Home Articles GIS Based Solutions for Waste Disposals

GIS Based Solutions for Waste Disposals

SM Ramasamy
[email protected]

C J Kumanan

K Palanivel
Centre for Remote Sensing
Bharathidasan University,
Khajamalai Campus, Tiruchirappalli

The phenomenal population explosion has led competitive and unplanned exploitation of the planet earth which has resulted irrepairable damage to the environment. Such improper exploitation of natural resources and the interaction of human beings with earth’s ecosystems has not only depleted the natural resources but also triggered off the natural morphodynamic processes of the earth which in turn are causing natural disasters and chains of environmental problems such as landslides, land subsidence, soil erosion, reservoir siltation, flooding, water logging, coastal erosion, etc. On the other hand, domestic, industrial and other wastes, whether these are of low or medium level wastes, are causing environmental pollution and have become a perennial problem for the mankind. However, while the human-induced environmental problems warrant detailed studies, the environmental pollution due to waste disposals can be overcome by selecting suitable sites through careful understanding of the lithospheric and hydrospheric conditions of the planet earth. The art of remote sensing is an excellent tool in mapping such lithospheric and hydrospheric parameters and the GIS is a proven tool in storing, retrieving, analysing and amalgamating all such parameters to select suitable sites for such waste disposals. The present paper brings out a certain newer package of information on how suitable sites can be identified for disposing wastes using remote sensing and GIS technologies.

The Newer Concepts
The sites which attain the credentials for waste disposals are normally the

  • Basinal / doubly plunging synclinal geological structures
  • Zones of least fracture density
  • Zones of deeper weathering
  • Regions of null slope
  • Regions of low drainage density
  • Arenas of deeper water level
  • Domains of poor natural recharge
  • Zones of centripetal groundwater flow etc.

The zones of doubly plunging synclinal geological structures extending to deeper levels are suitable because these structures can hold the wastes. Similarly, the zones of least lineament density will not allow the pollutant transport and contaminate the groundwater systems. The zones of deeper weathering is preferred so that such wastes discharged could percolate down and get stored.

While the regions of null or nil slope prevent the flowage of pollutants/wastes, the regions of drainage density minima facilitate percolation rather than runoff. The regions of water level deep, least natural recharge and centripetal groundwater flow are the further additional positive parameters which give credentials to the sites as these give least possibilities for the mixing up of the pollutants derived from the wastes into the groundwater systems and in case the pollutants reach the water table also such zones of centripetal groundwater flow will not allow them to get into the aquifer because of the eddying due to centripetal flow.

GIS Approach
Keeping this concept in mind, a model study was conducted in plains adjoining the Western Ghats in southwestern part of Tamil Nadu to demonstrate the concept of selecting suitable sites for the disposal industrial and domestic wastes.

In the said study, the basinal / doubly plunging synclinal structures were interpreted using satellite data and GIS image was generated buffering out such structures using GRAM GIS. The fracture density was worked out from the lineaments/fractures interpreted again from satellite data and the zones of least fracture density (total length of fractures per unit area) were buffered out in GIS environment (Fig. 1). In the same way, maps were prepared on the thickness of regolith cover by analysing the geophysical resistivity data and the zones of deeper weathering were buffered out in GIS (Fig. 2). The slope morphometry worked out from topographic sheets were converted into GIS image and the zones of null slope were identified. Similar buffered GIS raster images were generated showing the zones of poor drainage density (total length of drainages per unit area), deeper groundwater level, areas of natural recharge minima and the zones of centripetal groundwater flow.

Fig. 1: GIS Image – Lineament Density Minima (Green & Blue),Western Ghats, Tamil Nadu


Fig. 2: GIS Image – Zones of Deeper Weathering (Blue),Western Ghats, Tamil Nadu

After generating such different buffered raster GIS images, these were integrated utilising GIS overlay function in GRAM GIS and the final GIS integrated image was generated. To do so, firstly the buffered GIS image showing lineament density (Fig. 1) was overlaid on the GIS image showing basinal geological structures (Fig. 2). This has given a GIS image having polygons of three classes viz:

  • Basinal geological structures (Parameter-1)
  • Lineament density minima (Parameter-2)
  • Combined zones of basinal geological structures and lineament density maxima (combination of Parameters-1 and 2)

A model study was conducted in plains adjoining the Western Ghats to demonstrate the concept of selecting suitable sites for the disposal industrial and domestic wastes

Over the above integrated GIS image, buffered GIS image showing deeper weathering (Fig. 4) was overlaid. This has given a GIS image having polygons of 7 classes as follows:

  • Basinal geological structures (Parameter-1)
  • Lineament density minima (Parameter-2)
  • Zones of deeper weathering (Parameter-3)
  • Combined zones Parameters-1 & 2
  • Combined zones Parameters-1 & 3
  • Combined zones Parameters-2 & 3
  • Combined zones Parameters-1, 2 & 3

Thus, this overlay process was continued and all the buffered GIS images of all 9 geological, terrain and hydrological parameters were overlaid one after the other and the final integrated image was generated.

Such a final integrated GIS image has yielded a number of polygons of lands with 2 to 9 combinations of above geological, terrain and other hydrological parameters loaded in them. Obviously the area where more than 7 parameters were loaded were identified as the best suited zones for the waste disposals. For example, the areas where 9 & 10 parameters were loaded are shown in Figure 3. GIS, owing to its special credentials has the capability to show the numbers as well as the types of parameters loaded in each class of polygons from which the unloaded parameters can be identified and suitable measures can be suggested to correct such missing parameters and make that particular zone/polygon suitable for waste disposals.

For example, in a domain the lineament density(LD), thickness of fractured zones(TFZ), depth to bed rock(DBR), drainage density(DD), slope(SL), water level (WL), natural recharge(NR), landuse (LU) and the centripetal groundwater flow(CGF) are loaded which means, as far as these parameters are concerned the area is suitable for waste disposals. Whereas in that domain the parameters such as doubly plunging synclinal structure, thickness of top soil and thickness of weathered zone are not loaded.

Abbreviations used
BS – Basinal Structures
LD – Lineament Density
TTS – Thickness of Top Soil
TWZ – Thickness of Weathered Zone
TFZ – Thickness of Fractured Zone
DBR – Depth to Bed Rock
DD – Drainage Density
SL – Slope
WL – Water Level
NR – Natural Recharge
LU – Landuse/Land Cover
CGF – Centripetal Groundwater Flow

That means, there are no doubly plunging structures in that domain and thickness of weathered and fractured zone is less. In such areas, careful hydrofracturing can be done so as to push the pollutants as other conditions are favourable. At the same time, care should be taken so that such hydrofracturing does not create any conductivity to the aquifer. Similarly in certain polygons all parameters might have been loaded, but the lineament density minima might not have been loaded. That means, lineament/fracture density is high in that domain/polygon. In such polygons of land, the fractures can be grouted and thus the areas can be converted into suitable sites for waste disposals. Similarly, in certain polygons all parameters may be conducive for waste disposals but not for the water level deeper zones. In such areas, groundwater level can be depleted by pumping out and such areas can be used for waste disposals.

Conclusion
Thus, the present study has given certain newer concepts for site selection for waste disposals and some genetic ideas on the utility of GIS for similar purposes.