At present, 4.2 million deaths occur every year due to ambient air pollution, according to the World Health Organization. To reduce such figure, local administrations are enacting Air Quality Plans, for which accurate air quality monitoring is essential. Expanding monitoring networks to support local air quality planning is a complex task. On the one hand, the high costs of reference monitors make their large-scale adoption prohibitive, while the easily scalable low-cost sensors often feature significantly lower data quality and lack of calibration. Near-reference monitors have been voiced as a promising solution, as they exhibit limited costs, although specific studies assessing their performance against reference monitors are still missing. This article provides an in-depth assessment of three near-reference sensors’ performance, through their collocation with reference monitors from December 2021 onwards. Two sensors were positioned at high-traffic locations, while the third recorded background pollution levels in Milan, Italy. Sensors’ performance was quantified not only via the coefficient of determination (R²) and the regression model, but also with the Root Mean Squared Error, the Mean Normalized Bias (MNB), and the Coefficient of Variation (CV), which are often overlooked in the literature. Finally, a non-parametric Wilcoxon Signed-Rank test was performed to determine the statistical significance of observed differences. After a first measurement period, sensors were re-calibrated to also appraise their behavioral change, showing a general performance increase. Preliminary results show high correlation for all hourly-recorded pollutants, with peaks for Nitrogen Dioxide (NO₂) (R² = 0.92) and Black Carbon (R² = 0.91). Very low MNB was also recorded for NO₂ (MNB = 0.005), and for Carbon Monoxide (CO) (MNB = 0.0001). Similarly, promising CVs were consistently found for NO₂ (CV = 0.26), and for CO (CV = 0.31). Although preliminary, such results show the high potential of near-reference sensors to support urban air quality planning.