PM_2.5, generated via both direct emission and secondary formation, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM_2.5 components are often based on online or offline observations and numerical models, which are generally high economic cost- or labor-intensive. In this study, we develop a new method, named Multi-Tracer Estimation Algorithm (MTEA), to identify the primary and secondary components from routine observation of PM_2.5. By comparing with long-term and short-term measurements of aerosol chemical components in China and the United States, it is proven that MTEA can successfully capture the magnitude and variation of the primary PM_2.5 (PPM) and secondary PM_2.5 (SPM). Applying MTEA to the China National Air Quality Network, we find that (1) SPM accounted for 63.5% of the PM_2.5 in cities in southern China on average during 2014–2018, while the proportion dropped to 57.1% in the north of China, and at the same time the secondary proportion in regional background regions was ∼19% higher than that in populous regions; (2) the summertime secondary PM_2.5 proportion presented a slight but consistent increasing trend (from 58.5% to 59.2%) in most populous cities, mainly because of the recent increase in O3 pollution in China; (3) the secondary PM_2.5 proportion in Beijing significantly increased by 34% during the COVID-19 lockdown, which might be the main reason for the observed unexpected PM pollution in this special period; and finally, (4) SPM and O₃ showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but the correlations between total PM_2.5 and O₃ in these two regions, as determined from PPM levels, were quite different. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for future PM mitigation.

二次气溶胶,空气污染