Multivariate methods were successfully employed in a comprehensive scientometric analysis of geostatistics research, and the publications data for this research came from the Science Citation Index and spanned the period from 1967 to 2005. Hierarchical cluster analysis (CA) was used in publication patterns based on different types of variables. A backward discriminant analysis (DA) with appropriate statistical tests was then conducted to confirm CA results and evaluate the variations of various patterns. For authorship pattern, the 50 most productive authors were classified by CA into 4 groups representing different levels, and DA produced 92.0% correct assignment with high reliability. The discriminant parameters were mean impact factor (MIF), annual citations per publication (ACPP), and the number of publications by the first author; for country/region pattern, CA divided the top 50 most productive countries/regions into 4 groups with 95.9% correct assignments, and the discriminant parameters were MIF, ACCP, and independent publication (IP); for institute pattern, 3 groups were identified from the top 50 most productive institutes with nearly 88.0% correct assignment, and the discriminant parameters were MIF, ACCP, IP, and international collaborative publication; last, for journal pattern, the top 50 most productive journals were classified into 3 groups with nearly 98.0% correct assignment, and its discriminant parameters were total citations, impact factor and ACCP. Moreover, we also analyzed general patterns for publication document type, language, subject category, and publication growth.
Polycyclic aromatic hydrocarbons (PAHs) in two 210Pb dated sediment cores from the coastal East China Sea, strongly influenced by the discharge from the Yangtze River, were determined to help to reconstruct the economic development over the past century in East China. The variations in PAH concentrations and fluxes in the sediment cores were primarily due to energy structure change, severe floods and dam construction activities. The impact on PAHs by the river discharge overwhelmed the atmospheric depositions. The profiles of PAH fluxes and concentrations as well as compositions in the cores revealed the transformation from an agricultural economy to an industrial one especially after the 1990s' in the region. PAHs in the study area were dominated by pyrolytic sources.
The objective of this study was to detect the selenium level in the environment and the population of Zhoukoudian area, Beijing, and to discuss the influence of various factors on serum selenium level. The soil, drinking water, and foodstuff samples and venous blood samples of 401 individuals were obtained to determine the selenium level by gas chromatograph equipped with a (63)Ni electron capture detector (ECD). The selenium level was 0.210+/-0.013 microg/g in soil, 0.017 microg/L+/-0.002 in drinking water, 0.034+/-0.002 microg/g in rice, and 0.034+/-0.012 microg/g in wheat flour. This index showed that the Zhoukoudian area of Beijing was a moderate or marginal level selenium ecological landscape. The mean serum selenium level of the population was 75.01+/-28.35 microg/L, ranging between 35.2 and 160.4 microg/L. A total of 279 (69.6%) individuals exhibited serum selenium level below 80 microg/L, which is the lowest threshold for the activity of glutathione peroxidases (GPx) in vivo. A total of 35 (8.5%) individuals exhibited serum selenium level below 45 microg/L. It is widely recommended that below this value (45 microg/L) there is an increased risk of cardiovascular disease and cancer. Multiple linear regression analysis showed that serum selenium level was positively associated with body mass index (beta=0.137; P=0.011), serum total cholesterol TC (beta=0.785; P=0.000), however, negatively associated with systolic blood pressure (beta=-0.172; P=0.023), serum triglyceride (beta=-0.170; P=0.007), high density lipoprotein-cholesterol (beta=-0.121; P=0.027), and low high density lipoprotein-cholesterol (beta=-0.568; P=0.027).
To assess the contribution of sources to fine particulate organic carbon (OC) at four sites in North Carolina, USA, a molecular marker chemical mass balance model (MM-CMB) was used to quantify seasonal contributions for 2 years. The biomass burning contribution at these sites was found to be 30–50% of the annual OC concentration. In order to provide a better understanding of the uncertainty in MM-CMB model results, a biomass burning profile sensitivity test was performed on the 18 seasonal composites. The results using reconstructed emission profiles based on published profiles compared well, while model results using a single source test profile resulted in biomass burning contributions that were more variable. The biomass burning contribution calculated using an average regional profile of fireplace emissions from five southeastern tree species also compared well with an average profile of open burning of pine-dominated forest from Georgia. The standard deviation of the results using different source profiles was a little over 30% of the annual average biomass contributions. Because the biomass burning contribution accounted for 30–50% of the OC at these sites, the choice of profile also impacted the motor vehicle source attribution due to the common emission of elemental carbon and polycyclic aromatic hydrocarbons. The total mobile organic carbon contribution was less effected by the biomass burning profile than the relative contributions from gasoline and diesel engines.
[1] Size-segregated atmospheric aerosols (11 stages separating particles from <0.04 to >14.2 mu m) collected in the Arctic during the polar sunrise at Alert were analyzed for aerosol mass, dicarboxylic acids, and major inorganic ions. Oxalic, malonic, succinic, and glutaric acids were detected in all size ranges, with oxalic acid being dominant. Their concentrations maximized in the accumulation mode either at 0.24-0.40 or 0.40-0.8 mu m aerodynamic diameters, suggesting that diacids were mainly formed by gas-to-particle conversion via photochemical oxidation of nonmethane hydrocarbons and oxygenated organics originated from continental pollution sources. The relative abundances of oxalic acid were higher in the 0.24- to 0.4-mu m size particles (73-78%) than in supermicrometer particles (40-60%), indicating that oxalic acid is produced by gas phase oxidation of precursors followed by accumulation on preexisting particles. Mass size distributions of NH4+ and SO42- peaked in the accumulation mode similar to those of small diacids. The sea-salt enrichment factor of K+ (biomass burning tracer) relative to Na+ maximized in 0.1- to 0.8-mu m sizes, whereas those of Mg2+ and Ca2+ (dust tracers) in 0.4- to 7.8-mu m particles. Maximized chlorine loss and bromine enrichment were found at 0.4-0.8 and 0.24-0.4 mu m sizes, respectively. Concentrations of Br-, which typically showed a submicrometer maximum, increased significantly during an O-3 depletion event having a shift of size distribution to a supermicrometer mode. During this event, oxalic acid concentration relative to succinic acid increased in submicrometer mode (0.24-0.4 mu m), adding to a growing body of evidence supporting the hypothesis that halogen chemistry is important in the production and loss of oxalic acid in the arctic atmosphere.
The primary emission source contributions to fine organic carbon (OC) and fine particulate matter (PM2.5) mass concentrations on a daily basis in Atlanta, GA, are quantified for a summer (July 3 to August 4, 2001) and a winter (January 2-31, 2002) month. Thirty-one organic compounds in PM2.5 were identified and quantified by gas chromatography/mass spectrometry. These organic tracers, along with elemental carbon, aluminum, and silicon, were used in a chemical mass balance (CMB) receptor model. CMB source apportionment results revealed that major contributors to identified fine OC concentrations include meat cooking (7-68%; average: 36%), gasoline exhaust (7-45%; average: 21%), and diesel exhaust (6-41%; average: 20%) for the summer month, and wood combustion (0-77%; average: 50%); gasoline exhaust (14-69%; average: 33%), meat cooking (1-14%; average: 5%), and diesel exhaust (0-13%; average: 4%) for the winter month. Primary sources, as well as secondary ions, including sulfate, nitrate, and ammonium, accounted for 86 +/- 13% and 112 +/- 15% of the measured PM2.5 mass in summer and winter, respectively.
Based on measurements of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 μm or less) in January and August 2004, serious air pollution persists in Beijing. The chemical analysis included organic and elemental carbon, water-soluble ions, and elemental compositions. The positive matrix factorization (PMF) method was used to apportion the PM2.5 sources. The sources contributing dominantly to PM2.5 mass concentrations are coal combustion in winter and the secondary products in summer. Furthermore, the contributions from motor vehicles, road dusts and biomass burning could not be neglected. The products of biomass burning for winter heating in the area around Beijing could enter the urban area during quasi-quiescent weather conditions. In conclusion, some effective control measures were proposed to reduce the PM2.5 pollution in Beijing.
A substantial fraction of fine particulate matter (PM) across the United States is composed of carbon, which may be either emitted in particulate form (i.e., primary) or formed in the atmosphere through gas-to-particle conversion processes (i.e., secondary). Primary carbonaceous aerosol is emitted from numerous sources including motor vehicle exhaust, residential wood combustion, coal combustion, forest fires, agricultural burning, solid waste incineration, food cooking operations, and road dust. Quantifying the primary contributions from each major emission source category is a prerequisite to formulating an effective control strategy for the reduction of carbonaceous aerosol concentrations. A quantitative assessment of secondary carbonaceous aerosol concentrations also is required, but falls outside the scope of the present work.
Sources of carbonaceous aerosols collected from three sites of Chattanooga, TN (CH), Muscle Shoals, AL (MS), and Look Rock, TN (LR) in the Tennessee Valley Region (TVR) were apportioned using both organic tracer-based chemical mass balance (CMB) modeling and radiocarbon (14C) measurement and the results were compared. Eight sources were resolved by CMB, among which wood combustion (averaging 0.92μgm−3) was the largest contributor to primary organic carbon (OC) concentrations, followed by gasoline exhaust (0.35μgm−3), and diesel exhaust (0.18μgm−3). The identified primary sources accounted for 43%, 71%, and 14% of measured OC at CH, MS, and LR, respectively. Contributions from the eight primary sources resolved by CMB could explain 107±10% of ambient elemental carbon (EC) concentrations, with diesel exhaust (66±32%) and wood combustion (37±33%) as the most important contributors. The fossil fractions in total carbon determined by 14C measurements were in reasonably good agreement with that in primary (OC+EC) carbon apportioned by CMB in the MS winter samples. The comparison between the 14C and CMB results revealed that contemporary sources dominated other OC in the TVR, especially in summertime (84% contemporary).
A geographic information system (GIS)-based chemometric approach was applied to investigate the spatial distribution patterns of heavy metals in marine sediments and to identify spatial human impacts on global and local scales. Twelve metals (Zn, V, Ni, Mn. Pb, Cu Cd, Ba, Ha, Fe, Cr and Al) were surveyed twice annually at 59 sites in Hong Kong from 1998 to 2004. Cluster analysis classified the entire coastal area into three areas on a global scale, representing different pollution levels. Backward discriminant analysis, with 84.5% correct assignments, identified Zn, Pb, Cu, Cd, V, and Fe as significant variables affecting spatial variation on a local scale. Enrichment factors indicated that Cu. Cr, and Zn were derived from human impacts while Al, Ba, Mn, V and Fe originated from rock weathering. Principal component analysis further subdivided human impacts and their affected areas in each area, explaining 87%, 84% and 87% of the total variances, respectively. The primary anthropogenic sources in the three areas were (i) anti-fouling paint and domestic sewage, (ii) surface runoff, wastewater, vehicle emissions and marine transportation, and (iii) ship repainting, dental clinics, electronic/chemical industries and leaded fuel, respectively. Moreover, GIS-based spatial analysis facilitated chemometric methods. (c) 2007 Elsevier Ltd. All rights reserved.
