nonlinear regressionanaly-
siswithalocalrefinementprocedure. The qualityof theintegrationoftheRurcatchment
data is analysed by a comparison of monitoring data and predicted concentrations of de-
tergent and cleaning agent ingredients using actual consumption data of the two years
1993and 2000. The product moderesults show thatuse habitshave a largerinfluenceon
theresultsthanproductformulations. However,thelargestinfluenceiscausedbyvarying
wastewater treatment techniques. Boron and the surfactants are the most relevant deter-
gent ingredients. Furthermore, using different detergents for white and coloured laundry
lowersthepredicted emissionssignificantly.
Basedonthis methodology,sustainable development indicators(SDIs)for describingthe
aquatic aspects of household laundry are defined. CL is proposed as pressure indicator
and PRR as state indicator for describing aquatic aspects of the sustainability of house-
(cid:3)
hold laundry in a region. Different regions can be compared by normalising the CL by
the region’s population and expressing the PRR as a percentage of stretches in a region.
(cid:3)
Annually evaluating regional CLs and PRR s allows the assessment whether a region is
(cid:3)
movingtowardsa more sustainable state.
I
Acknowledgement
My sincere thanks go to Michael Matthies for being supervisor and to Olivier Jolliet for
being examiner of this thesis. I herewith also express many thanks to my colleagues at
the Institute of Environmental Systems Research and from the Intevation GmbH for any
help and assistance. In particular, I gratefully thank Andreas Beyer and Jo¨rg Klasmeier
for inspiring discussions and criticisms. Also, the linguistic support provided by Teresa
Gehrsis acknowledged.
I wish to thank the Henkel KGaA for the financial support of this thesis and especially
Frank RolandSchro¨derand ThorstenWindfor their cooperation.
Several people and institutions provided different kind of information. In this context,
I wish to thank F. Jo¨rrens, J. Lange, and L. Po¨rtner from the Wasserverband Eifel-
Rur (WVER), and also associates from the Staatliches Umweltamt Aachen, the Lan-
desumweltamt Nordrhein-Westfalen, and the Umweltbundesamt Berlin. Furthermore, ac-
knowledgement is being made toMark Huijbregts fromthe Universityof Amsterdam, to
UweKlinsmannfromtheBASFAG,andtoPeterRichnerfromCibaSpecialtyChemicals.
I will particularly remember the wonderful times spent at the Sustainable Development
Group and the Safety and Environmental Technology Group of the Swiss Federal Insti-
tutes of Technology at Lausanne and Zurich. I sincerely thank them for enabling me to
stayintheirinstitutesdoingresearch. Boththemomentsspentatworkaswellasenjoying
Swiss sceneryand culturewere highlymotivating.
Finally, I would like to express my thanks to my friends and family and especially to
Monika for supporting me each in their own way throughout the different stages of this
work.
II
Falstaff: You shall hear (...) they conveyed me into a buck-
basket.
Ford : A buck-basket?
Falstaff: Yea, a buck-basket: rammed me in with foul shirts
and socks, foul stockings, and greasy napkins; that,
master Brook, there was the rankest compound of
villainoussmell thateveroffendednostril.
WilliamShakespeare(1564 -1616): MerrywivesofWindsor
(ActIII, SceneV)

Contents
1. Introduction 1
1.1. The humanactivityandneed householdlaundry . . . . . . . . . . . . . . 1
1.2. Instruments,tools, methods, andmodels . . . . . . . . . . . . . . . . . . 3
1.3. Aimof thesis and researchquestions . . . . . . . . . . . . . . . . . . . . 5
1.4. Outline of dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2. Existing approaches 7
2.1. Environmental DecisionSupport Instruments . . . . . . . . . . . . . . . 7
2.1.1. Choiceof EDSIs . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2. LifeCycle Assessment . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3. EnvironmentalRisk Assessment . . . . . . . . . . . . . . . . . . 11
2.2. Reviewof LCIA methodsregarding aquaticecotoxicity . . . . . . . . . . 13
2.3. Applying GREAT-ER to theIttercatchment . . . . . . . . . . . . . . . . 15
2.3.1. The GREAT-ER model . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2. CasestudyItter catchment . . . . . . . . . . . . . . . . . . . . . 17
2.3.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4. Environmental assessment studiesrelatedto householdlaundry . . . . . . 25
2.4.1. Comprehensiveproductassessment ‘Washingand washingagents’ 25
2.4.2. Environmentalriskassessment of detergentchemicals . . . . . . 26
2.4.3. CHAINETcasestudy’Domesticwashingof clothes’ . . . . . . . 27
2.4.4. EuropeanLCI fordetergentsurfactantsproduction . . . . . . . . 28
2.4.5. Furtherrelatedstudies . . . . . . . . . . . . . . . . . . . . . . . 29
2.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3. The GREAT-ER product mode 31
3.1. Model Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1.1. Emission estimatesand aquaticfatemodelling . . . . . . . . . . 32
V
Contents
3.1.2. ‘Only-above-threshold’evaluation- PRR . . . . . . . . . . . . . 33
(cid:3)
3.1.3. ‘Less-is-better’evaluation -CL . . . . . . . . . . . . . . . . . . 34
3.2. Interpretation of theresults . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.1. ProductRisk Ratios . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.2. Critical Lengths . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3. Mixture toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4. Case study household laundry 39
4.1. Model detergents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2. Model households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3. Study areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4. Effectdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5. Considered substances . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.5.1. Anionic surfactants . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.5.2. Nonionic surfactants . . . . . . . . . . . . . . . . . . . . . . . . 44
4.5.3. Builders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.5.4. Bleachingagents . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.5.5. Opticalbrighteners . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.5.6. Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5. The Rur catchment - Integration into the GREAT-ER model 53
5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2. The Rurriverbasin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3. Data collection andassembly . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3.1. Rivernetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3.2. Riverattributes . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3.3. Dischargesite data . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.4. Monitoringdata . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.3.5. Additionalbackground data . . . . . . . . . . . . . . . . . . . . 59
5.3.6. Substancedata . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3.7. Generationof a GREAT-ER dataset . . . . . . . . . . . . . . . . 59
5.4. Hydrological modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.4.1. Nonlinearregression . . . . . . . . . . . . . . . . . . . . . . . . 60
5.4.2. Local refinement . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.4.3. ResultsobtainedintheRur catchment . . . . . . . . . . . . . . . 66
5.4.4. Discussionof methodology . . . . . . . . . . . . . . . . . . . . 67
VI
Contents
6. GREAT-ER simulations in the Rur catchment 69
6.1. Resultsapplying the 1993dataset . . . . . . . . . . . . . . . . . . . . . 69
6.1.1. Numberof Monte-Carlo shots . . . . . . . . . . . . . . . . . . . 69
6.1.2. Boron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.3. LAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.1.4. NTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.1.5. EDTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.2. Rur2000 dataset andmonitoring . . . . . . . . . . . . . . . . . . . . . . 84
6.2.1. Choiceof substances . . . . . . . . . . . . . . . . . . . . . . . . 84
6.2.2. Choiceof samplingsites . . . . . . . . . . . . . . . . . . . . . . 85
6.2.3. Samplingfrequency . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.4. Incorporationinto GREAT-ER . . . . . . . . . . . . . . . . . . 85
6.3. Resultsusing the2000data . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.3.1. Monitoringresults . . . . . . . . . . . . . . . . . . . . . . . . . 86
6.3.2. Boron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.3.3. Anionic surfactants . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.3.4. Nonionic surfactants-Alcohol Ethoxylate . . . . . . . . . . . . . 93
6.3.5. NTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.3.6. EDTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6.4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.1. Simulationusing1993data . . . . . . . . . . . . . . . . . . . . . 100
6.4.2. Simulationusing2000data . . . . . . . . . . . . . . . . . . . . . 101
6.4.3. Environmentalfateof substancesin lakes . . . . . . . . . . . . . 102
6.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7. Results of the product mode assessment 105
7.1. Main Rurscenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.1.1. PLAreference scenarios . . . . . . . . . . . . . . . . . . . . . . 106
7.1.2. Comparisonof products-Dosage accordingto product . . . . . . 109
7.1.3. Comparisonof products-Dosage accordingto usehabit . . . . . 113
7.1.4. Mixture toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.2. Differentiatingthenumberof substances inthePRR evaluation . . . . . 116
(cid:3)
7.3. Comparisonof catchments . . . . . . . . . . . . . . . . . . . . . . . . . 118
7.3.1. Comparisonof differentwastewatertreatmentoptions . . . . . . 119
7.3.2. Productcomparison indifferentcatchments . . . . . . . . . . . . 121
7.3.3. Comparisonof differentcatchments . . . . . . . . . . . . . . . . 123
VII
Contents
7.4. Assessment of inorganiccompounds . . . . . . . . . . . . . . . . . . . . 125
7.4.1. Sodium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.4.2. Silicon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7.4.3. Carbonate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7.4.4. Sulphate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
8. Discussion 133
8.1. Methodology of theproduct mode . . . . . . . . . . . . . . . . . . . . . 133
8.1.1. RelationtoERAand LCA . . . . . . . . . . . . . . . . . . . . . 133
8.1.2. CLandPRR evaluation . . . . . . . . . . . . . . . . . . . . . . 134
(cid:3)
8.1.3. Numberof substances consideredinthe PRR calculation . . . . 136
(cid:3)
8.1.4. Modelsensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8.1.5. Length-versus volume-basedaggregation . . . . . . . . . . . . . 138
8.1.6. ‘Less-is-better’versus ‘Only-above-threshold’ . . . . . . . . . . 139
8.2. Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
8.2.1. Referencescenarios andproduct comparison . . . . . . . . . . . 140
8.2.2. Relevanceof detergentingredients . . . . . . . . . . . . . . . . . 140
8.2.3. Dominanceof boron . . . . . . . . . . . . . . . . . . . . . . . . 141
8.2.4. Influenceof catchment characteristics . . . . . . . . . . . . . . . 142
8.2.5. Contributionof inorganiccompoundsto measuredconcentrations 143
8.2.6. Mixture toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . 143
8.3. Furtheraspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
8.3.1. Casestudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
8.3.2. Modelrestrictions . . . . . . . . . . . . . . . . . . . . . . . . . 144
9. Sustainable Development Indicators 147
9.1. SustainableDevelopment . . . . . . . . . . . . . . . . . . . . . . . . . . 147
9.2. Existing SDI approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 149
9.3. Indicators describingtheenvironmentalsustainability of householdlaundry150
9.3.1. Pressureindicators . . . . . . . . . . . . . . . . . . . . . . . . . 150
9.3.2. Stateindicators . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
9.3.3. Responseindicators . . . . . . . . . . . . . . . . . . . . . . . . 151
9.4. Discussionof proposedindicators . . . . . . . . . . . . . . . . . . . . . 152
10.Conclusions 153
10.1. Combination of LCAandERA . . . . . . . . . . . . . . . . . . . . . . . 153
10.2. Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
VIII