International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 10, Issue 6, November - December 2019, pp. 398-404, Article ID: IJARET_10_06_042 Available online at http://iaeme.com/Home/issue/IJARET?Volume=10&Issue=6 Journal Impact Factor (2019): 10.2216 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6480 and ISSN Online: 0976-6499 © IAEME Publication
ASSESMENT OF SURFACE WATER QUALITY IN COIMBATORE, TAMILNADU
Dr. Krishnakumar P Associate Professor and Head, Department of Civil Engineering, Rathinam Technical Campus, Coimbatore, Tamilnadu, India.
Sellakannu M Assistant Professor, Department of Civil Engineering, Rathinam Technical Campus, Coimbatore, Tamilnadu, India.
Athipathy M Assistant Professor, Department of Civil Engineering, Rathinam Technical Campus, Coimbatore, Tamilnadu, India.
Clement M Assistant Professor, Department of Civil Engineering, Rathinam Technical Campus, Coimbatore, Tamilnadu, India.
ABSTRACT The management of surface water quality is a major environmental challenge. Monitoring different sources of pollutant load contribution to the surface water is quite a difficult, laborious and expensive process which sometimes leads to analytical errors also. In the current study, lake water were collected from different locations in the Coimbatore urban to assess water quality for drinking purpose as well as for irrigation purpose by analysing the major cations (Ca2+, Mg2+, Na+, K+) and anions (HCO3−, Cl−, F−, SO42−, NO3−, CO32−) besides some physical and chemical parameters (pH, electrical conductivity, total alkalinity, and total hardness). It is clearly observed that the Kurichi Lake is less contaminated than other lakes because as it is directly connected to Noyyal river whereas Periyakulam Lake is more concentration in potassium, Nitrite, Free ammonia and magnesium due to disposal of domestic sewage water and industrial wastes. Vallankulam, Singanallur and Selvampathy lakes are highly contaminated due to disposal of domestic wastes by the surrounding area. The WQI showed that nearly 30% of the samples were good and the remaining was poor for drinking purpose. Through hierarchical cluster analysis two major groups of cluster were obtained from the sampling. Analysis based on sodium percentage, SAR and Kelley’s ratio showed that 60% of the samples are suitable for irrigation purpose. Statistical analysis like correlation and factor analysis have performed for delineation of ionic relationship.
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Keywords: Surface water, Contamination, Quality, Pollutants Cite this Article: Krishnakumar P, Sellakkannu N, Athipathy M and Clement M, Assesment of Surface Water Quality in Coimbatore, Tamilnadu, International Journal of Advanced Research in Engineering and Technology (IJARET), 10 (6), 2019, pp 398- 404. http://iaeme.com/Home/issue/IJARET?Volume=10&Issue=6
1. INTRODUCTION Lakes being stagnant water bodies are more prone to pollution than the rivers as in lakes self- purification process are less effective than rivers. Any contamination or pollution of lakes affects greatly the flora and fauna and also the human health if the water is used for domestic supply. The environmental health of any lake system depends upon the nature of that lake and its exposure to various environmental factors such as temperature, depth of water, wind speed, soil types and land uses of the catchment. Hence, lake water quality depends not only on natural processes such as precipitation inputs, erosion, and weathering of crustal material, etc. But also an anthropogenic influences like urban, industrial, and agricultural activities. In recent decades, population growth, agricultural practices and sewage runoff from urban areas have increased nutrient inputs many fold to the level of their natural occurrence, resulting in accelerated eutrophication. The lakes and reservoirs, all over India without exception, are in varying degrees of environmental degradation, might be due to encroachments, eutrophication (from domestic and industrial effluents) and silting. There has been a quantum jump in population during the preceding century without corresponding expansion of civic facilities due to which the lakes and reservoirs become the sink of contaminants especially in urban areas. Most urban and rural lakes have been vanished under the population pressure with worldwide environmental concerns Lakes are described as “the pearls on the plateau, “play a crucial role in water supply, fishing, waste disposal, lake reclamation, agriculture, and industry. However, the catchment area of these lakes has been subject to deforestation, agricultural intensification, soil erosion, urbanization, and industrialization. As a result, water pollution and water shortages have now become a severe problem which has received more and more attention from the public and the Government . For example, several water-quality monitoring programs have been initiated to track Lake water quality includes microbial, biological, chemical, and physical aspects. Chemical contamination of water sources may be due to certain industries and agricultural practices, or from natural sources. When toxic chemicals are present in drinking water, there is the potential that they may cause either acute or chronic health effects. In India, most of the population is dependent on surface water as the only source of drinking water supply. Surface water can become contaminated naturally or because of numerous types of human activities; residential, municipal, commercial, industrial, and agricultural activities can all affect surface water quality. Contamination of surface water can result in poor drinking water quality, loss of water supply, high clean-up costs, high costs for alternative water supplies, and potential health problems. A wide variety of materials have been identified as contaminants found in surface water. These include synthetic organic chemicals, hydrocarbons, inorganic captions, inorganic anions, pathogens, and radionuclides. The importance of water quality in human health has recently attracted a great deal of interest. In developing countries like India around 80% of all diseases are directly related to poor drinking water quality and unhygienic conditions. In India severe water scarcity is becoming common in several parts of the country, especially in arid and semi-arid regions. Geographic information system (GIS) has emerged as a powerful tool for storing, analyzing, and displaying spatial data and using these data for
http://iaeme.com/Home/journal/IJARET 399 [email protected] Krishnakumar P, Sellakkannu N, Athipathy M and Clement M decision making in several areas including engineering and environmental fields. Surface water can be optimally used and sustained only when the quantity and quality is properly assessed. GIS has been used in the map classification of surface water quality, based on correlating total dissolved solids (TDS) values with some aquifer characteristics or land use and land cover. Other studies have used GIS as a database system in order to prepare maps of water quality according to concentration values of different chemical constituents. In such studies, GIS is utilized to locate surface water quality zones suitable for different usages such as irrigation and domestic. A similar approach was adopted by pandey (2008) where GIS was used to prepare layers of maps to locate promising well sites based on water quality and availability. Chandra et al. (2007) proposed a GIS-based surface water quality index method which synthesizes different available water quality data (for example, Cl, Na, Ca) by indexing them numerically relative to the WHO standards. Water quality assessment involves evaluation of the physical, chemical, and biological nature of water in relation to natural quality, human effects, and intended uses, particularly uses which may affect human health and the health of the aquatic system itself. The use of GIS technology has greatly simplified the assessment of natural resources and environmental concerns, including surface water. In surface water studies, GIS is commonly used for integrating surface water quality assessment models with spatial data to create spatial decision support systems. A GIS-based study was carried out by Chandra et al. (1996) to determine the impact of urbanization on surface water quality. Achuthan (2010) have mapped urban surface water quality in Koyna, Turkey, using GIS. Ahn and Chon (1999) studied surface water contamination and spatial relationships among surface water quality, topography, geology, land use, and pollution sources using GIS in Seoul. GIS has been useful in establishing the spatial relationship between pollution level and its source in this study. ArcView GIS was used to map, query, and analyse the spatial patterns of surface water in north-central Texas that includes large percentages of both urban and agricultural land uses. Chandra (1999) produced surface water contamination risk and quality maps by using GIS in Southern Italy. It was suggested that the use of GIS techniques is vital in testing and improving the surface water contamination risk assessment methods. For any city, surface water quality map is important to evaluate the water safeness for drinking and irrigation purposes and also as a precautionary indication of potential environmental health problems. Considering the above aspects of surface water contamination and use of GIS in surface water quality mapping, the present study is undertaken to map the surface water quality in five lakes (Kurichi Lake, Ukadam Lake, Vallankulam Lake, singanallur and Perur Lake) in Coimbatore, Tamilnadu, India. The literature survey indicates that several researchers have made studies on surface water quality. Some have studied only physico-chemical parameters, while some have observed the parameters in a combined state. Further there are reports only on the detection of hydro-chemical factors. A systematic study is required to evaluate the water quality and to determine the factors affecting the water chemistry of the region integrated with GIS. Moreover such a study has not been carried in the five lakes region. This study aims to visualize the spatial variation of certain physico-chemical parameters through GIS.
2. OBJECTIVE The main objective of the research work are to make a surface water quality assessment based on the available physico-chemical data in Coimbatore district. The purposes of this assessment are To provide an overview of present surface water quality of the Coimbatore region. To identify the suitability of surface water for domestic purpose and irrigation purpose.
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3. STUDY AREA Our study area is Coimbatore. It is a district in the Tamil Nadu, India. Which lies between 1100’45”N to 11001’250`”N latitude and 76058`17”E to 76097`0”E. This District is bounded by Nilgiri District towards north, Idukki District of Kerala state towards south, Tiruppur District towards east and Palghat District of Kerala state towards west. Winds from the Arabian Sea are funnelled via the Palghat Pass resulting in a salubrious climate. During southwest monsoon, the place receives heavy rainfall. It has a moderate temperature and windy climate during summer period. The important crops which are cultivated are vegetables like tomato, green chili, lady's finger, brinjal, maize, cotton and ground nuts etc. Coconut trees are also well suitable for this soil and climate conditions, and they covered most of land part of the district. The major lakes in the area of Coimbatore are Ukadamperiyakulam, Kurichi Lake, Singanallur Lake, SungamValankulam and Selvampathy Lake.
Table 1.Sample location with latitude and longitude S.No Sample Location No.of.samples Longitude Latitude
1 Kurichi Lake 3 76.9701 10.9642
2 Periyakulam Lake 3 76.9629 10.9902
3 Vallankulam Lake 3 76.5225 10.5848
4 Singanallur Lake 3 76.9589 10.7824
5 Selvampathy Lake 3 76.9245 10.9622
Table 2. Total Turbidit Total Samples TDS EC PH alkalinit Calcium Magnesium Sodium Potassium y hardness y Sample 1 0.0 487.0 695.0 7.6 200.0 230.0 72.0 12.0 40.0 3.0 Sample 2 0.0 515.0 736.0 7.7 210.0 230.0 76.0 10.0 47.0 4.0 Sample 3 0.0 487.0 696.0 7.1 200.0 230.0 72.0 12.0 40.0 3.0 Sample 4 42.0 1195.0 1707.0 7.9 260.0 380.0 116.0 22.0 40.0 180.0 Sample 5 60.0 1280.0 1829.0 8.0 320.0 420.0 128.0 24.0 47.0 180.0 Sample 6 38.0 1166.0 1666.0 7.8 240.0 380.0 116.0 22.0 40.0 180.0 Sample 7 45.0 1075.0 1536.0 8.0 200.0 360.0 116.0 17.0 150.0 10.0 Sample 8 39.0 1151.0 1644.0 7.9 220.0 360.0 116.0 17.0 180.0 10.0 Sample 9 36.0 1110.0 1585.0 7.9 210.0 360.0 116.0 17.0 170.0 10.0 Sample 10 21.0 1561.0 2230.0 7.9 250.0 430.0 144.0 17.0 250.0 5.0 Sample 11 18.0 1715.0 2450.0 7.8 250.0 430.0 144.0 17.0 307.0 15.0 Sample 12 34.0 1568.0 2240.0 7.8 250.0 430.0 144.0 17.0 250.0 5.0 Sample 13 37.0 1151.0 1644.0 7.9 220.0 380.0 144.0 17.0 157.0 25.0 Sample 14 41.0 1519.0 2170.0 7.8 250.0 430.0 144.0 17.0 233.0 50.0 Sample 15 42.0 1072.0 1532.0 7.9 210.0 360.0 116.0 17.0 150.0 10.0 Min 0.0 487.0 695.0 7.1 200.0 230.0 72.0 10.0 40.0 3.0 Max 60.0 1715.0 2450.0 8.0 320.0 430.0 144.0 24.0 307.0 180.0 Std.div 18.3 388.3 554.8 0.2 32.4 73.2 26.0 3.8 93.0 70.4 Average 30.2 1136.8 1624.0 7.8 232.7 360.7 117.6 17.0 140.1 46.0
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4. INVESTIGATION RESULTS
Table 3.
Samples Iron Free ammonia Nitrite Nitrate Chloride Fluoride Sulphate Phosphate Total alkalinity Sample 1 0.2 0.2 0.0 2.0 85.0 0.4 6.0 0.2 200.0 Sample 2 0.2 0.2 0.0 2.0 90.0 0.4 6.0 0.3 210.0 Sample 3 0.0 0.2 0.0 2.0 90.0 0.4 5.0 0.2 200.0 Sample 4 2.9 5.0 0.1 42.0 315.0 0.6 54.0 5.3 260.0 Sample 5 4.3 5.6 0.1 44.0 310.0 0.6 62.0 5.7 320.0 Sample 6 3.0 5.0 0.1 41.0 315.0 0.6 52.0 5.3 240.0 Sample 7 2.8 3.0 0.1 42.0 315.0 0.6 54.0 5.2 200.0 Sample 8 2.9 2.8 0.1 42.0 315.0 0.6 54.0 5.3 220.0 Sample 9 2.9 2.5 0.1 42.0 315.0 0.6 54.0 5.3 210.0 Sample 10 2.4 2.4 0.1 40.0 390.0 0.6 205.0 5.7 250.0 Sample 11 2.2 2.3 0.1 41.0 460.0 0.6 201.0 5.3 250.0 Sample 12 3.4 3.3 0.1 40.0 390.0 0.6 192.0 5.3 250.0 Sample 13 2.9 2.3 0.1 40.0 300.0 0.6 86.0 5.7 220.0 Sample 14 3.4 2.3 0.1 40.0 445.0 0.6 104.0 5.3 250.0 Sample 15 3.4 3.3 0.1 42.0 305.0 0.6 54.0 5.3 210.0 Min 0.0 0.2 0.0 2.0 85.0 0.4 5.0 0.2 200.0 Max 4.3 5.6 0.1 44.0 460.0 0.6 205.0 5.7 320.0 Std.div 1.3 1.7 0.0 16.3 119.0 0.1 67.9 2.1 32.4 Average 2.5 2.7 0.1 33.5 296.0 0.6 79.3 4.4 1136.8
4.1. Surface Water Quality based on TDS
Table 4.
Parameter Ranges Classification Sample No No. of % of samples samples Davis and DeWiest TDS mg/l <500 Desirable for 1,3 2 13.34 drinking water 500-1000 Permissible for 2 1 6.67 drinking water 1000-3000 Useful for irrigation 4,5,6,7,8,9,10,11,12,13,14,15 12 80.04 >3000 Unfit for drinking Nil - - and irrigation Freeze and Cherry TDS mg/l <1000 Freshwater 1,2,3 3 20.01 1000-10000 Brackish water 4,5,6,7,8,9,10,11,12,13,14,15 12 80.04 10000- Saline water Nil - 100000 >1000000 Brine water Nil - -
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4.2. Water Quality Index and Relative Weight
Table 5. Chemical parameters BIS:10500 Weight Relative weight
1,991 (Wi) Wi =Wi/(∑wi)
pH 6.5-8.5 4 0.125 TDS 500-2000 4 0.125 TA 200-600 2 0.0625 TH 300-600 2 0.0625 Ca+ 75-200 2 0.0625 Mg+ 30-100 2 0.0625
No3 45-100 5 0.15625 Cl- 250-1000 3 0.09375 F- 1-1.5 4 0.125 2- SO4 200-400 4 0.125
∑Wi = 32 ∑wi = 1.000
4.3. Water Quality Index Result
Table 6. WQI Water Quality No.of Sample % of Samples 0-30 Excellent - - 30-50 Good - - 50-60 Moderate 4 26.68 60-70 Poor 4 26.68 70-80 Very poor 2 13.34 80-100 Unfit for drinking 5 33.35
5. CONCLUSION The present study of surface water samples in Coimbatore shows that the most water samples were exceeded within the BIS limits except few samples. This has the potential to affect human health adversely. The chemical composition of surface water shows that pH, potassium, and fluoride for all the samples were within limit. Based on samples are within acceptable limit and based on Davis and Dewiest, 13.34 % of samples were suitable for drinking water and 80.04% of samples useful for irrigation purpose. TA Nearly 80% of the samples were exceeded the permissible limit. Based on TA due to presence of carbonates, bicarbonates and hydroxides compound of Ca2+, Mg2+, Na+ and K+. Carbonate and bicarbonate may originate from microbial decomposition of organic matter. The total hardness shows that the samples were exceeded the permissible limit. The high percentage of water hardness due to presence of HCO3-, CO3-, SO42, Cl- and NO3. Nitrate, Calcium and Magnesium are found to be within the limit except, Water quality index of drinking water for analyzed sample shows that about 27 % of the samples were found to be moderate for drinking purpose, 26.67 % were poor, and 47 % were unfit for the drinking purpose. Hierarchical cluster analysis (HCA) shows three major groups of cluster. Group II, which is further subdivided into two subgroups, IIa and IIb clustered interprets in the
http://iaeme.com/Home/journal/IJARET 403 [email protected] Krishnakumar P, Sellakkannu N, Athipathy M and Clement M physicochemical composition, and all the parameter within limit. The stronger relation between the groups is due to anthropogenic impact and also agricultural practices. Group II is typically represented by the factors (Group II- Cl-, PO43- and NO3-) group of samples; they are influenced by human interference and extent of pollutants, agricultural activity. Sample number 10 comes under group III. To ensure a sustainable management of this resource in this fragile ecosystem, proper management of the water extraction structures and maintenance of the surface water sources are needed.
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