The findings of this analytical framework are consistent with previous research highlighting the complementary roles of fixed and mobile air quality monitoring sys-tems. Several studies (Snyder et al., 2013; Castell et al., 2017; Kumar et al., 2015) have emphasized that low-cost and mobile sensors significantly enhance spatial representa-tiveness, particularly in urban environments characterized by high spatial heterogene-ity. The present study supports these findings by demonstrating, through scenar-io-based modeling, that mobile monitoring configurations may theoretically achieve substantially greater spatial coverage compared to sparse fixed-station networks. Furthermore, previous works focusing on machine-learning calibration approaches (Zimmerman et al., 2018; Spinelle et al., 2015) primarily address environmental com-pensation and cross-sensitivity correction. In contrast, the present study supplements prior research by explicitly incorporating dynamic operational disturbances, such as traffic-induced vibration and immobilization effects, into a structured correction framework. This operational dimension remains underexplored in many existing cali-bration-based studies. Regarding cost-efficiency, earlier investigations have suggested that hybrid or mobile sensor networks may provide economically scalable solutions for re-source-constrained regions. The cost–benefit indicators developed in this study extend this discussion by quantitatively comparing spatial coverage efficiency and data gen-eration capacity per unit investment, thereby offering a structured economic assessment tailored to medium-sized industrial cities. While the conclusions align with the general consensus favoring hybrid or mo-bile-enhanced monitoring strategies, this study contributes by integrating uncertainty modeling, spatial coverage estimation, and long-term life-cycle cost analysis within a unified analytical framework specifically contextualized to Mohammedia. Rather than refuting prior research, the study consolidates and extends existing knowledge by adapting it to a structured decision-support perspective.

These results align with earlier research that has shown how fixed and mobile air quality monitoring systems work together effectively. Multiple studies (Snyder et al., 2013; Castell et al., 2017; Kumar et al., 2015) have pointed out that affordable mobile sensors greatly improve spatial coverage, especially in cities where air quality varies significantly across different areas. Our research confirms these conclusions by using scenario-based modeling to show that mobile monitoring setups could potentially cover much more ground than networks with only a few fixed stations. Past research on machine learning calibration methods (Zimmerman et al., 2018; Spinelle et al., 2015) has mainly focused on adjusting for environmental factors and fixing cross-sensitivity issues. Our study adds to this body of work by specifically including dynamic operational challenges like vibrations from traffic and equipment breakdowns in our correction system. This operational aspect has not been thoroughly examined in many calibration studies to date. When it comes to cost effectiveness, previous research has indicated that hybrid or mobile sensor networks could offer affordable solutions for areas with limited budgets. The cost-benefit measures we developed in this study build on this idea by providing numerical comparisons of spatial coverage efficiency and data production capacity relative to investment costs. This gives us a systematic way to evaluate economics specifically for medium-sized industrial cities. Although our findings support the widespread preference for hybrid or mobile-enhanced monitoring approaches, this study makes its own contribution by combining uncertainty modeling, spatial coverage calculations, and long-term lifecycle cost analysis into one comprehensive framework designed specifically for Mohammedia. Instead of challenging previous research, our study brings together and builds upon existing knowledge by adapting it for structured decision-making purposes.