This study is based on theoretical modeling and literature-derived parameters ra-ther than empirical field validation. Consequently, local calibration campaigns and ex-perimental validation studies are necessary to refine the proposed correction coeffi-cients and confirm spatial coverage assumptions under real operational conditions. Future research should focus on: Field-based validation of the dynamic correction model; Long-term seasonal and diurnal performance assessment; Integration of advanced machine learning algorithms for real-time calibration; Extension of the framework to other urban contexts with different industrial and spatial characteristics.

This research relies on theoretical modeling and parameters drawn from existing literature instead of direct field testing and validation. As a result, local calibration efforts and experimental validation work will be needed to improve the suggested correction factors and verify assumptions about spatial coverage in actual operating environments. Future research priorities should include: Validating the dynamic correction model through field studies; Evaluating performance across different seasons and times of day over extended periods; Incorporating advanced machine learning techniques for real-time calibration processes; Adapting the framework for use in other urban environments that have different industrial profiles and spatial features.