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Characterizing Spatiotemporal Variation of Trace Pollutants in Surface Water and Their Driving Forces

The expanding urbanisation, growing population, and industrial development are threatening global surface water quality. With increasing concern about surface-water quality, it is crucial to deeply understand the evolution of surface-water quality problems and comprehensively de-termine its fundamental driving forces. In this Dissertation, systematic work on the mechanisms of water pollution with trace elements has been carried out in three steps: i) to identify the sources contributing to surface water pollution by receptor-based models, ii) to determine the factors dominating the pollution risk transmission from sources to surface water by a source-based model, and iii) to capture the primary driving forces to the spatiotemporal variation in surface water pollution by Bayesian-based approaches. The following specific topics were ad-dressed based on five publications:
a) The temporal trends of trace metal pollution in the surface water were characterised by the Mann-Kendall test and the Generalised Additive Model.
b) The primary source contributors to the long-term trace metal pollution in a river system were determined by the Self-organised Map, Positive Matrix Factorization receptor model, and Bayesian multivariate receptor model. The distributions of the source contributions to trace metal pollution were estimated.
c) The risk transmission of trace pollutants in the surface water was estimated by a source-based dynamic model. The sensitivities of the risk to human activities, characteristics of wastewater treatment plants, and river flow regimes were evaluated.
d) The contributions of hydro-chemical factors, climate impact, and sampling methods to water pollution and data uncertainty were analysed by the Wavelet Analysis and Bayesian Net-work.
Both the models’ accuracy and robustness were evaluated by statistical analysis. The methods and results provided herein could improve the standard of statistical rigour and support the authorities’ decision-making.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:90559
Date26 March 2024
CreatorsWang, Zhenyu
ContributorsKrebs, Peter, Kolditz, Olaf, Yu, Longfei, Technische Universität Dresden
PublisherInstitut für Siedlungs- und Industriewasserwirtschaft
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relationurn:nbn:de:bsz:14-qucosa2-805409, qucosa:80540

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