International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 6, June 2017, pp. 75–84, Article ID: IJCIET_08_06_009 Available online at http://iaeme.com/Home/issue/IJCIET?Volume=8&Issue=6 ISSN Print: 0976-6308 and ISSN Online: 0976-6316

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APPLICATION OF QUAL2K MODEL FOR PREDICTION OF WATER QUALITY IN A SELECTED STRETCH OF

Ashwani S Research Scholar, Manipal Institute of Technology, Manipal University, Karnataka,

Vivek B Scientist, Centre for Water Resources Development and Management (CWRDM), Kunnamangalam, Kozhikode, India

Shilpa Ratnoji Associate Professor, Department of Civil Engineering, Manipal Institute of Technology, Karnataka, India

Jayakumar P Scientist, Centre for Water Resources Development and Management (CWRDM), Kunnamangalam, Kozhikode, India

Jainet P J Scientist, Centre for Water Resources Development and Management (CWRDM), Kunnamangalam, Kozhikode, India ABSTRACT Pamba River acts as a source for drinking, irrigation and is subjected to pollution loads. The application of QUAL2K model for a stretch of 20.63km of the river for prediction of water quality at where a WSS is proposed is studied here. The effect of point source from dairy farm at Cherukole is analysed. Samplings done during the monsoon and post monsoon showed how the water quality changed along the entire stretch of the river. Post monsoon and pilgrimage season data was used for calibration and validation of the model. The results show that the values predicted by the model are in good agreement with the measured values in majority of the stations, and that the water quality at the proposed location of WSS is within standards. Key words: Pamba River, QUAL2K, River pollution, Water Quality Prediction. Cite this Article: Ashwani S, Vivek B, Shilpa Ratnoji, Jayakumar P and Jainet P J. Application of Qual2K Model for Prediction of Water Quality in a Selected Stretch of Pamba River. International Journal of Civil Engineering and Technology, 8(6), 2017, pp. 75–84. http://iaeme.com/Home/issue/IJCIET?Volume=8&Issue=6

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1. INTRODUCTION Water is one of the basic necessities and is facing increasing demand due to population growth, industrialization and urbanization. The quality of the available water is getting depleted day by day due to, discharge of urban and industrial sewage, agricultural runoff etc. India has 14 major and 55 minor riverine systems and is often referred to as “land of rivers”. Human life is dependent on rivers for irrigation, potable water, transportation, generation of electricity etc. In addition, these rivers are worshipped as goddesses by the people of several regions of India. According to studies carried out by National Environmental Engineering Research Institute (NEERI), 70 percent of river water in India is polluted, mainly by wastes of domestic origin. The main consequence of river pollution is depletion of dissolved oxygen (DO) levels. If it falls below 2mg/l the survival of fishes will be affected and is considered to be one of the most important parameter, Other than that carbonaceous biochemical oxygen demand (CBOD), total nitrogen (TN), total phosphorous (TP), temperature and pH also determines quality of water. In order to achieve the designated standards of water quality, appropriate waste management measures have to be adopted so that capacity of the stream is not exceeded in the entire river[1].The complex relationships between waste loads from different sources and the resulting water quality is best described with the help of mathematical models. The selection of a model to predict the behaviour of system is based on the number of parameters it can model and the level of accuracy achieved [2].The prediction of water quality of a stretch of river Pamba has been done using the enhanced version of QUAL2K model which was developed by the United States Environmental Protection Agency (US EPA). The conservative and non-conservative parameters considered in the study are BOD, TN, TP, TSS and Alkalinity. Many water quality models were developed over the past years for various types of water bodies.QUAL2E water quality model developed during the earlier stages had many limitations. To overcome those limitations, QUAL2K was developed by Park and Lee in 2002[3]. The modifications in QUAL2K includes the expansion of computational structures and addition of new constituent interactions like algal BOD, de-nitrification and DO change caused by fixed plants. This model can simulate up to 16 water quality parameters along a river and its tributaries. The first step involves the segmentation of the river into a number of sub reaches. The assumptions required for proper running of the model are (a) the advective transport is based on the mean flow, (b) the water quality indicators are completely mixed over the cross-section and (c) the dispersive transport is correlated with the concentration gradient. Many studies have been carried out using different versions of QUAL2E series which includes QUAL2K and the modernized version known as QUAL2Kw. QUAL2K was applied for water quality modeling in the Bagmati River which concluded that, the model represented the field data almost accurately. Various water quality management options such as pollution loads modification and affixing of weirs for local oxygenation were adopted to enhance DO levels, The results showed that, local oxygenation is effective in raising DO levels[4]. Furthermore QUAL2K was used to determine the water environmental capacity of Hongqi River (China).The results obtained revealed that the simulated results correlated precisely with the measured data [5]. QUAL2K was used to model the polluted segment of Ndaguru River and to evaluate the performance of the model using correlation coefficient (R2) and standard error (SE) [6]. QUAL2K model was used for simulating the various water quality parameters of River Yamuna characterized by increased waste loading from sewers and drains. The results obtained proved the ability of QUAL2K for effective simulation of water quality [7].The impact of wastewater discharge on water quality of Zayandeh-rood River was studied by the application of QUAL2K with DO and BOD as the parameter of concern. The results showed that the model is perfectly reliable in modelling streams when

http://iaeme.com/Home/journal/IJCIET 76 [email protected] Application of Qual2K Model for Prediction of Water Quality in a Selected Stretch of Pamba River complex data are not available [5]. In most of the studies carried out in the application of QUAL2K model, it was observed that the model represents the field data quite well and guarantees the use of QUAL2K for future river water quality options [8].

2. PROBLEM DEFINITION Currently there are 18 Water Supply Schemes (WSS) both ongoing and proposed with Pamba as a source. It also serves as a source of water for irrigation schemes. The water quality is of serious concern especially in the mid-stretches because majority of the WSS are concentrated in this region. Wastewater flow to the river is mainly from towns located on the bank of the river, point sources from hospitals, small scale industries, institutions and pollution caused by the pilgrims during the pilgrimage season. The region generally experiences scarcity for water and Pamba is the only source by which even the basic necessities are met by the local inhabitants. Therefore it becomes a necessity to predict the water quality at ‘Cherukole’ where a new WSS is proposed. Prior studies have shown that outbreaks of water borne diseases occur as a result of severe pollution caused during the pilgrimage season. Absence of efficient sewage collection and treatment facilities at the pilgrim center of Sabarimala adds to the pollution load in the river. It is estimated that daily average sewage generated at Sabarimala is about 3.5 MLD which is discharged without sufficient treatment into the river. Coliform count in the river was reported as 40,000 to 46,000/100ml during pilgrimage seasons. Apart from this, the river is considered to be sacred and various religious rituals were done on the banks of river.

3. MATERIALS AND METHODS

3.1. Study Area The river Pamba has a length of 176 kms, and a catchment area of 2235 sq. km. It originates from Pulachimalai in the Western Ghats. Major portion of the river flows through and Alappuzha districts of and finally discharges to Vembanad Lake which is a Ramsar site. The river has five major tributaries namely Kakki, Arudi, Kakkad, Kal and Pambiyar. Many portions of the river becomes dry during summer seasons. The study area covers a stretch of 12.63 kms of Pamba River. Four sampling sites Viz: (AK), Mukkam (MK), Madammon (MM) and (RN) were selected for the study. Apart from this, at Cherukole a station 8.2 kms from the last sampling station mentioned earlier, where a WSS is proposed is also chosen and selected water quality parameters at the station were predicted using the water quality model QUAL2K. Fig. 1 shows the sampling sites for water quality monitoring chosen along with the river stretch.

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Figure 1 Map showing the study area in Pamba Basin

4. METHODOLOGY

4.1. Reach Segmentation The stretch of the river between Athikkayam to Cherukole has been segmented into 22 number of reaches of unequal lengths. Athikkayam was taken as the upstream boundary condition. The option of internal calculation was selected for the downstream boundary condition.Fig.2 shows the study area with the location of predicted and sampling stations along with the point source. Fig.3 shows the segmentation of the stretch with the location of point sources discharged to the river along with monitoring stations. There is only one point source of pollution, the rest of which has a very low flow value. The steady state data measured on 21/10/2016 in post-monsoon season was used for calibration. A calibration time step of 5.625 minute was set to avoid instability in the model [9]. The model was run until the system parameters were appropriately adjusted and reasonable agreement between model results and field measurements were achieved. In order to test the ability of the calibrated model to predict water quality under different conditions, the model was run using a different set of water quality data taken on 10/01/17 which represented the worst scenario.

Figure 2 Location of predicted and sampling stations with the point source

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Figure 3 Segmentation of the study stretch

4.2. Measurement of Field Data Water quality data of the river and wastewater of the point source which is that of a dairy farm were collected. For calibration post-monsoon season data was provided as input. For application of the model a comparatively shorter stretch of 12.63 kms was considered. Water samples were collected from eleven stations during the monsoon season and fourteen stations in the post-monsoon season. Four points along the river was selected where the water quality was of much concern due to the higher observed population status of the stretch concerned and also by the presence of Water Supply Schemes. Samples were collected in sampling bottles made of plastic each 2 liter and BOD bottles of 300ml capacities. Global Positioning System (GPS) was used to determine the latitude and longitude of the sampling points. Samples from the four locations Athikkayam, Mukkam, Madammon and Ranni were taken simultaneously at an interval of 2 hours. The samples collected were analyzed for pH, temperature, conductivity, Total Suspended Solids(TSS), Total Dissolved Solids(TDS), Salinity Dissolved Oxygen(DO), Biochemical Oxygen Demand (BOD), Nitrates, Alkalinity, Total phosphorous(TP), Inorganic phosphorous, Organic phosphorous, Nitrate nitrogen, Nitrite nitrogen, Ammonia nitrogen, Organic nitrogen, Total nitrogen and Chemical Oxygen Demand (COD) following the standard methods as per APHA [10].

4.3. Hydraulic Characteristics The head water flow of the river was computed from average flow velocity and cross sectional area of the river at corresponding stretches of river. Based on the field observations corresponding Manning’s coefficient values were used. [10].

4.4. Water Quality, Flow Data and Point Source Data The water quality and flow data during post-monsoon season used for calibration of the model is given in Appendix-1, and that used for validation of the model is given in Table 1, the growth of phytoplankton was neglected in the study since its contribution towards pollution abatement is not significant at the present rate of pollution observed in the river. In the present study the water quality model was used for predicting the five water quality parameters BOD, TSS, TN, TP and Alkalinity. The major point source is a dairy farm located at a distance of about 1km from the first station Athikkayam.

4.5. System Parameters The system parameters required by QUAL2K were obtained from a number of studies and literatures including Environment Protection Agency (EPA) guidance document, user manual of QUAL2K and documentation for the enhanced stream water quality model QUAL2E and

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QUAL2E-UNCAS [9] [11]. Internal calculation method was used to calculate re-aeration rate. Exponential model was chosen for oxygen inhibition of CBOD oxidation, nitrification and also for oxygen enhance of de-nitrification and bottom algae respiration.

Table 1 Validation set data Sampling stations Parameters in mg/l RN MM MK AK pH 7.27 7.86 7.30 7.42 EC (µs/cm) 42.20 26.60 81.78 84.45 TDS 23.20 14.60 44.78 46.00 TSS 17.50 22.50 22.50 27.50 Salinity (ppt) 0.03 0.02 0.05 0.06 DO 7.57 8.88 6.87 7.72 BOD 1.34 1.53 1.01 0.87 Alkalinity 13.00 8.50 15.00 15.50 Total phosphorous 0.0684 0.016 0.032 0.045 Inorganic phosphorous 0.0103 0.012 0.021 0.021 Organic Phosphorous 0.0581 0.004 0.010 0.024 Nitrite Nitrogen 0.002 0.006 0.002 0.004 Nitrate Nitrogen 0.19 0.50 1.22 0.99 COD 24.00 38.40 31.60 308.0 Where the stations are RN-Ranni, MM-Madammon, and MK-Mukkam, AK-Athikkayam

5. RESULTS AND DISCUSSION Five water quality parameters are considered in the study are BOD, TN, TP Alkalinity and TSS the output of the model gives the observed and the simulated concentrations of different water quality parameters along the selected stretch. Fig.4 shows the simulation of CBODf in the selected stretch. The black line shows the simulated values of CBOD. The observed data is shown by the squares. From the plot it can be inferred that the observed data is in close proximity with the simulated data for the first two stations which is not the trend seen for the remaining stations. This may be due to the influence of the source of dairy waste pollution in this stretch the dotted red lines indicate the maximum and minimum values.

Figure 4 Simulated CBOD profile along the study stretch

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The variation of TN along the stream is shown in Fig.5, the plot clearly indicates that the simulated and observed values of TN do not show much variation and is in accordance with the monitored values. The calibration of parameters was done using the data collected in the post- monsoon season. The results indicate that the simulated values of TN are in good agreement with the observed values. Here also the peak in the plot occurs at a distance of 4kms from the headwater which signifies the presence of the point source the effect of which has caused a maximum value, which gradually decreases along the length of the stream due to natural processes of degradation.

Figure 5 Simulated TN profile along the study stretch The variation of TP in the selected stretch obtained from the model is shown in Fig.6. The concentration of TP does not vary and remains almost constant for the observed values. This might be due to the low concentrations observed and therefore assimilation has no effect on total phosphorous since the concentration is negligible. The change of TP simulated by the model gradually increases from the first sampling point goes up to a maximum of 2600microgram/l and then declines to a near zero value towards downstream point.

Figure 6 Simulated TP profile along the study stretch The simulated values of TSS shown in Fig.7 goes in close proximity with the observed conditions, with the values being almost constant, the reason for this might be because it is a conservative pollutant and therefore variations along the stream are minimal other than at the point source locations. The simulated values falls within a range of 10 to 30mg/l the curves generated starts from a point gradually decreases showing minimal variations and remains nearly constant towards the lower reaches which is the same trend followed for the simulated data plotted by the model.

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Figure 7 Simulated TSS profile along the study stretch The simulation of alkalinity is shown in Fig.8. The simulated plot here shows very slight variations and remains nearly constant starting from the first point to the downstream point and stays within a range of 12 to 16mg/l, whereas the observed values do not follow the same trend with an exception occurring at the second point where it deviates from the curve with a dip. This might be due to the addition of acidic wastes which neutralizes with the alkaline nature of the waste which is let out.

Figure 8 Simulated Alkalinity profile along the study stretch

Table 2 The concentration of parameters predicted at Cherukole Parameter Name of Station AK MK MD RN CK CBOD(mg/l) 0.99 0.33 0.06 0 0 TN(mg/l) 0.02 4.43 4.75 2.3 0.26 TP(mg/l) 0.06 0.6 0.55 0.23 0.01 TSS(mg/l) 14.88 14.45 13.42 9.32 6.88 Alkalinity(mg/l) 15.09 15.07 15.06 14.39 13.04 AK-Athikkayam, MK-Mukkam, MD-Madammon, RN-Ranni, CK-Cherukole From the Table-2; it can be inferred that all the predicted water quality parameters at Cherukole (CK) are well within the allowable limits of drinking water standards and is safe to be used as a source for drinking.

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6. CONCLUSIONS Applications of river water quality models are extensive in the field of research as well as in the design and assessment of water quality management measures. The QUAL2K model has been used in the study for a 22.63 Km stretch of Pamba River. The impact of a dairy farm waste at the intake point of a water supply scheme at Cherukole is checked in the study. Five parameters given importance in the study are CBOD, TN, TP, TSS and Alkalinity. The resultant concentration of the above water quality parameters at this station is well within the limits prescribed for drinking water. The study also helps in assessing the potential of QUAL2K model for predicting the water quality along the river. This could be implemented as a valuable tool to design management strategies. Even though QUAL2K model is suitable for use in data limited conditions, studies have shown that when more data is used more accurate will be results obtained. It will also help in representing the real time conditions of the river. Future possibilities involves determination of allowable pollution loads for the stretch considered in the study where the point source data are altered until the predicted concentration stay within the limits prescribed.

ACKNOWLEDGEMENTS This research was supported by Kerala State Council for Science Technology and Environment (KCSTE), Government of Kerala. The authors are also grateful to the Executive Director, Centre for Water Resources Development and Management (CWRDM), for extending all the facilities of the institution.

REFERENCES [1] Campolo, M., Andreussi, P. and Soldati, A., 2002. Water quality control in the river Arno. Water Research, 36(10), pp.2673-2680 [2] Chapra, S. (1997) Surface Water Quality Modeling. The McGraw-Hill Companies, Inc. Intl. Edn. Bangkok. [3] Park, S.S. and Lee, Y.S., 2002. A water quality modeling study of the Nakdong River, Korea. Ecological Modelling, 152(1), pp.65-75. [4] Kannel, P.R., Lee, S., Kanel, S.R., Lee, Y.S. and Ahn, K.H., 2007. Application of QUAL2Kw for water quality modeling and dissolved oxygen control in the river Bagmati. Environmental monitoring and assessment, 125(1), pp.201-217. [5] Nakhaei, N. and Shahidi, A.E., 2010. Waste water discharge impact modeling with QUAL2K, case study: the Zayandeh-rood River (Doctoral dissertation, International Environmental Modelling and Software Society). [6] Hadgu, L.T., Nyadawa, M.O., Mwangi, J.K., Kibetu, P.M. and Mehari, B.B., 2014. Application of Water Quality Model QUAL2K to Model the Dispersion of Pollutants in River Ndarugu, Kenya. Computational Water, Energy, and Environmental Engineering, 3(04), p.162. [7] Ma, V., Nambi, I.M. and Suresh Kumar, G., 2011. Application of qual2k for assessing waste loading scenario in river Yamuna [8] Zhang, R., Qian, X., Li, H., Yuan, X. and Ye, R., 2012. Selection of optimal river water quality improvement programs using QUAL2K: A case study of Taihu Lake Basin, China. Science of the Total Environment, 431, pp.278-285. [9] Brown, L.C. and Barnwell, T.O., 1987. The enhanced stream water quality models QUAL2E and QUAL2E- UNCAS: documentation and user manual (p. 189). US

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Environmental Protection Agency. Office of Research and Development. Environmental Research Laboratory. [10] APHA.(2012) “Standard Methods for Examination of Water and Wastewater”, American Public Health Association WWA, Washington, D.C. 2012 [11] Chapra, S.C., Pelletier, G.J. and Tao, H., 2008. Documentation and User’s Manual QUAL2K: A Modeling Framework for Simulating River and Stream Water Quality, Version 2.11. Civil and Environmental Engineering Dept., Tufts University, Medford, MA, p.109. [12] H S Shah and J P Ruparelia, Applicability of Global Water Quality Trading Programs: An Indian Scenario. International Journal of Advanced Research in Engineering and Technology, 7(6), 2016, pp 37–44.

APPENDIX-1 Calibration Data Set, Average value of parameters observed in post-monsoon season

S N u Te S C P BO TD l i a P D p tr O a l m EC h h i O r (m a n D D o S a a p te ( i

s p

te m 0

ty (m e

(m

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(m

a

l

(ppt) g ( te s ) g a tu g m g / ) / te l / r g l / ) l r g ) l s ) /

)

e

/

l

l )

)

6 3 0 0 0 3 5 7 2 0 9 . . . . . S 1 1 . 6 . 5 0 0 1 1 8 0 . 1 . . . 6 3 6 2 1 0 8 5 3 5 3

3 7 2 5 1

4 0 0 3 4 7 0 7 0 6 . . . S 2 3 6 1 0 1 . . . . . 2 0 2 0 2 2 4 9 1 2 . . 8 6 1 3 6 3

3 4 8

8 0 0 0 3 3 7 1 0 b . . . . 3 S 3 0 2 1 0 5 . 8 . d 4 0 . 3 9 2 4 1 . . . 4 l 3 3 2 4 2

9 4 0 5

7 3 0 0 0 3 2 7 1 0 1 . . . . . S 0 8 . 4 . 9 5 0 0 1 3 0 . 4 . . . 6 9 9 2 7 6 7 1 6 4 2

9 9 8 3 3

7 0 0 3 3 7 1 0 0 b . . . S 5 3 1 0 2 . 6 . . 3 d 3 0 0 5 9 2 3 . . .

l 6 9 8 9 2 3

9 3 2

1 7 0 1 0 3 4 7 2 0 0 . . . . S 0 1 . 1 . 5 0 4 1 8 3 0 . 6 . . . 5 3 3 0 4

6 7 7 5 3

9 3 9 5 6

S a 9 0 0 0 3 3 7 2 0 1 m . . . . S 0 9 . 0 . 7 7 0 5 2 5 5 0 p 7 . . . . 9 2 7 0

8 6 2 3 3 6 l

9 4 3 8 i n g

S 9 5 0 1 0 4 7 2 0 2 t . . . . . a S 3 2 . 1 . 2 7 7 0 3 1 9 0 t 1 8 . . . 3 3 2 6 8 i 4 8 9 3 4 o

3 4 7 3 6 n s

7 3 0 2 0 3 4 7 2 0 6 . . . . . S 9 1 0 7 2 0 0 . 0 . . 5 0 9 4 9 3 2 3 7 . . . 9 6 5 9 3

9 3 4 6 4

7 0 2 0 3 3 7 1 0 1 S 4 . . . . 0 8 . 9 . 9 9 0 9 3 1 0 0 . . . . . 8 9 3 0 6 0 8 3 9 9 2 2

9 9 3 7

8 0 1 0 4 7 2 0 S 3 1 . . . . 3 0 . 0 . 3 0 1 1 1 1 0 . . 1 . . 8 6 9 2 1 9 1 1 2 4 3

9 9 4 9

7 3 0 1 0 3 6 2 0 S 9 . . . . . 4 3 4 0 1 1 1 . 2 . 1 . 9 0 5 6 3 6 2 1 5 . . 2 1 5 6 3

3 7 9 4 5

6 3 0 2 0 3 6 3 0 S 6 b . . . . . 3 9 0 6 1 1 2 2 . 1 . d 0 7 3 9 3 5 8 . . . 3 l 5 2 6 4

2 8 2 3 1

7 2 0 2 0 3 3 6 1 0 1 S . . . . . 1 5 . 8 . 1 7 7 0 3 1 1 9 0 . . . . 3 3 2 6 4 4 3 6 5 6 2 2 2 3 3 2 1

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