The Beijing government implemented a number of clean air action plans to improve air quality in the last 10 years, which contributed to changes in the concentration of fine particles and their compositions. However, quantifying the impacts of these interventions is challenging as meteorology masks the real changes in observed concentrations. Here, we applied a machine learning technique to decouple the effect of meteorology and evaluate the changes in the chemistry of nonrefractory PM1 (particulate matter less than 1 μm) in winter 2007, 2016, and 2017 as a result of the clean air actions. The observed mass concentrations of PM1 were 74.6, 90.2, and 36.1 μg m-3 in the three winters, while the deweathered concentrations were 74.2, 78.7, and 46.3 μg m-3, respectively. The deweathered concentrations of PM1, organics, sulfate, ammonium, chloride, SO2, NO2, and CO decreased by -38, -46, -59, -24, -51, -89, -16, and -52% in 2017 in comparison to 2007. On the contrary, the deweathered concentration of nitrates increased by 4%. Our results indicate that the clean air actions implemented in 2017 were highly effective in reducing ambient concentrations of SO2, CO, and PM1 organics, sulfate, ammonium, and chloride, but the control of nitrate and PM1 organics remains a major challenge.