Calibration model R code.

This study presents the calibration and performance analysis of a low-cost sensor (LCS) network for monitoring particulate matter with diameters ≤ 2.5 µm (PM2.5) in Bucharest, Romania. The InfoAer network comprised 44 Clarity Node-S sensors deployed across the city. The performance of sensors was evaluated against reference measurements from the National Environmental Protection Agency (NEPA) regulatory monitoring stations. The manufacturer’s pre-calibration significantly underestimated PM2.5 concentrations, particularly during the summer months, when meteorological conditions favor the formation of secondary aerosols. Nine spatial clusters with collocated InfoAer-NEPA measurements were identified, with one designated for calibration model development and the remaining eight for independent validation. Multiple seasonal calibration models were developed using temperature, relative humidity, and nitrogen dioxide as predictor variables in a multiple linear regression formulation. Calibration performance was compromised during hot, dry conditions, when PM2.5 concentrations were typically low, likely due to reduced aerosol scattering efficiency and increased measurement uncertainty. Using only temperature and relative humidity as predictors, the optimal model, selected via a two-step calibration process, substantially improved measurement accuracy. Pearson correlation coefficients improved from 0.06 to 0.65 and from −0.28 to 0.89 for the dry and humid seasons, respectively. However, considerable inter-sensor variability in calibration performance was observed, indicating the need for additional meteorological or chemical parameters in future calibration algorithms. Application of the calibration model to the entire InfoAer network revealed significant air quality violations across Bucharest. On average, calibrated sensors recorded more than 60 days of exceedances of the European Union’s daily PM2.5 limit value (25 μg m−3) per year, which far exceeded the permitted frequency of 35 exceedances per year. Prior to calibration, only 8 of 44 sensors (18%) exceeded this threshold; post-calibration analysis revealed violations at all monitoring locations. These results demonstrated the critical importance of proper LCS calibration for accurately assessing regulatory compliance and protecting public health.