Source contributions and spatiotemporal characteristics of PAHs in sediments: Using three‐way source apportionment approach

Abstract

Polycyclic aromatic hydrocarbon (PAHs) were measured in sediments from 29 sites throughout Taihu Lake in China during 2 seasons to investigate spatiotemporal characteristics and source contributions using a 3-way source apportionment approach to positive matrix factorization (PMF3). Seasonal and spatial variations of levels and toxicity suggested higher individual carcinogenic PAH concentrations and toxic equivalent quantity (TEQ) in the flooding season. Three-way PAHs dataset (PAH species, spatial variability, and seasonal variability) was analyzed by PMF3, and its results were compared with a widely used 2-way model (PMF2). Consistent results were observed: vehicular emission was the most important contributor (67.08% by PMF2 and 61.83% by PMF3 for the flooding season; 54.21% by PMF2 and 52.94% by PMF3 for dry season), followed by coal combustion and wood combustion in both seasons. The PMF-cluster analysis was employed to investigate spatial variability of source contributions. Findings can introduce the 3-way approach to apportion sources of PAHs and other persistent organic pollutants (POPs) in sediments, offering the advantage of accounting for information on 3-way datasets.

Spatial occurrence and sources of PAHs in sediments drive the ecological and health risk of Taihu Lake in China

“…Therefore, it was further derived the PAHs in ZB mainly from the petroleum source, and the main sources of GB and MB were the burning of grass, wood, and coal. While PAHs in other areas of Taihu Lake were from the combustion of liquid fossil fuels, which was consistent with the previous studies 48 , 49 .

Figure 4 Source apportionment of PAHs in the sediments of Taihu Lake.

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Spatial distribution and sources identification of polycyclic aromatic hydrocarbons in Wolong Lake, Northeast China

“…In the dry season, the concentration of TPAHs decreased gradually from southwest to northeast, with low concentrations of TPAHs near the boundary of the northeast. The spatial distribution of PAHs was similar to that from Tamamura et al (2009), although contrary to the results of Tian et al (2014) and Kwach et al (2009). The different spatial distribution of PAHs might generally be attributed to: 1) Seasonal effects, for example, in the wet season (June-September) PAHs were predominantly originating from street surface runoff, industrial discharges, and runoff from rural deposition of transported residues in combustion, while in the dry seasons, PAHs were mainly derived from the deposition of air particles (Zhao et al, 2014).…”

Source apportionment of non-methane hydrocarbons, NOx and H2S data from a central monitoring station in the Eagle Ford shale, Texas

“…As the NMF package was originally created for the Bioinformatics research community, for example as a tool for gene analysis, it was decided to compare its results with that of the more widely used source apportionment tool PMF2 used in environmental research (e.g. Ramadan et al, 2003;Begum et al, 2005;Chan et al, 2008;Dutton et al, 2010;Karnae and John, 2011;Rodenburg and Meng, 2013;Tian et al, 2014;Belis et al, 2015). For a direct comparison we selected the data set of Guha et al (2015) as it provided a similar set of measurements and NMHC species.…”

“…Under these conditions, local impacts from oil and gas exploration emissions in the shale area appear to be minimized, while emissions from vehicle traffic may be maximized as traffic impacts are amplified along the US-181 corridor and the neighboring town of Kenedy (Figure 1, Figure S -1a). Since ethane and propane emissions from car traffic are small as compared to the pentanes (Kawashima et al, 2006;Liu et al, 2014;Pang et al, 2014), an elevated pentane-to-propane ratio may be expected under southeasterly winds in summer. This feature of the Karnes City site is further illustrated via both pentane VMRs and the isopentane-to-pentane ratio, shown in Figure 4.…”