Use of statistical and GIS techniques to assess and predict concentrations of heavy metals in soils of Lahore City, Pakistan.

Soils from different land use areas in Lahore City, Pakistan, were analyzed for concentrations of heavy metals-cadmium (Cd), chromium (Cr), nickel (Ni), and lead (Pb). One hundred one samples were randomly collected from six land use areas categorized as park, commercial, agricultural, residential, urban, and industrial. Each sample was analyzed in the laboratory with the tri-acid digestion method.

Modelling categorical data to identify factors influencing concern for the natural environment in Iran.

Loglinear modelling techniques were used to identify the interactions and interrelationships underlying categorical environmental concern data collected from 9062 respondents in Iran. After fitting various loglinear models to the data, the most parsimonious model highlighted that a combination of interacting factors, namely educational attainment, age, gender, and residential location were responsible for influencing personal concern for the environment. Although high educational attainment had a close correspondence with high concern for the environment the loglinear results, when visualized with a geographical information system, demonstrated wide spatial variations in educational attainment and concern for the environment.

Use of loglinear models to assess factors influencing concern for the natural environment.

Since it is necessary to isolate the most significant factors influencing personal concern for the environment, this paper utilizes loglinear models for identifying the interactions and interrelationships underlying multidimensional environmental survey data. A field study in Guyana conducted face-to-face interviews with 1600 citizens. Acquired categorical data were then subjected to loglinear modeling techniques to determine what significance the factors education, age, residential location, and gender have on personal concern for the environment.