Contribution of meteorological factors to particulate pollution during winters in Beijing

Abstract Associated with its modernization, Beijing has experienced significant fine particulate matter (PM 2.5 ) pollution, especially in winter. In 2016, severe PM 2.5 pollution (PM 2.5  > 250 μg/m 3 ) lasted over 6 days and affected over 23 million people. A major challenge in dealing with this issue is the uncertainty regarding the influence of individual meteorological factors to the overall PM 2.5 concentration in Beijing. Thus, applying an empirical regression method to long-term ground-based PM 2.5 data and meteorological sounding measurements, we attempted to analyze the influence of individual meteorological factors on PM 2.5 pollution during winters in Beijing. We found that horizontal dilution and vertical aggregation plays a major role in PM 2.5 pollution during the winter of 2016. The impact of horizontal wind on PM 2.5 concentration in Beijing was mainly from its dilution, the dilution of northerly wind contributed 27.8% in 2016, far below its contribution in 2015 (32.2%). The contribution from the growing vertical aggregation observed in 2016 was mainly the result of both the lower height of the planetary boundary layer and the greater depth of the temperature inversion. The dilution of the planetary boundary layer height contributed 9.8% to PM 2.5 pollution in 2016, 5.4% lower than that in 2017. Compared with the temperature difference of the inversion layer, the temperature inversion depth better reflects the aggregated impact of temperature inversions to PM 2.5 , which was 10.9% in 2015, and the ratio rose to 14.3% in 2016. Relative humidity is also an important impacting factor, which contributed 41.0%, far higher than the ratio in 2017 (26.7%). Such results imply that we should focus on not only local emission control, but also horizontal atmospheric transport and meteorological conditions in order to provide a more accurate analysis of pollution mechanisms, conductive to air pollution governance in Beijing.