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April 2004 |
A GIS appraisal of heavy metals concentration in soil
APPRAISALS via GIS
New and innovative GIS-based research concepts and approaches are being developed by a variety of leading researchers, based on the recognition of the problems and
complexity of the phenomenon. In studied spatial model,
the base map in vector format was developed. This map was consisting of the major infrastructure of the city such as main roads, localities, administrative divisional boundaries and
sample points.
Database Development and Integration
One of the most important components of GIS is the development of database. Datasets from specific purpose are taken, in which the most significant available data includes geographical entities (i.e. map objects) and attribute data (e.g. concentration of heavy metals in soils). For any urban information system, database is a critical element in the structure of GIS.
To understand the geographical patterns of concentrations of heavy metals in urban soil of Karachi viz., Zinc (Zn),
Lead (Pb), Iron (Fe), Copper (Cu), Cadmium (Cd) and Manganese (Mn) samples were collected from 18 sites at the intersections of major roads, where traffic density was found to be high. Those samples were collected between 0 metre
and 10 metre from the road edge, and analysed through
lame atomic absorption spectroscopy (Ghauri et al., 1996). These sample tests were linked with mapped sample locations (Table 1).
Spatial Modelling
The term 'model' entered the lexicon in the 1960s when the idea of symbolically representing complex systems suddenly came of age (Batty, 2001). As computers have pervaded every corner of our world, the idea of a model no longer has the drawing power it once did. There are a number of spatial modelling methods available with respect to application, by virtue of efficacious computer tools (Mehdi et al., 2002). The main purpose of building a spatial model is to demarcate the locational distribution of heavy metals.
Surface Interpolation through IDW
Surface Interpolation uses a defined or selected set of all the samples to estimate each of the output grid's cell values. Inverse distance weighted (IDW) interpolation determines cell values using a linearly weighted combination of a set of sample points (Keith, 1997). The weight is a function of inverse distance. IDW allows controlling the significance of known points, upon the interpolated values, based upon their distance from the output point. This method provided accurate weighted interpolated surface grid as well as thematic isolines.
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