Abstract
<title>Abstract</title> <p>Dense urban carbon dioxide (CO\((_2)\)) monitoring is increasingly feasible via low-cost IoT networks, yet placing a finite sensor budget under complex municipal geometries remains a challenging combinatorial problem. We model the placement of \((N = 11)\) circular-footprint sensors of radius \((R = \SI{223}{m})\) on the University of Central Florida (UCF) campus as a geospatially constrained maximum-coverage facility-location problem. To ensure siting feasibility across complex land-use layouts, we develop a rigorous spatial screening pipeline that embeds building, water, and forest-class vegetation exclusions into the search space, converting rules into structural constraints. A quantum-inspired evolutionary optimizer (QIEO) operating over this candidate grid achieves \SI{72.73}{\percent} weighted priority-point coverage along high-traffic pedestrian corridors, a 2\((\times)\) improvement over the currently deployed campus baseline (\SI{36.29}{\percent}). The optimizer also outperforms a heuristic baseline solution (\SI{67.82}{\percent}) on this pedestrian-priority objective while enforcing siting constraints and maximizing feasible spacing. Furthermore, an inverse-problem formulation provides a transferable methodology for constructing hardware calibration curves, illustrated here for the UCF deployment, and demonstrates the nonlinear relationship between sensor count and fractional coverage to guide municipal investment. Finally, we analyze the spatial trade-offs between localized hotspot exposure and campus-wide representativeness, establishing this campus-scale deployment pipeline as a validated unit cell for hierarchical metro-scale extensions.</p>