Low-cost fine particle mass (PM2.5) sensors enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM2.5 sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units (and after rejecting several malfunctioning PurpleAir units). We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. Either model yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term qualitative comparisons of sites and providing long-term quantitative information for health impact studies. For the short-term case, either sensor can provide reasonably accurate concentration information (mean absolute error of ~4 µg/m3) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m3. These sensors are thus suitable for supplementing regulatory-grade instruments in sparsely monitored regions, neighborhood-scale monitoring, and for better understanding spatial patterns and temporal air quality trends across urban areas.