Ying-Ze Tian

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.

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.

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.

Long-term variation of the levels, compositions and sources of size-resolved particulate matter in a megacity in China.

To investigate the long-term trends and variations of the levels, compositions, size distribution and sources of particulate matter (PM), long-term monitoring campaigns of PM10 and PM2.5 were performed in a megacity in China (Chengdu) during the period from 2009 to 2011. The average concentration of PM10 was 172.01±89.80 μg/m(3) and that of PM2.5 was 103.15±59.83 μg/m(3), with an average PM2.5/PM10 of 0.60. Enrichments of the important species indicated that the fractions of crustal elements were higher in PM10 than those in PM2.5, while the abundance of organic carbon (OC) and secondary ions was enriched in the fine PM. Quantitative source apportionments of both PM10 and PM2.5 were performed by PMF. PM10 and PM2.5 in Chengdu were influenced by similar source categories, and their percentage contributions were in the same order: crustal dust was the highest contributor, followed by vehicular exhaust, secondary sulfate, secondary nitrate and cement dust. Crustal dust and cement dust contributed a higher percentage to PM10 than to PM2.5, while vehicular exhaust and secondary particles provided higher percentage contributions to PM2.5. In addition, PMF-HCA was performed to investigate the characteristics of the sources of the clustered samples, identifying three periods: crustal dust dominant-period, secondary sulfate dominant-period and comprehensive source influenced-period. Planting, reduction of precursors, and banning high-emission vehicles should be implemented to control crustal dust, secondary particles and vehicular exhaust in Chengdu. Furthermore, the size-resolved and the period-resolved control would be more effective.

Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method

To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn.

Source apportionment of perfluorinated compounds (PFCs) in sediments: Using three multivariate factor analysis receptor models

Understanding the levels, distribution and sources of perfluorinated compounds (PFCs) in sediments is of great significance for the management of aquatic environments. In this work, 26 sediment samples were collected from Dianchi Lake in China and ten PFCs compounds were measured. The concentrations of the total PFCs (∑PFCs) in the sediments ranged from 0.21 to 2.45 ng g(-1)dw (dry weight), with an average value of 0.95 ng g(-1)dw. PFOS was the most abundant compound among the ten PFCs with the average concentration of 0.33 ng g(-1)dw, followed by PFOA at 0.21 ng g(-1)dw. A two-dimensional HCA (hierarchical cluster analysis) heat map was depicted to analyze the spatial variation of individual PFCs compound and the possible origins in the sediments. Two groups were clustered by HCA, showing the possible source categories (PFOS-cluster and PFOA-cluster). Additionally, PCA-MLR, PMF and Unmix models were employed to quantitatively calculate the contribution of extracted sources. Three models concluded consistent results that PFOS-factor and PFOA-factor were the two main source categories for PFCs in the sediments. The contribution percentages were 43% (PCA-MLR), 48% (PMF) and 46% (Unmix) from the former source, and were 54% (PCA-MLR), 43% (PMF) and 44% (Unmix) from the latter source, respectively. The findings and the approaches used in this work can provide useful information for further study of source apportionment for PFCs in sediments and other environmental compartments.

Source apportionment of synchronously size segregated fine and coarse particulate matter, using an improved three-way factor analysis model.

Samples of PM₁₀ and PM₂.₅ were synchronously collected from a megacity in China (Chengdu) during the 2011 sampling campaign and then analyzed by an improved three-way factor analysis method based on ME2 (multilinear engine 2), to investigate the contributions and size distributions of the source categories for size segregated particulate matter (PM). Firstly, the synthetic test was performed to evaluate the accuracy of the improved three-way model. The same five source categories with slightly different source profiles were caught. The low AAE (average absolute error) values between the estimated and the synthetic source contributions (<15%) and the approachable estimated PM₂.₅/PM₁₀ ratios with the simulated ratios might indicate that the results of the improved three-way factor analysis might be satisfactory. Then, for the ambient PM samples, the mean levels were 206.65 ± 69.90 μg/m(3) (PM₁₀) and 130.47 ± 43.67 μg/m(3) (PM₂.₅). The average ratio of PM₂.₅/PM₁₀ was 0.63. PM₁₀ and PM₂.₅ in Chengdu were influenced by the same source categories and their percentage contributions were in the same order: crustal dust & coal combustion presented the highest percentage contributions, accounting for 58.20% (PM₁₀) and 53.73% (PM2.5); followed by vehicle exhaust & secondary organic carbon (18.45% for PM₁₀ and 21.63% for PM₂.₅), secondary sulfate and nitrate (17.06% for PM₁₀ and 20.91% for PM₂.₅) and cement dust (6.30% for PM₁₀ and 3.73% for PM₂.₅). The source profiles and contributions presented slightly different distributions for PM₁₀ and PM₂.₅, which could better reflect the actual situation. The findings based on the improved three-way factor analysis method may provide clear and deep insights into the sources of synchronously size-resolved PM.