Engineering-Based Source Apportionment and Water Quality Assessment for Sustainable River Basin Management: APCS–MLR Modeling

Іntroduction
Effective water resources engineering requires quantitative tools capable of identifying pollution sources and supporting basin-scale management decisions. The lower reach of the Styr River (Ukraine) integrates cumulative agricultural, municipal, and industrial pressures, creating complex hydrochemical dynamics. This study develops an engineering-oriented framework combining the Water Quality Index (WQI) with the Absolute Principal Component Score–Multiple Linear Regression (APCS–MLR) model to support evidence-based water resources planning and pollution control strategies.

Methods
Monthly monitoring (2018–2022) was conducted in the downstream section of the river, representing integrated basin conditions. Seven key parameters (T, DO, BOD₅, pH, PO₄³⁻, NO₃⁻, N–NH₃) were analyzed using standard hydrochemical methods. WQI was calculated to provide an aggregate ecological status indicator. Multivariate statistical analysis included Pearson correlation, Principal Component Analysis (PCA), and APCS–MLR modeling. Source contributions were quantified and model performance evaluated using R² and residual diagnostics.

Results
WQI values ranged from 63% to 87%, indicating seasonal transitions from unsatisfactory to good status. Correlation analysis revealed two dominant engineering-relevant process blocks: a biogenic–organic loading complex (BOD₅, nutrients) and a hydro–oxygen regime (T–DO interaction). PCA identified three principal factors explaining 88.1% of total variance: agro–municipal (45.7%), natural–thermal (27.3%), and technogenic (15.1%). APCS–MLR quantification demonstrated that agro–municipal sources contribute 74.64% of total variability, while natural–thermal and technogenic influences account for 11.85% and 13.51%, respectively.

Conclusion
The integrated WQI–APCS–MLR approach provides a robust engineering tool for source identification and quantitative apportionment in river basins. Results indicate that nutrient and organic loads from agricultural and municipal activities are the dominant drivers of water quality degradation. The proposed framework supports the design of targeted mitigation measures, optimization of wastewater infrastructure, and sustainable water resources management in accordance with modern river basin engineering principles.