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Zhang Y.-S.,University of Science and Technology of China | Zhang H.-Y.,Jinan Academy of Environmental science | Luan S.-J.,University of Science and Technology of China | Luan S.-J.,PKU HUST Shenzhen Hong Kong Institution | And 5 more authors.
Zhongguo Huanjing Kexue/China Environmental Science | Year: 2015

Biomass burning are typical combustion sources of PAHs in rural China. In this study, emissions of gaseous and particulate PAHs from typical biomass burning types were measured by laboratory simulations using a self-designed dilution chamber system under real combustion conditions. Rice straw, corn stalk, peanut stem and soybean stalk were burned in a cook stove to simulate water boiling tests. Rice straw, corn stalk, peanut stem, litchi leaves, and leaves from Ficus virens and Ficus microcarpa were burned in an open platform to simulate field burning activities. Emission factors of three typical burning types, including crop residues open burning, foliage open burning and indoor crop residue combustion were higher than previously reported values, respectively. Spectral distributions of emitted PAHs from above burning types were similar. Medium to high-ring PAHs accounted for 22.2% to 28.8% of total PAHs emitted from biomass burning. PAHs diagnostic ratios as indicators of certain pollutants sources may introduce significant uncertainty, when they were adopted in source apportionments of atmospheric PAHs. ©, 2015, Chinese Society for Environmental Sciences. All right reserved.


Xin R.B.,Shandong University of Science and Technology | Jiang Z.F.,Shandong University of Science and Technology | Li N.,Shandong University of Science and Technology | Hou L.J.,Jinan Academy of Environmental science
Advanced Materials Research | Year: 2013

In order to obtain high precision results of urban air quality forecast, we propose a shortterm predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results. © (2013) Trans Tech Publications, Switzerland.


Luo J.,Shandong University | Cui Z.J.,Shandong University | Du S.Y.,Jinan Academy of Environmental science | Fan G.L.,Jinan Academy of Environmental science
Advanced Materials Research | Year: 2013

In order to investigate the distributions and characters of sixteen (U.S. EPA) polycyclic aromatic hydrocarbons (PAHs) in PM10, samples were collected from three districts in Jinan, P.R. China during 2004-2005 and 2008-2009. The total concentrations of 16 PAHs were 33.6-627.3 ng·m-3 in 2004-2005 and 37.4-696.0 ng·m-3 in 2008-2009. Annual variations were slight. A small increase of PAH level presented from 2004-2005 to 2008-2009, while four and five aromatic rings accounted for 70% of all PAHs. An obvious seasonal trend of PAH abundance was observed. The highest concentrations of PAHs were measured in winter. Benzo(k)fluoranthene (BkF, 4-ring) and fluoranthene (FluA, 5-ring) were the most abundant PAHs in winter. Spatial variations of PAHs were obviously impacted by local pollution sources. In non-heating seasons, the district had more industries and traffic presented the most PAHs; in heating seasons, the district that had higher population density appeared more contamination for the domestic heating. © (2013) Trans Tech Publications, Switzerland.


Liu G.,Jinan Academy of Environmental science | Du S.,Jinan Academy of Environmental science
Advanced Materials Research | Year: 2012

The nano-sized Al/AlN powder was immersed in deionized water to investigate their hydrolysis behavior from 40°C to 80°C. The Al/AlN powder hydrolysis behavior was observed by measuring the pH of the suspension, whereas XRD and TEM analyses were employed for the characterization of the hydrolytic products. The hydrolysis at the higher temperature was different from that at the lower temperature. The nano fibrous crystalline boehmite was formed mainly over 80°C, while the bayerite (Al(OH 3)) coexisted with the boehmite predominantly below 70°C. The hydrolytic product showed the higher specific surface area at 70°C than that of the hydrolytic product at other temperatures. The highest specific surface area of hydrolytic product was 145.84m 2/g. © (2012) Trans Tech Publications.


Luo J.,Shandong University | Cui Z.J.,Shandong University | Du S.Y.,Jinan Academy of Environmental science | Fan G.L.,Jinan Academy of Environmental science
Advanced Materials Research | Year: 2013

To explore the PCB pollution characteristics in the airborne particulate matter, seven indicative polychlorinated biphenyls (PCBs) in atmospheric PM10 were determined weekly in Jinan, China. PCB concentrations, profiles, temporal distribution pattern and the correlations between Σ7PCBs and the meteorological conditions were analyzed, aiming to quantify the pollution level of PCBs in urban air. All the samplers were extracted by accelerated solvent extraction (ASE) and analyzed by gas chromatography (GC). The monthly concentrations of Σ7PCBs were 81.4-2335.2 fg/m3. A distinct seasonal variation of PM10-associated PCBs exhibited. Higher concentrations of PCBs in cold weather than warm weather was found. PCB28 and PCB52 were the major components of PM10-associated PCBs in cold weather, while PCB 118 and PCB 180 dominated in warm weather. The fresh emission sources in cold weather, such as the burning of coal for domestic heating, were suggested to be a major influence factor. A significant correlation (p<0.05) was found between the total PCB congeners and atmospheric pressure. Heavy rainfall can also impact PM10-PCBs significantly. The weak correlation coefficient between atmospheric PCBs and wind speed suggested that the inefficient dispersion and no significant pollution sources around. © (2013) Trans Tech Publications, Switzerland.

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