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. 4.6 Limitations of the Study Although the proposed analytical framework provides a structured comparison between fixed and mobile air quality monitoring systems, several limitations must be acknowledged.

This analytical framework produces results that align with earlier research emphasizing 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 shown that affordable mobile sensors greatly improve spatial coverage, especially in urban areas where pollution levels vary significantly across short distances. Our research confirms these observations by using scenario-based modeling to show that mobile monitoring setups could theoretically provide much broader spatial coverage than networks with few fixed stations. Previous research on machine learning calibration methods (Zimmerman et al., 2018; Spinelle et al., 2015) has mainly focused on environmental adjustments and fixing cross-sensitivity issues. Our study adds to this existing work by directly including dynamic operational challenges like traffic vibrations and equipment immobilization problems within an organized correction system. This operational aspect has received limited attention in many current calibration studies. When it comes to cost effectiveness, past research has indicated that hybrid or mobile sensor networks might offer affordable scaling options for areas with limited resources. The cost-benefit measures we developed in this study expand on this topic by providing numerical comparisons of spatial coverage efficiency and data production capacity per dollar invested. This creates a systematic economic evaluation designed specifically for medium-sized industrial cities. Although our findings support the widespread preference for hybrid or mobile-enhanced monitoring approaches, this study makes a unique contribution by combining uncertainty modeling, spatial coverage assessment, and long-term lifecycle cost analysis into one comprehensive analytical framework specifically designed for Mohammedia. Instead of challenging previous research, our study builds upon and expands current knowledge by adapting it for structured decision-making purposes. 4.6 Study Limitations While this analytical framework offers a systematic comparison of fixed and mobile air quality monitoring systems, we must recognize several important limitations.