In terms of cost-effectiveness, previous research has shown that mobile sensor networks can provide affordable solutions for areas with limited financial resources. The cost–benefit indicators developed in this study build on this idea by offering quan-titative comparisons of spatial coverage efficiency and data generation capacity relative to investment costs. This provides a more systematic basis for evaluating economic feasibility in medium-sized industrial cities. Although our findings are consistent with the growing interest in mobile air qual-ity monitoring approaches, this study makes a specific contribution by combining un-certainty analysis, spatial coverage estimation, and long-term life-cycle cost assessment within a single analytical framework tailored to the case of Mohammedia. Rather than challenging previous research, our study consolidates and extends existing knowledge by adapting it to support structured decision-making in resource-constrained urban environments.

Previous studies have demonstrated that mobile sensor networks offer budget-friendly options for regions facing financial constraints. Building on this foundation, the cost-benefit metrics we developed in this research provide numerical assessments that compare spatial coverage effectiveness and data collection capabilities against investment expenses. This creates a more organized approach for determining economic viability in mid-sized industrial cities. While our results align with the increasing focus on mobile air quality monitoring methods, this research contributes uniquely by integrating uncertainty evaluation, spatial coverage analysis, and comprehensive life-cycle cost evaluation into one cohesive analytical approach designed specifically for Mohammedia. Instead of disputing earlier findings, our work strengthens and broadens current understanding by modifying it to enable systematic decision-making processes in urban areas with limited resources.