Abstract
Nowadays, society and regulatory authorities claim rigorous environmental risk and exposure assessment procedures. Prediction models of emerging contaminants may provide results within these assessment practices. This review gathers models that have been used by scientific researchers in order to predict emerging contaminant concentrations in rivers. A description of PhATE, GREAT-ER, WASP, SWMM, EUSES, QUAL2E, ChemCAN and AQUASIM models is provided. After reviewing more than 40 scientific applications of these emerging contaminant models, PhATE and GREAT-ER result to be the most used tools in the literature. Overall most applications point out the utility and necessity of these models. In any case, uncertainty is always related to any model outcomes. Thus, an analysis of propagated uncertainty in emerging contaminant basic processes is reviewed. Results indicate that the apparent contaminant emission from the population is the most significant issue in terms of propagated uncertainty. All considered factors suggest that there is still potential for further development of emerging contaminant models and that there is still the necessity of complementing the applications with measured data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- API:
-
Active pharmaceutical ingredient
- EC:
-
Emerging contaminants
- EU:
-
European Union
- GIS:
-
Geographic information system
- PEC:
-
Predicted environmental concentration
- PhRMA:
-
Pharmaceutical Research and Manufacturers of America
- PNEC:
-
Predicted no-effect concentration
- UK:
-
United Kingdom
- US and USA:
-
United States of America
- USEPA:
-
US Environmental Protection Agency
- WWTP:
-
Wastewater treatment plant
References
Salas JD, Govindaraju RS, Anderson M, Arabi M, Francés F, Suarez W, Lavado-Casimiro WS, Green TR (2014) Introduction to hydrology. In: Wang LK, Yang CT (eds) Modern water resources engineering. Humana Press Springer, New York, pp 1–126
Feijtel T, Boeije G, Matthies M et al (1997) Development of a geography-referenced regional exposure assessment tool for European rivers – great-er contribution to great-er #1. Chemosphere 34(11):2351–2373
Anderson PD, D’Aco VJ, Shanahan P et al (2004) Screening analysis of human pharmaceutical compounds in US surface waters. Environ Sci Technol 38:838–849
Lehner B, Grill G (2013) Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol Process 27:2171–2486
Mackay D (2001) Multimedia environmental models: the fugacity approach, 2nd edn. Lewis, Boca Raton
Mackay D, Di Guardo A, Paterson S et al (1996) Assessment of chemical fate in the environment using evaluative, regional and local-scale models: Illustrative application to chlorobenzene and linear alkylbenzene sulfonates. Environ Toxicol Chem 15(9):1638–1648
Boeije G, Vanrolleghem P, Matthies M (1997) A geo-referenced aquatic exposure prediction methodology for “down-the-drain” chemicals. Wat Sci Technol 36(5):251–258
Keller V (2006) Risk assessment of “down-the-drain” chemicals: search for a suitable model. Sci Total Environ 360:305–318
Cunningham VL, D’Aco VJ, Pfeiffer D et al (2011) Predicting concentrations of trace organic compounds in municipal wastewater treatment plant sludge and biosolids using the PhATETM model. Integr Environ Assess Manag 8(3):530–542
Park J, Kim MH, Choi K et al (2007) Environmental risk assessment of pharmaceuticals: model application for estimating pharmaceutical exposures in the Han River Basin. Korea Environment Institute, Seoul
Hannah R, D'Aco VJ, Anderson PD et al (2009) Exposure assessment of 17a-ethinylestradiol in surface waters of the United States and Europe. Environ Toxicol Chem 28(12):2725–2732
Cunningham VL, Constable DJC, Hannah RE (2004) Environmental risk assessment of paroxetine. Environ Sci Technol 38:3351–3359
Cunningham VL, Binks SP, Olson MJ (2009) Human health risk assessment from the presence of human pharmaceuticals in the aquatic environment. Regul Toxicol Pharmacol 53(1):39–45
Robinson PF, Liu QT, Riddle AM et al (2007) Modeling the impact of direct phototransformation on predicted environmental concentrations (PECs) of propranolol hydrochloride in UK and US rivers. Chemosphere 66(4):757–766
Cunningham VL, Perino C, D'Aco VJ et al (2010) Human health risk assessment of carbamazepine in surface waters of North America and Europe. Regul Toxicol Pharmacol 56(3):343–351
Schwab BW, Hayes EP, Fiori JM (2005) Human pharmaceuticals in US surface waters: a human health risk assessment. Regul Toxicol Pharmacol 42(3):296–312
Caldwell DJ, Mastrocco F, Nowak E et al (2010) An assessment of potential exposure and risk from estrogens in drinking water. Environ Health Perspect 118(3):338–344
Anderson PD, Johnson AC, Pfeiffer D et al (2012) Endocrine disruption due to estrogens derived from humans predicted to be low in the majority of US surface waters. Environ Toxicol Chem 31(6):1407–1415
Hosseini NAA, Parker WJJ, Matott LSL (2013) Modelling concentrations of pharmaceuticals and personal care products in a Canadian watershed. Can Water Resour J 37(3):191–208
Chieng B (2012) Descriptive assessment and amendment of the simple treat model. Faculty of Engineering, Lund University, Sweden
Struijs J (1996) Simple Treat 3.0: a model to predict the distribution and elimination of chemicals by sewage treatment plants. National Institute of Public Health and Environmental Protection, Bilthoven, RIVM Report No. 719101025
Arlos MJ (2013) Characterization and modelling of selected antiandrogens and pharmaceuticals in highly impacted reaches of Grand River Watershed in Southern Ontario. University of Waterloo, Ontario
Alder AC, Schaffner C, Majewsky M et al (2010) Fate of b-blocker human pharmaceuticals in surface water: comparison of measured and simulated concentrations in the Glatt Valley Watershed, Switzerland. Water Res 44(3):936–948
Johnson AC, Keller V, Williams RJ et al (2007) A practical demonstration in modeling diclofenac and propranolol river water concentrations using a GIS hydrology model in a rural UK catchment. Environ Pollut 146:155–165
Schowanek D, Webb S (2000) Examples of exposure assessment simulation for pharmaceuticals in river basins with the GREAT-ER 1.0 system. In: KVIV Seminar ‘Pharmaceuticals in the Environment’, Brussels, 9 March 2000
Van de Meent D (1993) SimpleBox: a generic multimedia fate evaluation model. National Institute of Public Health and Environmental Protection, Bilthoven, RIVM Report No 672720001
Klasmeier J, Matthies M (2001) Application of the geography-referenced environmental assessment tool for European rivers (GREAT-ER) in the catchment of the River Main (Germany). Institute of Environmental System Research. University of Osnabrück, Osnabrück
Price OR, Munday DK, Whelan MJ (2009) Data requirements of GREAT-ER: Modelling and validation using LAS in four UK catchments. Environ Pollut 157(10):2610–2616
Wind T, Werner U, Jacob M, Hauk A (2004) Environmental concentrations of boron, LAS, EDTA, NTA and triclosan simulated with GREAT-ER in the river Itter. Chemosphere 54(8):1135–1144
Johnson AC, Jürgens MD, Williams RJ et al (2008) Do cytotoxic chemotherapy drugs discharged into rivers pose a risk to the environment and human health? An overview and UK case study. J Hydrol 348(1–2):167–175
Hüffmeyer N, Klasmeier J, Matthies M (2009) Geo-referenced modeling of zinc concentrations in the Ruhr river basin (Germany) using the model GREAT-ER. Sci Total Environ 407(7):2296–2305
Schröder FR, Schulze C, Matthies M (2002) Concentration of LAS and boron in the Itter. Comparison of measured data with results obtained by simulation with the GREAT-ER software. Environ Sci Pollut Res Int 9(2):130–135
Schulze C, Matthies M (2001) Georeferenced aquatic fate simulation of cleaning agent and detergent ingredients in the river Rur catchment (Germany). Sci Total Environ 280(1–3):55–77
Sabaliunas D, Webb SF, Hauk A et al (2003) Environmental fate of triclosan in the River Aire Basin, UK. Water Res 37(13):3145–3154
Price OR, Williams RJ, Van Egmond R et al (2010) Predicting accurate and ecologically relevant regional scale concentrations of triclosan in rivers for use in higher-tier aquatic risk assessments. Environ Int 36(6):521–526
Sumpter JP, Johnson AC, Williams RJ et al (2006) Modeling effects of mixtures of endocrine disrupting chemicals at the river catchment scale. Environ Sci Technol 40(17):5478–5489
Aldekoa J, Medici C, Osorio V et al (2013) Modelling the emerging pollutant diclofenac with the GREAT-ER model: application to the Llobregat River Basin. J Hazard Mater 263:207–213
Holt MS, Fox KK, Burford M et al (1998) UK monitoring study on the removal of linear alkylbenzene sulphonate in trickling filter type sewage treatment plants. Contribution to GREAT-ER project # 2. Sci Total Environ 210–211:255–269
Schowanek D, Webb S (2002) Exposure simulation for pharmaceuticals in European surface waters with GREAT-ER. Toxicol Lett 131(1–2):39–50
Verdonck F, Boeije G, Schowanek D et al. (2000) Geography-referenced regional exposure tool for European rivers (GREAT-ER): a case study for the Rupel basin. Proceedings of the 4th international conference on integrating GIS and environmental modeling (GIS/EM4), Banff, Alberta, 2–8 September 2000
Young AR, Grew R, Holmes MG (2003) Low flows 2000: a national water resources assessment and decision support tool. Water Sci Technol 48(10):119–126
Wind T (2004) Prognosis of environmental concentrations by geo-referenced and generic models: a comparison of GREAT-ER and EUSES exposure simulations for some consumer-product ingredients in the Itter. Chemosphere 54(8):1145–1153
Di Toro DM, Fitzpatrick JJ, Thomann RV (1983) Documentation for water quality analysis simulation program (WASP) and model verification program (MVP). Envir Res Lab EPA-600/3-81-044
Rygwelski KR, Richardson WL, Endicott DD (1999) A screening-level model evaluation of atrazine in the Lake Michigan basin. J Great Lakes Res 25(1):94–106
Metcalf and Eddy (1971) Storm water management model, volume I – final report. US Environmental Protection Agency, Washington, EPA Report No 11024 DOC 07/71 (NTIS PB-203289)
Jang S, Cho M, Yoon J et al (2007) Using SWMM as a tool for hydrologic impact assessment. Desalination 212(1–3):344–356
Struijs J, Van de Meent D, Stoltenkamp J (1991) SimpleTreat: a spreadsheet-based box model to predict the fate of xenobiotics in a municipal waste water treatment plant. National Institute of Public Health and Environmental Protection, Bilthoven, RIVM Report No. 670208002
Carlsson C, Johansson AK, Alvan G et al (2006) Are pharmaceuticals potent environmental pollutants? Part II: Environmental risk assessments of selected pharmaceutical excipients. Sci Total Environ 364:88–95
Jaworska JS, Schowanek D, Feijtel TCJ (1999) Environmental risk assessment for trisodium [S, S]-ethylene diamine disuccinate, a biodegradable chelator used in detergent applications. Chemosphere 38(15):3597–3625
Kawamoto K, MacLeod M, Mackay D (2001) Evaluation and comparison of multimedia mass balance models of chemicals fate: application of EUSES and ChemCAN to 68 chemicals in Japan. Chemosphere 44(4):599–612
Schwartz S, Berding V, Matthies M (2000) Aquatic fate assessment of the polycyclic musk fragrance HHCB: scenario and variability analysis in accordance with the EU risk assessment guidelines. Chemosphere 41(5):671–679
Webster E, Mackay D, Di Guardo A et al (2004) Regional differences in chemical fate model outcome. Chemosphere 55(10):1361–1376
Cubillo F, Rodriguez B, Barnwell TO (1992) A system for control of river water-quality for the community of Madrid using Qual2e. Water Sci Technol 26(7–8):1867–1873
Drolc A, Končan JZ (1999) Calibration of QUAL2E model for the Sava River (Slovenia). Water Sci Technol 40(10):111–118
Ghosh NC, McBean EA (1998) Water quality modeling of the Kali River, India. Water Air Soil Pollut 102(1–2):91–103
Ning SK, Chang NB, Yang L et al (2001) Assessing pollution prevention program by QUAL2E simulation analysis for the Kao-Ping River Basin, Taiwan. J Environ Manage 61(1):61–76
McAvoy DC, Masscheleyn P, Peng C et al (2003) Risk assessment approach for untreated wastewater using the QUAL2E water quality model. Chemosphere 52(1):55–56
Okasaki S, Fono L, Sedlak DL et al. (2002) Assessment of the unintentional reuse of municipal wastewater. In: Abstract of the fall meeting 2002, American Geophysical Union
Managaki S, Enomoto I, Masunaga S (2012) Sources and distribution of hexabromocyclododecanes (HBCDs) in Japanese river sediment. J Environ Monit 14:901–907
Reichert P (1994) Aquasim a tool for simulation and data analysis of aquatic systems. Water Sci Technol 30(2):21–30
Buser HR, Poiger T, Muller MD (1998) Occurrence and fate of the pharmaceutical drug diclofenac in surface waters: rapid photodegradation in a lake. Environ Sci Technol 32(22):3449–3456
Poiger T, Buser HR, Müller MD (2001) Photodegradation of the pharmaceutical drug diclofenac in a lake: pathway, field measurements, and mathematical modeling. Environ Toxicol Chem 20(2):256–263
Poiger T, Kari FG, Giger W (1999) Fate of fluorescent whitening agents in the River Glatt. Environ Sci Technol 33(4):533–539
Tixier C, Singer HP, Canonica S et al (2002) Phototransformation of triclosan in surface waters: a relevant elimination process for this widely used biocides – laboratory studies, field measurements, and modeling. Environ Sci Technol 36(16):3482–3489
Zepp RG, Cline DM (1977) Rates of direct photolysis in aquatic environment. Environ Sci Technol 11(4):359–366
Refsgaard JC, van der Sluijs JP, Højberg AL et al (2007) Uncertainty in the environmental modelling process – a framework and guidance. Environ Model Software 22(11):1543–1556
Uusitalo L, Lehikoinen A, Helle I et al (2015) An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ Model Software 63:24–31
Ort C, Lawrence MG, Rieckermann J et al (2010) Sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in wastewater systems: are your conclusions valid? A critical review. Environ Sci Technol 44(16):6024–6035
Renard B, Kavetski D, Kuczera G (2010) Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors. Water Resour Res 46(5). doi:10.1029/2009WR008328
Acuña V, Von Schiller D, García-Galán MJ et al (2015) Occurrence and in-stream attenuation of wastewater derived pharmaceuticals in Iberian rivers. Sci Total Environ 503–504:133–141
Clara M, Kreuzinger N, Strenn B et al (2005) The solids retention time—a suitable design parameter to evaluate the capacity of wastewater treatment plants to remove micropollutants. Water Res 39(1):97–106
Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation and confirmation of numerical models in the earth sciences. Science 263(5147):641–646
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Aldekoa, J., Marcé, R., Francés, F. (2015). Fate and Degradation of Emerging Contaminants in Rivers: Review of Existing Models. In: Petrovic, M., Sabater, S., Elosegi, A., Barceló, D. (eds) Emerging Contaminants in River Ecosystems. The Handbook of Environmental Chemistry, vol 46. Springer, Cham. https://doi.org/10.1007/698_2015_5017
Download citation
DOI: https://doi.org/10.1007/698_2015_5017
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29374-5
Online ISBN: 978-3-319-29376-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)