International Journal of

Water Resources and

Environmental

Engineering

Volume 4 Number 8 August 2012

ISSN-2141-6613

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Prof. T. Murugesan Universiti Teknologi PETRONAS, Malaysia Specialization: Chemical Engineering Malaysia. Dr. Sadek Z Kassab Mechanical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt At Present: Visting Professor,Mechanical Engineering Department,Faculty of Engineering & Technology, Arab Academy for Science,Technology & Maritime Transport, Alexandria, Egypt Specialization: Experimental Fluid Mechanics Egypt. Dr. Minghua Zhou College of Environmental Science and Engineering, Nankai University Specialization: Environmental Engineering (Water Pollution Control Technologies) China. Dr. Hossam Hamdy Elewa National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt. Specialization: Hydrogeological and Hydrological applications of Remote Sensing and GIS Egypt.

Dr. Mohamed Mokhtar Mohamed Abdalla Benha University Specialization: Surface & Catalysis Egypt. Dr. Michael Horsfall Jnr University of Port Harcourt Specialization: (chemistry) chemical speciation and adsorption of heavy metals Nigeria. Engr. Saheeb Ahmed Kayani Department of Mechanical Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, Pakistan.

Editorial Board

Prof. Hyo Choi Dept. of Atmospheric Environmental Sciences College of Natural Sciences Gangneung-Wonju National University Gangneung city, Gangwondo 210-702 Specialization: Numerical forecasting of Rainfall and Flood, Daily hydrological forecasting , Regional & Urban climate modelling -wind, heat, moisture, water Republic of Korea Dr. Adelekan, Babajide A. Department of Agricultural Engineering, College of Engineering and Technology, Olabisi Onabanjo Specialization: Agricultural and Environmental Engineering, Water Resources Engineering, Other Engineering based Water-related fields. Nigeria Dr. Rais Ahmad Department of Applied Chemistry F/O Engineering & Technology Aligarh Muslim University specialization: Environmetal Chemistry India Dr. Venkata Krishna K. Upadhyayula Air Force Research labs, Tyndall AFB, Panama City, FL, USA Specialization: Environmental Nanotechnology, Biomaterials, Pathogen Sensors, Nanomaterials for Water Treatment Country: USA Dr. R. Parthiban Sri Venkateswara College of Engineering Specialization - Environmental Engineering India Dr. Haolin Tang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology Specialization: Hydrogen energy, Fuel cell China

Dr. Ercument Genc Mustafa Kemal University (Aquaculture Department Chairman, Faculty of Fisheries, Department of Aquaculture, Branch of Fish Diseases, Mustafa Kemal University,31200,Iskenderun, Hatay, Turkey) Specialization: Environmental (heavy metal), nutritional and hormonal pathologies, Parasitic infections prevalences and their histopathologies in aquatic animals Turkey Dr. Weizhe An KLH Engineers, Inc., Pittsburgh, PA, USA. Specialization: Stormwater management, urban hydrology, watershed modeling, hydrological engineering, GIS application in water resources engineering. USA Dr. T.M.V. Suryanarayana Water Resources Engineering and Management Institute, Faculty of Tech. and Engg.,The Maharaja Sayajirao University of Baroda, Samiala - 391410, Ta. & Dist.:Baroda. Specialization: Water Resources Engineering & Management, Applications of Soft Computing Techniques India Dr. Hedayat Omidvar National Iranian Gas Company Specialization: Gas Expert Iran Dr. Ta Yeong Wu School of Engineering Monash University Jalan Lagoon Selatan, Bandar Sunway, 46150, Selangor Darul Ehsan Specialization: Biochemical Engineering; Bioprocess Technology; Cleaner Production; Environmental Engineering; Membrane Technology. Malaysia.

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International Journal of Medicine and Medical SciencesInternational Journal of Water Resources and Environmental Engineering

Table of Contents: Volume 4 Number 8 August 2012

ences ARTICLES

Research Articles Development, utilization and statistical evaluation of Hardy Cross pipe network analysis softwares 252 Salihu Lukman, Abubakar Ismail, Abusufyan Ibrahim and Badruddeen Sani Saulawa

Water-supply augmentation options in water-scarce countries 263 Tala Qtaishat Assessment of atmospheric moisture using hygroscopic salts in dry- and-wet climate of Nigeria 270 A. O. Eruola, G. C. Ufoegbune, A. A. Amori and I. O. Ogunyemi

Ecofriendly management of mixed coconut oil cake waste for lipase production by marine Streptomyces indiaensis and utilization as detergent additive 275 B. Sathya Priya, T. Stalin and K. Selvam

International Journal of Water Resources and Environmental Engineering Vol. 4(8), pp. 252-262, August 2012 Available online at http://www.academicjournals.org/IJWREE DOI: 10.5897/IJWREE11.125 ISSN 1991-637X ©2012 Academic Journals

Full Length Research Paper

Development, utilization and statistical evaluation of Hardy Cross pipe network analysis softwares

Salihu Lukman1*, Abubakar Ismail2, Abusufyan Ibrahim2 and Badruddeen Sani Saulawa2

1Civil Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

2Department of Water Resources and Environmental Engineering, Ahmadu Bello University, Zaria-Nigeria.

Accepted 6 April, 2012

Pipe network analysis is an essential aspect in the design of water distribution networks. The Hardy Cross method is one of the methods commonly employed in pipe network analysis. Two softwares are developed using the Hardy Cross method for analysis of flow in pipe networks based on FORTRAN and Visual BASIC (VB) programming languages. HARDY CROSS1 (FORTRAN-based) and PNET Expert (VB-based) are proposed. They differ only in the algorithm for computing the friction factor for head loss computation within pipeline; the former utilizes the Moody’s formulation while the later uses the Barr’s equation. Previous study proved that Moody’s equation has lower total errors than Barr’s equation, thus accounting for the differences in their algorithm implementation and results. Evaluation and analysis of these softwares using statistical methods (total error, coefficient of determination, coefficient of correlation, and reliability) are presented with particular attention to accuracy, validity and good fitness of each of the software. Two flow rate problems are used to demonstrate the applicability of the softwares. The study showed that the overall total errors (for the two Problems) for the flow rates are 1.33 and 0.60, for PNET Expert and HARDY CROSS1 respectively. Coefficients of determination values are 0.999976 for HARDY CROSS1 and 0.999948 PNET Expert. The values of coefficient of correlation were 0.999988 and 0.999974 for HARDY CROSS1 and PNET Expert respectively. It is then concluded that, the HARDY CROSS1 is the best option, because it gave the highest coefficient of determination, lowest total errors, and lowest reliability value. Its better performance is related to the algorithm utilized for head loss computation. Key words: Hardy Cross, pipe network analysis, flow rate, statistical evaluation, head loss, friction factor.

INTRODUCTION Pipe network analysis, as described by Featherstone and Nalluri (1997) involves the determination of pipe flow rates and pressure heads, which satisfy continuity and energy conservation equations. Water distribution net-work analysis provides the basis for the design of new systems and the extension of existing ones. Design criteria such as specified minimum flow rates and pressure distributions across a network are affected by the arrangement and sizes of the pipes and distribution of the outflows. Since a change in diameter in one pipe

*Corresponding author. E-mail: salihulukman@yahoo.com. Tel: +966501968813.

length will affect the flow and pressure distribution everywhere, network design is not an explicit process.

Common challenges encountered in the distribution system are those due to pressure and discharge in certain areas of a city where the elevation is relatively high or the place is far away from the water treatment plant. Webber (1971) posited that the network of distribution mains is nearly always the most expensive item of equipment in a water undertaking. Also, the cost of its upkeep generally represents a large proportion of the annual maintenance budget. It is therefore incumbent upon the water engineer to devote some considerable care to the design and simulation of the most efficient distribution system and this entails an accurate prediction of the flows and pressures in the various pipe

components. Distribution networks should be designed with sole aim of ensuring safe working pressures without compromising the supply efficiency, resilience of system components and avoiding unnecessary cost implications.

According to Chadwick and Morfett (1998), all these limiting values of flows and pressure heads can only be achieved through proper network analysis. Meeting this challenge requires a comprehensive network modelling capability that includes the rapid and accurate calculation of various designs and operating parameters.

The most commonly used computational techniques for pipe network are: Linear theory, Hardy Cross and Newton-Raphson methods (Oke, 2007; Jeppson, 1976; Featherstone and Nalluri, 1997; Wood and Rayes, 1981; Wood and Charles, 1972). Other methods of pipe network analysis include equivalent diameter method, electric analyzer, gradient method and analysis using optimization technique (Brkic, 2011; Brkic, 2009; Ormsbee, 2006; Morley et al., 2001). A brief review of the theoretical framework of each of these methods along with critique of the relative advantages and/or limitations of each method was presented by Ormsbee (2006), Basha and Kassab, (1996), Waheed (1992) and Lee (1983).

Details of the principles of the Hardy Cross method can be found in standard texts and articles (Jeppson, 1976; Brkic, 2009; Ormsbee, 2006; Cross, 1936; Brkic, 2011; Babatola et al., 2008; Featherstone and Nalluri, 1997). There exists a lot of softwares developed using the linear theory and Newton-Raphson techniques, but rarely do one finds Hardy Cross based software. This may be attributed to the easier convergence of linear theory and Newton-Raphson techniques over the Hardy Cross method which could sometimes take long period of time to converge to a solution and in some cases, may fail to converge at all for complex distribution networks. The rate of convergence of the solution depends on the assumed flow initialization (Ormsbee, 2006; Basha and Kassab, 1996; Gay and Middleton, 1971). Jeppson (1976) developed a FORTRAN program for the analysis of pipe networks using the Hardy Cross method. This program has enjoyed so much reputation among researchers. But when Jeppson’s program was studied closely, the following drawbacks were eminent and manifested.

i. There was no provision for calculating the Reynolds number and friction factor for each loop continuously as the flows within each pipe is corrected. ii. The program calculates the Reynolds number and the friction factor of each pipe using the initial guess of the flow rate and maintains the results throughout the execution of the program. There was an omission of a

negative sign in finding the error in the flows that is, Q ,

which is given by

(1)

Lukman et al. 253 where h is the head loss in a pipe based in the estimated flow Q. iii. The assignment statement for the correction of the assumed flows is wrong.

Consequent upon the aforementioned problems inherent in Jeppson’s program, there was the need to develop another program using FORTRAN language that will eradicate the above drawbacks. The Hardy Cross method is widely used in comparison with the other methods, because there is less number of loops than nodes in a water distribution system, and consequently, lesser computations are required to balance the system (Waheed, 1992). Developers of most new pipe network analysis algorithms, use Hardy Cross method as a benchmark for validation of their new algorithms (Brkic, 2011; Sarbu, 2011; Brkic, 2009; Gay and Middleton, 1971). These and many more, give the motivation for using Hardy Cross method in this study. The objectives of this study are to develop softwares using FORTRAN and Visual BASIC (VB) programming languages based on the Hardy Cross method of analysis of pipe networks that can handle small and large distribution systems to solve flow rates and head losses in pipes; and to use the developed softwares to solve two problems of distribution networks and compare the results obtained based on statistical data evaluation criteria. These problems consist of two and three loops pipe network as shown in Figures 1 and 2, respectively. MATERIALS AND METHODS

The head balance criterion is that the algebraic sum of the head losses around any closed loop is zero; the sign convention that clockwise flows (and the associated head losses) are positive is adopted. Details of the steps for the Hardy Cross method may be found in standard texts and articles (Brkic, 2011; Brkic, 2009; El-Bahrawy, 1997; Featherstone and Nalluri, 1997).

Flow rate problems employed To evaluate the softwares developed in this study, two-loop (Figure 1) and three-loop (Figure 2) pipe networks are adopted from the literature (Featherstone and Nalluri, 1997), and pipe and loop numbers (IDs) were assigned. Outflow at each node and assumed flow directions were drawn as illustrated in Figs. 1 and 2. Analysis of the pipe networks was carried out manually and the results serve as the expected values of each fitting procedure. Analysis results (final discharges and head losses) were then used for statistical evaluations using total error, coefficient of correlation, coefficient of determination and reliability tests.

Description of the softwares

Visual FORTRAN 90 compiler and Visual BASIC 6.0 are used for creating, running and compiling HARDY CROSS1 and PNET Expert respectively. Codes for HARDY CROSS1 are developed according to an earlier study by Lukman, 2004. HARDY CROSS1 has a default input data file name as INPUT PROJECT.SI and an

254 Int. J. Water Res. Environ. Eng.

Loop number

Pipe number

Flow direction

200

60 50

40 50

6

1

2

5

4

3 2

1

200 mm

150 mm

600 m

150 mm

20

0 m

10

0 m

m

20

0 m

10

0 m

m

600m 500 m

200 mm

600 m

Figure 1. Pipe characteristics: lengths (m), diameters (mm), nodal demands (l/s) and assumed flow directions for Problem 1.

30 300m

150mm

7

1

2

3

Loop number

Pipe number

Flow direction

150 mm 150 mm

300 m

150 mm

15

0 m

15

0 m

m

15

0 m

20

0 m

m

15

0 m

15

0 m

m

300 m

15

0 m

15

0 m

m

400 m

20

0 m

m

30

0 m

400 m

200 mm

10

9

8

6

5

4

3

2 1

230

50 20

40 30

20

40

Figure 2. Pipe lengths (m), diameters (mm), nodal demands (l/s) and assumed flow directions (Problem 2).

output data file name as OUTPUT PROJECT.SI. HARDY CROSS1 displays only the final values of flows and head losses in pipes. The input data file is to be created by the user, while the output data file is to be created by the compiler when it is running. The two file names can be changed as the user wishes.

Pseudocode for HARDY CROSS1 is presented as follows:

i. Create input and output data and result files respectively. ii. Enter the number of pipes, number of loops, maximum number of iterations, fluid kinematic viscosity, stopping criterion error term,

twice the value of acceleration due to gravity. iii. Enter diameters, lengths, roughness coefficient and assumed flow for each pipe in the network. iv. Compute relative roughness for all pipes v. Enter loop ID no (identification number, starting with the first loop). vi. Print loop ID (starting with the first loop). vii. Enter the number of pipes in that loop together with the pipe IDs positive or negative, depending on the flow direction. viii. Take the absolute value of the assumed flows.

Lukman et al. 255

Figure 3. PNET expert home page.

ix. Compute Reynolds number, friction factor (using Moody’s equation), coefficient of head loss and head loss. x. Compute the numerator of equation (1). xi. Compute the denominator of equation (1). xii. Repeat steps (e)-(k) for the next pipe. xiii. Compute equation (1). xiv. Correct the flows xv. Repeat steps (f)-(n) for all the loops in the pipe network. xvi. Has the target stopping criterion been met? xvii. If no, then repeat steps (f)-(n), that is, next iteration. xviii. If yes, then print the final flow rates in pipes. xix. Using the final flow rates, compute and print the head losses for all pipes.

xx. Stop. Pipe Network Expert (PNET Expert) Version 1.0 is windows-based application software that is designed purposely for pipe network analysis (Figure 3). The software is developed using VB language by one of the authors according to Abusufyan (2009). Figure 4 is used to input data relating to the total number of pipes to be used in the network, fluid type, viscosity and number of loops. Figure 5 shows where all pipe details should be entered, edited and saved. After data have been processed, users have the options of viewing individual loop results, final results or printing or saving the results in notepad application. The Pseudocode for PNET Expert is similar to that of HARDY CROSS1 except that steps (d) and (f) are

256 Int. J. Water Res. Environ. Eng.

Figure 4. General input screen.

Figure 5. Loop inputs screen.

eliminated. In addition, Barr’s equation was used in step (i) as against Moody’s equation. PNET Expert and HARDY CROSS1 are applied to solve Problems 1 and 2 whose diagrams are contained in Figures 1 and 2. A sample loop input screen is shown in Figure 5. A simplified flowchart for PNET Expert and HARDY CROSS1 is shown in Figure 6. Where the two softwares will differ is basically in the method for computing the friction factor.

RESULTS AND DISCUSSION Using HARDY CROSS1, PNET Expert and manual calculation to analyse the networks in Figure 1 and 2, the results in Tables 1 and 2 are obtained for Problems 1 and 2 respectively. Problem 1 consists of 2 loops, 6 pipes and 5 nodes, while Problem 2 is made up of 3 loops, 10 pipes and 8 nodes. From the analysis results in Tables 1 and 2, HARDY CROSS1 and PNET Expert are able to reproduce the expected flow rate and headloss values within certain range of accuracy which is to be thoroughly evaluated using statistical evaluation tools. Statistical evaluation Total error Total error, TE, represents the overall or total error that may occur in a test result due to both the imprecision (random error) and inaccuracy (systematic error) of the measurement procedure (Oke, 2007). The total error, which is the sum of the squares of the errors between the obtained values and the predicted values, can be interpreted as a measure of variation in the values predicted unexplained by the values in obtained data (Oke and Akindahunsi, 2005). The lower the value of total error, the higher is the accuracy, validity, and good fitness of the software (Oke, 2007; Douglas et al., 2010). Total error (Err

2) can be computed using Equation 2:

(2) Observed values are those obtained from the softwares whereas calculated values are computed manually from the benchmark problems. Table 3 shows the computation of total error for each of the software. The total errors are 0.6024 and 1.3325 for HARDY CROSS1 and PNET Expert respectively. These results indicate that HARDY CROSS1 has the least error, followed by PNET Expert. The larger error in the PNET Expert software may be attributed to the fact that the two softwares were developed using different friction factor formulae. PNET Expert uses Barr’s equation while HARDY CROSS1 uses Moody’s equation. Babatola et al. (2008) proved that Moody’s equation has lower total errors than Barr’s equation. The former should be preferred over the later

Lukman et al. 257 because of its explicit case and its availability as a chart. Coefficient of determination and correlation coefficient

The coefficient of determination, r2, is useful because it

gives the proportion of the variance (fluctuation) of one variable that is predictable from the other variable. It is a measure that allows us to determine how certain one can be in making predictions from a certain model/graph. It represents the percent of the data that is the closest to the line of best fit. The coefficient of determination is such that 0 < r

2 < 1, and denotes the strength of the linear

association between two variables. It is mathematically expressed as the square of the correlation coefficient (Montgomery et al., 2010).

(3)

Higher values of r2 indicate higher accuracy, validity and

good fitness of the software. Like total error, r2 values are

0.9999765 and 0.9999480 for HARDY CROSS1 and PNET Expert respectively (Table 4). These results indicate that HARDY CROSS1 has a higher coefficient of determination than PNET Expert. The fact that the r

2

values are almost equal to 1 indicates how well the softwares can predict the pipe flows (Table 4). The coefficient of correlation (r) can be interpreted as the proportion of expected data variation that can be explained by the obtained data. Higher values of r indicate higher accuracy, validity and good fitness of the software. r can be expressed as follows:

(4)

A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally described as weak. These values can vary based upon the "type" of data being examined. A study utilizing scientific data may require a stronger correlation than a study using social science data (Montgomery et al., 2010). Like total error, r values are 0.9999882and 0.9999740 for HARDY CROSS1 and PNET Expert respectively (Table 4). These results indicate that HARDY CROSS1 has a higher correlation coefficient than PNET Expert. The fact that the r values are almost equal to 1 indicates how well the softwares can predict the pipe flows. Reliability Reliability of any software depends on its accuracy and

258 Int. J. Water Res. Environ. Eng.

Compute Reynolds No., friction factor, head loss, for each pipe in the loop

Start

Data input for the

entire network:

No. of pipes, loops,

constants, etc

Enter loop ID, no. of

pipes in the loop and

pipe IDs (-ve or +ve)

Last loop

?

Stopping

criterion met?

Print final

flow rates

Print head loss

Stop

Initial flow guess for all pipes

Compute relative roughness for all pipes in the network

Compute flow correction for the loop

Correct flows for pipes within the loop

Continue simulation starting

from the 1st loop

Compute head loss for all pipes in the network

No

Yes

Yes

No

Start

Data input for the entire network:

No. of pipes, loops, constants, etc

Enter loop ID, no. of pipes in the

loop and pipe IDs (-ve or +ve)

Last loop?

Stopping criterion met?

Print final flow rates

Print number of

iterations used

Figure 6. Flowchart for HARDY CROSS1 and PNET Expert.

Lukman et al. 259

Table 1. Summary of flows and head losses for Problem 1.

Pipe ID

HARDY CROSS1 PNET Expert Expected Result

Observed

flow (l/s)

Observed

headloss (m)

Observed

flow (l/s)

Observed headloss (m)

flow (l/s) headloss

(m)

1 104.74 20.84 104.57 18.30 104.80 20.84

2 0.63 0.02 0.75 0.01 0.65 0.00

3 95.25 20.80 95.43 18.28 95.20 20.84

4 45.06 21.54 45.33 18.425 45.40 21.22

5 4.54 0.76 4.67 0.54 4.42 0.74

6 44.94 20.45 44.67 17.89 44.42 21.11

Table 2. Summary of flows and head losses for Problem 2.

Pipe ID

HARDY CROSS1 PNET expert Expected result

Observed

flow (l/s)

Observed

headloss (m)

Observed

flow (l/s)

Observed

headloss (m)

flow (l/s)

headloss

(m)

1 136.33 31.05 136.02 28.54 136.50 31.05

2 56.52 3.52 56.22 3.52 56.50 3.52

3 9.81 0.03 9.80 0.01 10.00 0.03

4 2.45 5.30 2.06 4.61 2.50 5.30

5 53.67 0.62 53.99 0.51 53.50 0.62

6 93.67 4.12 93.99 3.03 93.50 4.12

7 29.81 2.10 29.80 1.83 30.00 2.10

8 24.07 0.33 24.15 0.25 24.00 0.33

9 13.88 22.00 13.95 20.16 14.00 22.00

10 26.12 11.19 26.05 10.22 26.00 11.19

Table 3. Values of total error for Problems 1 and 2.

HARDY CROSS1 PNET Expert

Yobs (l/s) Ycal (l/s) (Yobs - Ycal)2 Yobs (l/s) Ycal (l/s) (Yobs - Ycal)

2

104.74 104.8 0.0036 104.57 104.80 0.0511

0.63 0.65 0.0004 0.75 0.65 0.0104

95.25 95.20 0.0025 95.43 95.20 0.0511

45.06 45.40 0.1156 45.33 45.40 0.0055

4.54 4.42 0.0144 4.67 4.42 0.0645

44.94 44.42 0.2704 44.67 44.42 0.0645

136.33 136.50 0.0289 136.02 136.50 0.2343

56.52 56.50 0.0004 56.22 56.50 0.0795

9.81 10.00 0.0361 9.80 10.00 0.0412

2.45 2.50 0.0025 2.06 2.50 0.1901

53.67 53.50 0.0289 53.99 53.50 0.2352

93.67 93.50 0.0289 93.99 93.50 0.2352

29.81 30.00 0.0361 29.80 30.00 0.0412

24.07 24.00 0.0049 24.15 24.00 0.0237

13.88 14.00 0.0144 13.95 14.00 0.0025

26.12 26.00 0.0144 26.05 26.00 0.0025

Total error 0.6024 Total error 1.3325

260 Int. J. Water Res. Environ. Eng.

Table 4. Computation of coefficient of determination and correlation coefficient for Problems 1 and 2.

HARDY CROSS1 PNET Expert

Yobs (l/s) Ycal (l/s) (Yobs-Ycal)2

Yobs (l/s) Ycal (l/s) (Yobs-Ycal)2

104.74 104.80 0.0036 3410.93 104.57 104.80 0.0510 3391.56

0.63 0.65 0.0004 2089.12 0.75 0.65 0.0100 2077.98

95.25 95.20 0.0025 2392.50 95.43 95.20 0.0510 2409.74

45.06 45.40 0.1156 1.63 45.33 45.40 0.0050 1.02

4.54 4.42 0.0144 1746.98 4.67 4.42 0.0650 1735.80

44.94 44.42 0.2704 1.951 44.67 44.42 0.0650 2.77

136.33 136.50 0.0289 8098.76 136.02 136.50 0.2340 8042.35

56.52 56.50 0.0004 103.70 56.22 56.50 0.0800 97.64

9.81 10.00 0.0361 1334.21 9.80 10.00 0.0410 1335.16

2.45 2.50 0.0025 1926.06 2.06 2.50 0.1900 1960.09

53.67 53.50 0.0289 53.78 53.99 53.50 0.2350 58.49

93.67 93.50 0.0289 2240.43 93.99 93.50 0.2350 2270.34

29.81 30.00 0.0361 273.14 29.80 30.00 0.0410 273.57

24.07 24.00 0.0049 495.81 24.15 24.00 0.0240 492.08

13.88 14.00 0.0144 1053.45 13.95 14.00 0.0030 1048.91

26.12 26.00 0.0144 408.72 26.05 26.00 0.0030 411.56

46.34

46.34

r2

0.999976497 r2

0.999947967

r 0.999988249 r 0.999973983

Table 5. Reliability values of the softwares when applied to Problem 1.

HARDY CROSS1 PNET Expert

Yobs (l/s) Ycal (l/s) (ln Ycal - ln Yobs)*100 Yobs (l/s) Ycal (l/s) (ln Ycal - ln Yobs)*100

104.74 104.80 0.0573 104.574 104.80 0.2159

0.63 0.65 3.1253 0.752 0.65 -14.5764

95.25 95.20 -0.0525 95.426 95.20 -0.2371

45.06 45.40 0.7517 45.326 45.40 0.1631

4.54 4.42 -2.6787 4.674 4.42 -5.5876

44.94 44.42 -1.1638 44.674 44.42 -0.5702

RD 0.0392 RD -20.5922

validity. The statistical approach developed to address reliability of any method is the testing of hypothesis that there is no difference between the expected results and the softwares (Oke, 2007). Sartory (2005) describes statistical relative difference between results obtained with the softwares and the expected results as follows:

(5) A lower RD value indicates that the software is reliable. Reliability values for the first and second Problems are 0.039, -20.592 and 4.273, 20.856 for HARDY CROSS1 and PNET Expert respectively (Tables 5 and 6). These results indicate that HARDY CROSS1 has a higher

reliability than PNET Expert. Low reliability of the PNET Expert software may be attributed to the fact that the two softwares were developed using different friction factor formulae. PNET Expert uses Barr’s equation (which has lower reliability than Moody’s equation) while HARDY CROSS1 uses Moody’s equation.

Conclusion

The following conclusions can be drawn based on the study, that: 1. HARDY CROSS1 and PNET Expert softwares presented herein are accurate, valid, reliable and have

Lukman et al. 261

Table 6. Reliability values of the softwares when applied to Problem 2.

HARDY CROSS1 PNET expert

Yobs (l/s) Ycal (l/s) (ln Ycal - ln Yobs)*100 Yobs (l/s) Ycal (l/s) (ln Ycal - ln Yobs)*100

136.33 136.50 0.1246 136.02 136.50 0.3552

56.52 56.50 -0.0354 56.22 56.50 0.5004

9.81 10.00 1.9183 9.80 10.00 2.0509

2.45 2.50 2.0203 2.06 2.50 19.1645

53.67 53.50 -0.3173 53.99 53.50 -0.9025

93.67 93.50 -0.1817 93.99 93.50 -0.5174

29.81 30.00 0.6353 29.797 30.00 0.6790

24.07 24.00 -0.2912 24.154 24.00 -0.6396

13.88 14.00 0.8608 13.95 14.00 0.3578

26.12 26.00 -0.4605 26.05 26.00 -0.1921

RD 4.2733 RD 20.8561

good fitness for their application in pipe network simulation. 2. With particular reference to accuracy, HARDY CROSS1 could be a better software of choice than PNET Expert. 3. PNET Expert software can be used as substitute to HARDY CROSS1 when the need arises. This is because of its user-friendliness, use of graphical user interface (GUI) and users do not require knowledge of Visual BASIC prior to its use. 4. Differences in results from HARDY CROSS1 and PNET Expert are attributed to the differences in the algorithm implementation for friction factor computation. Nomenclature: Aq: Expected discharges (l/s) Bq: obtained discharges (l/s) RD: reliability r

2:

coefficient of determination r: coefficient of correlation

obsiY : obtained experimental values

caliY : expected values of each fitting procedure 2Err : total error

n: number of data points

caliY : average of expected values Q: assumed flow

Q: flow correction K: coefficient of head loss ACKNOWLEDEGEMENT The authors wish to acknowledge the support given by Civil Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for this

study. REFERENCES Abusufyan I (2009). Software Development for Pipe Network

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International Journal of Water Resources and Environmental Engineering Vol. 4(8), pp. 263-269, August 2012 Available online at http://www.academicjournals.org/IJWREE DOI: 10.5897/IJWREE12.024 ISSN 1991-637X ©2012 Academic Journals

Full Length Research Paper

Water-supply augmentation options in water-scarce countries

Tala Qtaishat

Agricultural Economics and Agribusiness Department, Agriculture College, The University of Jordan, Amman, Jordan. E-mail: t.Qtaishat@ju.edu.jo or talaqtaishat@yahoo.com.

Accepted 13 July, 2012

Growing demand for water resources due to increased population and improved living standards, have prompted public agencies and others in the Fertile Crescent (FC), a semi-arid region, to seek better ways to manage water. This paper discusses water supply-augmentation options (e.g., desalination, water importing, wastewater treatment, recycling, water conservation, reducing evapotranspiration and storage) to alleviate water scarcity generally, and in the FC countries in particular. Introduction of conventional and non-conventional measures to augment water supplies as well as narrowing of the gap between water supply and demand in water-scarce countries and regions was done. A conceptual supply augmentation method operationalized with secondary data suggests water supply augmentation is feasible in this region. Marginal cost (MC) principle was used to demonstrate optimal water supply by step-wise adoption of the supply-augmentation methods with the lowest MC. Three supply-augmentation options were most promising in the FC: (1) desalination of brackish water, (2) reducing evapotranspiration, and (3) water conservation. These three options can potentially add as much as 630 million cubic meter (MCM) over the next two decades, helping to solve the water-scarcity problem while considering sustainability and water quality for present and future uses. Key words: Water scarcity, Fertile Crescent (FC), supply-augmentation options, marginal cost (MC).

INTRODUCTION Water is a vital component for all life forms and necessary for human and economic development. Water is essential to food security. It is required for a quality environment for humans and other life forms. However, water scarcity is a critical resource constraint for economic growth and development of the Fertile Crescent (FC),

1the Arabian Peninsula

2 and Egypt

(Haddadin, 2002). Water resources in the FC consist of ground (renewable and non-renewable) and surface water, with treated wastewater used increasingly for irrigation. Development of water resources has been hindered by regional political considerations and the high costs of water transportation infrastructure (Taha, 2006).

Water shortages constrain economic development, negatively impact urban industries and adversely affect the environment (United Nations, 2003). Further, many

1Including Iraq, Syria, Lebanon, Jordan, Palestine and Israel. 2Including the Republic of Yemen and the Gulf Cooperation Council members

which are the State of Kuwait, the Kingdom of Bahrain, the State of Qatar, the

United Arab Emirates, the Sultanate of Oman and the Kingdom of Saudi Arabia.

FC countries lack an integrated and comprehensive approach to address water shortages. Securing additional water can ameliorate water scarcity, ceteris paribus. However, reducing evapotranspiration, capturing rainwater with micro- and macro-storage dams (building dams), desalination of seawater and brackish groundwater, wastewater reuse and importation of water from neighboring countries via virtual water can all augment water supply. Conservation, or using current water supplies more efficiently, can also augment water supply. However, in the real world, sustainable supply-augmentation options must adhere to economic principles by considering costs, benefits, and constraints.

Water situation in the FC countries

More than half of the countries in the FC are ranked in the world‘s lowest 10% of annual, per capita total renewable water resource availability (Table 1). Of the FC countries, Iraq has the greatest supply of total annual

264 Int. J. Water Res. Environ. Eng. Table 1. Water availability in the FC.

Country Ranking* Total renewable water resources (MCM/cap/year)

Total internal renewable water

resources

(MCM/year)

Surface water:

Produced

internally

(MCM/year)

Groundwater:

Produced

internally

(MCM/year)

Palestinian territories 179 52.0 500 0.00 500

Jordan 170 179.0 680 400 500

Israel 167 276.0 750 250 500

Lebanon 149 1,261.0 128,500 97,300 49,300

Syria 141 1,622.0 7,000 4,800 4,200

Iraq 108 3,287.0 35,200 34,000 1,200

*Rank of FC countries among 182 countries according to their annual, per capita total renewable water resource availability from the least (182) to the most (1). Source: Adapted from World Water Development Report (WWDR, 2003).

renewable water resources per capita at 3,287 MCM/cap/year (Table 1). The Palestinian territories have the least total annual renewable water resources per capita in the FC with only 52 MCM/cap/year. Jordan and Israel also have fewer than a million MCM/cap/year in renewable water resources. Lebanon and Syria have 1.2 and 1.6 MCM/cap/year, respectively. Lebanon has the greatest internal renewable water resources in the region with more than 1.2 MCM/year. The surface water and groundwater together are about 146,600 MCM/year which means that the water situation in Lebanon is better than the other FC courtiers. The Palestinian METHODOLOGY Three overall categories of options for increasing water supply in the FC include (1) reallocating water to its highest and best use, (2) finding actual substitutes for water, and (3) augmenting water supply. Herein we consider the water supply augmentation options, including reducing evapotranspiration, capturing rainwater with micro and macro dams, conserving water, desalinating seawater and brackish water, treating wastewater, and importing water from neighboring countries. However, all these options must be assessed based on both efficiency and geopolitical feasibility. The marginal cost (MC) principle is used to select the order in which supply-augmentation options could be implemented efficiently. The marginal cost (MC) principle helps to achieve economically optimal water supply by adopting the supply-augmentation method with the lowest MC first. In brief, the option where the next unit of water can be obtained at the least cost is the most efficient.

Conceptual model

To illustrate the conceptual model we assume there are two supply-augmentation options (A and B). Factors that affect option A and B are held constant. Also, the model assumes options A and B are already in operation at some level, so there are no additional start-up costs. A practical issue with water supply is the ability to either scale-up existing supply options or to start from nothing with a new water supply option which usually includes heavy initial costs. Thus, hereafter we have assumed scaling-up an existing option, rather than investing in a start-up.

Both options A and B provide an optimal quantity of water (q1) (Figure 1). The switch point is reached by allocating water to the least cost use at the margin, until the costs equal the marginal benefits of an additional unit of water. At the switch point, efficient choice is to switch to the supply-augmentation option with a lower MC per unit.

We used water cost and marginal cost data for supply-augmentation options from the literature. The cost data used to operationalize the conceptual supply augmentation model came from several studies (FAO, 2009; Al-Mutaz, 2005; El-Sadek, 2010; World Bank, 2007; United Nations, 2003). Secondary water data at the country or regional scale are generally not sufficiently robust to employ precise optimization methods. Reducing evapotranspiration Water that evaporates from soil, water, or artificial surfaces is removed by plants through transpiration is a bio-physical phenomenon called evapotranspiration (ET). Reducing ET could help alleviate water-poverty. ET is influenced by several factors including rainfall patterns, air and soil temperature, wind speed, soil characteristics and type of vegetative cover. About 85% of total surface water initially available for use in the FC is lost to ET (Shannag and Al-Adwan, 2000), illustrating a potential place to ‗save‘ water. Annual evaporation volumes at high temperatures and under direct exposure to the sun in the Middle East may reach 1.5 to 2.5 m3/m2 of water surface (Varma, 1996). In Israel, 70 to 80% of average annual precipitation evaporates (Shevah, 2008).

ET can be feasibly reduced in the FC on a small, localized scale. Building dams and reservoirs in deep valleys with a correspondingly smaller surface area to overall volume ratio can reduce water loss to ET. Mechanical wind fences and parasol-type floats could also be used to prevent water loss due to evaporation (Gökbulak and Özhan, 2006; Segal and Burstein, 2010). Segal and Burstein (2010) concluded that parasol-type floats reduced water loss in proportion to the protected surface area.

Subsurface storage has also been shown to reduce ET and lower the risk of surface water contamination (Hut et al., 2008).

Monolayers have been used to reduce water evaporation from large dams when the conditions are favorable. Monolayers are thin chemical films as little as one molecule thick which produce a diffusion barrier on the water surface reducing evaporation (Barnes, 2008). Barnes (2008) used findings from small projects to estimate monolayer costs. The potential volume of water gain was about 15.18 MCM. The average total cost (ATC) was estimated to be $1.92/m3, average variable cost (AVC) was $0.82/ m3 and the

Qtaishat 265

Figure 1. Conceptual Water-Supply Augmentation Model for Two Options (A and B).

marginal cost was $0.83 /m3 (McJannet et al., 2008). Davenport et al. (1976) estimated the cost of reducing ET was about $1.3/m3 while Gay (1988) estimated the cost to reduce ET would be $0.8/m3.

The main constraint to reducing ET is technology. Additional research is needed to develop technologies and reduce the cost of ET reduction techniques. Reducing ET could potentially conserve as much as 50 MCM by year 2030 in the FC. Capturing runoff by building dams Reservoir storage is a time-tested supply augmentation tool (Tullos et al., 2009). Reservoirs behind dams collect water in one time period for use in a future time period and function as storage pools to provide water during periods of water shortages. Dams (water storage) in the FC region provide water for agriculture, commercial, municipal, hydropower and recreation uses (World Commission on Dams [WCD], 2000). However, dams and dam construction have biophysical, socioeconomic, geopolitical and environmental impacts (Adams and Hughes, 1986). Dams can negatively affect ecosystems, hydrology and water quality and disrupt existing cultural and economic institutions (Poff and Hart, 2002).

Applying the MC principle to reservoirs would involve increasing storage at existing sites through operational changes, rather than developing new sites.

Sub-surface groundwater dams also capture rainfall and store it for livestock, irrigation and domestic use (Hut et al., 2008). A subsurface dam stores groundwater with a ―cut-off wall‖ across a groundwater channel. The sub-surface technology is preferred for numerous reasons including increasing the capacity of traditional wells, simplicity and less expensive to construct, replicable and easily maintained by the community, and less contamination of water. For example, sand dams have made a substantial impact on more than 100,000 people in Kenya. Sand dams are a relatively low cost measure that improves individuals‘ access to water (Lasage et al., 2008). A sand dam is a subsurface dam built across a seasonal river. Sand and gravel are accumulated upstream of the dam, which is raised progressively before each rainy season until it reaches an appropriate height to provide water storage.

The Al-Wehdah dam project on the Yarmouk River, the border between Syria and Jordan, is an FC example of reservoir storage. The project was funded by the government of Jordan, the Arab Fund for Economic and Social Development, and the Abu Dhabi Fund for Development in 2003. Dam capacity was about 1,144,000 m3. Construction costs were $135 million (Molle et al., 2008) and

266 Int. J. Water Res. Environ. Eng. operation and maintenance (O&M) costs were about $7.03 million/year. The operation and maintenance (O&M) costs include labor, administration, clean-up operations, electricity, rehabilitation and resettlement, environmental and forest aspects, the catchment area treatment and drainage system cost, and others. Average total cost (ATC) was $4.72 /m3, average variable costs (AVC) were $0.25/m3 and the marginal cost was $1.87 /m3 (Molle et al., 2008).

The feasible potential quantity of water that can be gained annually from building surface and subsurface dams in the FC is 280 MCM by 2030 (FAO, 2009). The lack of research and development about the importance of dams as well as the high costs of construction and operation of dams are the main constraints to the dam-building option. However, micro and groundwater storage dams may be readily adopted in the next two decades. Desalination Among the options for water-supply augmentation is desalination of saline groundwater, brackish drainage water and seawater. Desalination in the FC is receiving considerable attention from scientists, resource planners, policy-makers and other stakeholders. Desalination removes dissolved minerals from seawater and brackish water. Desalination is not a new technology; in fact studies from centuries ago discussed distillation of drinking water from seawater by Mediterranean and Near East civilizations (Abu Zeid, 2000). Water desalination in the FC can be a technically and economically efficient option to produce additional quality water (Ammary, 2007). Desalination of Red Sea water by reverse osmosis (RO) and brackish groundwater desalination by nano-filtration could be technically viable and economically feasible (Afonso et al., 2004). RO is a relatively low MC option, reducing the content of organic and inorganic matter in water at a relatively low marginal cost ($0.36/m3) (Afonso et al., 2004).

The existing Ashkelon desalination facility in Israel is expected to operate for 25 years, from 2002 to 2027. Facility production is expected to rise to 750 MCM by 2020 (de la Torre, 2008). The total cost of desalinated water from the Ashkelon plant, consisting of contracted total water price and the government‘s own project-related costs, is $0.53 /m3. About 42% of the water cost covers energy costs, variable O&M costs, membranes and chemicals costs. The remaining 58% covers capital expenditure and fixed costs. The average total cost (ATC) is about $1.00/m3, average variable cost (AVC) is $0.85/m3 and the marginal cost is $0.53/m3 (de la Torre, 2008; Kronenberg, 2004).

In 2010, water desalination provided 30 MCM in the FC, and by 2030, desalination is projected to provide about 170 MCM (Al-Mutaz, 2005; El-Sadek, 2010; World Bank, 2007; United Nations, 2003). In the FC, the marginal cost of treated brackish water ranged from US$0.30 to US$1.00, while, for seawater desalination, this cost ranged from US$0.84 to US$1.70 (Glueckstern, 2004). Use of desalination technologies in the FC is quite new when compared to the Gulf States where it has been used since 1957, but interest is growing as conventional water resources became fully allocated. Desalination is currently used primarily in industrial and tourism sectors because of the high cost of seawater desalination. The use of desalination for other purposes (agriculture and municipal) will depend on technological improvements that result in reduced overall and marginal costs. Wastewater reuse The U.S. Environmental Protection Agency (EPA) defines wastewater reuse as reusing treated wastewater in agricultural and industrial processes. In the FC, water reuse is an existing tool for managing scarce water resources. Overtime, wastewater reuse has

changed from simply irrigating field crops with untreated wastewater to a sophisticated reclamation process for agricultural, industrial and domestic reuse (Durham et al., 2005).

Wastewater treatment and reuse as a tool for addressing food and water security in the Middle East and North Africa (MENA) was introduced by Faruqui (2002). The most practical solution for water scarcity is reuse of domestic wastewater for some non-potable municipal purposes, such as flushing toilets, irrigating green spaces, and for agriculture. Reusing wastewater is cheaper than developing new water supplies and protects existing sources of valuable fresh water from overexploitation (Faruqui, 2002).

The As-Samra wastewater treatment plant in Jordan was funded by USAID to replace the existing wastewater treatment plant. The project budget was $169 million, with half from USAID and the rest from the Jordanian government (Al-Zboon and Al-Ananzeh, 2008). The As-Samra plant is the largest wastewater treatment plant in Jordan and can treat about 75% of the 267,000 m3 of wastewater collected each day (Ammary, 2007). The project began in 2000 and was completed in 2007. The plant is expected to be viable until 2025.

The government buys water from the As-Samra plant for approximately $1.1 /m3 (Al-Zu‘bi, 2007). The average cost for O&M of treating wastewater in waste stabilization ponds ranges from $0.15 to $0.9 /m3. The total cost of the As-Samra wastewater treatment plant includes depreciation, salary, electricity, operation and maintenance, chemicals, sludge disposal and contracted testing. The average total cost (ATC) is about $1.51 /m3, average variable cost (AVC) is $0.53 /m3 and the marginal cost is $1.23 /m3 (Mohsen, 2007).

Wastewater treatment is assumed to become much more widely adopted in the next two decades because it is an applicable and feasible technology (Mohsen, 2007). However, the main constraints for wastewater recycling in Israel and the Palestinian territories for irrigation and other appropriate industrial and municipal uses are potential contamination and long term reliability (Yaron, 1999). Investment and operation costs for wastewater treatment and reuse are high. However, treated wastewater is increasingly being used for agricultural irrigation. Many efforts, such as increasing awareness and information campaigns, are needed to encourage participatory approaches. Importation of water from neighboring countries and virtual water Water importation in the FC can be actual, physical water or virtual water. Virtual water is importing food with high water use in its production, thereby having the burden of the water input borne by the food producing country. In addition to processed food, it may be rational to import high water-consuming crops (that is, virtual water) from countries with adequate water (Shuval, 2006). For example, Israel‘s annual ‗virtual‘ water imports are approximately three times its available internal water resource (Phillips et al., 2006). Israel also imports about 80% of its food and the Palestinians import over 65% of their food.

Israel and Turkey signed an agreement in 2004 that allowed Israel to import 50 MCM/year of fresh water from the Manavgat River system in Turkey for the next 20 years. The net cost of these physical water imports was estimated at US$0.73 to US$1.36 per m3. That cost covers the tankers, bags and loading and unloading terminals (Yedioth, 2004; Friedman, 2004). The total minimum Manavgat River flow recorded was 60 m3/s (that is, 1892.16 MCM per year). In other words, a volume of up to 1,892 MCM per year may be available from the Manavgat River (Friedman, 2004).

Many studies indicate that political conflict will be the main limiting factor for water-importation, especially physical water. Political uncertainty limits multi-national projects in the region. Strong collaborative institutions, at both national and regional

Qtaishat 267 Table 2. Water-supply augmentation cost, potential volume (MCM) for different years and constraints for each option.

Supply augmentation options

Average prices (2008)* ($/M

3)

Expected prices 2030

Potential volumes (MCM) Constraints

2010 2030

Brackish desalination 0.54 Decreasing 30 170 High cost and ecological impact

Sea water desalination 1.70 Decreasing

Water importation 1.55 Increasing 60 140 Geopolitical, technical, high cost and pollution concern

Building storage dams 1.87 Increasing 120 280 High cost and little of research

Wastewater Reuse 1.23 Decreasing 80 230 High cost and water quality

Water conservation 0.85 Increasing 10 60 Low social incentive, cost and unorganized plan

Reducing ET 0.83 Decreasing 0 50 Global climate change

*Prices from different years were adjusted to 2008 using the GDP deflator. Source: Al-Mutaz (2005), Alrosoroff (2004), Friedman (2004) and Mohesn (2007). levels, will be required for transboundary water agreements in the FC (Swedish Ministry for Foreign Affairs [SMFA], 2001). There is hope, that, through transboundary cooperation, local stakeholders‘ participation and policy-makers‘ regional analysis, the conflict can be recognized and people can resolve disputes. Political conflicts will limit water imports over the coming decades, but importing water as food products (virtual water) is an efficient option. The potential volumes of water from importation in 2030 could be 140 MCM.

Water conservation (demand management) Water conservation increases the water available for all uses and can expand water availability and improve water quality. The main constraint for water conservation in the FC countries, or its potential for savings, is that consumers, water authorities, are unorganized and lack sufficient incentives. There are many water losses and other forms of waste in the FC. There is a lack of national and international water conservation plans to address the many examples of water loss through wasteful processes. For example, farmers in the FC consider the cost of adopting new irrigation techniques as a part of a water-conservation system to be high. That belief tends to discourage adaptation of more efficient irrigation systems (Helming, 1993). The farmers have neither appropriate nor adequate incentives to consume water in an efficient way.

Water conservation through water demand-supply management can take many forms, including provisions to reduce losses through technical measures that will improve the efficiency of water consumption. Rationing programs to increase public awareness together with incentives may also promote water conservation. A water-conservation management plan for the Jordan basin region will likely need to incorporate both supply- and demand-oriented measures to maximize economic and environment efficiencies (Berkoff, 1994).

The approximate marginal cost of water from all water conservation measures is about $0.85/m3 and the projected potential quantity of water that can be obtained is about 10 MCM. By 2030, the projections for water conservation in the FC could be 50 MCM (Arlosoroff, 2004). By 2030, the challenges of inefficient water pricing mechanisms and the lack of public awareness about conservation could be solved resulting in more water from conservation.

RESULTS AND DISCUSSION Three specific supply-augmentation options were most promising in the FC: (1) desalination of brackish water, (2) reducing evapotranspiration and (3) water conservation. These three options have the lowest marginal costs among all options reviewed. MC of reducing evapotranspiration was $0.83/m

3, for water

conservation was $0.85 /m3 and for brackish desalination

was $0.54 /m3. Additional research is needed to address

technical and economic constraints. The potential volume of water that could be added by

each method varies (Table 2) based on both technology and the principle of increasing marginal cost. That is, there becomes a point where option A‘s MC becomes higher than option B‘s MC (that is, the switch point). Marginal cost (MC) also varies across space and over time for each option. The following plan considers the MC of each supply-augmentation option to assist decisions makers to prioritize choices. Furthermore, precise economic analysis involving AFC, ATC and AVC; technical analysis, and socio-political assessments will be necessary as plan components are implemented over time.

Assuming a perfect market where Price (P) = Marginal Cost (MC); the costs of water-supply augmentation options are adjusted to 2008 (Table 2). Desalination was the lowest marginal cost option to reduce water scarcity. Total water supply in 2010 was about 300 MCM from all sources. By 2030, the total water supply could feasibly be increased by about 630 MCM in the FC (Figure 2).

Conclusion

With continuing world population growth and a widening gap between water supply and demand, supply-augmentation options, such as water importation,

268 Int. J. Water Res. Environ. Eng.

Figure 2. Water-supply augmentation amounts (MCM) from six options over time (2010 and 2030) in the FC countries.

wastewater treatment, desalination of brackish water and seawater, water storage in dams and water conservation will be implemented to help address water-scarcity worldwide.

Brackish desalination, reducing evapotranspiration and water conservation are least costly, at the margin, at this time in the FC. FC countries may need to cooperate to overcome water shortages. Supply-augmentation options require transboundary support and regional cooperation. Policy-makers might start using supply-augmentation options efficiently not only to overcome water shortages, but also to resolve long-standing political conflicts and to invigorate economic growth and promote stability in the region.

The development of options with high capital invest-ments is further limited by environmental and ecological impacts along with public awareness. The FC countries currently lack resources and face technological issues to implement most of supply-augmentation options.

Dams and water importation systems are examples of supply-augmentation options limited by high cost and other political and economic constraints. An efficient mix of water-supply augmentation options will eventually likely be adopted. Most of the literature showed that the major mission given to the engineers was to evaluate the effectiveness and efficacy of various options. The assessment of supply-augmentation options would help extend the process of identifying packages of implementation options and help proceed with the plan.

A comprehensive approach is needed. A broad strategy can highlight the need for improving and mana-ging the available water resources and for finding new

water-supply options. This broad, general approach is the necessary groundwork for a more detailed strategic plan that could feasibly add as much as 630 MCM over the next two decades, helping solve the water-scarcity problem while considering sustainability and water quality for present and future uses. REFERENCES Abu Zeid M (2000). ―Desalination in Egypt Between the Past and Future

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Adams WM, Hughes FMR (1986). ―The Environmental Effects of Dam Construction in Tropical Africa: Impacts and Planning Procedures.‖ Geoforum 17(3-4):403-410.

Afonso M, Jaber J, Mohsen M (2004). ―Brackish Groundwater Treatment by Reverse Osmosis in Jordan.‖ Desalination 164(2):157-171.

Al-Mutaz I (2005). ―Hybrid RO MSF Desalination: Present Status and Future Perspectives.‖ International Forum on Water–Resources, Technologies and Management in the Arab World, 8-10 May 2005, University of Sharjah, Sharjah, United Arab Emirates.

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Al-Zu‘bi Y (2007). ―Application of Multicriteria Analysis for Ranking and Evaluation of Waste Water Treatment Plants and its Impact on the Environment and Public Health: Case Study from Jordan.‖ J. Appl. Sci. Res. 3(2):155-160.

Ammary B (2007). ―Wastewater Reuse in Jordan: Present Status and Future Plans.‖ Desalination 211(1-3):164-176.

Arlosoroff S (2004). ―Water Demand Management–A Strategy to Deal with Scarcity.Israel–A Case Study.‖ Second Israeli-Palestinian International Conference on Water for Life in the Middle East, Antalya, Turkey, 10-14 October 2004. Israel- Palestine Center for Research and Information, Jerusalem.

Barnes G (2008). ―The Potential for Monolayers to Reduce the

Evaporation of Water from Large Water Storages.‖ Agric. Water

Manage. 95(4):339-353. Berkoff J (1994). A Strategy for Managing Water in the Middle East and

North Africa. World Bank, Washington, D.C. Davenport DC, Anderson JE, Gay LW, Kynard BE, Bonde EK, Hagan

RM (1976). ―Phreatophyte Evapotranspiration and Its Potential Reduction without Eradication.‖ Am. Water Resour. Assoc. 15 (5):1293-1300.

De la Torre A (2008). ―Efficiency optimization in SWRO plant: high efficiency & low maintenance pumps.‖ Desalination, 221(1-3):151-157.

Durham B, Angelakis AN, Wintgens T, Thoeye C, Sala L (2005). ―Water Recycling and Reuse. A Water Scarcity Best Practice Solution.‖Technical paper at Environmental Technologies. Retrieved date: 7/20/2010, Available at: http://technologies.ew.eea.europa.eu/technologies/resourc_mngt/water_use/Water_recycling_and_reuse_a_water_scarcity_solution._Final.doc/.

El-Sadek A (2010). ―Water Desalination: An Imperative Measure for Water Security in Egypt.‖ Desalination 250(3):876-884.

Faruqui N (2002).―A Brief on Wastewater Treatment and Reuse for Food and Water Scarcity.‖ Retrieved date: 8/20/2010, Available at: http://www.idrc.ca/en/ev-44039-201-1-DO_TOPIC.html.

Food and Agriculture Organization of the United Nations (FAO). 2009. ―Aquastat Water Report 34.‖ Retrieved date: 9/3/2010, Available at: http://www.fao.org/nr/water/aquastat/main/index.stm.

Friedman I (2004). ―The Management of Our Water Resources is a National Disaster.‖ The Back Page with Dan Zaslavsky. Retrieved date: 4/3/2010, Available at: www.TheJerusalemReport.Com

Gay LW (1988). ―A portable Bowen ratio system for ET measurements.‖ P.p.625-632, In: L.C. James and M.J. English (ed.), Proc. Planning Now for Irrigation and Drainage in the 21st Century, ASCE, New York, NY.

Glueckstern P (2004). ―History of Desalination Cost Estimations.‖ Proceedings of the International Conference on Desalination Costing, Limassol, Cyprus.

Gökbulak F, Özhan S (2006). ―Water Loss Through Evaporation from Water Surfaces of Lakes and Reservoirs in Turkey.‖ Official Publication of the European Water Association, EWA. Istanbul University, Istanbul, Turkey.

Haddadin M (2002). ―Water Issues in the Middle East Challenge and Opportunities.‖ Water Policy 4:205-222.

Helming S (1993).―Planning for the Future: The Cost of Water.‖ Paper presented at EDI Water Management Workshop, July 1993, Victoria Falls, Zimbabwe.

Hut R, Ertsen M, Joeman N, Vergeer N, Winsemiusand H Giesen N (2008). ―Effects of Sand Storage Dams on Groundwater Levels with Examples from Kenya.‖ Phys. Chem. Earth 33(1-2):56-66.

Kronenberg G (2004). ―The largest SWRO plant in the world — Ashkelon 100 million m3/y BOT project.‖ Desalination 166(4):457-463.

Lasage R, Aerts J, Mutiso G, de Vries A (2008). ―Potential for Community Based Adaptation to Droughts: Sand Dams in Kitui, Kenya.‖ Phys. Chem. Earth 33(1-2):67-73.

McJannet D, Cook F, Knight J, Burn S (2008). Evaporation Reduction by Monolayers: Overview, Modeling and Effectiveness. Urban Water Security Research Alliance Technical Report No. 6. The University of QueensLand, Australia.

Mohsen M (2007). ―Water Strategies and Potential of Desalination in Jordan.‖ Desalination 203(1-3):27-46.

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Water Pricing Policies Overly Optimistic?‖ Agric. Water Manage. 95(4):427-438.

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Poff NL, Hart DD (2002). ―How Dams Vary and Why it matters for the Emerging Science of Dam Removal.‖ Bioscience 52(8):59-68.

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Shevah Y (2008). ―Irrigation and Agriculture Experience and Options in Israel.‖ Contributing paper prepared for Thematic Review IV.2: Assessment of Irrigation Options. Retrieved date: 08/02/2010, Available at:http://www.dams.org/docs/kbase/contrib/opt159.pdf.

Shuval H (2006). ―The Role of ‗Virtual Water‘ in the Water Resources Management of the Arid Middle East.‖ Chapter 12 in Water Resources in the Middle East: The Israeli-Palestinian Water Issues: From Conflict to Cooperation. Springer, New York.

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Taha S (2006). National Water Master Plan in Jordan.Conference of the Water Directors of the Euro-Mediterranean and South Eastern European Countries, Athens, Greece.

Tullos D, Bryan T, Liermann C (2009). ―Introduction to the Special Issue: Understanding and Linking the Biophysical, Socioeconomic and Geopolitical Effects of Dams.‖ J. Environ. Manag. 90(3):S203-S207.

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World Bank (2007). Making the Most of Scarcity: Accountability for Better Water Management in the Middle East and North Africa. MENA Development Report, World Bank, Washington, D.C..

World Commission on Dams (2000). Dams and Development: A New Framework for Decision-Making. Earthscan, London.

World Water Development Report (WWDR) (2003). Water for People, Water for Life. UNESCO and Berghahn, Paris.

Yaron D (1999). ―An Approach to the Problem of Water Allocation to Israel and Palestinian Entity.‖ Resour. Energy Econ. 16(4):271-286.

Yedioth A (2004). ―Israel is Buying Water from Turkey.‖ Tel Aviv. Retrieved date: 01/27/2010, Available at: http://www.turkishweekly.net/news/66974/israel-considers-buying-water-from-turkey-.html.

International Journal of Water Resources and Environmental Engineering Vol. 4(8), pp. 270-274, August 2012 Available online at http://www.academicjournals.org/IJWREE DOI: 10.5897/IJWREE12.025 ISSN 1991-637X ©2012 Academic Journals

Full Length Research Paper

Assessment of atmospheric moisture using hygroscopic salts in dry-and-wet climate of Nigeria

A. O. Eruola1,2*, G. C. Ufoegbune1, A. A. Amori1 and I. O. Ogunyemi2

1University of Agriculture, Abeokuta, Ogun State, Nigeria.

2Yaba College of Technology, Yaba, Lagos, Nigeria.

Accepted 19 July, 2012

Atmospheric moisture content plays an important role in climate changes studies. Atmospheric moisture content was determined using four different hygroscopic salts, viz Sodium chloride (NaCl), Potassium chloride (KCl), Zinc sulphate (ZnSO4) and Magnesium chloride (MgCl2) salts and hygrometer. Evaluation of different hygroscopic salts for the determination of humidity was based on the ability of the salts to absorb atmospheric moisture. The affinity of salts for atmospheric water from measured value was compared with the hygrometer measured value. Data were subjected to one-way ANOVA using the Genstat statistical package (Release 4.24 Discovery Edition) to determine the weekly average of the moisture content and bulb depression. Duncan Multiple Range Test (DMRT), T-test and a linear regression model equation was generated with the fitness within 95%. The model of the regression was tested to determine the accuracy of equation compared with wet and dry bulb thermometer depression value. Magnesium chloride (MgCl2) offers the best result with significant coefficient (P = 0.01 and 0.05) and depression value of wet and dry bulb thermometer. The relative reliability of the use of hygroscopic salts in terms of accuracy of both measured and extrapolated humidity data utilization showed that these salts could be used in place of the usual hygrometer. Key words: Hygroscopic salts, hygrometer, atmospheric moisture, humidity.

INTRODUCTION Most studies on climate change focus on changes in temperature and precipitation neglecting the atmospheric moisture (relative humidity) which is equally very important not only because of its effect on human comfort but also because either excessively moist or excessively dry conditions can be detrimental to equipment, furniture, buildings and the like (Mitchell et al., 1995). Atmospheric water vapor is one of the most important factors in determining earth's weather and climate. It play a vital role as a greenhouse gas, as an environmental condition which influences the growth of the plants, health of man, pollution of environment (Nieuwolt, 1972) and also transpiration rate which is determined by a balance between the amount of energy available to convert water from the liquid to vapor phase and the moisture gradient. Studies have shown that humidity measurement is among the more difficult problems in basic meteorology. *Corresponding author. E-mail: layosky@yahoo.com.

In particular, the most widely used hygrometer (psychrometer) in Nigeria is not reliable (Smadi, 2006). One major problem in the use of hygrometer (psychrometer) is the use of water rather than air (which is a much less effective heat transfer medium) for hygrometers calibration (Murray, 1967). The use of water causes hygrometer to be subjected to drift consequently requiring regular recalibration. A further difficulty is that most hygrometers sense relative humidity rather than the absolute amount of water present. However, research has shown that some hygrometer works on the principle of absorbed moisture in which a known volume of gas passes over a hygroscopic or moisture-absorbing material. The use of moisture-absorbing materials has been acclaimed to be the most accurate way of mea-suring humidity (Jackson, 1986b). Research into the application of humidity have been extensive in nearly all the eco-climatic zones of Nigeria; however, in contrast research endeavor in humidity measurement particularly the use of hygroscopic or moisture-absorbing materials is rare. It is therefore not surprising that humidity

Eruola et al. 271

Table 1. Weekly mean of hygrometer depression value and changing and non-changing salts moisture content difference.

Weeks *NaCl **NaCl *KCl **KCl *ZnS04 **ZnSO4 *MgCl2 **MgCl2 Hygrometer depression

1 0.87 0.988 0.678 0.83 0.662 0.706 1.272 1.19 1.2

2 0.414 0.85 0.238 0.578 0.188 1.098 2.232 2.176 2.1

3 0.762 0.248 0.29 0.182 0.476 0.178 1.844 0.726 1.8

4 0.142 0.2 0.134 0.096 0.092 0.064 1.7 0.208 1.8

5 0.152 0.166 0.11 0.11 0.086 0.008 2.052 0.208 1.8

6 0.172 0.084 0.102 0.028 0.064 0.014 1.716 0.208 2.1

7 0.362 0.15 0.2176 0.216 0.214 0.248 2.092 0.19 2.1

8 0.148 0.378 0.134 0.714 0.1 0.394 2.088 1.8 2.1

*, ** changing and non-changing salts, respectively.

measurement is restricted to the use of hygrometer (psychrometer). This study was therefore taken up to evaluate some hygroscopic salts for determining relative humidity in Abeokuta, South West Nigeria. MATERIALS AND METHODS Study area This study was conducted at the meteorological station of the University of Agriculture in the Odeda area of Abeokuta in Ogun State, South-Western Nigeria. It is located 100 km north of Lagos and 80 km south-west of Ibadan and covers an area extent of 1256 km2. The state is characterized by a tropical climate with distinct wet and dry seasons. The wet season is associated relatively with the prevalence of the moist maritime southerly monsoon from Atlantic Ocean and dry season by the continental North Easterly Harmattan winds from the Sahara desert. The area is located within a region characterized by bimodal rainfall pattern (commences in March and is plentiful in July and September, with a short dry spell in August). The long dry period extends from November to March. The annual rainfall ranges between 1400 and 1500 mm in Abeokuta and environs. The region is characterized by relatively high temperature with mean annual air temperature being about 30°C. The greatest variation in temperature is experienced in July (25.7°C) and in February (30.2°C). The humidity is lowest (37 to 54%) at the peak of dry season in February and highest at the peak of the rainy season between June and September (78 to 85%).

Determination of atmospheric moisture content Atmospheric moisture content was determined using four different hygroscopic salts, viz Sodium Chloride (NaCl), Potassium Chloride (KCl), Zinc Sulphate (ZnSO4) and Magnesium Chloride (MgCl2) salt. Based on their affinity for atmospheric water, 25 g of each hygroscopic salts were weighed into 50 mm diameter beaker and stored in a container kept in a Stevenson’s screen which also contained the wet and dry bulb hygrometer used as control measurement. The experiment involved salt samples which are changed weekly *NaCl, *MgCl2, *ZnSO4 and *MgCl2; and those not changed all through the study **NaCl, **MgCl2, **ZnSO4 and **MgCl2. There was also an overflow arrangement under every beaker.

Daily measurement was carried out for moisture adsorbed by each salt changed weekly; however those not changed were determined by weighing using digital scale and also the wet and dry bulb depression were taken. The weekly average of the moisture

content and bulb depression were compared using analysis of variance, Duncan Multiple Range Test (DMRT), T-test and a linear regression model equation was generated with the fitness within 95%. The model of the regression was tested to determine the accuracy of equation compared with wet and dry bulb thermometer depression value.

RESULTS AND DISCUSSION

A comparison of four hygroscopic salts, viz Sodium chloride (NaCl), Potassium chloride (KCl), Zinc sulphate (ZnSO4) and Magnesium chloride (MgCl2) salts and hygrometer was made while measuring the amount of atmospheric water vapor; and this show that salts varied greatly in their ability to measure relative humidity (Table 1 and Figure 1). The amount of atmospheric water vapor varied with the hygroscopic salt used in measurement in the study area. However, the Magnesium chloride (MgCl2) salt, in particular, weekly changed salt agreed closely in its relation to depression value obtained in the use of hygrometer as observed in Figure 1. This may be due to the fact that MgCl2 salt has the strongest affinity for atmospheric water. The Magnesium chloride (MgCl2) salt, irrespective of pattern of exposure (changed or unchanged) is in agreement to depression value obtained in the use of hygrometer. However, the unchanged MgCl2 salt is closer in value to other three salts, although better than others.

Table 2 shows that there is a significant difference between the salt samples; and the depression value of wet and dry bulb from the p-value of the F statistic (0.000) is less than 5% level of significance irrespective of pattern of exposure of salts (changed of unchanged).

Duncan Multiple Range Test (DMRT) was employed to compare the salts and the depression value of wet and dry bulb (Tables 3 and 4). Table 3 shows that there is no significant difference (P < 0.05) between the KCl, ZnSO4 and NaCl since they belong to the same category. However, the depression value of wet and dry bulb thermometer and the MgCl2 moisture absorption belong to the same category since their p-value is greater than 5% level of significance. From Table 4, the non-changing salts are significantly different from the

272 Int. J. Water Res. Environ. Eng.

1 2 3 4 5 6 7 8

*NaCl

**KCl **ZnSO4

**NaCl

*MgCl

*KCl

*ZnSO4

**MgCl

Figure 1. Weekly mean of hygrometer depression value and changing and non-changing salts moisture content difference.

Table 2. Analysis of variance for salts sample and hygrometer depression.

Parameter Degree of freedom P < 0.05 Result

Changing salts 199 0.000 Significant

Unchanging salts 199 0.000 Significant

Table 3. Duncan analysis of changing salts and hygrometer depression.

Changing salts N Subset for alpha = 0.05

1 2

KCl 40 0.2347

ZnSO4 40 0.2875

NaCl 40 0.3778

Standard difference 40 1.8625

MgCl2 40 1.8785

Significance 0.232 0.887

Means for groups in homogeneous subsets are displayed.

Table 4. Duncan test for unchanging salts and hygrometer depression.

Non changing salts N Subset for alpha = 0.05

1 2

ZnSO4 40 0.3347

KCl 40 0.3378

NaCl 40 0.3830

MgCl2 40 0.6348

Standard difference 40 1.8625

Significance 0.057 1.000

Means for groups in homogeneous subsets are displayed.

Eruola et al. 273

Table 5. Correlation test between the changing MgCl2 and depression of hygrometer.

Parameter Correlation Standard difference Magnesium chloride salt

Depression value

Pearson correlation 1 0.993

Significance (2-tailed) 0.000

N 40 40

Magnesium chloride salt

Pearson correlation 0.993 1

Significance (2-tailed) 0.000

N 40 40

** Correlation is significant at the 0.01 level (2-tailed).

Table 6. T-test between the changing MgCl2 and hygrometer depression. Paired samples (a) test (b) correlations and (c) statistics.

(a) Paired samples test

Model

Paired differences

t df Significance (2-

tailed) Mean Standard deviation

Standard error mean

95% Confidence Interval of the difference

Lower Upper

Pair 1 Magnesium chloride – depression

0.01385 0.08216 0.01316 -0.01279 0.04048 1.052 38 0.299

(b) Paired samples correlations

N Correlation Significance

Pair 1 Magnesium chloride and depression

39 0.993 0.000

(c) Paired samples statistics

Mean N Standard deviation Standard error mean

Pair 1 Magnesium chloride 1.8728 39 0.65198 0.10440

Depression 1.8590 39 0.67812 0.10859

standard difference.

Generally, the Magnesium chloride (MgCl2) salt, showed more reliable estimate of relative humidity than the other three salts. Since it was observed that there is a link between the changing MgCl2 and depression value of wet and dry bulb thermometer, correlation test was carried out between the variables and presented in Table 5.

The correlation between the MgCl2 and the standard difference is 0.993 meaning that there is a positive correlation between the variables.

Table 6 shows paired sample T-test of changing MgCl2 and depression of hygrometer. It was observed that there is no significant difference between the MgCl2 moisture content value and hygrometer depression; since the p-value is greater than 5% level of significance we say that there is no significant difference between the MgCl2 and depression.

Table 7a and b shows linear regression model for changing MgCl2 and depression of hygrometer from the coefficient table. The linear regression equation is given

as:

Y = -.076 + 1.032X

Where Y = Depression value of wet and dry bulb thermometer. X = Magnesium chloride salt moisture content.

Test of linearity

Since the p–value of t–statistic (0.000) is less than 5% level of significance used for this analysis, we say that the coefficient is significant in the model and contribute to the model (Table 7b).

From Table 8, it was observed that the independent variable explained the variation in the dependent variable as the p–value of the F-statistic (0.000) is less than 5% level of significance used for the analysis. From the model summary table, the R

2 model is 0.986 meaning that the model is

98.6% fit and it is a very good model.

Test running the model equation

With the linear regression equation being generated,

274 Int. J. Water Res. Environ. Eng.

Table 7. Linear regression model for changing MgCl2 and depression of hygrometer (a) Coefficients (b) ANOVA.

(a) Coefficients (Dependent variable: standard difference)

Model Unstandardized coefficients Standardized coefficients

T Significance B Standard error Beta

1 Constant -0.076 0.040 -1.899 0.065

Magnesium chloride salt 1.032 0.020 0.993 51.457 0.000

(b) ANOVA (Dependent variable: standard difference)

Model Sum of squares df Mean square F Sig.

1 Regression 17.246 1 17.246 2.648E3 0.000a

Residual 0.248 38 0.007

Total 17.494 39 a Predictors: (Constant), Magnesium chloride salt.

Table 8. Model summary for changing MgCl2 and depression of hygrometer.

Model R R2 Adjusted R

2 Standard error of the estimate

1 0.993a 0.986 0.985 0.08070

a Predictors: (Constant), Magnesium chloride salt.

Table 9. Showing linear model equation result after substituting for MgCl2 value (Y = -0.076 + 1.032 X).

Days Value of morning and afternoon

difference in MgCl2 readings Depression value of wet and

dry bulb thermometer Linear regression equation

result

1 1.86 2.0 1.84

2 2.62 2.5 2.63

3 3.87 4.0 3.92

4 2.33 2.5 2.33

this prompted a four days observation back to the field to collect data for MgCl2 to be input in the equation and compared with the depression value of wet and dry bulb thermometer result being taken on same days of second visit to the field. It could be observed from Table 9 that the result of the linear regression equation after substituting for the value of MgCl2 in the equation gave an approximate value of the depression value of wet and dry bulb thermometer.

Conclusion

In the study area characterized by an irregular sequence of relative humidity which varies with lowest (37 to 54%) at the peak of dry season in February and highest (78 to 85%) at the peak of the rainy season between June and September (Bello, 1997), it was concluded that, where the

use of wet and dry bulb hygrometer is not available or needs to be tested, the use of hygroscopic salt particularly Magnesium chloride (MgCl2) offers comparatively desirable result. However, the reliability of results from hygroscopic salts considered depends on replacement of

salts at interval and the personnel. Where the bulk of data are obtained by extrapolation from an unchanged salt a less reliable result can be expected. REFERENCES Bello NJ (1997). An investigation of the characteristics of the onset and

cessation of the rains in Nigeria. Theor. Appl. Climatol. 54(3-4):161-173.

Jackson IJ (1986b). Tropical rainfall and surface water’ in Developing World Water. Grosvenor Press Int. pp. 42-43.

Mitchell JFB, Johns TC, Gregory JM, Tett FB (1995). Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature 376:501-504.

Murray FW (1967). On the computation of saturation vapour pressure. J. Appl. Meteorol. 6:203-204.

Nieuwolt S (1972). Rainfall variability in Zambia. J. Trop. Geogr. 12:46-57.

Smadi MM (2006). Observed abrupt changes in minimum and maximum temperatures in Jordan in the 20th century. Am. J. Environ. Sci. 2:114-120.

International Journal of Water Resources and Environmental Engineering Vol. 4(8), pp. 275-280, August 2012 Available online at http://www.academicjournals.org/IJWREE DOI: 10.5897/IJWREE12.027 ISSN 1991-637X ©2012 Academic Journals

Full Length Research Paper

Ecofriendly management of mixed coconut oil cake waste for lipase production by marine Streptomyces

indiaensis and utilization as detergent additive

B. Sathya Priya1*, T. Stalin1 and K. Selvam2

1Research and development centre, Bharathiar University,Coimbatore-641046, Tamil Nadu, India.

2Department of Biotechnology, Dr. N. G. P Arts and Science College, Coimbatore-641014, Tamil Nadu, India.

Accepted 14 June, 2012

The utilization of agro waste for the production of lipase enzyme was one of the ecofriendly methods for the management of waste. From 34 actinomycetes strains screened from sediments of Tiruchendhur coastal areas of Tamil Nadu, India, 26 strains exhibited lipase activity. The marine actinomycete strain MAC 7 was used for the production of extracellular lipase by using mixed coconut oil cake waste as substrate. The strain showed maximum lipase activity at pH 9 and temperature 55°C. The solid state fermentation was carried out for 8 days with 80% moisture content. The lipase extracted from marine actinomycete was highly alkaline and thermophilic in nature. The enzyme was further utilized as a good detergent additive. Key words: Actinomycete, coconut oil cake, lipase, Streptomyces indiaensis, solid state fermentation, wheat bran.

INTRODUCTION The aquatic world contains a wide variety of living species and represents greatest potential for discovering new enzymes (Cherif et al., 2007). Microbial enzymes are relatively more stable and active with extraordinary properties (Bull et al., 2000; Ghosh et al., 2005). Lipases (triacylglycerol acylhydrolases, EC 3.1.1.3) are the enzymes that catalyze the hydrolysis and the synthesis of esters formed from glycerol and long chain fatty acids. Lipases are more useful (Sharma et al., 2001) in food additives, clinical reagents and detergent additives, medicines and biodegradation of plastics such as polyhydroxyalkanoates (PHA) and polycaprolactone (PCL) (Jaeger et al., 1995; Mochizuki et al., 1995).

The partial decomposition of solid waste produces leachate and affects ground water and land environment.

*Corresponding author. E-mail: sbspriya11@gmail.com. Tel: 9994641760.

It also causes bad odour and increases chance for pathogens which cause serious diseases to organisms. So, solid wastes are used as sources for the production of novel enzymes like lipase. This is one of the best methods for the management of solid waste in an ecofriendly way. Solid state fermentation (SSF) has more advantages than submerged fermentation (SMF) due to low capital investment, simplification of the fermentation media, absence of complex machinery, reduced energy requirement and improved product recovery, more thermostable (Lonsane et al ., 1985; Pandey et al., 1999). Mixed solid substrate fermentation (Benjamin and Pandey, 1998; Imandi and Garapathi, 2007) was a novel process for enhanced lipase production by Candida rugosa and Yarrowia lipolytica. The maximum lipase activity was observed in Bacillus subtilis in solid state fermentation using ground nut oil cake as substrate (Chaturvedi et al., 2010). Thermostable lipases from (Ahmed et al., 2009; Dutta and Ray, 2009; Nawani and Kaur, 2000) many Pseudomonas and Bacillus sp. have

276 Int. J. Water Res. Environ. Eng. been isolated and studied. The actinomycete Streptomyces griseochromogenes isolated from shrimp pond showed higher lipase activity (Gunalakshmi et al., 2008). MATERIALS AND METHODS Isolation and screening of lipase producing actinomycetes from sediment sample The marine sediment samples were collected from two different points of Tiruchendur coastal areas of Tamil Nadu, India. The collected samples were transported immediately to the laboratory. The samples were air dried and were incubated at 55°C for 10 min. The samples were serially diluted and spread on actinomycete isolation agar with pH 9 and incubated for 7 to 14 days and the plates were observed for the appearance of actinomycete colonies. The bacterial and fungal contaminations were controlled by the addition of each 20 and 100 mg/L of nystatin and cycloheximide with agar media. The pure actinomycetes cultures were maintained on nutrient agar media. The actinomycetes were grown on tributyrin agar plates and the zone of clearance was observed due to the hydrolysis of tributyrin (Hun et al., 2003; Nair and Kumar, 2007). The actinomycetes that showed the maximum zone of clearance was selected for further analysis. Solid state fermentation Modified mineral salt solution was used for the inoculation of the strain. The media composition (g/ 100 ml) was magnesium sulphate 0.05 g, dipottasium hydrogen phosphate 0.1 g, sodium chloride 3 g, ferrous sulphate 0.001 g, manganous chloride 0.001 g, zinc sulphate 0.001 g. It was incubated at 55°C in an incubator shaker at 180 rpm for 24 h. The different wastes with the following combinations were used for the enzyme production: Substrate 1) coconut oil cake (10 g), substrate 2) soymeal + coconut oil cake (5 g + 5 g), substrate 3) coconut oil cake (3.3 g) + soymeal (3.4 g) + wheat bran (3.3 g). They were dried at room temperature to reduce the moisture content and ground to the desired size. Each substrate (1, 2, 3) of 10 g was added with 80 ml of modified (SS) mineral salt solution and sterilized. To the sterilized media, 10 ml of inoculum was added. Each flask with substrates 1, 2 and 3 was incubated at 55°C in an incubator shaker at 180 rpm for 8 days. Optimization of different parameters The different parameters like pH of the medium ranging from 6 to 11, temperature (35 to 60°C), moisture content (50 to 100%), substrate concentration (5 to 15%) and sodium chloride concen-tration (1 to 5%) were optimized for enzyme production. All the experiments were carried out in 250 ml Erlenmeyer flask containing 100 ml of medium. It was incubated for 8 days at 55°C in an incubator shaker at 180 rpm. Enzyme extraction The crude enzyme from the fermented substrate was extracted by using 0.05 m sodium phosphate buffer (pH 9.0).The fermented substrate was mixed with 100 ml of buffer and was kept in the rotary shaker (180 rpm) at 55°C for 1 h. The raw extract was obtained by centrifugation at 10,000 rpm for 15 min at 4°C. The clear supernatant obtained from centrifugation was used to determine the enzyme activity.

Lipase assay Lipase activity was measured by titrimetric method using olive oil emulsion method (Watnabe et al., 1977). The reaction mixture with 5 ml of olive oil emulsion (25 ml olive oil and 75 ml 2% polyvinyl alcohol), 4 ml of 0.2 M tris buffer, 1 ml of 110 mM CaCl2 and 1 ml enzyme solution was incubated for 30 min at 55°C. The control containing boiled inactivated enzyme (at 100°C for 5 min) was treated similarly. After incubation, the enzyme activity was blocked by 20 ml of acetone ethanol (1:1) mixture and the liberated free fatty acid was titrated against 0.05 M NaOH using phenolphthalein as an indicator. One unit of lipase was defined as the amount of enzyme, which liberates 1 µmol of fatty acid/ min under standard assay conditions. The enzyme activity was expressed as IU/ml. Partial purification of enzyme The crude enzyme was precipitated by the addition of 60% (v/v) volume of chilled acetone and stored overnight at -4ºC. It was centrifuged at 10,000 rpm for 10 min. The precipitate was suspended in sodium phosphate buffer and incubated overnight at 4°C. The enzyme was dialyzed against same phosphate buffer. It was loaded on column (2.5 × 17 cm) of DEAE Sephadex A-50 already equilibrated with sodium phosphate buffer. The purified enzyme was collected and stored at 4°C for further use. RESULTS AND DISCUSSION Screening of potential isolate for lipase activity Out of 26 lipase producing actinomycetes screened from sediments of Tiruchendhur coastal areas of Tamil Nadu, India, MAC 7 strain was selected due to larger clear zone formation on Tributyrin agar medium. It was identified as Streptomyces indiaensis using 16S rRNA sequencing (Genbank accession number for nucleotide sequence: JQ801298).

Influence of additives on lipase activity Figure 1 showed the moderate increase of enzyme activity from 3

rd to 5

th day and the highest lipase activity

was observed on the 4th day (220.8 ± 0.20 IU/ml) by

using mixed waste (coconut oil cake with inducers soy meal and wheat bran) than coconut oil cake and combined oil cake with soy meal. From the 4

th day

onwards, the lipase yield was decreasing slowly due to the consumption of nutrients by the microbes. The lipase activity (175.6 ± 0.25 IU/ml) observed in mixed coconut oil cake with soymeal was greater than coconut oil cake (141.6 ± 0.15 IU/ml) as substrate in the 4

th day. The

results indicated that soymeal and wheat bran act as inducers and additives for the lipase production. So, the combination of both with oil cake increased the lipase production. Manoj et al. (2010) reported that the lipase production was more in groundnut oil cake than in coconut oil cake. The lipase activity was highest in mixed waste of bagasse and wheat bran (Imandi and Garapathi, 2007) than wheat bran and bagasse used as substrate.

Priya et al. 277

Figure 1. Influence of coconut oil cake with additives on lipase activity.

Figure 2. Effect of different substrate concentration on lipase activity.

Optimization of substrate From Figure 2, it was observed that 10 g of substrate showed maximum lipase production than 5 g (133.3 ± 0.47 IU/ml) and 15 g (95.8 ± 0.30 IU/ml) due to its easier penetration by the microbes. The less lipase production at higher substrate level was due to low mass transfer rate and difficulty in penetration of the organism (Rao et al., 2003). The mixed waste of bagasse and wheat bran (10 g) showed highest lipase activity (Imandi and Garapathi, 2007). Effect of pH, temperature and incubation time on enzyme production The maximum lipase activity was observed in pH 9 (Figure 3) and at a temperature of 55°C (Figure 4). Gunalakshmi et al. (2008) observed highest activity for marine actinomycete strain at 55°C and pH 8. pH is an important parameter required for the growth of microbes in the respective media. The results indicated that there is a strong influence of alkaline pH on lipase production. Figure 3 showed that the alkaline nature of marine

actinomycete was due to its maximum activity at pH 9. It also exhibited higher enzyme activity on pH 10 (204.6 ± 0.15 IU/ml) and 11 (191.6 ± 0.32 IU/ml). It showed the least value for pH 6 (66.6 ± 0.20 IU/ml). It exhibited maximum activity at higher temperature in the range of 55°C. The higher enzyme activity was also observed in the temperatures 50°C (200.9 ± 0.85 IU/ml) and 60°C (191.7± 0.17 IU/ml). The results show the stability of the enzyme was from pH 8 to 11 and at temperatures ranging from 45 to 60°C. So the marine actinomycete preferred more alkaline and thermal conditions for maximum enzyme production. Effect of moisture content on lipase activity The lipase activity was maximum at 80% moisture content. The maximum lipase activity was observed at 80% moisture content in Y. lipolytica (Imandi and Garapathi, 2007). At 70% moisture content, the activity was slightly higher (175.5 ± 0.50 IU/ml) than at 50% moisture content. From Figure 5, it was observed that the optimum moisture content for lipase production was 80%. The enzyme activity was high in favourable moisture

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278 Int. J. Water Res. Environ. Eng.

Figure 3. Effect of different pH on lipase activity.

Figure 4. Effect of different temperature on lipase activity.

Figure 5. Effect of different moisture content on lipase activity.

conditions of 70 to 80%. It was lowest at 50% (50.7 ± 0.25 IU/ml). Low moisture contents lead to the reduction

of solubility of nutrients and higher moisture contents lead to decrease in the porosity due to the stickiness of media

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Figure 6. Effect of sodium chloride on lipase activity.

(Lonsane et al., 1985).

Sodium chloride tolerance

The marine actinomycete showed highest activity at 3% sodium chloride concentration (Figure 6). The salt concentration of 4% also showed an increased activity of 195.7 ± 0.43 IU/ml. It showed the sodium chloride tolerance level from 2 to 4%. It showed the least value for 1% sodium chloride concentration (137.5 ± 0.55 IU/ml). So, this organism preferred alkaline pH condition for maximum growth and highest lipase production. The maximum lipase activity was observed at 4% sodium chloride concentration (Gunalakshmi et al., 2008).

Application of lipase as detergent additive

The partially purified lipase enzyme was used as an additive in the detergent industry. Four multistained (grease, oil, mud and pickle) white pieces of cloth (5 cm × 5 cm) were taken in four flasks with 100 ml of water each. One flask was used as control. Detergent (Surf Excel-5 mg/ml) was put in the second flask. Enzyme (1 ml) was put in the third flask while the fourth flask contained both enzyme and detergent. All the flasks were incubated at 55°C for 30 min and the observations were recorded before and after incubation. After incubation, the pieces of cloth were rinsed with water and dried. The enzyme showed a good result of cleaning the multistained cloths. The enzyme extract with the combination of detergent removed the stain successfully. It showed the effective utilization of enzyme extract as a powerful detergent additive.

Conclusions

The results presented in the Figures 1 to 6 show that mixed waste with inducers had the highest lipase

production than single waste used as substrate. The addition of mixture of soy meal and wheat bran induced the lipase production. The marine actinomycete S. indiaensis preferred alkaline conditions (e.g. pH 9, higher temperatures 55°C) for maximum lipase activity. So this marine actinomycete was used for enhanced production of lipase enzyme and utilization as additive in detergent industries. In the solid state, fermentation mixed waste (with inducers) used as substrate showed an efficient ecofriendly management of waste and it reduced the environmental pollution.

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