Assessing wastewater micropollutant loads with approximate bayesian computations

Environmental Science and Technology

Volumn 45, Issue 10, 2011, Pages 4399-4406

  Rieckermann, Jörg ^a   Anta, Jose ^b   Scheidegger, Andreas ^a   Ort, Christoph ^c  

^a SWISS FEDERAL INSTITUTE OF AQUATIC SCIENCE AND TECHNOLOGY   (Switzerland)

^b UNIVERSITY OF A CORUÑA   (Spain)

^c UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ABC METHOD; APPROXIMATE BAYESIAN; ATTENTION TO DETAILS; BENZOTRIAZOLES; CATCHMENT PROPERTIES; DATA SERIES; ENVIRONMENTAL MODEL; ENVIRONMENTAL PROCESS; ENVIRONMENTAL SYSTEMS ANALYSIS; HIGH RESOLUTION; LIKELIHOOD FUNCTIONS; MICROPOLLUTANTS; MODEL-BASED; PHARMACEUTICALS AND PERSONAL CARE PRODUCTS; PREDICTIVE CAPABILITIES; STATISTICAL INFERENCE; TOTAL NITROGEN; WATER QUALITY MODELING;

ALGORITHMS; BAYESIAN NETWORKS; CATCHMENTS; FORMAL METHODS; STATISTICAL METHODS; STOCHASTIC SYSTEMS; SYSTEMS ANALYSIS; WASTEWATER; WATER QUALITY;

STOCHASTIC MODELS;

BENZOTRIAZOLE DERIVATIVE; NITROGEN;

ALGORITHM; BAYESIAN ANALYSIS; CATCHMENT; CHEMICAL POLLUTANT; ENVIRONMENTAL MODELING; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; POLLUTION MONITORING; STOCHASTICITY; UNCERTAINTY ANALYSIS; WASTEWATER; WATER QUALITY;

APPROXIMATE BAYESIAN COMPUTATION; ARTICLE; BAYES THEOREM; POLLUTANT; SAMPLING ERROR; SEWER; STOCHASTIC MODEL; WASTE WATER; WATER QUALITY;

BAYES THEOREM; ENVIRONMENTAL MONITORING; MODELS, CHEMICAL; MODELS, STATISTICAL; MONTE CARLO METHOD; NITROGEN; SEWAGE; TRIAZOLES; WASTE DISPOSAL, FLUID; WATER POLLUTANTS, CHEMICAL; WATER POLLUTION, CHEMICAL;

EID: 79956002338     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es1030432     Document Type: Article

References (29)

1. Gottschalk, F.; Sonderer, T.; Scholz, R. W.; Nowack, B. Modeled environmental concentrations of engineered nanomaterials (TiO2, ZnO, Ag, CNT, fullerenes) for different regions Environ. Sci. Technol. 2009, 43, 9216-9222
2. Ort, C.; Gujer, W. Sampling for representative micropollutant loads in sewer systems Water Sci. Technol. 2006, 54, 169-176 (Pubitemid 44755606)
3. Rauch, W.; Brockmann, D.; Peters, I.; Larsen, T. A.; Gujer, W. Combining urine separation with waste design: an analysis using a stochastic model for urine production Water Res. 2003, 37, 681-689 (Pubitemid 35434165)
4. Ort, C.; Lawrence, M. G.; Reungoat, J.; Mueller, J. F. Sampling for PPCPs in wastewater systems: Comparison of different sampling modes and optimization strategies Environ. Sci. Technol. 2010, 44, 6289-6296
5. Ort, C.; Lawrence, M. G.; Rieckermann, J.; Joss, A. Sampling for pharmaceuticals and personal care products (PPCPs) and illicit drugs in wastewater systems: Are your conclusions valid? A critical review Environ. Sci. Technol. 2010, 44, 6024-6035
6. Qian, S. S.; Reckhow, K. H. Combining model results and monitoring data for water quality assessment Environ. Sci. Technol. 2007, 41, 5007-5013 (Pubitemid 47121100)
7. Beaumont, M. A.; Zhang, W. Y.; Balding, D. J. Approximate Bayesian Computation in population genetics Genetics 2002, 162, 2025-2035 (Pubitemid 36114682)
8. Marjoram, P.; Molitor, J.; Plagnol, V.; Tavaré, S. Markov chain Monte Carlo without likelihoods Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 15324-15328 (Pubitemid 38020988)
9. Sisson, S. A.; Fan, Y.; Tanaka, M. M. Sequential Monte Carlo without likelihoods Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 1760-1765 (Pubitemid 46239852)
10. van Nuijs, A. L. N.; Castiglioni, S.; Tarcomnicu, I.; Postigo, C.; de Alda, M. L.; Neels, H.; Zuccato, E.; Barcelo, D.; Covaci, A. Illicit drug consumption estimations derived from wastewater analysis: A critical review. Sci. Total Environ. 2010, DOI:10.1016/j.scitotenv.2010.05.030.
11. Ort, C.; Schaffner, C.; Giger, W.; Gujer, W. Modeling stochastic load variations in sewer systems. Water Sci. Technol. 2005, 52, 113-122.
12. Gelman, A.; Carlin, J. B.; Stern, H. S.; Rubin, D. B. Bayesian Data Analysis; Chapman & Hall: London, 1995.
13. Tavaré, S.; Balding, D. J.; Griffiths, R. C.; Donnelly, P. Inferring coalescence times from DNA sequence data Genetics 1997, 145, 505-518 (Pubitemid 27068401)
14. Toni, T.; Welch, D.; Strelkowa, N.; Ipsen, A.; Stumpf, M. P. H. Approximate Bayesian Computation scheme for parameter inference and model selection in dynamical systems J. R. Soc., Interface 2009, 6, 187-202
15. Sisson, S. A.; Fan, Y. Likelihood-free Markov chain Monte Carlo. In Handbook of Markov Chain Monte Carlo; Brooks, S. P.; Gelman, A.; Jones, G.; Meng, X.-L., Eds.; Chapman and Hall/CRC Press: New York, 2010.
16. Del Moral, P.; Doucet, A.; Jasra, A. Sequential Monte Carlo samplers J. R. Stat. Soc., Ser. B: Stat. Methodol. 2006, 68, 411-436 (Pubitemid 43742903)
17. Schaffner, C.; Giger, W. Anticorrosive benzotriazoles as contaminants in wastewaters and rivers Chimia 2004, 58 (7/8) 453
18. Geigy. Geigy Scientific Tables; Ciba Pharmaceutical Co.: Bassel, Switzerland, 1981.
19. Wegmann, D.; Leuenberger, C.; Excoffier, L. Efficient Approximate Bayesian Computation coupled with Markov chain Monte Carlo without likelihood Genetics 2009, 182, 1207-1218
20. Beaumont, M. A.; Cornuet, J. M.; Marin, J. M.; Robert, C. P. Adaptive Approximate Bayesian Computation Biometrika 2009, 96, 983-990
21. Del Moral, P.; Doucet, A.; Jasra, A. An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation; Imperial College: London, 2009.
22. Peters, G. W.; Fan, Y.; Sisson, S. A. On sequential Monte Carlo, partial rejection control and Approximate Bayesian Computation. arXiv:0808.3466v2 [stat.CO], 2009.
23. MacKay, D. J. C. Information Theory, Inference and Learning Algorithms; Cambridge University Press: England, 2003; p 052164298.
24. Csillery, K.; Blum, M. G. B.; Gaggiotti, O. E.; Francois, O. Approximate Bayesian Computation (ABC) in practice Trends Ecol. Evol. 2010, 25, 410-418
25. Joyce, P.; Marjoram, P. Approximately sufficient statistics and bayesian computation Stat. Appl. Genet. Mol. Biol. 2008, 7
26. Secrier, M.; Toni, T.; Stumpf, M. P. H. The ABC of reverse engineering biological signalling systems Mol. BioSyst. 2009, 5, 1925-1935
27. Csilléry, K.; Blum, M. G. B.; Gaggiotti, O. E.; Francois, O. Approximate Bayesian Computation (ABC) in practice Trends Ecol. Evol. 2010, 25, 410-418
28. Beven, K.; Binley, A. The future of distributed models: model calibration and uncertainty prediction Hydrol. Processes 1992, 6, 279-298
29. Mantovan, P.; Todini, E. Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology J. Hydrol. 2006, 330, 368-381 (Pubitemid 44528546)