Statistical characterization of the hydrochemical data’s of groundwater in the arid land of Wadi AdDawasir area, Saudi Arabia : A probabilistic assessment

The main purpose of this research is to determine the quality of ground water in Wadi AdDawasir by the assessmentof 12 chemical parameters: pH, EC, Eh, TDS, TH, Ca2+, Cl–, HCO3–, Mg2+, Na+, NO3–, and SO42–. Statistical analyseswere carried out using descriptive statistics, histograms, and normal quantile plots. The SPSS 15 software package(SPSS Inc. 2006) and JMPIN (version 4.0.4) were used as the main statistical software. Many locations withinthe study area show thatf pH, EC, HCO3–and Na+ values exceed permissible limits. The concentration of anions isin the order SO42–>Cl–>HCO3–. Some of the analyzed parameters approach a normal distribution, as both their skewnessand kurtoses are close to zero. However, skewness for some parameters such as Mg2+ and HCO3–is high. Kurtosisfor most of the elements varies from moderate to low. Only pH, HCO3– and SO42– have kurtoses. Both the resultsof cluster tree and geochemical features of variables could be generally classifi ed into three main groups. Group1 is comprised of Na and SO42–. The relationships within this group are strong. Group 2 consisted of Mg2+, NO3–,pH, HCO3–, and Ca2+. The fact that this group has a close relationship with group 1 demonstrates that the increase inthe concentration of some elements could be the same. Group 3 is comprised of TH, Cl–, Eh, and EC.</p

Multivariate statistical analysis of urban soil contamination by heavy metals at selected industrial locations in the Greater Toronto area, Canada

A good understanding of urban soil contamination with metals and the location of pollution sources due to industrialization and urbanization is important for addressing many environmental problems. The results are reported here of an analysis of the metals content in urban soils samples next toindustrial locations in the Greater Toronto Area (GTA) in Ontario, Canada. Theanalyzed metals are Cr, Mn, Fe, Ni, Cu, Zn, and Pb. Multivariate geostatistcalanalysis (correlation matrix, cluster analysis, principal component analysis) is used to estimate soil chemical content variability. The correlation matrix exhibits a positive correlation with Mn, Fe, Cu, Zn, Cd, and Pb. The principal component analysis (PCA) displays two components. The first component explains the major part of the total variance and is loaded heavily with Cr, Mn, Fe, Zn,and Pb, and the sources are industrial activities and traffic flows. The second component is loaded with Ni, and Cd, and the sources could be lithology andtraffic flow. The results of the cluster analysis demonstrate three major clusters: 1) Mn-Zn, 2) Pb-Cd-Cu and Cr, 3) Fe-Ni. The geo-accumulation index and the pollution load index are determined and show the main I geovalues to be in the range of 0-1.67; the values indicate that the soil samples studied for industrial locations in the GTA are slightly to moderately contaminated with Cr, Fe, Cu, Zn, and Cd, and moderately contaminated with Pb,while Ni, and Mn fall in class "0". Regarding the pollution load ingindex (PLI), the lowest values are observed at stations 6, 7, 9, 10, 11, 12,25, 27 and 28, while the highest values are recorded for stations 1, 5, 6, 13,14, 16, 17, 18, 20, 22 and 24, and very high PLI readings are seen for stations 5, 13, 16, 17, 18, 22 and 24. These data confirm that the type of industries, especially metallurgical and chemical related ones, in the study area, in addition to high traffic flows, are the main sources for soil pollution in the GTA

Chapter 4 Geographic information system spatial data structures, models, and case studies

This chapter provides a basic overview of geographic information systems (GISs) as well as a summary of basic concepts encountered with GISs. Specifically, it touches on the various spatial data structures and models used by GISs to represent geographical information. First, general concepts related to information organization and data structure are briefly described and related to the different ways of representing real-world geographical data and information in GISs. Second, different perspectives on information organization are discussed, including different types of spatial relationships processed by GISs as well as the underlying information organization structure within GISs. Finally, the concept of data is investigated, as well as the purpose of databases with respect to GISs, including the various methods of modeling real-world data, relationships, and processes into databases

Environmental evaluation of New Mexico stream sediment chemistry using the National Uranium Resource Evaluation (NURE) program data

Lognormality of geochemical data is a fundamental principal in geochemistry. Recent studies challenge details of this concept, and data sets need to be reevaluated with these new ideas in mind. The present study is a part of such testing of traditional ideas; however it is also a stand alone study of the surface chemistry of a large region, the state of New Mexico. The environmental evaluation presented in this study is based on chemical analyses of stream sediments from the National Uranium Resource Evaluation (NURE) data set. The statewide database consists of 27,798 stream sediment sites. Twenty-four elements are selected which are Al, Ba, Ca, Ce, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Nb, Ni, Pb, Sc, Sr, Th, Ti, U, V, and Zn. Univariate, bivariate, and multivariate statistics and GIS techniques are applied to classify the elements and to identify geochemical signatures, either natural or anthropogenic, with the purpose of finding their sources. The elements are spatially analyzed by mapping their concentration distributions, anomalous element locations, and the factor scores that resulted, and these features are integrated as modified GIS theme layers, which are then compared with geologic maps, hydrologic basins, and mining districts maps. The study concludes that (1) the distribution of the elements in stream sediments in New Mexico shows that most of the variability is controlled by the bed rock chemistry. (2) Anthropogenic sources have local influence in the geochemistry of the stream sediments in New Mexico. (3) The mafic factor consists of Co, Cr, Cu, Fe, Ni, Sc, Ti, V and Zn and it also clusters in the Rio Grande rift and Jemez lineament. (4) The REE factor consists of Ce, La and U, and it has strong, localized, clusters in the Organ Mountains, Boot Heel, San Andres Mountains and El Capitan Mountains. (5) Mineral exploration and contamination assessment are definitely feasible with the use of the NURE data set and the statistical analyses performed in this study