Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter PM_2.5, traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve PM_2.5 relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064-nm backscatter lidar data from the NASA Cloud-Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1-D ensemble-based variational technique (1-D EnsVar) to perform vertically-resolved retrievals of speciated aerosol extinction and surface PM_2.5. Next, we evaluated the performance of 1-D EnsVar retrievals of PM_2.5 and extinction through an independent validation using measurements from spaceborne, airborne, and ground-based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. With the advantage of active remote sensing, this approach is fully capable of performing aerosol retrievals during both daytime and nighttime scenes. Given the unique capability of CATS to process vertical profile data in near real-time, this work demonstrates the powerful utility of spaceborne lidar for improving air quality forecasting. While this pilot study is not yet performed within a cycling data assimilation system, the algorithm developed here can easily be integrated in such systems. Results presented may be useful in other applications as well, including the validation of aerosol transport models, improving passive satellite retrievals of PM_2.5, and developing data assimilation techniques for future lidar platforms.