Water Quality and Pollution Load at Stretch-I
The stretch-I is about 180 kms. This stretch, covering a total area of 13065.22 km2, is subdivided into three sub-watersheds SW1 (1705.17), SW2 (3545.4) and SW3 (7814.65) km2. A WQM station accompanies each one of these sub-watersheds. The WQMs 1194, 36 and 37 respectively fall within the sub-watersheds SW1, SW2 and SW3. The coverage of Krishna channel within these subwatersheds are respectively 40.92, 300.84 and 531.10 Km2. About 19 water quality parameters, the physical parameters temperature, run-off and turbidity and the chemical parameters pH, hardness, conductivity, alkalinity, DO, BOD, COD, Fcoli, Total Coliform, Nitrogen, Chlorine, Sulphur, Sodium, Calcium, Magnesium and TKN, were studied from their monsoon and non-monsoon readings. While computing the pollution load (Table 3) it was assumed that the river flows in the stretch 365 days a year (a perennial river). The exposure of total population to pollution load in each subwatershed as shown in this table is to correlate their growth trend.
Generally along the stretch-I, turbidity and the chemical parameters BOD, COD, Na, Mg, Ca, Cl, TKN and Sulphate show slightly increasing trend over the years (1984-1997) in the downstream direction of river flow. Parameters like pH, N and DO don't show much of variation from the mean. However, the water quality readings of Fcoli and Tcoli are slightly decreasing along the downstream direction. In the individual WQM station the trend in BOD and COD loads, the indicators of organic pollution, show positive and the COD values are quite higher than BOD. The minimum and maximum BOD values during 1997 were 227 and 13241 tonnes year-1 whereas the COD values were 655 and 33453 tonnes year-1. The BOD and COD loads of the stretch, are showing sharp positive trend from 1990 onwards (figure 2 a,b,c,d). These indicated that the inflow of pollutants to river has been increasing after 1990. Amongst all the chemical parameters, the load of magnesium was the maximum. The highest Mg-load obtained was 224416 tonnes year-1 in 1988 for SW3. If the load of each pollutant is listed in terms of their total contribution in an year, the sequence in descending order for these pollutants will be Mg, Ca, Na, Sulphate, Cl, N, COD and BOD. First five major pollutants in the sequence are generally from the agricultural sources and the last two are both from both domestic and industrial sources.
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).
Conclusions
GIS has been utilised in the storage and retrieval of attribute data such as water quality parameters (pollution loads), population density and fertiliser consumption over the spatial database (map) of Satara-Sangli stretch in the Krishna basin. This database was useful in motoring the trend of pollution load and population growth in the entire watershed between 1984 to 1997. With the aid of map comparison utility in GIS pollution map could be compared with the population, fertiliser and industry location maps. Satara-Sangli stretch of the Krishna river is polluted grossly by the human-induced activities in the subwatersheds. The factors for acute pollution of water are:- the intensive use of fertilisers and pesticides in the agricultural land, growth of medium to big size sugar and distillery factories and very high growth of population leading to high domestic load from urban setup. Amongst the physical parameters turbidity values increased and the same results were witnessed after 1990 for chemical parameters such as BOD, COD, Na, Mg, Ca, Cl, TKN and sulphate. For all the pollutants load values increased abruptly for the subwatersheds along the downstream direction. There has been a good relationship between the pollution parameters with the population density. About 32 lakhs people got exposed to the pollution in 1991. The growth of people synchronised with that of the growth in industries. About 8 major sugar factories were responsible for the most of industrial effluents. Of all sources, the share of agriculture to water consumption and water pollution was the highest. Agricultural sources contributed to 91 % of total waste discharge while the same for domestic and industrial sources were 4.5 % each. It is very much indispensable that some standard economically feasible technologies be adopted to mitigate and reciprocate the process of water quality degradation, and restore the quality back to its normal. The river zonation suggested in this paper on the basis of subwatershed approach is fairly better in terms of areal coverage and pollution control.
References
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