Gui-Rong Liu

Historical trends of concentrations, source contributions and toxicities for PAHs in dated sediment cores from five lakes in western China.

In this work, sixteen U.S. EPA priority PAH compounds in the dated sediment cores were detected from five lakes in western China. In most lakes, the concentrations of the total PAHs (ΣPAHs) increased from the deep layers to the surface sediments. Two source categories, i.e. vehicular emission and biomass & domestic coal combustion were identified by Unmix, a factor analysis receptor model to explore the source contributions of PAHs in the dated sediments. The source apportionment results showed that biomass & domestic coal combustion contributed larger proportion of PAHs in the five lakes. The toxicities of PAHs in the dated sediments, assessed by BaP equivalent (BaPE) values showed that the BaPE increased gradually from the deep layers to the surface sediments in most lakes. For the first effort, the contribution of each source to BaPE was apportioned by Unmix-BaPE method, and the result indicated that the vehicular emission posed the highest toxic risk. The percentage contribution of vehicular emission for PAHs and BaPE also increased from the deep layers to the surface sediments, while biomass & domestic coal combustion exhibited the opposite tendency.

A new receptor model-incremental lifetime cancer risk method to quantify the carcinogenic risks associated with sources of particle-bound polycyclic aromatic hydrocarbons from Chengdu in China

PM10 and PM2.5 samples were simultaneously collected during a one-year monitoring period in Chengdu. The concentrations of 16 particle-bound polycyclic aromatic hydrocarbons (Σ16PAHs) were measured. Σ16PAHs concentrations varied from 16.85 to 160.24 ng m(-3) and 14.93 to 111.04ngm(-3) for PM10 and PM2.5, respectively. Three receptor models (principal component analysis (PCA), positive matrix factorization (PMF), and Multilinear Engine 2 (ME2)) were applied to investigate the sources and contributions of PAHs. The results obtained from the three receptor models were compared. Diesel emissions, gasoline emissions, and coal and wood combustion were the primary sources. Source apportionment results indicated that these models were able to track the ΣPAHs. For the first time, the cancer risks for each identified source were quantitatively calculated for ingestion and dermal contact routes by combining the incremental lifetime cancer risk (ILCR) values with the estimated source contributions. The results showed that gasoline emissions posed the highest cancer risk, even though it contributed less to Σ16PAHs. The results and method from this work can provide useful information for quantifying the toxicity of source categories and studying human health in the future.

Vertical characteristics of levels and potential sources of water-soluble ions in PM10 in a Chinese megacity

To investigate the vertical characteristics of ions in PM10 as well as the contributions and possible locations of their sources, eight water-soluble ions were measured at four heights simultaneously along a meteorological tower in Tianjin, China. The total ion concentrations showed a general decreasing trend with increasing height, ranging from 64.94μgm(-3) at 10m to 44.56μgm(-3) at 220m. NH4(+), SO4(2-) and NO3(-) showed higher height-to-height correlations. In addition, relationships between ions are discussed using Pearson correlation coefficients and hierarchical clustering analysis (HCA), which implied that, for each height, the correlations among NH4(+), SO4(2-) and NO3(-) were higher. Finally, sources were identified qualitatively by the ratio of certain ions and quantitatively by principal component analysis/multiple linear regression (PCA/MLR) and positive matrix factorisation (PMF). Secondary sources played a dominant role for PM10 and water-soluble ions at four heights and became more important at greater heights (the percentage contributions were 43.04-66.41% for four heights by PCA/MLR and 46.93-67.62% by PMF). Then, the redistributed concentration field (RCF) combined with PCA/MLR and PMF was applied, which indicated the high potential source regions. The vertical characteristics of the levels, relationships, source contributions and locations would support the effective management of the water-soluble ions in particulate matter.

Chemical characteristic and toxicity assessment of particle associated PAHs for the short-term anthropogenic activity event: during the Chinese New Year's Festival in 2013.

PM10 and PM2.5 samples were simultaneously collected during a period which covered the Chinese New Years (CNY) Festival. The concentrations of particulate matter (PM) and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The possible source contributions and toxicity risks were estimated for Festival and non-Festival periods. According to the diagnostic ratios and Multilinear Engine 2 (ME2), three sources were identified and their contributions were calculated: vehicle emission (48.97% for PM10, 53.56% for PM2.5), biomass & coal combustion (36.83% for PM10, 28.76% for PM2.5), and cook emission (22.29% for PM10, 27.23% for PM2.5). An interesting result was found: although the PAHs are not directly from the fireworks display, they were still indirectly influenced by biomass combustion which is affiliated with the fireworks display. Additionally, toxicity risks of different sources were estimated by Multilinear Engine 2-BaP equivalent (ME2-BaPE): vehicle emission (54.01% for PM10, 55.42% for PM2.5), cook emission (25.59% for PM10, 29.05% for PM2.5), and biomass & coal combustion source (20.90% for PM10, 14.28% for PM2.5). It is worth to be noticed that the toxicity contribution of cook emission was considerable in Festival period. The findings can provide useful information to protect the urban human health, as well as develop the effective air control strategies in special short-term anthropogenic activity event.

Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR) method.

An improved physically constrained source apportionment (PCSA) technology using the Multilinear Engine 2-species ratios (ME2-SR) method was proposed and applied to quantify the sources of PM10- and PM2.5-associated polycyclic aromatic hydrocarbons (PAHs) from Chengdu in winter time. Sixteen priority PAH compounds were detected with mean ΣPAH concentrations (sum of 16 PAHs) ranging from 70.65 ng/m(3) to 209.58 ng/m(3) and from 59.17 ng/m(3) to 170.64 ng/m(3) for the PM10 and PM2.5 samples, respectively. The ME2-SR and positive matrix factorization (PMF) models were employed to estimate the source contributions of PAHs, and these estimates agreed with the experimental results. For the PMF model, the highest contributor to the ΣPAHs was vehicular emission (81.69% for PM10, 82.06% for PM2.5), followed by coal combustion (12.68%, 12.11%), wood combustion (5.65%, 4.45%) and oil combustion (0.72%, 0.88%). For the ME2-SR method, the highest contributions were from diesel (43.19% for PM10, 47.17% for PM2.5) and gasoline exhaust (34.94%, 32.44%), followed by wood combustion (8.79%, 6.37%), coal combustion (12.46%, 12.37%) and oil combustion (0.80%, 1.22%). However, the PAH ratios calculated for the factors extracted by ME2-SR were closer to the values from actual source profiles, implying that the results obtained from ME2-SR might be physically constrained and satisfactory.

Further insights into the composition, source, and toxicity of PAHs in size‐resolved particulate matter in a megacity in China

Concentrations of particulate matter with an aerodynamic diameter less than 10 μm (PM10 ) and PM with an aerodynamic diameter less than 2.5 μm (PM2.5 ), and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The average concentrations of PM10 and PM2.5 reached 209.75 μg/m(3) and 141.87 μg/m(3) , respectively, and those of ΣPAHs were 41.46 ng/m(3) for PM10 and 36.77 ng/m(3) for PM2.5 . The mass ratio concentrations were 219.23 μg/g and 311.01 μg/g in PM10 and PM2.5 , respectively. Three sources and their contributions for PAHs were obtained. For individual input mode, diesel exhaust contributed 46.77% (PM10 ) and 41.12% (PM2.5 ) for mass concentration and 48.69% (PM10 ) and 39.47% (PM2.5 ) for mass ratio concentration; gasoline exhaust contributed 31.02% (PM10 ) and 39.47% (PM2.5 ) for mass concentration and 28.95% (PM10 ) and 36.46% (PM2.5 ) for mass ratio concentration; and coal combustion contributed 22.22% (PM10 ) and 19.41% (PM2.5 ) for mass concentration and 22.36% (PM10 ) and 15.89% (PM2.5 ) for mass ratio concentration. For combined input mode, the same source categories were obtained. Source contributions to PM10 and PM2.5 were diesel exhaust (40.70% and 36.64%, respectively, for mass concentration; 49.19% and 38.47%, respectively, for mass ratio concentration), gasoline exhaust (35.09% and 38.47%, respectively, for mass concentration; 32.50% and 33.43%, respectively, for mass ratio concentration), and coal combustion (24.21% and 24.89%, respectively, for mass concentration; 18.31% and18.17%, respectively, for mass ratio concentration). Source risk assessment showed that vehicle emission was a significant contributor. The findings can help elucidate sources of PAHs and provide evidence supporting further applications of the Unmix model and additional studies about PAHs. Environ Toxicol Chem 2015;34:480-487.

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

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.