Bayesian-Based Approaches to Exploring the Long-Term Alteration in Trace Metals of Surface Water and Its Driving Forces

Abstract

Trace metal pollution poses a serious threat to the aquatic ecosystem. Therefore, characterizing the long-term environmental behavior of trace metals and their driving forces is essential for guiding water quality management. Based on a long-term data set from 1990 to 2019, this study systematically conducted the spatiotemporal trend assessment, influential factor analysis, and source apportionment of trace elements in the rivers of the German Elbe River basin. Results show that the mean concentrations of the given elements in the last 30 years were found in the order of Fe (1179.5 ± 1221 μg·L–1) ≫ Mn (209.6 ± 181.7 μg·L–1) ≫ Zn (52.5 ± 166.2 μg·L–1) ≫ Cu (5.3 ± 5.5 μg·L–1) > Ni (4.4 ± 8.3 μg·L–1) > Pb (3.3 ± 4.4 μg·L–1) > As (2.9 ± 2.3 μg·L–1) > Cr (1.8 ± 2.4 μg·L–1) ≫ Cd (0.3 ± 1.1 μg·L–1) > Hg (0.05 ± 0.12 μg·L–1). Wavelet analyses show that river flow regimes and flooding dominated the periodic variations in metal pollution. Bayesian network suggests that the hydrochemical factors (i.e., TOC, TP, TN, pH, and EC) chemically influenced the metal mobility between water and sediments. Furthermore, the source apportionment computed by the Bayesian multivariate receptor model shows that the given element contamination was typically attributed to the geogenic sources (17.5, 95% confidence interval: 13.1–17.6%), urban and industrial sources (22.1, 18.0–27.2%), arable soil erosion (24.2, 16.4–31.5%), and historical anthropogenic activities (35.2, 32.8–43.3%). The results provided herein reveal that both the hydrochemical influence on metal mobility and the chronic disturbance from anthropogenic activities caused the long-term variation in trace metal pollution.