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Spatial modelling approach to water pollution monitoring in the sugar belt of Maharashtra along the Krishna river
Source Identification
GIS was used to organise both
spatially and temporally and presenting graphically the pollution load data for
each subwatershed over the period 1984 to 1997. For each pollutant the load data
for four years was presented which included years of minimum and maximum
pollution loads and the pollution loads of starting and ending years. One such
case for 'Mg' is shown in figure 3 a,b,c,d. The spatial variation of all the
pollutants showed a steady increase in the load towards the downstream
direction. This is due to two reasons - (i) the flow rate (cumecs) of river
increased in the downstream direction and (ii) the increase in concentration of
water quality parameters, though inconsistent, downstream due to addition of
wastes from upstream and additional streams. One of the facts for additional
increase of concentration downstream is due to increasing number of sugar
factories. In 1997, the number of major sugar factories in SW1, SW2 and SW3 were
zero, three and five respectively. The effluents those come out of these
factories and surrounding urban setup are added to the stream as a fresh input.
The waste discharge of these large and medium sugar factories and the
surrounding urban setup are in the order of 13400 and 1524 cubic meter per day
respectively. The growth or density of population has increased highly along the
downstream which has produced such a large quantity of domestic wastes. Between
1951 and 1991, the population growth km-2 in Satara and Sangli were 121 and 141
respectively. The growth of population synchronised with the growth in factories
and the consumption rate of fertilisers. The consumption quantities of
fertilisers in the agricultural land have increased by more than 3-fold in
Satara and 4-fold in Sangli. This explains why the pollution loads from
agricultural sources such as Mg, Ca, Na, Sulphate, Cl and N are continuously
increasing along the downstream direction.
The spatial relationship
between the pollutants (BOD & COD) and the population growth was correlated
using three estimators contingency coefficient, Tschuprow's T and Cramer's V.
The estimators showed good relationships (V=0.67, T=0.56, Contingency
Coefficient=0.56) between BOD and population growth and COD and population
growth. Therefore, using the overlay techniques the composites BOD - population
growth and COD-population growth were produced. The results in composite were
classified into good, bad, very bad and worst (see figures 4 & 5). For
example, good regions have low population growth and low BOD. In a similar way
the relationships between the rate of fertiliser consumption with BOD and COD
were estimated. The estimators showed again good relationship. These analyses
supported the fact that the population rise is a dominant factor to increasing
pollution load due to domestic and agricultural sources in the downstream
direction.
Pollutant Balance
Industrial and domestic wastes contribute to the
major rise in BOD and COD concentrations. The total estimated pollution load for
Satara and Sangli from agricultural, domestic and industrial (sugar and others)
sources are shown in the table 4. The waste disposals from sugar and distillery
factories are the prime sources of BOD and COD loads. The total amount of waste
that is being generated, treated and discharged from sugar and other industries
in Satara and Sangli districts are mentioned in the table 5. In table 6, the
balance of pollutants is estimated for 3 subwatersheds. Since the total
pollution load for the stretch is coming from SW1 to SW3 through SW2, the share
of each subwatershed to pollution load has been computed. In each subwatershed
the total addition (fresh input) is estimated. By subtracting the total waste
assimilation capacity (WAC) of river from the total addition, the result becomes
the net addition of load that will go into the subsequent subwatersheds in the
downstream.
Suggestion on River Water Quality Restoration Through
Zonation
Buffer zones are used in proximity analysis where the distance
from either side of river bank is an important criterion in determining
suitability or risk. Buffer zones provide storage for floods and pollution
control. Buffer strips made of uneven vegetation (grasses, shrubs, trees)
attenuate runoff pollutants that would otherwise reach the body of water. The
methods of creating buffer zones on both sides of river bank, also known as
corridors, are called as river zonation. The present study suggests over an
existing river zonation method. The Satara- Sangli stretch is classified into
zone A-II by MPCB but the present study found 3 clearly demarcated zones A-I,
A-II and A-III contained in SW1, SW2 and SW3 respectively. Each of these
subwate rsheds was used for buffer zones demarcating the red, orange and green
zones. The buffering distance for each zone was considered as per MPCB's
distance criteria (MPCB, 1997). The characteristics of watersheds influence the
pollution load at substretches due to the water quality properties of streamlets
those are induced artificially and naturally. For better understanding of
pollution loads in a stretch (watershed), it is essential to know the addition
of pollutants at substretches. In other way saying, the pollution load is better
dependent on the subwatershed (substretch) characteristics than the flow
channel. It is suggested that the river zonation at stretch-I should be done in
the subwatersheds rather than along the flow channel. The comparison of these
two types of river zonation is shown in figures 6 and 7. The total areal
coverage as per flow channel zonation are 2849 km2 (within 1 km) and 1845 km2
(within 1-2 kms) and the same from the present suggestion of subwatershed
zonation are 1653 km2 (within 3, 1, 0.5 kms) and 1897 km2 (within 8, 2, 1 kms).
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