Low-cost air quality sensors measuring air quality at fine spatio-temporal resolutions, typically suffer from sensor drift and interference. Field calibration is typically performed at one location, while little is known about the spatial transferability of correction factors. We evaluated three calibration methods using a year of hourly nitrogen dioxide (NO2) observations from low-cost sensors, collocated at two sites with a conventional monitor as reference: (1) an iterative Bayesian approach for daily estimation of the parameters in a multiple linear regression model, (2) a daily updated correction factor and (3) a correction factor updated only when concentrations are uniformly low. We compared the performance of the calibration methods in terms of temporal stability, spatial transferability, and sensor specificity. We documented drift within the 1-year period. The correction factor updated under uniformly low concentrations performed poorly. The iterative Bayesian approach and daily correction factor reduced the root mean squared error (RMSE) by 21–46% at the calibration locations, but did not reduce RMSE at the other location. By examining the posterior distributions of the regression coefficients, we found that the poor spatial transferability is consistent with different responses of individual sensors to environmental factors. We conclude that the spatial and temporal variability in the calibration parameters requires them to be updated regularly, including sensor-specific recalibrations.