We developed a new algorithm with low economic/technique costs to identify primary and secondary components of PM_2.5. Our model was shown to be reliable by comparison with different observation datasets. We systematically explored the patterns and changes in the secondary PM_2.5 pollution in China at large spatial and time scales. We believe that this method is a promising tool for efficiently estimating primary and secondary PM_2.5, and has huge potential for future PM mitigation.