Assessment of parameter uncertainty for non-point source pollution mechanism modeling: A Bayesian-based approach

Yan Xueman; Lu Wenxi; An Yongkai; Dong Weihong

doi:10.1016/j.envpol.2020.114570

Assessment of parameter uncertainty for non-point source pollution mechanism modeling: A Bayesian-based approach

Environ Pollut. 2020 Aug;263(Pt A):114570. doi: 10.1016/j.envpol.2020.114570. Epub 2020 Apr 18.

Authors

Yan Xueman¹, Lu Wenxi², An Yongkai³, Dong Weihong⁴

Affiliations

¹ Key Laboratory of Groundwater Resources and Environment of Ministry of Education, Jilin University, Changchun, 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China; Institute of Water Resources and Environment, Jilin University, Changchun, 130021, China.
² Key Laboratory of Groundwater Resources and Environment of Ministry of Education, Jilin University, Changchun, 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China. Electronic address: luwenxi@jlu.edu.cn.
³ Key Laboratory of Groundwater Resources and Environment of Ministry of Education, Jilin University, Changchun, 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China.
⁴ Institute of Water Resources and Environment, Jilin University, Changchun, 130021, China; College of Construction Engineering, Jilin University, Changchun, 130026, China.

PMID: 33618467
DOI: 10.1016/j.envpol.2020.114570

Abstract

Uncertainty assessment of parameters associated with non-point source pollution mechanism modeling are crucial for improving the effectiveness of pollution controlling. In this study, an approach based on Bayesian inference and integrated Markov chain Monte Carlo and multilevel factorial analysis has been developed, and it can not only apply straightforward Bayesian inference to assess parameter uncertainties, but also quantitatively investigate the main and interactive effects of multiple parameters on the model response variables by measuring the specific variations of model outputs. Its applicability and advantages are presented through the application of the Soil and Water Assessment Tool to Shitoukoumen Reservoir Catchment in northeast China. This study investigated the uncertainties of a set of sensitive parameters and their multilevel effects on model response variables, including average annual runoff (AAR), average annual sediment (AAS) and average annual total nitrogen (AAN). Results revealed that (i) soil conservation service runoff curve number for moisture condition II (CN2) had a positive effect on all response variables; (ii) available water capacity of the soil layer (SOL_AWC) had a negative effect on all response variables; (iii) the universal soil loss equation support practice (USLE_P) had a positive effect on AAS and AAN, and little effect on AAR; while the nitrate percolation coefficient (NPERCO) had a positive effect on AAN, and little effect on AAS and AAR; and (iv) the interactions amongst parameters had obvious interdependent effects on the model response variables, for example, the interaction between CN2 and SOL_AWC had a major impact on AAR. The above findings can improve the simulating and predicting capabilities of non-point source pollution mechanism model. Overall, this study highlights that the proposed approach represents a promising solution for uncertainty assessment of model parameters in non-point source pollution mechanism modeling.

Keywords: Bayesian inference; Markov chain Monte Carlo; Multilevel factorial analysis; Parameter uncertainty; Soil and water assessment tool.

MeSH terms

Bayes Theorem
China
Environmental Monitoring
Models, Theoretical
Non-Point Source Pollution*
Uncertainty