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In September 2013, China began to implement a series of policies in tackling severe air pollution. This paper aims to explore the diversity and effectiveness of its air pollution control policies at the city level. A city-level pollution control policy indicator is constructed for 35 Chinese cities during the last two air pollution control action periods from 2014 to 2017 and 2018 to 2020. Additionally, this paper employs the panel vector autoregression model (PVAR) to estimate the impact of air pollution control policies on air pollution reduction. The empirical results show that, in terms of the two main air quality indicators, PM_2.5 and PM₁₀, China’s air pollution control policies have helped improve the air quality over the last several years. The study concludes that air quality improvement should depend on coordinated strategies for controlling various pollutants that involve the collaboration of government and industries.

Based on Daubechies wavelet transform method, this paper studied the time series of daily average concentration for PM2.5 and PM10, analyzed their variation law and mutation characteristics, a simple comparison is made between the concentration variation law of PM2.5 and PM10. Wavelet transform method is feasible and effective for studying the variation law of the air pollution index time series, the results can be expected to provide decision support for Beijing's atmospheric environmental governance.