Distribution Characteristics and Source Apportionment of Winter Carbonaceous Aerosols in a Rural Area in Shandong, China

Abstract

PM2.5 samples were collected for 15 consecutive days in a rural area in Shandong from January to February 2022. The carbon components and water-soluble ions in PM2.5 were measured, and the distribution characteristics and sources of the carbonaceous aerosols were analysed. It was found that the concentrations of PM2.5 in the region were high in winter (55.79–236.11 μg/m³). Organic carbon (OC) and elemental carbon (EC) accounted for 11.61% and 4.57% of PM2.5, respectively. The average concentrations of OC (19.01 μg/m³) and EC (7.49 μg/m³) in PM2.5 were high. The mean value of secondary organic carbon (SOC), estimated by the minimum R squared (MRS) method, was 14.76 μg/m3, accounting for a high proportion of OC (79.41%). Four OC fractions (OC1, OC2, OC3, and OC4) were significantly correlated with SOC, indicating that the OC components contained a large amount of SOC. OC3, OC4, EC1, and OC2 dominated (accounting for 80% of TC) among the eight carbon fractions. Water-soluble organic carbon (WSOC, 12.82 μg/m³) and methanol-soluble organic carbon (MSOC) (16.28 μg/m³) accounted for 67.47% and 84.99% of OC, respectively, indicating that SOC accounted for a high proportion of OC. The proportion of eight water-soluble ions in PM2.5 was 47.48%. NH4+ can neutralise most of the SO42− and NO3−, forming (NH4)2SO4 and NH4NO3, while Cl− mainly exists in the form of KCl and MgCl2. The ratios of some typical components showed that PM2.5 was not only affected by local combustion sources, but also by mobile sources. The cluster analysis results of the backward trajectory model showed that primary and secondary sources in Shandong Province had a great impact on PM2.5 (64%). The analysis results of the positive matrix factorisation (PMF) model showed that the sources of PM2.5 in the region included mobile sources, primary combustion sources, secondary sources, and dust sources, among which secondary sources contributed the most (60.46%).

Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements

“…It was then Millet et al who first argued that a proper POC/EC ratio should minimize the correlation between EC and SOC, later termed as the minimum R squared (MRS) method and further formulated by Wu and Yu . Since then, the MRS method has been examined and applied in various studies. − Nonetheless, as we shall see later, there is an inherent flaw within the calculation of the MRS method.…”

Seasonal Distribution and Source Apportionment of Chemical Compositions in PM2.5 in Nanchang, Inland Area of East China

“…The sources of PM 2.5 were complex and diverse and the distribution characteristics were varied in different regions [10]. It is important to study the chemical composition of PM 2.5 and quantitatively analyse its sources for regional air-pollution prevention and control.…”