Geography Reference
In-Depth Information
Table 9. (Continued)
Parameters Used in Water Quality
Index
S. No. Name of Index
Country
Source
Fuzzy Surface
Water Quality
Index
Temperature, pH, DO, BOD,
Coliforms, dissolved inorganic N,
total P, total solids and turbidity
Lermontov et
al. (2009)
10
Brazil
pH, EC, Na, Cl, SO
4
, total alkalinity,
total hardness, Ca, Mg, Fe, F, NO
3
,
NO
2
, Mn, Zn, Cd, Cr, Pb, Cu, Ni,
total coliform, salmonella
Groundwater
Quality Index
Ramesh et al.
(2010)
11
India
In brief, the GIS-based WQI formulation process involves generation of
representations for the spatial variability of originally scattered point
measurements and the multiple transformations of water quality data into a
corresponding index rating value related to water quality. The steps involved
in the formulation of GIS-based WQI proposed Babiker et al. (2007) are
described in the subsequent section.
7.1. Computing Normalized Difference Maps
In the first step, spatial maps (C) representing distribution of
concentrations of the water quality parameters over the space are constructed
for each parameter from the point sample values by spatial interpolation
technique within GIS environment.
Thereafter, observed spatial concentrations (C
obs
) of the water quality
parameters are related to their maximum desirable limits (C
mdl
) prescribed by
the WHO (2006) on pixel-by-pixel basis using a GIS-based normalized
difference index (ND
index
) as follows (Babiker et al., 2007):
ND
C
C
C
C
index
obs
mdl
obs
mdl
(34)
Values of the resulted ND
index
for each pixel range between -1 and 1.
