Source Apportionment of PM_2.5 Using Hourly Measurements of Elemental Tracers and Major Constituents in an Urban Environment: Investigation of Time‐Resolution Influence

Abstract

We demonstrate with field data the benefit of using high‐time‐resolution chemical speciation data in achieving more robust source apportionment of fine particulate matter (PM2.5) using positive matrix factorization (PMF). Hourly composition data were collected over a month in Shanghai, including four inorganic ions, 13 elements, organic, and elemental carbon. PMF analysis of the hourly data set (PMF1h) resolves eight factors: secondary nitrate/sulfate, vehicular/industrial emissions, coal combustion, secondary sulfate, tire wear, Cr and Ni point source, residual oil combustion, and dust, with the first three being the major ones and each contributing to >20% of PM2.5 mass. To characterize the benefit gained from time resolution, we carried out separate PMF analyses of 4‐ and 6‐hr averaged data of the same data set (PMF6h and PMF4h). PMF6h and PMF4h produce an eight‐factor solution sharing similar factors to those by PMF1h but show less stability and more mixing in source profiles. Profile mixing was especially noticeable for tire wear, coal combustion, and Cr and Ni point source in PMF6h, as the 6‐hr averaging significantly decreased between‐sample variability and increased rotational ambiguity. While the three sets of PMF solutions were similar in contributions for factors with major species as source markers (e.g., secondary nitrate/sulfate), larger variations existed for factors with trace species as markers due to mixing of major species in the profiles and higher rotational uncertainties in PMF4h and PMF6h. Our results indicate that hourly time series of elements and major components could achieve more robust source apportionment through better capturing of diurnal‐scale dynamics in source activities.

Characteristics and Sources of Hourly Trace Elements in Airborne Fine Particles in Urban Beijing, China

“…Factor 5, characterized by high loadings of As, Se, Pb, and Zn, seems to be coal combustion; it contributed 7.6% of the total TE mass. As and Se have been found to be associated with coal combustion (Liu et al, ; Wang, Qiao, Zhou, et al, ; Zhu et al, ). As shown in Figure S6, the diurnal variations in this factor show a lower contribution during the daytime, and they present high values from November to March when substantial amounts of coal are used for domestic heating in Beijing.…”

“…Factor 4 is characteristic of vehicle emission, as it has high inputs of Cu, Zn, Ba, and Fe. Cu, Zn, Ba, and Fe are tracers of non-exhaust vehicle emission (Dall'Osto et al, 2013;Wang, Qiao, Zhou, et al, 2018). K, Mn, and Pb contribute moderately in this factor.…”

Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland

“…2 and 3a respectively. The firework factors are mostly dominated by K, S, Cl, Ti, Cu, Ba and Bi, which are the chemical elemental species of fireworks (Moreno et al, 2007;Wang et al, 2007;Vecchi et al, 2008;Perrino et al, 2011;Tian et al, 2014;Kong et al, 2015;Lin, 2016;Pongpiachan et al, 2018). Ba and Cu compounds are used to produce green and blue fireworks.…”

Development and application of a mass closure PM<sub>2.5</sub> composition online monitoring system

“…A semicontinuous OC/EC analyzer with 1 h time resolution could obtain the concentrations EC and OC, which accounted for 225 approximately 30% of the PM2.5 (Liu et al, 2017). Wang et al (2018) https://doi.org/10.5194/amt-2020-77 Preprint. Discussion started: 20 March 2020 c Author(s) 2020.…”

“…To obtain more comprehensive information on chemical species in PM2.5, some previous studies have employed several online instruments to simultaneously measure the organic carbon, element carbon, important elements, and water-soluble inorganic ions in PM2.5 (Gao et al, 2016;Peng et al, 2016b;Wang et al, 2018;Huang et al, 2019;Liu et al, 2019). Although major species of PM2.5 have been measured in these research studies, the sum concentration of all the determined species was not close to the PM2.5 mass concentrations, which was partly because OM has not been measured, which is required to measure PM2.5 mass closure (Hand et al, 2011;Wang et al, 2018). OM can be calculated by multiplying the OC concentrations by a multiplier that is an estimation of the average molecular weight per carbon weight for the organic aerosol.…”