Diagnostic evaluation of a small watershed via multivariate statistical analysis and a positive matrix factorization receptor model

Abstract This study employs multivariate statistical techniques, water quality index (WQI), and a positive matrix factorization (PMF) receptor model to identify pollution sources entering a river, and evaluate the water quality. The study aims to establish strategies for effective water quality management in a watershed by identifying water quality characteristics using principal component analysis (PCA), and evaluating the effect of each pollution source using the PMF model. Through PCA, we identified organic matter and nutrients (e.g., nitrogen and phosphorus) as the primary sources of pollution with a significant impact on the target watershed. The PMF receptor model showed that the pollution sources included organic matter (29.61%), chlorophyll (22.52%), and nitrogen-based nutritive salts (19.80%). Furthermore, the WQI revealed a decrease in the calculated values in urban districts; site 1 (85.1) showed the highest value, whereas sites 5 (64.0) and 6 (63.8) showed lower values. The overall water quality remained safe above the moderate level. To maintain safe water quality and ensure effective management practices, it is imperative to consistently monitor the treated water flowing into the river from domestic sewage and industrial wastewater treatment facilities, and implement countermeasures against various non-point pollution sources. By selecting the sections affecting the target watershed and presenting the main factors and contributions of pollution sources, this study provides a range of methods for water quality management through scientific and precise analysis. The diverse analysis techniques employed in this study can be applied to future water quality evaluations.