Yin-Chang Feng

Potential source contributions and risk assessment of PAHs in sediments from Taihu Lake, China: Comparison of three receptor models

In this work, three receptor models (Principal Component Analysis-Multiple Linear Regression (PCA-MLR) model, Unmix model and Positive Matrix Factorization (PMF) model) were employed to investigate potential source apportionment of PAHs in sediments from Taihu Lake, China. A total of 15 priority PAHs in 29 sediments from Taihu Lake were measured, with ∑PAHs (sum of 15 PAHs) concentrations ranging from 209 to 1003 ng g(-1) dw. Source apportionment results derived from three different models were similar, indicating that the highest contribution to ∑PAHs was from vehicular emission (53.6-54.3%), followed by coal combustion (23.8-28.8%) and wood combustion (11.9-16.0%). The contribution of mixed wood and coal combustion source identified by PCA-MLR was 41.3%. For the first time the risk assessment for each identified source category was quantitatively calculated by combining the BaP equivalents (BaPE) values with estimated source contributions. The results showed that vehicular emission posed the highest toxic risk, with BaPE values of 26.9-31.5 ng g(-1) dw, and the BaPE values for coal combustion and wood combustion were 6.56-15.6 ng g(-1) dw and 2.94-6.11 ng g(-1) dw, respectively. The distributions of contribution and BaPE for each identified source category were studied as well, and showed similar trends among the sampling sites, for each source category.

Using geoaccumulation index to study source profiles of soil dust in China

Source apportionment studies of TSP (atmospheric particulate matter with aerodynamic diameters < or = 100 microm) and PM10 (atmospheric particulate matter with aerodynamic diameters < or = 10microm) have revealed that soil dust is an important source of these particulates in China. In this study, the contamination of soil dust was assessed through the use of a geoaccumulation index (I(geo)). The mass concentration profiles of 17 elements (Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, Ba, and Pb) were established for urban soil dusts. Geochemical compositions of soils from 15 cities were used to represent background urban soil compositions. The results of this study indicated that a number of cities are severely polluted by particulates containing Ca, Cr, Ni, and Cu in both size fractions (TSP and PM10). Contamination with Zn, Pb, Co, and Br was moderate to severe (I(geo) > 2). The Al and Fe concentrations were not high enough for them to be considered contaminants.

Concentrations and sources of PAHs in surface sediments of the Fenhe reservoir and watershed, China

Sixteen PAHs in surface sediments at 28 sites throughout Fenhe reservoir and watershed were measured. The ∑PAHs concentrations ranged from 539.0 to 6281.7 with the mean of 2214.8ng/g. The 2-3 rings PAHs, contributing 55 percent to ∑PAHs, were the dominant species. Twenty-eight sites were grouped into three segments: Fenhe principal stream, estuaries of main branch streams, and Fenhe reservoir. ∑PAHs was highest in the estuaries of main branch streams. The ecological risk assessment was studied by biological thresholds. The results showed levels of PAHs might cause mild but not acute adverse biological effects. In addition, PAHs ratios, PCA/MLR and hierarchical clustering analysis were applied to evaluate the possible sources. Coal combustion (35 percent), diesel and gasoline emissions (29 percent and 16 percent, respectively) might be the important sources. For sites in Fenhe reservoir, the major sources were complex, while other two segments were mainly influenced by coal combustion source.

Relationships between PAHs and PCBs, and quantitative source apportionment of PAHs toxicity in sediments from Fenhe reservoir and watershed

Sedimentary samples from 28 sites throughout the Fenhe reservoir and upstream watershed in China were measured, to study the relationships between PAHs and PCBs as well as perform quantitative source apportionment of toxicity. The distributions of ∑16PAHs and ∑123PCBs showed high correlation. The ∑7C-PAHs (total concentrations of seven carcinogenic PAHs) varied from 87.7 to 2005.0ngg(-1)dw, and the ∑6DL-PCBs (total concentrations of six dioxin-like PCB congeners) were n.d.-5.96ngg(-1)dw. Toxicity and biological risk were assessed using toxic equivalent quantity (TEQ) and sediment quality guideline quotient (SQGQ). The BaP played the dominant role for TEQPAH at most sites (37.17-89.40%), although the CHR showed the highest concentration level. PCB-81 contributed the highest TEQPCB. High correlations were observed between ∑16PAHs and ∑7C-PAHs, ∑123PCBs and ∑6DL-PCBs, ∑7C-PAHs and ∑6DL-PCBs as well as TEQPAH and TEQPCB. Furthermore, quantitative source apportionments for PAHs concentrations and TEQ were carried out, combining the positive matrix factorization (PMF) with the formula of TEQ. For most sites, the vehicular sources were the largest contributors to TEQPAH (46.58%), while coal combustion sources were the highest contributor for PAHs concentrations (43.31%). The relatively higher correlations between PCBs and contributions of coal combustion to PAHs might imply the similar distribution of industrial sources for PAHs and PCBs.

Insights into the chemical characterization and sources of PM2.5 in Beijing at a 1-h time resolution

As the widespread application of online instruments penetrates the environmental fields, it is interesting to investigate the sources of fine particulate matter (PM2.5) based on the data monitored by online instruments. In this study, online analyzers with 1-h time resolution were employed to observe PM2.5 composition data, including carbon components, inorganic ions, heavy metals and gas pollutants, during a summer in Beijing. Chemical characteristics, temporal patterns and sources of PM2.5 are discussed. On the basis of hourly data, the mean concentration value of PM2.5 was 62.16±39.37 μg m(-3) (ranging from 6.69 to 183.67 μg m(-3)). The average concentrations of NO3(-), SO4(2-), NH4(+), OC and EC, the major chemical species, were 15.18±13.12, 14.80±14.53, 8.90±9.51, 9.32±4.16 and 3.08±1.43 μg m(-3), respectively. The concentration of PM2.5 varied during the online-sampling period, initially increasing and then subsequently decreasing. Three factor analysis models, including principal component analysis (PCA), positive matrix factorization (PMF) and Multilinear Engine 2 (ME2), were applied to apportion the PM2.5 sources. Source apportionment results obtained by the three different models were in agreement. Four sources were identified in Beijing during the sampling campaign, including secondary sources (38-39%), crustal dust (17-22%), vehicle exhaust (25-28%) and coal combustion (15-16%). Similar source profiles and contributions of PM2.5 were derived from ME2 and PMF, indicating the results of the two models are reasonable. The finding provides information that could be exploited for regular air control strategies.

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.

Use of a nonnegative constrained principal component regression chemical mass balance model to study the contributions of nearly collinear sources.

In this study, a nonnegative constrained principal component regression chemical mass balance (NCPCRCMB) model was used to solve the near collinearity problem among source profiles for source apportionment. The NCPCRCMB model added the principle component regression route into the CMB model iteration. The model was tested with the synthetic data sets, which involved contributions from eleven actual sources, with a serious near collinearity problem among them. The actual source profiles were randomly perturbed and then applied to create the synthetic receptor. The resulting synthetic receptor concentrations were also randomly perturbed to simulate measurement errors. The synthetic receptors were separately apportioned by CMB and NCPCRCMB model. The result showed that source contributions estimated by the NCPCRCMB model were much closer to the true values than those estimated by the CMB model. Next, five real ambient data sets from five cities in China were analyzed using the NCPCRCMB model to test the model practicability. Reasonable results were obtained in all cases. It is shown that the NCPCRCMB model has an advantage over the traditional CMB model when dealing with near collinearity problems in source apportionment studies.

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 and stable carbon isotopic characterization for PAHs in aerosol emitted from two indoor sources.

Polycyclic aromatic hydrocarbons (PAHs) are with great environmental concerns due to their toxic, mutagenic, and carcinogenic properties. The harmful effects caused by PAHs emitted from indoor sources may be more direct and serious. In this study, PAHs in aerosol from two typical indoor sources, the cooking fume (CF) and environmental tobacco smoke (ETS) were collected by simulation emission in a chamber. Eighteen PAHs were analyzed by GC/MS and GC/C/IRMS. The chemical profiles of these two sources were described. Results indicated that Fluoranthene, Pyrene and Fluorene in CF, and Fluorene, Phenanthrene, Indeno(1,2,3-cd)pyrene and Benz(a)anthracene in ETS, relative quantity of which were variable in a smaller range, can be regarded as tracers of indoor PAHs sources. There are distinct differences among the ratios of Benz(a)anthracene/Chrysene, Benzo(e)pyrene/Benzo(a)pyrene and Indeno(1,2,3-cd)pyrene/Benzo(g,h,i)perylene between CF and EST. Distribution of delta(13)C of individual PAHs in ETS samples ranged from -21.76 per thousand to -29.32 per thousand, wider than that in CF samples (-22.94 per thousand to -28.39 per thousand). The delta(13)C of Phenanthrene, Benz(a)anthracene, Benzo(b)fluoranthene, Benzo(k)fluoranthene and Indeno(1,2,3-cd)pyrene between these two sources showed great differences. The (13)C was enriched in low molecule weight compounds of CF samples and in high molecule weight compounds of ETS samples.