A Climate-Driven Dynamic Model for Highway Emissions in Arid Cities Modifying AP-42 and EEA Algorithms with Silt Loading, Building Geometry, and Fuel Density Parameters

Accurate assessment of vehicular air pollution in arid urban environments remains a challenge because standard emission models often overlook localized influences such as climate-driven dust resuspension and urban canyon effects. This study develops an enhanced modeling framework that integrates critical regional parameters into established algorithms to improve estimates of traffic-related emissions, including PM10, PM2.5, CO, and NO2. The US EPA’s AP-42 algorithm was modified to incorporate a novel highway width-to-building height ratio (I/H) and a climate-driven dynamic silt loading model derived from satellite data, while the European EEA algorithm was refined by introducing an explicit fuel density correction (ρ). The framework was applied and validated on two representative highways in Jordan—an industrial corridor and an urban-commercial artery—using continuous sensor-based measurements. Results indicate substantial improvement in predictive performance, with reductions of 60–77% in normalized difference for particulate matter and 72% for CO. The model successfully distinguished between emission regimes, capturing a seasonal silt-loading peak of approximately 17.5 g/m2 during autumn at the industrial site, compared to more stable, traffic-dominated emissions along the urban corridor. Although NO2 performance showed modest gains (4–40%) due to complex photochemical processes, the overall framework proved to be a robust and reliable tool for air quality assessment in arid cities. This adaptable approach provides a foundation for targeted air pollution management, and future work will integrate real-time dispersion dynamics and photochemical modules to better capture secondary pollutant formation.