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Modelling and Evaluating the Aquatic Fate of Detergents

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Modelling and Evaluating the Aquatic Fate of Detergents 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 Osnabruck¨ January 3 ¢¡ , 2001 Supervisor: Prof. Dr. M. Matthies, Department of Mathematics and Computer Science, University of Osnabruck,¨ D-49069 Osnabruck,¨ 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) and Environmental Risk Assessment of chemicals (ERA) are combined in this methodol- ogy. The core consists of the Geography-referenced Regional Exposure Assessment Tool for European Rivers (GREAT-ER), which calculates concentrations of ‘down-the-drain’ chemicals in surface waters due to point releases. In order to simulate the aquatic fate of detergents, a new GREAT-ER emission model is developed, called GREAT-ER product mode, which calculates calculates concentration increases of detergent ingredients in sur- face waters based on product formulations and assumptions concerning washing habits. Two evaluation methods, the Critical Length (CL) and the Product Risk Ratio (PRR £ ), are defined for evaluating the results. CL is the sum of mean concentration increases, di- vided by substance-specific no effect concentrations (NECs), over all river stretches and all ingredients weighted by the lengths of the stretches. PRR £ is the (percentual) number of river stretches in a region, in which the x-percentiles of the predicted concentration increases of at least one ingredient exceed a substance-specific NEC. The emission model requires input data that can be derived from the functional unit of an LCA, which allows an assessment of other impact categories by using any existing LCA method. The methodology is applied to a case study which is based on scenarios given in the com- 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 simple hydrological model that combines a nonlinear regression analy- sis with a local refinement procedure. The quality of the integration of the Rur catchment 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 1993 and 2000. The product mode results show that use habits have a larger influence on the results than product formulations. However, the largest influence is caused by varying wastewater treatment techniques. Boron and the surfactants are the most relevant deter- gent ingredients. Furthermore, using different detergents for white and coloured laundry lowers the predicted emissions significantly. Based on this methodology, sustainable development indicators (SDIs) for describing the 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- 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. Annually evaluating regional CLs and PRR £ s allows the assessment whether a region is moving towards a 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 Jor¨ g Klasmeier for inspiring discussions and criticisms. Also, the linguistic support provided by Teresa Gehrs is acknowledged. I wish to thank the Henkel KGaA for the financial support of this thesis and especially Frank Roland Schroder¨ and Thorsten Wind for their cooperation. Several people and institutions provided different kind of information. In this context, I wish to thank F. Jorrens,¨ J. Lange, and L. Portner¨ 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 to Mark Huijbregts from the University of Amsterdam, to Uwe Klinsmann from the BASF AG, and to Peter Richner from Ciba Specialty Chemicals. 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 stay in their institutes doing research. Both the moments spent at work as well as enjoying Swiss scenery and culture were highly motivating. 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 villainous smell that ever offended nostril. William Shakespeare (1564 - 1616): Merry wives of Windsor (Act III, Scene V) Contents 1. Introduction 1 1.1. The human activity and need household laundry . 1 1.2. Instruments, tools, methods, and models . 3 1.3. Aim of thesis and research questions . 5 1.4. Outline of dissertation . 6 2. Existing approaches 7 2.1. Environmental Decision Support Instruments . 7 2.1.1. Choice of EDSIs . 7 2.1.2. Life Cycle Assessment . 8 2.1.3. Environmental Risk Assessment . 11 2.2. Review of LCIA methods regarding aquatic ecotoxicity . 13 2.3. Applying GREAT-ER to the Itter catchment . 15 2.3.1. The GREAT-ER model . 16 2.3.2. Case study Itter catchment . 17 2.3.3. Results . 20 2.3.4. Discussion . 23 2.4. Environmental assessment studies related to household laundry . 25 2.4.1. Comprehensive product assessment ‘Washing and washing agents’ 25 2.4.2. Environmental risk assessment of detergent chemicals . 26 2.4.3. CHAINET case study ’Domestic washing of clothes’ . 27 2.4.4. European LCI for detergent surfactants production . 28 2.4.5. Further related studies . 29 2.5. Conclusions . 29 3. The GREAT-ER product mode 31 3.1. Model Principles . 31 3.1.1. Emission estimates and aquatic fate modelling . 32 V Contents 3.1.2. ‘Only-above-threshold’ evaluation - PRR £ . 33 3.1.3. ‘Less-is-better’ evaluation - CL . 34 3.2. Interpretation of the results . 34 3.2.1. Product Risk 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. Effect data . 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. Bleaching agents . 47 4.5.5. Optical brighteners . 49 4.5.6. Miscellaneous . 50 5. The Rur catchment - Integration into the GREAT-ER model 53 5.1. Introduction . 53 5.2. The Rur river basin . 54 5.3. Data collection and assembly . 54 5.3.1. River network . 54 5.3.2. River attributes . 55 5.3.3. Discharge site data . 57 5.3.4. Monitoring data . 58 5.3.5. Additional background data . 59 5.3.6. Substance data . 59 5.3.7. Generation of a GREAT-ER data set . 59 5.4. Hydrological modelling . 59 5.4.1. Nonlinear regression . 60 5.4.2. Local refinement . 62 5.4.3. Results obtained in the Rur catchment . 66 5.4.4. Discussion of methodology . 67 VI Contents 6. GREAT-ER simulations in the Rur catchment 69 6.1. Results applying the 1993 data set . 69 6.1.1. Number of 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. Rur 2000 data set and monitoring . 84 6.2.1. Choice of substances . 84 6.2.2. Choice of sampling sites . 85 6.2.3. Sampling frequency . 85 6.2.4. Incorporation into GREAT-ER . 85 6.3. Results using the 2000 data . 86 6.3.1. Monitoring results . 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. Simulation using 1993 data . 100 6.4.2. Simulation using 2000 data . 101 6.4.3. Environmental fate of substances in lakes . 102 6.5. Conclusions . 104 7. Results of the product mode assessment 105 7.1. Main Rur scenarios . 105 7.1.1. PLA reference scenarios . 106 7.1.2. Comparison of products - Dosage according to product . 109 7.1.3. Comparison of products - Dosage according to use habit . 113 7.1.4. Mixture toxicity . 116 7.2. Differentiating the number of substances in the PRR £ evaluation . 116 7.3. Comparison of catchments . 118 7.3.1. Comparison of different wastewater treatment options . 119 7.3.2. Product comparison in different catchments . 121 7.3.3. Comparison of different catchments . 123 VII Contents 7.4. Assessment of inorganic compounds . 125 7.4.1. Sodium .
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