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Tracing nutrient pollution pathways from road dust to water bodies in South Tianjin using remote sensing and machine
Muhammad Nabil Ashraf1, Suiliang Huang1, Deze Liu1
1Key Laboratory of Pollution Processes and Environmental Criteria of the Ministry of Education, Key Laboratory of Urban Ecological Environment Rehabilitation and Pollution Control of Tianjin, Numerical Stimulation Group for Water Environment, College of Environmental Science and Engineering Nankai University, Tianjin 300350, China.
Abstract:
Nutrient pollution from nitrogen (N) and phosphorus (P) poses major environmental and health risks, yet its dynamics in urban settings remain underexplored. This study presents one of the first comprehensive assessments of N and P pollution in road dust and adjacent water bodies in South Tianjin, China. We introduce three advances: (1) a two-intersection-line sampling strategy to capture spatial gradients across roadway types; (2) a quantitative evaluation of nutrient transfer from road dust to water, highlighting dominant pathways; and (3) an integrated framework combining chemical analysis, remote sensing (RS), and machine learning (ML) for source apportionment. Results revealed a distinct N-dominated pattern in suburban areas, contrasting with P-dominated trends in western urban districts. Ratios shifted markedly: TP:TN in road dust declined from > 1 (urban) to < 0.35 (agricultural), while TDP:TDN in water rose from 0.006 to 0.0875, indicating changes in transfer mechanisms. SHAP analysis showed total atmospheric deposition explained 65.5 % of TN and 75.5 % of TP:TN, while distance accounted for 83.7 % of TP variation. Nutrient concentrations in dust and water confirmed terrestrial aquatic transfer. The Gradient Boosting Model achieved high accuracy (R² = 0.91 for N; 0.83 for P). Our scalable RS-ML-chemical approach enables efficient source identification and monitoring, with broad applicability to urban nutrient pollution management.
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