Our work quantifies how much sensor placement affects emission-rate inversion for candidate urban facilities and road segments relative to other design decisions, given that monitoring networks are sited for concentration mapping. We built a paired benchmark covering 30 synthetic cities that places five design decisions on one error scale. Under matched transport, covering traffic in the source space changes normalized source error by 0.74, and dictionary fidelity and sensor calibration by about 0.68 each, against 0.13–0.15 across placement criteria; under an independent stability-dependent dispersion generator, the coverage effect shrinks to the order of the placement effect. Across five error types at matched severity, source identification degrades more slowly than emission rates, with similar patterns for every tested type. Unmodeled mixing height is among the most common errors (0.41–0.53), and an accurate external estimate can recover much of it. A truth-free residual flags severe model errors of every tested type (AUC 0.89–0.95). For seven Chengdu NO₂ stations, poor out-of-sample skill, unstable support, and a large residual led us to withhold estimates. Where major inventory omissions or calibration errors exist, correcting them may reduce error more than switching among the placement criteria tested; identification capacity, rates, and residuals should be reported separately.