Socioeconomic drivers of riverine biochemical oxygen demand: Insights from interpretable machine learning.

Biochemical oxygen demand (BOD5) is a comprehensive indicator for assessing organic pollution and water quality in rivers. Socioeconomic factors, such as regional population pressure, urbanization intensity, and land use, affect riverine BOD5 by altering aquatic environmental conditions. However, the underlying driving mechanism remains insufficiently understood. This study combined interpretable machine learning (IML) with partial least squares structural equation modeling (PLS-SEM) to develop an analytical framework for the factors influencing riverine BOD5. Our findings indicated that variations in urbanization intensity and socioeconomic patterns across the watershed significantly influence river water quality characteristics, with BOD5 showing a strong correlation with these water quality parameters. The developed Random Forest model confirmed the substantial contribution of nitrogen and phosphorus compounds to predicting riverine BOD5, identifying TKN as the critical driver. Furthermore, path analysis using PLS-SEM revealed that socioeconomic factors exert an indirect influence on BOD5 concentrations by mediating the discharge of nitrogen and phosphorus nutrients into surface waters. In highly urbanized areas, riverine BOD5 was strongly influenced by socioeconomic activities, whereas in less urbanized regions, it is governed more by natural processes than by socioeconomic drivers. This study elucidates the impact of socioeconomic development on riverine BOD5, providing scientific insights for deeply understanding the driving mechanisms of river pollution and fostering sustainable environmental and socioeconomic development.