Adaptive Technological Processes for Urban Road Network Maintenance in Mountainous Terrain: Mathematical Modeling and Hydrochemical Verification

Non-exhaust particulate emissions from road dust represent a major air-quality concern in mountain-basin cities, where restricted atmospheric dispersion promotes pollutant accumulation. This study develops an adaptive framework for urban road maintenance that integrates road-dust resuspension modelling, a nonlinear model of pavement friction response to de-icing material dosage, computational fluid dynamics analysis of street-canyon effects, and snow-cover hydrochemical monitoring. Field sampling was conducted at eight road sites and one background site in Almaty, Kazakhstan, during three campaigns in the 2024–2025 winter season. Meltwater pH, turbidity, and color were measured and incorporated into a diagnostic index for identifying road sections requiring maintenance adjustments. The measured pH ranged from 5.5 to 9.3, while January turbidity reached 68 mg/L at the most polluted site. The modelling results indicated that the principal improvement in pavement friction occurred within a de-icing material dosage range of 40–60 g/m2, whereas further dosage increases produced limited additional friction benefits. Scenario calculations suggested that adaptive dosing and the replacement of mechanical sweeping with vacuum or hydrodynamic cleaning at high-resuspension sites could reduce material consumption by 20–30% and potentially decrease road-dust resuspension by 30–50%. The proposed framework provides a feedback-based approach for balancing pavement safety, maintenance efficiency, and environmental performance; however, its predicted effects require validation through continuous particulate-matter and road-condition monitoring.