Estimation of Secondary PM<sub>2.5</sub> in China and the United States using a Multi-Tracer Approach

Abstract. PM2.5, generated via both direct emissions and secondary formations, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM2.5 components are often based on online observations or lab analyses, which are generally high economic cost and 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 PM2.5. By comparing with the long-term and short-term measurements of aerosol chemical components in China, as well as aerosol composition network in the United States, MTEA is proved to be able to successfully capture the magnitude and variation of the primary PM2.5 (PPM) and secondary PM2.5 (SPM). Applying MTEA to China national air quality network, we find that 1) SPM accounts for 63.5 % of PM2.5 in southern cities of China averaged for 2014–2018, while in the North the proportion drops to 57.1 %, and at the same time the secondary proportion in regional background regions is ~19 % higher than that in populous regions; 2) the summertime secondary PM2.5 proportion presents 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 PM2.5 proportion in Beijing significantly increases by 34 % during the COVID-19 lockdown, which might be the main reason of the observed unexpected PM pollution in this special period; and at least, 4) SPM and O3 show similar positive correlations in the BTH and YRD regions, but the correlations between total PM2.5 and O3 in these two regions are quite different as PPM levels determines. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for the future PM mitigation.