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

Zhenyu Wang

AutorIn

Zhenyu Wang

Titel

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

Zitierfähige Url:

https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-905598

Schriftenreihe

Dresdner Berichte

Bandnummer

Erstveröffentlichung

2024

Datum der Einreichung

16.05.2023

Datum der Verteidigung

24.08.2023

ISSN

1615-083X

Abstract (EN)

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.

Freie Schlagwörter (EN)

Source apportionment, Surface water, Trace pollutants, Pollutant transport

Klassifikation (DDC)

577

Klassifikation (RVK)

AR 22400

GutachterIn