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Environmental Risk Assessment of Emerging Contaminants Using Degradation Data

  • Lanqing Hong
  • Zhi-Sheng Ye
  • Ran Ling
Article
  • 67 Downloads

Abstract

The degradation behavior of an emerging contaminant is a key factor in its environmental risk assessment. Existing risk assessment methods based on EC degradation data commonly neglect the time-varying volatility of the degradation, the possible correlations in degradation between different ECs, and the estimation errors. To fill the gaps, this paper proposes an EC risk assessment framework based on the Wiener process. We first focus on degradation data from competitive experiments, which are adopted to evaluate a useful risk indicator, i.e., the bimolecular rate constant of a degradation reaction. A two-dimensional Wiener process model is developed to capture the degradation behaviors of the target EC and a reference contaminant in the experiment. Point and interval estimations of desired quantities, including the rate constant and the degradation half-life, are developed. We further extend the model to the multivariate case, which is applicable to waste water treatment where multiple ECs degrade in a mixed solution. A risk indicator for the mixed solution is proposed, based on which a minimal treatment time can be determined. Both point and interval estimation procedures of the risk indicator and the minimal treatment time are proposed. Two EC degradation datasets are used to demonstrate the proposed methodologies.   Supplementary materials accompanying this paper appear on-line.

Keywords

Interval estimation Multi-dimensional degradation model Risk indicator Stochastic process 

Notes

Acknowledgements

We are grateful to the editor, the associate editor, and the referee for their insightful comments that have lead to a substantial improvement of an earlier version of the paper. This work was supported in part by the Natural Science Foundation of China (71601138), Singapore AcRF Tier 1 funding (R-266-000-113-114), and the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE).

Supplementary material

13253_2018_326_MOESM1_ESM.rar (8 kb)
Supplementary material 1 (rar 8 KB)
13253_2018_326_MOESM2_ESM.rar (4 kb)
Supplementary material 2 (rar 4 KB)

References

  1. Arnold, W. A. and Roberts, A. L. (2000), “Pathways and kinetics of chlorinated ethylene and chlorinated acetylene reaction with Fe (0) particles,” Environmental Science & Technology, 34(9), 1794–1805.CrossRefGoogle Scholar
  2. Benitez, F. J., Acero, J. L., Real, F. J., Roldan, G., and Rodriguez, E. (2013), “Photolysis of model emerging contaminants in ultra-pure water: kinetics, by-products formation and degradation pathways,” Water Research, 47(2), 870–880.CrossRefGoogle Scholar
  3. Buxton, G. V., Greenstock, C. L., Helman, W. P., and Ross, A. B. (1988), “Critical review of rate constants for reactions of hydrated electrons, hydrogen atoms and hydroxyl radicals (\(\cdot \text{ OH }/\cdot \!\! \text{ O }^-\)) in aqueous solution,” Journal of Physical and Chemical Reference Data, 17(2), 513–886.CrossRefGoogle Scholar
  4. Carrico, C., Gennings, C., Wheeler, D. C., and Factor-Litvak, P. (2015), “Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting,” Journal of Agricultural, Biological, and Environmental Statistics, 20(1), 100–120.MathSciNetCrossRefMATHGoogle Scholar
  5. Casella, G. and Berger, R. L. (2002), Statistical Inference, Duxbury Pacific Grove, CA.MATHGoogle Scholar
  6. Chen, P. and Ye, Z.-S. (2018), “Uncertainty quantification for monotone stochastic degradation models,” Journal of Quality Technology, 50(2), 207–219.CrossRefGoogle Scholar
  7. Gmurek, M., Rossi, A. F., Martins, R. C., Quinta-Ferreira, R. M., and Ledakowicz, S. (2015), “Photodegradation of single and mixture of parabens–kinetic, by-products identification and cost-efficiency analysis,” Chemical Engineering Journal, 276, 303–314.CrossRefGoogle Scholar
  8. Gupta, A. K. and Nagar, D. K. (1999), Matrix Variate Distributions, CRC Press.Google Scholar
  9. Hannig, J., Iyer, H., and Patterson, P. (2006), “Fiducial generalized confidence intervals,” Journal of the American Statistical Association, 101(473), 254–269.MathSciNetCrossRefMATHGoogle Scholar
  10. Hong, L. and Ye, Z.-S. (2017), “When is acceleration unnecessary in a degradation test?” Statistica Sinica, 27, 1461–1483.MathSciNetMATHGoogle Scholar
  11. Hong, L., Ye, Z.-S., and Josephine, K. S. (2018), “Interval estimation for Wiener processes based on accelerated degradation test data,” IISE Transactions, (to appear).Google Scholar
  12. Iyengar, S. (1985), “Hitting lines with two-dimensional Brownian motion,” SIAM Journal on Applied Mathematics, 45(6), 983–989.MathSciNetCrossRefMATHGoogle Scholar
  13. Lin, A. Y.-C., Yu, T.-H., and Lin, C.-F. (2008), “Pharmaceutical contamination in residential, industrial, and agricultural waste streams: risk to aqueous environments in Taiwan,” Chemosphere, 74(1), 131–141.CrossRefGoogle Scholar
  14. Ling, R., Chen, J. P., Shao, J., and Reinhard, M. (2018), “Degradation of organic compounds during the corrosion of ZVI by hydrogen peroxide at neutral pH: Kinetics, mechanisms and effect of corrosion promoting and inhibiting ions,” Water Research, 134, 44–53.CrossRefGoogle Scholar
  15. Liu, X., Al-Khalifa, K. N., Elsayed, E. A., Coit, D. W., and Hamouda, A. S. (2014), “Criticality measures for components with multi-dimensional degradation,” IIE Transactions, 46(10), 987–998.CrossRefGoogle Scholar
  16. Mazille, F., Schoettl, T., Klamerth, N., Malato, S., and Pulgarin, C. (2010), “Field solar degradation of pesticides and emerging water contaminants mediated by polymer films containing titanium and iron oxide with synergistic heterogeneous photocatalytic activity at neutral pH,” Water Research, 44(10), 3029–3038.CrossRefGoogle Scholar
  17. Pal, A., Gin, K. Y.-H., Lin, A. Y.-C., and Reinhard, M. (2010), “Impacts of emerging organic contaminants on freshwater resources: review of recent occurrences, sources, fate and effects,” Science of the Total Environment, 408(24), 6062–6069.CrossRefGoogle Scholar
  18. Petrie, B., Barden, R., and Kasprzyk-Hordern, B. (2015), “A review on emerging contaminants in wastewaters and the environment: current knowledge, understudied areas and recommendations for future monitoring,” Water Research, 72, 3–27.CrossRefGoogle Scholar
  19. Porat, B. and Friedlander, B. (1986), “Computation of the exact information matrix of Gaussian time series with stationary random components,” IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(1), 118–130.MathSciNetCrossRefGoogle Scholar
  20. Steinfeld, J. I., Francisco, J. S., and Hase, W. L. (1989), Chemical Kinetics and Dynamics, Prentice Hall Englewood Cliffs, New Jersey.Google Scholar
  21. Stork, L. G., Gennings, C., Carter, W. H., Teuschler, L. K., and Carney, E. W. (2008), “Empirical evaluation of sufficient similarity in dose–response for environmental risk assessment of chemical mixtures,” Journal of Agricultural, Biological, and Environmental Statistics, 13(3), 313–333.MathSciNetCrossRefMATHGoogle Scholar
  22. Weerahandi, S. (1993), “Generalized confidence intervals,” Journal of the American Statistical Association, 88(423), 899–905.MathSciNetCrossRefMATHGoogle Scholar
  23. Whitmore, G., Crowder, M., and Lawless, J. (1998), “Failure inference from a marker process based on a bivariate Wiener model,” Lifetime Data Analysis, 4(3), 229–251.CrossRefMATHGoogle Scholar
  24. Xu, Y., Nguyen, T. V., Reinhard, M., and Gin, K. Y.-H. (2011), “Photodegradation kinetics of p-tert-octylphenol, 4-tert-octylphenoxy-acetic acid and ibuprofen under simulated solar conditions in surface water,” Chemosphere, 85(5), 790–796.CrossRefGoogle Scholar
  25. Ye, Z.-S., Wang, Y., Tsui, K.-L., and Pecht, M. (2013), “Degradation data analysis using Wiener processes with measurement errors,” IEEE Transactions on Reliability, 62(4), 772–780.CrossRefGoogle Scholar
  26. You, L., Nguyen, V. T., Pal, A., Chen, H., He, Y., Reinhard, M., and Gin, K. Y.-H. (2015), “Investigation of pharmaceuticals, personal care products and endocrine disrupting chemicals in a tropical urban catchment and the influence of environmental factors,” Science of the Total Environment, 536, 955–963.CrossRefGoogle Scholar
  27. Zhai, Q. and Ye, Z.-S. (2018), “Degradation in common dynamic environments,” Technometrics, (to appear).Google Scholar

Copyright information

© International Biometric Society 2018

Authors and Affiliations

  1. 1. Industrial Systems Engineering and ManagementNational University of SingaporeSingaporeSingapore
  2. 2. National University of Singapore Suzhou Research InstituteSuzhouChina
  3. 3. Civil and Environmental EngineeringNational University of SingaporeSingaporeSingapore

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