Low-cost sensors are useful tools for the collection of air quality data, augmenting the existing regulatory monitoring networks and providing an unprecedented opportunity to increase their spatial coverage. This study presents a calibration process of a low-cost PM sensor (PurpleAir PA-II, PAir) in ambient conditions in the city of Patras, Greece, during 18 months of 2017–2018. The hourly PM1 and PM2.5 measurements using the original sensor values were reasonably well correlated (R2 = 0.82 for PM1 and R2 = 0.56 for PM2.5) with the reference instrument, but with a high mean bias and root mean square error. There was a small improvement of around 10% for the daily averages. For PM1–2.5 (particles with diameters between 1 and 2.5 μm), PM2.5–10 (diameters between 2.5 and 10 μm) and PM10, the performance of the low-cost sensors was poor in this area with R2 < 0.37 in all cases. The response of the PAir sensor for PM1 and PM2.5 changed significantly compared to the reference instrument during periods with high dust (or other coarse particle) concentrations. These periods were excluded and a simple linear calibration was then developed for the rest of the fine PM measurements. A method for the identification of these high dust periods based on regional model predictions is proposed. This calibration reduces the relative mean error for hourly PM1 to 19% (1.1 μg m−3) and for PM2.5 to 18% (1.1 μg m−3). The corresponding root mean square errors are 25% (1.4 μg m−3) for hourly PM1 and 25% (1.6 μg m−3) for PM2.5. The biases of the corrected values are, as expected, practically zero. Surprisingly, the relative humidity had a negligible effect on fine PM measurements of the PAir in this location and for the conditions of the study. Display omitted •Low-cost PM sensors were calibrated in ambient conditions.•The original sensor values were reasonably well correlated.•The sensor response was different during periods with high dust.•The relative humidity had a negligible effect on fine PMs.