Abstract
Abstract. The South China Sea (SCS) is a receptor of numerous natural and anthropogenic aerosol species from throughout greater Asia. A combination of several developing countries, archipelagic and peninsular terrain, a strong Asian monsoon climate, and a host of multi-scale meteorological phenomena make the SCS one of the most complex aerosol–meteorological systems in the world. However, aside from the well-known biomass burning emissions from Indonesia and Borneo, the current understanding of aerosol sources is limited, especially in remote marine environments. In September 2011, a 2-week research cruise was conducted near Palawan, Philippines, to sample the remote SCS environment. Size-segregated aerosol data were collected using a Davis Rotating Uniform size-cut Monitor (DRUM) sampler and analyzed for concentrations of 28 elements measured via X-ray fluorescence (XRF). Positive matrix factorization (PMF) was performed separately on the coarse, fine, and ultrafine size ranges to determine possible sources and their contributions to the total elemental particulate matter mass. The PMF analysis resolved six sources across the three size ranges: biomass burning, oil combustion, soil dust, a crustal–marine mixed source, sea spray, and fly ash. Additionally, size distribution plots, time series plots, back trajectories and satellite data were used in interpreting factors. The multi-technique source apportionment revealed the presence of biogenic sources such as soil dust, sea spray, and a crustal–marine mixed source. Anthropogenic sources were also identified: biomass burning, oil combustion, and fly ash. Mass size distributions showed elevated aerosol concentrations towards the end of the sampling period, which coincided with a shift of air mass back trajectories to southern Kalimantan. Covariance between coarse-mode soil dust and fine-mode biomass burning aerosols were observed. Agreement between the PMF and the linear regression analyses indicates that the PMF solution is robust. While biomass burning is indeed a key source of aerosol, this study shows the presence of other important sources in the SCS. Identifying these sources is not only key for characterizing the chemical profile of the SCS but, by improving our picture of aerosol sources in the region, also a step forward in developing our understanding of aerosol–meteorology feedbacks in this complex environment.
Highlights
In the midst of several developing countries, the South China Sea (SCS) is a receptor for a multitude of natural and anthropogenic sources of aerosol
The elemental contribution to the total PM2.5 mass was estimated as the summed contributions of the reconstructed sulfate, sea salt, and soil components according to formulas from Malm and Hand (2007) and Chow et al (2015)
PM2.5 Teflon filters have been observed to show lower concentrations than rotating-drum impactors for several elements, attributed to insufficient background subtractions when computing for filter concentrations (Venecek et al, 2016)
Summary
In the midst of several developing countries, the South China Sea (SCS) is a receptor for a multitude of natural and anthropogenic sources of aerosol. It is known to be impacted by dust storms and industrial pollution from China (Wang et al, 2011; Atwood et al, 2013a) and by biomass burning emissions from the Maritime Continent (Balasubramanian et al, 2003; Lin et al, 2007; Cohen et al, 2010a, b; Wang et al, 2011; Reid et al, 2013, 2015, 2016) The transport of such emissions is enabled by the long atmospheric residence times of fine particles (Cohen et al, 2010a), potentially creating regional and global concerns through their effects on radiative forcing (Nakajima et al, 2007; Boucher et al, 2013; Lin et al, 2014; Ge et al, 2014) and cloud properties (Sorooshian et al, 2009; Lee et al, 2012; Boucher et al, 2013; Ross et al, 2018). Biomass burning is a significant contributor to the region’s cloud condensation nuclei (CCN) budget in all years, as are the region’s significant anthropogenic emissions (Balasubramanian et al, 2003; Field et al, 2008; Reid et al, 2012, 2013, 2015, 2016; Atwood et al, 2017)
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