Recently, highly spatially dense air quality monitoring networks using low-cost sensors have been attempted worldwide. However, the quality of data from these sensor networks remains to be validated. This study assessed the potential of low-cost sensors for spatially dense air quality monitoring. Thirty sets of air quality sensor nodes for CO, NO2, O3, PM2.5, and PM10 were custom-built to evaluate their consistency in measurement, both among the sensor nodes and between the sensor node and instruments that use Federal Reference/Equivalent Methods (FRMs/FEMs) in the real atmosphere under two distinctly differing meteorological conditions (summer and winter) in Seoul and Busan, Korea. We found that commercially available low-cost sensors possess great potential as monitors for short-term air quality studies in urban areas, at least for one-month periods, given that (1) the self-consistency among the 30 sensors was high (R2 > 0.93), (2) the consistency between the sensors and the FRM/FEM instruments was reasonably high (R2 > 0.87 overall for the periods of comparison ), and (3) the consistency both among the sensors and between the sensors and the FRM/FEM instruments remained stable throughout the summer and the winter. However, vigorous data post-processing is needed to obtain reliable air quality data. For longer-term or temporally discontinuous monitoring, several issues must be addressed, including the limited lifetime of sensors, the degradation in sensor performance over time, and the long warm-up times for gaseous pollutant sensors. The O3 sensors required minimal post-processing correction, and the particulate matter and CO sensors agreed well with the FEM instruments after appropriate scale correction, but the NO2 sensors required additional efforts to correct for the effects of meteorological conditions and interfering materials. Overall, our results suggest that when investigating spatiotemporally heterogeneous distributions of air pollutants in various urban environments, a three-dimensional sensor network can be a useful tool for short-term monitoring, as long as data are corrected properly.