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Development of computer automated decision support system for surface water quality assessment
Computers & Geosciences 51 (2013) 129–134
Contents lists available at SciVerse ScienceDirect
Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo
Development of computer automated decision support system for surface water quality assessment Asheesh Sharma n, Madhuri Naidu, Aabha Sargaonkar Environmental System Design and Modelling Division, CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
a r t i c l e i n f o
abstract
Article history: Received 2 May 2012 Received in revised form 23 August 2012 Accepted 12 September 2012 Available online 23 September 2012
The Overall Index of Pollution (OIP) is a single number that expresses the overall water quality by integrating measurements of 14 different physicochemical, toxicological, and bacteriological water quality parameters. It provides a simple and concise method for water quality classification as, ‘Excellent’, ‘Acceptable’, ‘Slightly Polluted’, ‘Polluted’, and ‘Heavily Polluted’. OIP values range from 0 to 16. A high OIP value signals poor water quality, while a low value signals good water quality based on the classification scheme developed for India. In this paper, we present a computer-automated, userfriendly, and standalone Surface Water Quality Assessment Tool (SWQAT), which calculates OIP values and displays it on Google map. The software is developed in VB.Net and SQL database. The software application is demonstrated through water quality assessment of two rivers of India, namely Cauvery and Tungabhadra. OIP values are estimated at 10 sampling stations on the river Cauvery and at eight sampling stations on the river Tungabhadra. The Cauvery river OIP scores in the range 0.85–7.91 while for Tungabhadra river, it is in range 2.08 to 8.97. The results are useful to analyze the variations in the water quality of different sites at different times. SWQAT improves understanding of general water quality issues, communicates water quality status, and draws the need for and effectiveness of protection measures. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Water quality assessment Water quality index Google earth plug-in Software
1. Introduction Water quality is a complex subject, which involves interplay of physical, chemical, hydrological, and biological parameters. Water quality analysis is one of the essential steps for protection and management of water bodies at national level. In India, Central Water Commission (CWC) is responsible for collecting, keeping, and publishing statistical data related to water bodies (CWC, 1945). It acts as the central bureau of information for water quality. Besides CWC, Central Pollution Control Board (CPCB) is also a governing body for water quality recording and management in India (MINARS, 2007–08). For rational planning and management programs, CPCB needs to know the nature and extent of water quality degradation in surface water bodies. To ensure whether the water quality at a particular location is within the standards or not, CPCB regularly analyzes the water samples (The Water (Prevention and Control of Pollution) Act, 1974). Frequency of sampling (random and cyclic) depends on the water quality variation. Large frequency of sampling is required, if variations are more in a short interval of time. The task of evaluating the overall water quality of a river is difficult and complex, especially when the analysis involves many
n
Corresponding author. Tel./fax: þ 91 712 2249990. E-mail address: [email protected] (A. Sharma).
0098-3004/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cageo.2012.09.007
samples and measuring multiple parameters per sample. In addition to this, the water quality varies according to the type of usage. A certain water quality may be good enough for the purpose of irrigation but it may not be good for drinking purpose. Furthermore, the criterion of an ‘acceptable water quality’ varies from region to region and from time to time. In order to address these issues, it is required to transform the water quality information into a single index value, which is understandable and useful for decision-making (Bharti and Katyal, 2011). A water quality index (WQI) is a mathematical tool employed to transform the bulk of water quality data into a single number using numerical expressions. WQI indicates the level of pollution and acceptability of water. Some of the water quality indices that are frequently employed in public domain for the purpose of water quality assessment include, NSF Water Quality Index (NSFWQI), British Columbia Water Quality Index (BCWQI), Canadian Water Quality Index (CWQI), Oregon Water Quality Index (OWQI), and the Florida Stream Water Quality Index (FWQI) (Said et al., 2004). Despite the availability of water quality data, mathematical tool and computer knowledge, efforts are rarely made to develop a user-interactive tool for water quality analysis. Sarkar and Abbasi (2006) attempted development of a software tool, QUALIDEX to generate WQIs. However, its application is not yet reported. As WQIs are highly subjective in nature, water quality assessment is influenced by the index used, weights assigned, and classification
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scheme adopted. Therefore, in view of the routine and strategic water quality monitoring projects in India, an attempt is made to develop a user-interactive and standalone tool, Surface Water Quality Assessment Tool (SWQAT). It computes OIP values by assigning dynamic weights to water quality parameters and provides water quality classification in Indian context that is useful in regular water quality assessment. The paper describes various modules of SWQAT and presents its application for two major rivers of India.
2. Background of OIP Sargaonkar and Deshpande (2002) have developed OIP for water quality assessment under Indian conditions using 14 water quality parameters (Fig. 1). A general classification scheme used for water quality assessment is depicted in Fig. 2. The scheme reflects the status of the water quality in terms of pollution effects of the parameters under consideration (Sargaonkar and Deshpande, 2003). The value function curves used for calculating the OIP are presented in Appendix A. In value function curves, the measured values of water quality parameters are on X-axis, while sub-index (Si) values are on Y-axis. For a particular value of a water quality parameter on X-axis the corresponding sub-index (Si) value is obtained using the value function curve of that parameter. The water quality parameters are weighted dynamically as per their significance at a particular site and dynamic OIP is estimated based on the linear combination of all the individual pollution sub-indices (Si) values, as given in Appendix A (Sargaonkar et al., 2008).
Fig. 1. Water quality parameters.
3. Description of SWQAT In the present study, a computer-automated tool SWQAT has been developed to generate and operate on OIP. The software is developed in VB.Net and SQL database. An open source Google Earth plug-in (2004) is integrated with the software for online display of OIP values on Google map. The software can also store water quality data for future use. In the first step of using SWQAT, the user inputs values for 14 or available number of water quality
Fig. 2. Water quality classification.
OIP calculation module Sub-index evaluation Input module Surface water body/river details WQI parameters concentration
Average, Fixed, Dynamic weighted OIP evaluation Database (MS SQL server) Visual interpretation module Graphical representation on MS-Excel Geo–spatial mapping on Google Earth Fig. 3. Conceptual framework of SWQAT.
Fig. 4. Processing options available in SWQAT.
Decision making on water quality
A. Sharma et al. / Computers & Geosciences 51 (2013) 129–134
parameters. In the second step of the process, the program uses inputted values of the water quality parameters to compute OIP. In the third and final step, the results are presented in a graphical mode with output OIP values and water quality classification being shown on the map for a sampling location. The primary objective is to fulfill the urgent need of a diagnostic tool for the overall assessment of the status of water quality of individual water sources in any region, with respect to the different water uses at regular intervals. Geographical displays of software results are useful for the policy makers and researchers in water quality assessment and pollution control.
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enables the user to insert/save, delete, and view water quality data using ‘Insert’, ‘Delete’ and ‘View’ buttons respectively, as shown in Fig. 5. ‘Close’ button is used to close the module.
4. Basic architecture of SWQAT SWQAT comprise of three modules, namely- Input module, OIP calculation module and Visual interpretation module. The basic architecture of the software is depicted in Fig. 3. 4.1. Input module The main interface of the software has three main tabs, namely user registration, data entry, view database, and overall index of pollution as depicted in Fig. 4. The water quality parameter values monitored at particular site (river/lakes and stations) and at particular frequency (yearly, seasonally or monthly) are entered through this module. Water quality data is stored in SQL database, which is connected to the software through the Open Database Connectivity (ODBC). A user-friendly interface developed within the software
Fig. 5. SWQAT water quality input form.
Fig. 6. Computation of sub-index and OIP.
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4.2. OIP calculation module OIP calculation module is shown in Fig. 6. The sub-index values for all the parameters at a particular site are calculated using ‘Calculate Sub-Index’ button in this module. Three options are available for OIP computation as ‘Average’, ‘Fixed’, and ‘Dynamic’ OIP. User selects one of the options and clicks on ‘Calculate OIP’ button for OIP calculation. In the average OIP method, average of all sub-indices is estimated while in fixed and dynamic OIP method, user-defined and dynamic weights are assigned respectively to the sub-index values of parameters, and then linear combination of all sub-indices is calculated for weighted OIP. In dynamic weighting
method, weights are estimated according to the pollution effect of different parameters at particular sampling station. Hence, higher weights are assigned to the parameter that significantly contributes to the overall pollution. In most of the cases dynamic weighting method performs better than the remaining two methods of OIP calculation (Sargaonkar et al., 2008). 4.3. Visual interpretation module The results of OIP calculation module may be viewed in the form of compact spreadsheets through this module. For each run, the corresponding summary data are stored within the software,
Fig. 7. Water quality classification for river Tungabhadra.
Table 1 Computed OIP at 10 sampling stations in Cauvery river. S.no.
Longitude
Latitude
Station id
Station name
Year
Month
OIP
Water classification
1 2 3 4 5 6 7 8 9 10
76.408567 76.436481 76.409967 76.187389 76.665861 76.695928 76.689061 76.710997 76.686519 76.844156
12.372828 12.390472 12.485386 12.543833 12.371903 12.376447 12.377761 12.420061 12.426422 12.316164
Cauvery1 Cauvery2 Cauvery3 Cauvery4 Cauvery5 Cauvery6 Cauvery7 Cauvery8 Cauvery9 Cauvery10
K R Nagar1 K R Nagar2 K R Nagar3 K R Nagar4 Sri Ranga1 Sri Ranga2a Sri Ranga2b Sri Ranga3a Sri Ranga3b Sri Ranga4
2007 2007 2007 2007 2007 2007 2007 2007 2007 2007
January January January January January January January January January January
0.85 1.12 1.87 1.16 1.15 2.03 7.91 1.12 4.46 0.85
Excellent Acceptable Acceptable Acceptable Acceptable Slightly polluted Polluted Acceptable Polluted Excellent
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which can be accessed through GUI interface. General format of the results generated by OIP calculation module is depicted in Fig. 7. The software has built-in feature of geospatial display of water quality data and OIP values on Google maps. User can select details of a particular sampling station and OIP value stored in the database for the mapping purpose. It also provides visual representation of water quality classification as ‘Excellent’, ‘Acceptable’, ‘Slightly Polluted’, ‘Polluted’, or ‘Heavily Polluted’ obtained for various stations.
eastern slope of the Western Ghats in the state of Karnataka. Industrial pollution and mining activities on Tungabhadra riverbanks have declined the water quality of the river. Altogether, the Tungabhadra river pollution has affected 1,000,000 people in the sub-basin as most of the villages use the river water for drinking, bathing, livestock, fishing as well as for irrigating crops (Striver, 2007). Water quality data of Cauvery and Tungabhadra rivers were recorded during January 2007 for water quality assessment (India water portal, 2007))
5. Case study
6. Results and discussion
Water quality data of two major rivers, Cauvery and Tungabhadra are used in the present study for the software application. The Cauvery river is one of the significant sources of water supply for domestic, agricultural, and industrial usage in Tamil Nadu State, India. The Cauvery river basin is around 72,000 km2. Rising in southwestern Karnataka, it flows southeast some 765 km to enter the Bay of Bengal (Raja et al., 2008). The other river considered in the study is Tungabhadra, which is formed by the confluence of two rivers, the Tunga river and the Bhadra river. The river flows down the
OIP values for 10 sampling locations on the river Cauvery and eight sampling locations on the river Tungabhadra are computed using SWQAT and presented in Tables 1 and 2 respectively. The calculated OIP values of the Cauvery river sampling stations range from 0.85 to 7.91 while OIP values of the Tungabhadra sampling station range from 2.08 to 8.97. Water quality at most of the sampling stations of Cauvery river is classified as ‘Excellent’ and ‘Acceptable’. At three sampling stations, namely ‘Sri Ranga2a’, ‘Sri Ranga 2b’, ‘Sri Ranga 3b’, the water quality is classified as
Table 2 Computed OIP at eight sampling stations in Tungabhadra river. S.no.
Longitude
Latitude
Station id
Station name
Year
Month
OIP
Water classification
1 2 3 4 5 6 7 8
75.954033 75.805947 75.816144 75.78125 75.800297 75.791847 75.775847 75.675803
14.447347 14.611572 14.579703 14.480828 14.543503 14.436594 14.450347 14.014431
Tung1 Tung2 Tung3 Tung4 Tung5 Tung6 Tung7 Tung8
Bethur Halla Sarthihalla RB SarthiHalla Harallapura C HPF outfall New bridge C Shiva nagar LB Sulekere Halla
2007 2007 2007 2007 2007 2007 2007 2007
January January January January January January January January
2.50 2.64 2.52 2.08 2.60 3.36 8.97 2.60
Slightly Slightly Slightly Slightly Slightly Slightly Heavily Slightly
Fig. 8. Mapping of computed OIP values on Google Earth.
polluted polluted polluted polluted polluted polluted polluted polluted
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‘Slightly Polluted’ and ‘Polluted’. The water quality of Tungabhadra river at all the sampling stations, except ‘Shiva nagar LB’ is classified as ‘Slightly Polluted’. The water quality at worst affected sampling station ‘Shiva nagar LB’, is classified as ‘Heavily Polluted’, which is a matter of concern. It signals urgent need of control measures for water quality protection at that station. To point out the possible causes of pollution, OIP values are displayed on Google maps. Around ‘Shiva nagar LB’, the landuse is mostly agriculture and rural settlements. The pollution in river is mainly caused by anthropogenic activities near this sampling station. The water quality classification of ‘Shiva nagar LB’ with OIP values and major landuse in surrounding are mapped on Goggle earth, as shown in Fig. 8.
7. Conclusion A computer automated tool ‘SWQAT’ for water quality assessment and management is developed in this paper. It has following key features:
Real time generation of OIP by just entering the values of the
component parameters in the respective database for number of rivers and sampling stations. Assessment of the overall status of water quality based on 14 different water quality parameters. Mapping of sampling station, water quality classification, and OIP values on Google maps.
Acknowledgments The authors are grateful to the Director, Council of Scientific and Industrial Research-National Environmental Engineering Research Institute for providing encouragement, necessary infrastructure to carry out the research, and kind permission to publish the paper. Google Earth plug-in freely available on Google website, is also acknowledged.
Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.cageo.2012.09.007.
References Bharti, N., Katyal, D., 2011. Water quality indices used for surface water vulnerability assessment. International Journal of Environmental Sciences 2 (1), ISSN 0976–4402. Central Water Commission (CWC), 1945. Available from: /http://www.cwc.gov. in/main/webpages/activities.htmlS. Central Pollution Control Board, (CPCB), New Delhi, India. Available from: /http:// www.cpcb.nic.in/upload/NewItems/NewItem_116_Guidelinesof%20waterqualitymonitoring_31.07.08.pdfS. Google Earth plug-in, 2004. Available from: /http://www.google.com/earth/down load/ge/agree.htmlS. India water portal, Water quality status of rivers Cauvery and Tungabhadra January–June 2007. Available from: /http://www.indiawaterportal.org/S. MINARS, 2007–08. Guidelines for water quality monitoring, Central Pollution Control Board, Parivesh Bhawan East Arjun Nagar, Delhi-32, India. Available from: /http://www.rainwaterharvesting.org/Crisis/river-krishna.htmS. Raja, P., Amarnath, A.M., Elangovan, R., Palanivel., M., 2008. Evaluation of physical and chemical parameters of river Kaveri, Tiruchirappalli, Tamil Nadu, India. Journal of Environmental Biology 29 (5), 765–768. Sargaonkar, A.P., Deshpande, V., 2002. Development of an Overall Index of Pollution for surface water based on a general classification scheme in Indian context. Environmental Monitoring and Assessment 89, 43–67. Sargaonkar, A.P., Deshpande, V.A., 2003. A general classification of water quality in Indian context. Indian Journal of Environmental Health 44 (3), 231–237. Sargaonkar, A.P., Gupta, A., Devotta, S., 2008. Dynamic weighting system for water quality index. Water Sciences and Technology-WST (58.6), 1261–1271. Said, A., Stevens, D.K., Sehlke, G., 2004. An innovative index for evaluating water quality in streams. Environment Management 34 (3), 406–414. Sarkar, C., Abbasi, S.A., 2006. QUALIDEX—a new software for generating water quality indices. Environmental Monitoring and Assessment 119, 201–231. Striver., 2007. The Tungabhadra river basin. In: Proceedings of the First Stakeholder Meeting. Available from: /http://www.indiawaterportal.org/sites/india waterportal.org/files/Striver_0.pdfS. The Water (Prevention and Control of Pollution) Act, 1974. Available from: /www.cpcb.nic.in/upload/NewItems/(1)%20Wateract1974%20.docS.
Contents lists available at SciVerse ScienceDirect
Computers & Geosciences journal homepage: www.elsevier.com/locate/cageo
Development of computer automated decision support system for surface water quality assessment Asheesh Sharma n, Madhuri Naidu, Aabha Sargaonkar Environmental System Design and Modelling Division, CSIR-National Environmental Engineering Research Institute, Nagpur 440020, Maharashtra, India
a r t i c l e i n f o
abstract
Article history: Received 2 May 2012 Received in revised form 23 August 2012 Accepted 12 September 2012 Available online 23 September 2012
The Overall Index of Pollution (OIP) is a single number that expresses the overall water quality by integrating measurements of 14 different physicochemical, toxicological, and bacteriological water quality parameters. It provides a simple and concise method for water quality classification as, ‘Excellent’, ‘Acceptable’, ‘Slightly Polluted’, ‘Polluted’, and ‘Heavily Polluted’. OIP values range from 0 to 16. A high OIP value signals poor water quality, while a low value signals good water quality based on the classification scheme developed for India. In this paper, we present a computer-automated, userfriendly, and standalone Surface Water Quality Assessment Tool (SWQAT), which calculates OIP values and displays it on Google map. The software is developed in VB.Net and SQL database. The software application is demonstrated through water quality assessment of two rivers of India, namely Cauvery and Tungabhadra. OIP values are estimated at 10 sampling stations on the river Cauvery and at eight sampling stations on the river Tungabhadra. The Cauvery river OIP scores in the range 0.85–7.91 while for Tungabhadra river, it is in range 2.08 to 8.97. The results are useful to analyze the variations in the water quality of different sites at different times. SWQAT improves understanding of general water quality issues, communicates water quality status, and draws the need for and effectiveness of protection measures. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Water quality assessment Water quality index Google earth plug-in Software
1. Introduction Water quality is a complex subject, which involves interplay of physical, chemical, hydrological, and biological parameters. Water quality analysis is one of the essential steps for protection and management of water bodies at national level. In India, Central Water Commission (CWC) is responsible for collecting, keeping, and publishing statistical data related to water bodies (CWC, 1945). It acts as the central bureau of information for water quality. Besides CWC, Central Pollution Control Board (CPCB) is also a governing body for water quality recording and management in India (MINARS, 2007–08). For rational planning and management programs, CPCB needs to know the nature and extent of water quality degradation in surface water bodies. To ensure whether the water quality at a particular location is within the standards or not, CPCB regularly analyzes the water samples (The Water (Prevention and Control of Pollution) Act, 1974). Frequency of sampling (random and cyclic) depends on the water quality variation. Large frequency of sampling is required, if variations are more in a short interval of time. The task of evaluating the overall water quality of a river is difficult and complex, especially when the analysis involves many
n
Corresponding author. Tel./fax: þ 91 712 2249990. E-mail address: [email protected] (A. Sharma).
0098-3004/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cageo.2012.09.007
samples and measuring multiple parameters per sample. In addition to this, the water quality varies according to the type of usage. A certain water quality may be good enough for the purpose of irrigation but it may not be good for drinking purpose. Furthermore, the criterion of an ‘acceptable water quality’ varies from region to region and from time to time. In order to address these issues, it is required to transform the water quality information into a single index value, which is understandable and useful for decision-making (Bharti and Katyal, 2011). A water quality index (WQI) is a mathematical tool employed to transform the bulk of water quality data into a single number using numerical expressions. WQI indicates the level of pollution and acceptability of water. Some of the water quality indices that are frequently employed in public domain for the purpose of water quality assessment include, NSF Water Quality Index (NSFWQI), British Columbia Water Quality Index (BCWQI), Canadian Water Quality Index (CWQI), Oregon Water Quality Index (OWQI), and the Florida Stream Water Quality Index (FWQI) (Said et al., 2004). Despite the availability of water quality data, mathematical tool and computer knowledge, efforts are rarely made to develop a user-interactive tool for water quality analysis. Sarkar and Abbasi (2006) attempted development of a software tool, QUALIDEX to generate WQIs. However, its application is not yet reported. As WQIs are highly subjective in nature, water quality assessment is influenced by the index used, weights assigned, and classification
130
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scheme adopted. Therefore, in view of the routine and strategic water quality monitoring projects in India, an attempt is made to develop a user-interactive and standalone tool, Surface Water Quality Assessment Tool (SWQAT). It computes OIP values by assigning dynamic weights to water quality parameters and provides water quality classification in Indian context that is useful in regular water quality assessment. The paper describes various modules of SWQAT and presents its application for two major rivers of India.
2. Background of OIP Sargaonkar and Deshpande (2002) have developed OIP for water quality assessment under Indian conditions using 14 water quality parameters (Fig. 1). A general classification scheme used for water quality assessment is depicted in Fig. 2. The scheme reflects the status of the water quality in terms of pollution effects of the parameters under consideration (Sargaonkar and Deshpande, 2003). The value function curves used for calculating the OIP are presented in Appendix A. In value function curves, the measured values of water quality parameters are on X-axis, while sub-index (Si) values are on Y-axis. For a particular value of a water quality parameter on X-axis the corresponding sub-index (Si) value is obtained using the value function curve of that parameter. The water quality parameters are weighted dynamically as per their significance at a particular site and dynamic OIP is estimated based on the linear combination of all the individual pollution sub-indices (Si) values, as given in Appendix A (Sargaonkar et al., 2008).
Fig. 1. Water quality parameters.
3. Description of SWQAT In the present study, a computer-automated tool SWQAT has been developed to generate and operate on OIP. The software is developed in VB.Net and SQL database. An open source Google Earth plug-in (2004) is integrated with the software for online display of OIP values on Google map. The software can also store water quality data for future use. In the first step of using SWQAT, the user inputs values for 14 or available number of water quality
Fig. 2. Water quality classification.
OIP calculation module Sub-index evaluation Input module Surface water body/river details WQI parameters concentration
Average, Fixed, Dynamic weighted OIP evaluation Database (MS SQL server) Visual interpretation module Graphical representation on MS-Excel Geo–spatial mapping on Google Earth Fig. 3. Conceptual framework of SWQAT.
Fig. 4. Processing options available in SWQAT.
Decision making on water quality
A. Sharma et al. / Computers & Geosciences 51 (2013) 129–134
parameters. In the second step of the process, the program uses inputted values of the water quality parameters to compute OIP. In the third and final step, the results are presented in a graphical mode with output OIP values and water quality classification being shown on the map for a sampling location. The primary objective is to fulfill the urgent need of a diagnostic tool for the overall assessment of the status of water quality of individual water sources in any region, with respect to the different water uses at regular intervals. Geographical displays of software results are useful for the policy makers and researchers in water quality assessment and pollution control.
131
enables the user to insert/save, delete, and view water quality data using ‘Insert’, ‘Delete’ and ‘View’ buttons respectively, as shown in Fig. 5. ‘Close’ button is used to close the module.
4. Basic architecture of SWQAT SWQAT comprise of three modules, namely- Input module, OIP calculation module and Visual interpretation module. The basic architecture of the software is depicted in Fig. 3. 4.1. Input module The main interface of the software has three main tabs, namely user registration, data entry, view database, and overall index of pollution as depicted in Fig. 4. The water quality parameter values monitored at particular site (river/lakes and stations) and at particular frequency (yearly, seasonally or monthly) are entered through this module. Water quality data is stored in SQL database, which is connected to the software through the Open Database Connectivity (ODBC). A user-friendly interface developed within the software
Fig. 5. SWQAT water quality input form.
Fig. 6. Computation of sub-index and OIP.
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4.2. OIP calculation module OIP calculation module is shown in Fig. 6. The sub-index values for all the parameters at a particular site are calculated using ‘Calculate Sub-Index’ button in this module. Three options are available for OIP computation as ‘Average’, ‘Fixed’, and ‘Dynamic’ OIP. User selects one of the options and clicks on ‘Calculate OIP’ button for OIP calculation. In the average OIP method, average of all sub-indices is estimated while in fixed and dynamic OIP method, user-defined and dynamic weights are assigned respectively to the sub-index values of parameters, and then linear combination of all sub-indices is calculated for weighted OIP. In dynamic weighting
method, weights are estimated according to the pollution effect of different parameters at particular sampling station. Hence, higher weights are assigned to the parameter that significantly contributes to the overall pollution. In most of the cases dynamic weighting method performs better than the remaining two methods of OIP calculation (Sargaonkar et al., 2008). 4.3. Visual interpretation module The results of OIP calculation module may be viewed in the form of compact spreadsheets through this module. For each run, the corresponding summary data are stored within the software,
Fig. 7. Water quality classification for river Tungabhadra.
Table 1 Computed OIP at 10 sampling stations in Cauvery river. S.no.
Longitude
Latitude
Station id
Station name
Year
Month
OIP
Water classification
1 2 3 4 5 6 7 8 9 10
76.408567 76.436481 76.409967 76.187389 76.665861 76.695928 76.689061 76.710997 76.686519 76.844156
12.372828 12.390472 12.485386 12.543833 12.371903 12.376447 12.377761 12.420061 12.426422 12.316164
Cauvery1 Cauvery2 Cauvery3 Cauvery4 Cauvery5 Cauvery6 Cauvery7 Cauvery8 Cauvery9 Cauvery10
K R Nagar1 K R Nagar2 K R Nagar3 K R Nagar4 Sri Ranga1 Sri Ranga2a Sri Ranga2b Sri Ranga3a Sri Ranga3b Sri Ranga4
2007 2007 2007 2007 2007 2007 2007 2007 2007 2007
January January January January January January January January January January
0.85 1.12 1.87 1.16 1.15 2.03 7.91 1.12 4.46 0.85
Excellent Acceptable Acceptable Acceptable Acceptable Slightly polluted Polluted Acceptable Polluted Excellent
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which can be accessed through GUI interface. General format of the results generated by OIP calculation module is depicted in Fig. 7. The software has built-in feature of geospatial display of water quality data and OIP values on Google maps. User can select details of a particular sampling station and OIP value stored in the database for the mapping purpose. It also provides visual representation of water quality classification as ‘Excellent’, ‘Acceptable’, ‘Slightly Polluted’, ‘Polluted’, or ‘Heavily Polluted’ obtained for various stations.
eastern slope of the Western Ghats in the state of Karnataka. Industrial pollution and mining activities on Tungabhadra riverbanks have declined the water quality of the river. Altogether, the Tungabhadra river pollution has affected 1,000,000 people in the sub-basin as most of the villages use the river water for drinking, bathing, livestock, fishing as well as for irrigating crops (Striver, 2007). Water quality data of Cauvery and Tungabhadra rivers were recorded during January 2007 for water quality assessment (India water portal, 2007))
5. Case study
6. Results and discussion
Water quality data of two major rivers, Cauvery and Tungabhadra are used in the present study for the software application. The Cauvery river is one of the significant sources of water supply for domestic, agricultural, and industrial usage in Tamil Nadu State, India. The Cauvery river basin is around 72,000 km2. Rising in southwestern Karnataka, it flows southeast some 765 km to enter the Bay of Bengal (Raja et al., 2008). The other river considered in the study is Tungabhadra, which is formed by the confluence of two rivers, the Tunga river and the Bhadra river. The river flows down the
OIP values for 10 sampling locations on the river Cauvery and eight sampling locations on the river Tungabhadra are computed using SWQAT and presented in Tables 1 and 2 respectively. The calculated OIP values of the Cauvery river sampling stations range from 0.85 to 7.91 while OIP values of the Tungabhadra sampling station range from 2.08 to 8.97. Water quality at most of the sampling stations of Cauvery river is classified as ‘Excellent’ and ‘Acceptable’. At three sampling stations, namely ‘Sri Ranga2a’, ‘Sri Ranga 2b’, ‘Sri Ranga 3b’, the water quality is classified as
Table 2 Computed OIP at eight sampling stations in Tungabhadra river. S.no.
Longitude
Latitude
Station id
Station name
Year
Month
OIP
Water classification
1 2 3 4 5 6 7 8
75.954033 75.805947 75.816144 75.78125 75.800297 75.791847 75.775847 75.675803
14.447347 14.611572 14.579703 14.480828 14.543503 14.436594 14.450347 14.014431
Tung1 Tung2 Tung3 Tung4 Tung5 Tung6 Tung7 Tung8
Bethur Halla Sarthihalla RB SarthiHalla Harallapura C HPF outfall New bridge C Shiva nagar LB Sulekere Halla
2007 2007 2007 2007 2007 2007 2007 2007
January January January January January January January January
2.50 2.64 2.52 2.08 2.60 3.36 8.97 2.60
Slightly Slightly Slightly Slightly Slightly Slightly Heavily Slightly
Fig. 8. Mapping of computed OIP values on Google Earth.
polluted polluted polluted polluted polluted polluted polluted polluted
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‘Slightly Polluted’ and ‘Polluted’. The water quality of Tungabhadra river at all the sampling stations, except ‘Shiva nagar LB’ is classified as ‘Slightly Polluted’. The water quality at worst affected sampling station ‘Shiva nagar LB’, is classified as ‘Heavily Polluted’, which is a matter of concern. It signals urgent need of control measures for water quality protection at that station. To point out the possible causes of pollution, OIP values are displayed on Google maps. Around ‘Shiva nagar LB’, the landuse is mostly agriculture and rural settlements. The pollution in river is mainly caused by anthropogenic activities near this sampling station. The water quality classification of ‘Shiva nagar LB’ with OIP values and major landuse in surrounding are mapped on Goggle earth, as shown in Fig. 8.
7. Conclusion A computer automated tool ‘SWQAT’ for water quality assessment and management is developed in this paper. It has following key features:
Real time generation of OIP by just entering the values of the
component parameters in the respective database for number of rivers and sampling stations. Assessment of the overall status of water quality based on 14 different water quality parameters. Mapping of sampling station, water quality classification, and OIP values on Google maps.
Acknowledgments The authors are grateful to the Director, Council of Scientific and Industrial Research-National Environmental Engineering Research Institute for providing encouragement, necessary infrastructure to carry out the research, and kind permission to publish the paper. Google Earth plug-in freely available on Google website, is also acknowledged.
Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.cageo.2012.09.007.
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