Abstract
Abstract. Identification of various emission sources and quantification of their contributions comprise an essential step in formulating scientifically sound pollution control strategies. Most previous studies have been based on traditional offline filter analysis of aerosol major components (usually inorganic ions, elemental carbon – EC, organic carbon – OC, and elements). In this study, source apportionment of PM2.5 using a positive matrix factorization (PMF) model was conducted for urban Shanghai in the Yangtze River Delta region, China, utilizing a large suite of molecular and elemental tracers, together with water-soluble inorganic ions, OC, and EC from measurements conducted at two sites from 9 November to 3 December 2018. The PMF analysis with inclusion of molecular makers (i.e., MM-PMF) identified 11 pollution sources, including 3 secondary-source factors (i.e., secondary sulfate; secondary nitrate; and secondary organic aerosol, SOA, factors) and 8 primary sources (i.e., vehicle exhaust, industrial emission and tire wear, industrial emission II, residual oil combustion, dust, coal combustion, biomass burning, and cooking). The secondary sources contributed 62.5 % of the campaign-average PM2.5 mass, with the secondary nitrate factor being the leading contributor. Cooking was a minor contributor (2.8 %) to PM2.5 mass while a significant contributor (11.4 %) to the OC mass. Traditional PMF analysis relying on major components alone (PMFt) was unable to resolve three organics-dominated sources (i.e., biomass burning, cooking, and SOA source factors). Utilizing organic tracers, the MM-PMF analysis determined that these three sources combined accounted for 24.4 % of the total PM2.5 mass. In PMFt, this significant portion of PM mass was apportioned to other sources and thereby was notably biasing the source apportionment outcome. Backward trajectory and episodic analysis were performed on the MM-PMF-resolved source factors to examine the variations in source origins and composition. It was shown that under all episodes, secondary nitrate and the SOA factor were two major source contributors to the PM2.5 pollution. Our work has demonstrated that comprehensive hourly data of molecular markers and other source tracers, coupled with MM-PMF, enables examination of detailed pollution source characteristics, especially organics-dominated sources, at a timescale suitable for monitoring episodic evolution and with finer source breakdown.
Highlights
Airborne PM2.5 has attracted increased global attention due to its well-recognized impact on climate, visibility, and human health (Chow et al, 2004; Liu et al, 2016; Foley et al, 2010)
Through this work we demonstrate that the comprehensive hourly data of molecular markers and other source tracers have significantly enhanced our ability in resolving organics-dominated PM2.5 sources and the source apportionment could be achieved at a timescale suitable for monitoring episodic evolution
We carried out a source apportionment study through utilizing hourly measured PM2.5 and its chemical components, including water-soluble inorganic ions, carbonaceous species, and trace elements, and organic molecular markers which were measured at every odd hour in a 3-week field campaign in winter in urban Shanghai, a megacity in the Yangtze River Delta region, China
Summary
Airborne PM2.5 (i.e., particulate matter with an aerodynamic diameter of less than 2.5 μm) has attracted increased global attention due to its well-recognized impact on climate, visibility, and human health (Chow et al, 2004; Liu et al, 2016; Foley et al, 2010). Identifying the pollution sources and quantifying their contributions to ambient PM2.5 are of fundamental significance for PM reduction and air quality improvement Compared with other methods, such as chemical mass balance (CMB) and multilinear engine (ME-2), positive matrix factorization (PMF; Paatero and Tapper, 1994) does not need to input source profiles and is able to provide as model outcome both the source profiles and contributions of various sources (Wang et al, 2018; Zhou et al, 2019). PMF relies on marker species to separate and identify different source factors, and in principle more comprehensive data sets, especially chemical data of high source specificity, would enable more accurate and finer source breakdown for potential sources contributing to PM2.5
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