Application of Synthetic Data to Establish the Working Framework for Multivariate Statistical Analysis of River Pollution Traceability - The Heavy Metals in Nankan River, Taiwan

Abstract This study applied multivariate statistical analysis (MSA) to the synthetic data simulated by the river water quality model to investigate how two pollution sources with different characteristics and contributions affect the results of MSA. The results showed that when assessing the number and possible locations of pollution sources based on the results of cluster analysis (CA), hydrological information about surface water should be obtained to improve the accuracy of the results; when applying principal component analysis (PCA), the results of the second principal component (PC2) and the Pearson correlation coefficients among the pollutants should both be included, which can add more information about the characteristics of pollutant sources. In addition, this study found that the solid and liquid partition coefficients (Kd) of pollutants can affect the interpretation of the PCA results, so the Kd values should be determined before tracing the pollution sources to facilitate the evaluation of the source characteristics and potential targets. This study established a working framework for surface water pollution traceability to enhance the effectiveness of pollution traceability.