Shaofei Kong

A seasonal study of polycyclic aromatic hydrocarbons in PM2.5 and PM2.5-10 in five typical cities of Liaoning Province, China.

Fourteen polycyclic aromatic hydrocarbons (PAHs) in PM(2.5) and PM(2.5-10) samples collected in five cities (Shenyang, Anshan, Jinzhou, Fushun and Dalian), Liaoning Province, China in 2004 and 2005 were analyzed by using a HPLC equipped with fluorescence and UV detectors. Results showed total PAHs concentrations in PM(2.5) and PM(2.5-10) were in the range of 75.32-1900.89 ng m(-3) and 16.74-303.24 ng m(-3), respectively. 90% of the total PAHs were in PM(2.5). PAHs in PM(2.5) had a winter to summer ratio varying from 6.5 to 125.8 while PAHs in PM(2.5-10) had a ratio ranging from 1.7 to 37.6. Total PAHs concentrations were most abundant at residential/commercial sites and were fewest at an industrial site for both PM(2.5) and PM(2.5-10). Urban background sites showed unexpected higher PAHs concentrations. Total BaP equivalent concentration (BaPeq) for PM(2.5) ranged from 7.80 to 88.42 ng m(-3) in different function zones. Similarities of PAHs profiles between sampling sites and between fine and coarse fractions were compared by coefficient of divergence which indicated that remarkable differences in PAHs compositions existed. Principal component analysis (PCA) associated with diagnostic ratios revealed coal combustion and vehicle emission were the major sources for PM(2.5) and PM(2.5-10) associated PAHs.

Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China.

Atmospheric particulate matter (PM(2.5), PM(10) and TSP) were sampled synchronously during three monitoring campaigns from June 2007 to February 2008 at a coastal site in TEDA of Tianjin, China. Chemical compositions including 19 elements, 6 water-solubility ions, organic and elemental carbon were determined. principle components analysis (PCA) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions with the assistance of NSS SO(4)(2)(-), the mass ratios of NO(3)(-) to SO(4)(2)(-) and OC to EC. Air mass backward trajectory model was compared with source apportionment results to evaluate the origin of PM. Results showed that NSS SO(4)(2)(-) values for PM(2.5) were 2147.38, 1701.26 and 239.80 ng/m(3) in summer, autumn and winter, reflecting the influence of sources from local emissions. Most of it was below zero in summer for PM(10) indicating the influence of sea salt. The ratios of NO(3)(-) to SO(4)(2)(-) was 0.19 for PM(2.5), 0.18 for PM(10) and 0.19 for TSP in winter indicating high amounts of coal consumed for heating purpose. Higher OC/EC values (mostly larger than 2.5) demonstrated that secondary organic aerosol was abundant at this site. The major sources were construction activities, road dust, vehicle emissions, marine aerosol, metal manufacturing, secondary sulfate aerosols, soil dust, biomass burning, some pharmaceutics industries and fuel-oil combustion according to PCA. Coal combustion, marine aerosol, vehicular emission and soil dust explained 5-31%, 1-13%, 13-44% and 3-46% for PM(2.5), PM(10) and TSP, respectively. Backward trajectory analysis showed air parcels originating from sea accounted for 39% in summer, while in autumn and winter the air parcels were mainly related to continental origin.

Diversities of phthalate esters in suburban agricultural soils and wasteland soil appeared with urbanization in China

The distribution of six priority phthalic acid esters (PAEs) in suburban farmland, vegetable, orchard and wasteland soils of Tianjin were obtained with gas chromatography-mass spectrometer analysis in 2009. Results showed that total PAEs varied from 0.05 to 10.4 μg g(-1), with the median value as 0.32 μg g(-1). Di-(2-ethylhexyl) phthalate and di-n-butyl phthalate are most abundant species. PAEs concentrations for the four types of soils exhibited decreasing order as vegetable soil > wasteland soil > farmland soil > orchard soil. PAEs exhibited elevated levels in more developed regions when compared with other studies. The agricultural plastic film could elevate the PAEs contents in soils. Principal component analysis indicated the emission from cosmetics and personal care products and plasticizers were important sources for PAEs in suburban soils in Tianjin. The higher PAEs contents in wasteland soils from suburban area should be paid more attention owing to large amounts of solid wastes appeared with the ongoing urbanization.

A land use regression for predicting NO2 and PM10 concentrations in different seasons in Tianjin region, China

Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area; and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season), 0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating season), and 0.096 (PM10, non-heating season). These intercept values might imply an areas background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NO2 compared with PM10.

Risk assessment of heavy metals in road and soil dusts within PM2.5, PM10 and PM100 fractions in Dongying city, Shandong Province, China

15 road and 14 soil dust samples were collected from an oilfield city, Dongying, from 11/2009-4/2010 and analyzed by inductively coupled plasma-mass spectroscopy (ICP-MS) for V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd and Pb within PM(2.5), PM(10) and PM(100) fractions synchronously. Metal concentrations, sources and human health risk were studied. Results showed that both soil and road dust exhibited higher values for Mn and Zn and lower values for Co and Cd for the three fractions. Mass concentration ratios of PM(2.5)/PM(10) and PM(10)/PM(100) for metals in road and soil dust indicate that most of the heavy metals tend to concentrate in fine particles. Geoaccumulation index and enrichment factors analysis showed that Cu, Zn and Cd exhibited moderate or heavy contamination and significant enrichment, indicating the influence of anthropogenic sources. Vanadium, Cr, Mn and Co were mostly not enriched and were mainly influenced by crustal sources. For Ni, As and Pb, they ranged from not enriched to moderately enriched and were influenced by both crustal materials and anthropogenic sources. The conclusions were confirmed by multivariate analysis methods. Principle component analysis revealed that the major sources were vehicle emission, industrial activities, coal combustion, agricultural activities and crustal materials. The risk assessment results indicated that metal ingestion appeared to be the main exposure route followed by dermal contact. The most likely cause for cancer and other health risks are both the fine particles of soil and road dusts.

A land use regression model incorporating data on industrial point source pollution

Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land use regression (LUR) models for SO2, NO2 and PM10 for Tianjin, China. Traffic volumes, road networks, land use data, population density, meteorological conditions, physical conditions and satellite-derived greenness, brightness and wetness were used for predicting SO2, NO2 and PM10 concentrations. We incorporated data on industrial point sources to improve LUR model performance. In order to consider the impact of different sources, we calculated the PSIndex, LSIndex and area of different land use types (agricultural land, industrial land, commercial land, residential land, green space and water area) within different buffer radii (1 to 20 km). This method makes up for the lack of consideration of source impact based on the LUR model. Remote sensing-derived variables were significantly correlated with gaseous pollutant concentrations such as SO2 and NO2. R2 values of the multiple linear regression equations for SO2, NO2 and PM10 were 0.78, 0.89 and 0.84, respectively, and the RMSE values were 0.32, 0.18 and 0.21, respectively. Model predictions at validation monitoring sites went well with predictions generally within 15% of measured values. Compared to the relationship between dependent variables and simple variables (such as traffic variables or meteorological condition variables), the relationship between dependent variables and integrated variables was more consistent with a linear relationship. Such integration has a discernable influence on both the overall model prediction and health effects assessment on the spatial distribution of air pollution in the city region.

Health risk assessment for vehicle inspection workers exposed to airborne polycyclic aromatic hydrocarbons (PAHs) in their work place

Inhalatory and dermal exposures of on-duty vehicle inspection workers to polycyclic aromatic hydrocarbons (PAHs) in Beijing were investigated from April 18 to May 17, 2011. Exposure levels to particulate PAHs for the vehicle inspection workers at gasoline, bus and diesel lines were found to be 56.07 ng m(-3), 111.72 ng m(-3) and 199.80 ng m(-3), respectively. A probabilistic risk assessment framework was integrated with the toxic equivalence factors (TEFs) and the incremental lifetime cancer risk (ILCR) approaches to quantitatively estimate the exposure risk for vehicle inspection workers of the three work lines. The median values of inhalation risk were estimated to be 3.7 × 10(-7), 5.0 × 10(-7) and 1.37 × 10(-6), respectively, while the median dermal ILCR values were 7.05 × 10(-6), 6.98 × 10(-6) and 1.28 × 10(-5), respectively for gasoline, bus, and diesel inspection workers. Total ILCR was higher than the acceptable risk level of 10(-6), indicating unacceptable potential cancer risk.