11 Sep 2023
 | 11 Sep 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Short-term Source Apportionment of Fine Particulate Matter with Time-dependent Profiles Using SoFi: Exploring the Reliability of Rolling Positive Matrix Factorization (PMF) Applied to Bihourly Molecular and Elemental Tracer Data

Qiongqiong Wang, Shuhui Zhu, Shan Wang, Cheng Huang, Yunsen Duan, and Jian Zhen Yu

Abstract. Positive matrix factorization (PMF) has been widely used to apportion the sources of fine particulate matter (PM2.5) by utilizing PM chemical speciation data measured at receptor site(s). Traditional PMF, which typically relies on long-term observational datasets of daily or lower time resolution to meet the required sample size, has its reliability undermined by changes in source profiles, thus it is inherently ill-suited for apportioning sporadic sources or ephemeral pollution events. In this study, we explored short-term source apportionment of PM2.5 using a set of hourly chemical speciation data over a period of thirty-seven days in the winter of 2019–2020. PMF run with campaign-wide data as input (PMFref) was initially conducted to obtain reference profiles for the primary source factors. Subsequently, short-term PMF analysis was performed using the Source Finder Professional (SoFi Pro). The analysis sets a window length as the first 18 d of the campaign and constrained the primary source profiles using the a-value approach embedded in SoFi software. Rolling PMF was then conducted with a fixed window length of 18 d and a step of 1 d using the remaining dataset. By applying the a-value constraints to the primary sources, the rolling PMF effectively reproduced the individual primary sources, as evidenced by the slope values close to unity (i.e., 0.9–1.0). However, the estimation for the firework emission factor in the rolling PMF was lower compared with the PMFref (slope: 0.8). These results suggest the unique advantage of short-term PMF analysis in accurately apportioning sporadic sources. Although the total secondary sources were well-modelled (slope: 1.0), larger biases were observed for individual secondary sources. The variation in source profiles indicated higher variability for the secondary sources, with average relative differences ranging from 42 % to 173 %, while the primary source profiles exhibited much smaller variabilities (relative differences of 8–26 %). This study suggests that short-term PMF analysis with the a-value constraints in SoFi can be utilized to apportion primary sources accurately, while future efforts are needed to improve the prediction of individual secondary sources.

Qiongqiong Wang et al.

Status: open (until 23 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1846', Anonymous Referee #1, 20 Sep 2023 reply
  • RC2: 'Comment on egusphere-2023-1846', Anonymous Referee #2, 21 Sep 2023 reply

Qiongqiong Wang et al.

Qiongqiong Wang et al.


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Short summary
We investigated short-term source apportionment of PM2.5 utilizing rolling positive matrix factorization (PMF) and online PM chemical speciation data, which included source-specific organic tracers collected over a period of 37 days during the winter of 2019–2020 in suburban Shanghai, China. The findings highlight that by imposing constraints on the primary source profiles, short-term PMF analysis successfully replicated both the individual primary sources and the total secondary sources.