Environmental Risk Assessment of Emerging Contaminants Using Degradation Data

  • Lanqing Hong
  • Zhi-Sheng Ye
  • Ran Ling


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.


Interval estimation Multi-dimensional degradation model Risk indicator Stochastic process 



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)


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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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