Ecological risk and machine learning based source analyses of trace metals in typical surface water.

Surface water is threatened by trace metal pollution due to increasing anthropogenic activities. Therefore, an appropriate source identification was essential to reduce the ecological risk posed by the given pollutants. In this study, shallow and deep learning approaches trained by a registered environmental dataset of discharge sources were employed to classify the potential emission sources of trace metals in the Elbe River, Germany. The results showed that the overall concentration rank of the given metals was Zn (226.5 ± 526.5 μg·L-1) > Ni (5.6 ± 4.7 μg·L-1) > Cu (5.3 ± 5.8 μg·L-1) > As (3.3 ± 3.7 μg·L-1) > Pb (2.9 ± 5.2 μg·L-1) > Cr (1.8 ± 2.5 μg·L-1) > Cd (1.3 ± 3.1 μg·L-1) in seven tributaries and the mainstream of the Elbe River, among which the tributary Triebisch had the highest risk quotient over 86. Random Forest outperformed other algorithms with the highest Kappa median values of 0.59 and the lowest Hamming-loss values of 0.22 in extraction of the majority voted class. Then, the source apportionment conducted by random forest suggested that wastewater disposal and metal industrial emissions were the source contributors in the tributary Triebisch (probabilities: 0.39, 0.3), upstream segment (0.45, 0.25), and downstream segment (0.32, 0.23) of the given river. Additional sources of mineral industry emissions were found in the upstream segment (0.21) and downstream segment (0.22). The data provided herein suggest that random forest would be an effective approach to identify pollutants in aquatic environments and could assist source-oriented adaptive management.