RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).

Measurment of gas-liquid two-phase slug flow with a Venturi meter based on blind source separation

We propose a novel flow measurement method for gas–liquid two-phase slug flow by using the blind source separation technique. The flow measurement model is established based on the fluctuation characteristics of differential pressure(DP) signals measured from a Venturi meter. It is demonstrated that DP signals of two-phase flow are a linear mixture of DP signals of single phase fluids. The measurement model is a combination of throttle relationship and blind source separation model. In addition, we estimate the mixture matrix using the independent component analysis(ICA) technique. The mixture matrix could be described using the variances of two DP signals acquired from two Venturi meters. The validity of the proposed model was tested in the gas–liquid twophase flow loop facility. Experimental results showed that for most slug flow the relative error is within 10%.We also find that the mixture matrix is beneficial to investigate the flow mechanism of gas–liquid two-phase flow.

Identification of coastal water quality by multivariate statistical techniques in two typical bays of northern Zhejiang Province,East China Sea

The Hangzhou Bay（HZB） and Xiangshan Bay（XSB）, in northern Zhejiang Province and connect to the East China Sea（ECS） were considerably affected by the consequence of water quality degradation. In this study, we analyzed physical and biogeochemical properties of water quality via multivariate statistical techniques. Hierarchical cluster analysis（HCA） grouped HZB and XSB into two subareas of different pollution sources based on similar physical and biogeochemical properties. Principal component analysis（PCA） identified three latent pollution sources in HZB and XSB respectively and emphasized the importance of terrestrial inputs, coastal industries as well as natural processes in determining the water quality of the two bays. Therefore, proper measurement for the protection of aquatic ecoenvironment in HZB and XSB were of great urgency.

PM_(2.5) in an industrial district of Zhengzhou,China:Chemical composition and source apportionment

Zhengzhou is a developing city in China, that is heavily polluted by high levels of particulate matter. In this study, fine particulate matter （PM2.5） was collected and analyzed for their chemical composition （soluble ions, elements, elemental carbon （EC） and organic carbon （OC）） in an industrial district of Zhengzhou in 2010. The average concentrations of PM2.5 were 181, 122, 186 and 211 μg/m3 for spring, summer, autumn and winter, respectively, with an annual average of 175 μg/m3, far exceeding the PM2.5 regulation of USA National Air Quality Standards （15 μg/m3）. The dominant components of PM2.5 in Zhengzhou were secondary ions （sulphate and nitrate） and carbon fractions. Soluble ions, total carbon and elements contributed 41%, 13% and 3% of PM2.5 mass, respectively. Soil dust, secondary aerosol and coal combustion, each contributing about 26%, 24% and 23% of total PM2.5 mass, were the major sources of PM2.5, according to the result of positive matrix factorization analysis. A mixed source of biomass burning, oil combustion and incineration contributed 13% of PM2.5. Fine particulate matter arising from vehicles and industry contributed about 10% and 4% of PM2.5, respectively.

Trace metals in atmospheric fine particles in one industrial urban city: Spatial variations, sources, and health implications

Trace metals in PM2.5 were measured at one industrial site and one urban site during September, 2010 in Ji＇nan, eastern China. Individual aerosol particles and PM2.5 samples were collected concurrently at both sites. Mass concentrations of eleven trace metals （i.e., Al, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Ba, and Pb） and one metalloid （i.e., As） were measured by inductively coupled plasma atomic emission spectroscopy （ICP-AES）. The result shows that mass concentrations of PM2.5 （130μg/m3） and trace metals （4.03 μg/m3） at the industrial site were 1.3 times and 1.7 times higher than those at the urban site, respectively, indicating that industrial activities nearby the city can emit trace metals into the surrounding atmosphere. Fe concentrations were the highest among all the measured trace metals at both sites, with concentrations of 1.04 ixg/m 3 at the urban site and 2.41 Itg/m3 at the industrial site, respectively. In addition, Pb showed the highest enrichment factors at both sites, suggesting the emissions from anthropogenic activities existed around the city. Correlation coefficient analysis and principal component analysis revealed that Cu, Fe, Mn, Pb, and Zn were originated from vehicular traffic and industrial emissions at both sites; As, Cr, and part of Pb from coal-fired power plant; Ba and Ti from natural soil. Based on the transmission electron microscopy analysis, we found that most of the trace metals were internally mixed with secondary sulfate/organic particles. These internally mixed trace metals in the urban air may have different toxic abilities compared with externally mixed trace metals.

Chemical characteristics of trace metals in PM10 and their concentrated weighted trajectory analysis at Central Delhi,India

Trace metals associated with PM10 aerosols and their variation during day and nighttime as well as during different seasons have been studied for the year 2012. PCA analysis suggested5 PCs,which accounted for 86.8% cumulative variance. PC1 accounted for 30% with a significant loading of metals of anthropogenic origin,while PC2 showed 28% variance with the loading of metals of crustal origin. These trace metals showed seasonal distinct day and night time characteristics. The concentrations of Cu,Pb,and Cd were found to be higher during nighttime in all the seasons. Only Fe was observed with significantly higher mean concentrations during daytime of all seasons except monsoon. The highest mean values of Cu,Cd,Zn,and Pb during post-monsoon might be attributed to winds advection over the regions of waste/biomass burning and industrial activities in Punjab and Haryana regions.Furthermore,concentration weighted trajectory analysis suggested that metals of crustal origin were contributed by long-range transport while metals of anthropogenic and industrial activities were contributed by regional/local source regions.