Although air pollution is largely due to anthropogenic emission, the observed pollution levels in a city are confounded by meteorological conditions and regional transportation of pollutants. However, effective air quality management requires measures for local emissions of the city. With a data selection algorithm, we choose calm episodes after strong cleaning processes to measure the growth of three air pollutants (PM_2.5, NO₂ and SO₂) before the arrival of transported pollution. Panel data regression models are used to analyze the episode data from the quasi-experiments to quantify the local emission in three North China cities from March 2013 to February 2019. The study reveals a significant reduction in the average hourly growth rates for PM_2.5 and SO₂ in 2017-2018 as compared to the levels in 2013 in almost all seasons and cities. However, the local emission with respect to NO₂ was little changed for almost all seasons and cities. The study also finds the winter growth rates of PM_2.5 in Beijing were comparable to those in the heavy industrialized Tangshan and Baoding, even the PM_2.5 hourly growth rates for winter 2018 in Beijing were higher than those in Tangshan and Baoding, revealing Beijing’s substantial emission.