Abstract
Abstract. Organic aerosols (OAs), which consist of thousands of complex compounds emitted from various sources, constitute one of the major components of fine particulate matter. The traditional positive matrix factorization (PMF) method often apportions aerosol mass spectrometer (AMS) organic datasets into less meaningful or mixed factors, especially in complex urban cases. In this study, an improved source apportionment method using a bilinear model of the multilinear engine (ME-2) was applied to OAs collected during the heavily polluted season from two Chinese megacities located in the north and south with an Aerodyne high-resolution aerosol mass spectrometer (HR-ToF-AMS). We applied a rather novel procedure for utilization of prior information and selecting optimal solutions, which does not necessarily depend on other studies. Ultimately, six reasonable factors were clearly resolved and quantified for both sites by constraining one or more factors: hydrocarbon-like OA (HOA), cooking-related OA (COA), biomass burning OA (BBOA), coal combustion (CCOA), less-oxidized oxygenated OA (LO-OOA) and more-oxidized oxygenated OA (MO-OOA). In comparison, the traditional PMF method could not effectively resolve the appropriate factors, e.g., BBOA and CCOA, in the solutions. Moreover, coal combustion and traffic emissions were determined to be primarily responsible for the concentrations of PAHs and BC, respectively, through the regression analyses of the ME-2 results.
Highlights
Atmospheric aerosols are generating increasing interest due to their adverse effects on human health, visibility and the climate (IPCC, 2013; Pope and Dockery, 2006)
The results show that biomass burning OA (BBOA) and coal combustion OA (CCOA) are separated from each other in the 7- and 8-factor solutions for Qingdao and that better signals for unmixed and stable hydrocarbon-like OA (HOA) with low O / C ratios of 0.17 or 0.18 emerged in the 7- to 10-factor solutions for Dongguan
In order to prove the improvement of using the anchor profiles generated by the unconstrained positive matrix factorization (PMF) run with the same local datasets, we run the ME-2 analysis using the anchor profiles available in the literature, with the results shown in Table S5 and S6
Summary
Atmospheric aerosols are generating increasing interest due to their adverse effects on human health, visibility and the climate (IPCC, 2013; Pope and Dockery, 2006). Many studies focus on organic aerosols (OAs) because they contribute 20–90 % to the total submicron mass (Jimenez et al, 2009; Zhang et al, 2007). An AMS provides online quantitative mass spectra of non-refractory components from the submicron aerosol fraction with a high temporal resolution (i.e., seconds to minutes) (Canagaratna et al, 2007). The total mass spectra can be assigned to both several inorganic compounds and the organic fraction through mass spectral fragmentation tables (Allan et al, 2004). To further investigate the different types of organic fractions, numerous studies have exploited the positive matrix factorization (PMF) algorithm and apportioned the AMS organic mass spectra in terms of their source emissions or formation processes (Zhang et al, 2011). PMF is a standard multivariate factor analysis tool
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