ETHIOPIAN JOURNAL OF SCIENCE AND TECHNOLOGY VOLUME …

ETHIOPIAN JOURNAL OF SCIENCE AND TECHNOLOGY VOLUME 8, NUMBER 1 (JANUARY, 2015)

EDITORIAL BOARD

EDITOR-IN-CHIEF Professor Mulugeta Kibret Bahir Dar University, Department of Biology Email: [email protected], [email protected]: +251-582-266597Fax: +251-582-264066Mailing address: P.O. Box 79Bahir Dar, Ethiopia

ASSOCIATE EDITORS Dr Abera Kechi, Ethiopian Institute of Textile and Fashion Technology, BDUDr Amare Benore, Department of Physics, Science College, BDUDr Awoke Andargie, Department of Mathematics , Science College, BDUMr Bayeh Abera, Department of Microbiology, Immunology and Parasitology, College of Medicine and Health Sciences, BDUDr Eneyew Tadesse, School of Chemical and Food Engineering, BDUDr Essey Kebede, Department of Statistics, Science College, BDUDr Hailu Mazengia, Department, of Animal Science College of Agriculture and Environ-mental Sciences, BDUDr Meareg Amare, Department of Chemistry, Science College, BDUDr Solomon Tesfamariam, School of Mechanical and Industrial Engineering, BDUDr Wassie Anteneh, Department of Biology, Science College, BDUMr Zelalem Liyew, Department of Earth Science, Science College, BDU

ADVISORY BOARD

Professor Gezahegn Yirgu, Addis Ababa University, EthiopiaDr Gizaw Mengistu, Addis Ababa University, EthiopiaDr Habtu Zegeye, Botstwana University, Botswana Dr Melaku Walle, Bahir Dar University, EthiopiaDr Mesfin Tsige, University of Akron, Ohio, USADr Mohammed Tesemma, Spelman College, Atlanta, USADr Mulugeta Bekele, Addis Ababa University, EthiopiaDr Mulugeta Gebregziabher, University of Medical South Carolina, USAProfessor Murali Mohan, SP-Mahila University, India Professor Robert McCrindle, Tshwane University of Technology, South AfricaDr Semu Mitiku, Addis Ababa University, EthiopiaDr Solomon Harrar, University of Montana, USA Dr Solomon Libsu, Bahir Dar University, EthiopiaProfessor Srinivas Rao, Andhra University, IndiaProfessor Teketel Yohannes, Addis Ababa UniversityProfessor Temesgen Zewotir, University of KwaZulu Natal, South Africa Dr Tesfaye Baye, University of Cincinnati, USA

Dr Abebe Getahun, Addis Ababa University, Ethiopia Professor Abera Mogessie, Karl-Franzense University of Graz, AustriaProfessor Afework Bekele, Addis Ababa University, EthiopiaDr Berahnu Abraha, Bahir Dar University, EthiopiaProfessor Bhagwan Singh Chandravanshi, Addis Ababa University, EthiopiaDr Emmanuel Vreven, Royal Museum of Central Africa, BelgiumDr Endawoke Yizengaw, Institute for Scientific Research, Boston College, USADr Eshetie Dejen, Inter-governmental Authority on Development(IGAD), DjiboutiDr Firew Tegegne, Bahir Dar University, EthiopiaDr Gebregziabher Kahsay, Bahir Dar University, EthiopiaProfessor Geog Güebitz, University of Natural Resources and Life Science, Vienna, AustriaDr Getachew Adamu, Bahir Dar University, Ethiopia

© Science College, Bahir Dar University, 2015

ETHIOPIAN JOURNAL OF SCIENCE AND TECHNOLOGY VOLUME 8, NUMBER 1 (JANUARY, 2015)

CONTENTS


Bioethanol production from finger millet (Eleusine coracana) straw Teshager Mekonnen Tekaligne, Abebe Reda Woldu, Yeshitila Asteraye Tsigie

1

Water quality characteristics and pollution levels of heavy metals in Lake Haiq, Ethiopia

Aregawi Teklay and Meareg Amare

15

Prevalence and antibiogram of Shigella and Salmonella spp. from under five children with acute diarrhea in Bahir Dar Town

Maritu Admassu, Gebremariam Yemane, Mulugeta Kibret, Bayeh Abera, Endalkachew Nibret and Melaku Adal

27

Spawning migration of Labeobarbus species to some tributary rivers of Lake Tana, Ethiopia

Gizachew Teshome, Abebe Getahun, Minwyelet Mingist and Wassie Anteneh

37

Application of energy storage units in an interconnected hydro power system

V. Rajaguru, B.V.S. Prahasith Dasari, Abebe Tsegaye

51

Author Guidelines

Ethiop. J. Sci. & Technol. 8(1) 1- 13, 2015 1

Bioethanol production from finger millet (Eleusine coracana) straw

Teshager Mekonnen Tekaligne, Abebe Reda Woldu, Yeshitila Asteraye Tsigie*

Bahir Dar University, College of Science, Department of Chemistry, P.O. Box 79, Bahir Dar, Ethiopia

ABSTRACTThe possibility of producing bioethanol from the biomass of finger millet straw was studied. The effects of temperature, acid concentration, hydrolysis time, and substrate concentration were investigated. The result showed that a maximum sugar content of 79.04 and 82.01 %w/w was achieved using phenol-sulfuric acid and Fehling method, respectively, from hydrolysis of 10 % biomass concentration at 2 % sulfuric acid, 35oC reaction temperature, and 4 days of hydrolysis time. The optimized hydrolyzate sample was fermented at optimized pH 6.0, 4 g/L yeast concentration, 32.5 oC reaction temperature, 4 days of fermentation time, and maximum of 7.28 %w/v of ethanol content was obtained using Pycnometer measurement. In general, the bioethanol achieved from FMS (7.28 %) at optimized conditions were highly promising and hence, it can be employed as an alternative lignocellulosic feedstock for bioethanol production rather than using food crops such as corn, sugarcane, etc.

Keywords: Acid hydrolysis, bioethanol, finger millet straw, fermentation, Saccharomyces cerevisiae.DOI: http://dx.doi.org/10.4314/ejst.v8i1.1

INTRODUCTION

Ethanol production through biotechnological methods has acquired considerable interest due to possible utilization of bioethanol as an alternative fuel. The rises in prices and environmental problems caused by fossil fuels have contributed to this recent interest of alternative energy sources. Consequently, research efforts have become more focused on low-cost lignocellulosic materials derived from agricultural and forest residues along with herbaceous materials and municipal wastes (Yeshitila Asteraye et al., 2013). Utilization of bioethanol can reduce the world’s dependence on fossil fuels, in addition to decreasing net emissions of greenhouse gases. Burning fossil fuels such as coal and oil release CO2, which is a major cause of global warming (Erdei et al., 2010),

while bioethanol is clean, safe, environmentally friendly. Besides, the short round of growing plants, burning fuel made from them does not contribute CO2 to the atmosphere (Kumar et al., 2009; Zhao and Xia, 2010). Ethanol contains 35% oxygen that facilitates total combustion of fuel and hence decreases particulate emission that causes health problem to living things (Ali et al., 2011). About 6,000 varieties of millet with different colors like pale yellow, gray, white, and red are found in the world. Originally, the millet varieties were originated from both Africa and Asia. Finger millet was taken to India and Europe about 3,000 years ago and at the beginning of the Christian era, respectively (Molla Fentie, 2012).Finger millet can be grown in marginal lands beneath low input system and in a broad range of altitudes. The seeds can be stored for a long period

______________________________*Corresponding author: [email protected]

©This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/CC BY4.0).

2 Teshager Mekonnen et al.

and it has a high malting quality. It is least prone to insect pests and diseases. In Ethiopia, finger millet is the 6th important crops after teff, wheat, maize, sorghum and barley. It comprises about 5 percent of the total land devoted to cereals. It is mainly grown in North Gondar, West Gojam, West Wollega, some parts of Tigray, and Benishangul Gumuz regional states of Ethiopia (Singh and Raghuvanshi, 2012).Straw is one of the plentiful lignocellulosic waste feedstocks in the world. It is a byproduct of cere-al crop production and a great bioresource (Singh and Raghuvanshi, 2012﴿. The study utilized straws of finger millet from Pawe Woreda agricultural re-search center and farmer farm lands, Ethiopia, as a major raw material for the production of bioetha-nol using Saccharomyces cerevisiae.

EXPERIMENTAL

Materials

Drying oven (GALLENKAMP), electrical grinder (ZAIBA super blender), electrical balance (ae ADAM, PW 124), UV-Vis spectrometer (NV203 spectrophotometer), fermentation and distillation set up, pycnometer (50 mL, KW 14/23), hydrometer (Araometer nach Dichte fur schwefelsaure Temp. 20oC), digital pH meter (pH meter 3310, JENWAY), ICP-OES (ICP-spectrometer, ULTMA-2), and heat mantle (Labmaster, isopad, type LMUL/ER/1L with 220/240 volts), were the equipment used in this work.

Chemicals

Methylene blue indicator, Fehling A and Fehling B (the detail is mentioned in Fehling method below), sulfuric acid, D (+) – glucose as standard (PANREAC, MONTPLET and ESTEBAN SA, Barcelona. Madrid), and calcium hydroxide were the major chemicals used in this study. Moreover yeast (Saccharomyces cerevisiae) was the biological

material that was used in this study to facilitate the fermentation process.

Sample Collection and Preparation

Finger millet straw (13 kg) was collected in poly-ethene bags from Pawe Woreda Agricultural Re-search Center (Benshangul Gumz, Ethiopia) and farmer farm lands. It was cut by sickle into pieces of about 3-5 cm length for drying and grinding. The sample was dried through direct sunlight to obtain an easily crushable material. After drying, the sample was ground with electrical grinder. The maximum ground particle size of 1-2 mm.

Pretreatment

100 mL of a 0.5% sulfuric acid was added to 50 g of the sample to remove lignin, reduce cellulose crystallinity and increase the porosity of the materi-als. The mixture was heated to 125 - 130 oC under a pressure of 25 psi for 1 hr. The pretreated sam-ple was collected and used in the subsequent step. Confirmatory test by iodine (test for the presence of starch) was carried out. Appearance of helically coiled blue complex ascertained that the pretreated material was actually free of lignin (Mishra et al, 2011).

Hydrolysis of the Pretreated Samples

50 g of pretreated FMS was used for the triplicate experiments of hydrolysis and the factors investi-gated for hydrolysis were time (1-5 days), tempera-ture (25, 30, 35, 40 and 45 oC), acid concentration (0 - 4 %), and biomass concentration (6.25, 7.14, 8.33, 10.00, 12.5, and 16.61 % w/v). The effect of substrate (biomass) concentration was studied by adding 50 g sample in 800, 700, 600, 500, 400 and 300 mL of distilled water containing 2 % of sulfuric acid.The mixture was transferred to glass bottles and sealed to avoid vaporization of acid due to heat. The liquid fraction of the hydrolysate sample was filtered and its sugar content was determined by Fehling and phenol-sulfuric acid methods.


Fehling methodThe Fehling method was conducted as described elsewhere (Periyasamy et al., 2009; Adane Muche and Sahu, 2014; Sahu, 2014). The filtered hydro-lyzed sample solution (50 mL) was neutralized with the required amount of 4 M NaOH and 2.5 M HCl and the solution was made up to a volume of 300 mL and taken into the burette. Then, 5 mL of Fehling A (prepared by dissolving 34.6 g of cop-per (II) sulfate pentahydrate in 500 mL distilled water) and 5 mL of Fehling B solutions (prepared by dissolving 125 g of potassium hydroxide and 173 g of potassium sodium tartrate tetrahydrate in 500 mL of distilled water) were taken and mixed with 90 mL of distilled water in 250 mL Erlen-meyer flask to which methylene blue indicator was added. The solution in the flask was titrated with solution in the burette under boiling situation un-til departure of blue color and the volume of the titrate causing brick red color was noted. For each sample the sugar content was calculated by using Eqn. (1). Sugar content (%)=

VfmL.300 x100% (1)

Where: f is Fehling factor (0.051), V is the volume of titrant used.

Phenol-sulfuric acid methodGlucose dehydrates to furfural derivative (hy-droxymethylfurfural) in hot acidic medium, when this derivative reacts with phenol, it develops de-tectible color (Dubois, 1956) and has 490 nm ab-sorption maxima. Phenol-sulfuric acid method is a widely used colorimetric method for determi-nation of carbohydrate concentration in aqueous solutions. The total sugar concentration was deter-mined by using UV-visible spectrophotometer at 490 nm wavelength of glucose absorbance. Cali-bration curve was obtained for series of standard glucose solutions and the regression equation was used to calculate the total sugar concentration in

the sample. The standard procedure of this meth-od is as follows. A 1 mL aliquot of a sample solu-tion was mixed with 1 mL of 5% aqueous solu-tion of phenol in a test tube. Subsequently, 5 mL of concentrated sulfuric acid was added rapidly to the mixture. The test tubes were kept al room temperature for 10 min at room temperature, and then placed in a water bath for 20 min for color development. Its absorption at 490 nm was record-ed. Blank solution was prepared in the same way as above, except that the 1 mL aliquot of a sample solution was replaced by distilled water (Albalas-meh et al., 2013).

Preparation of Standard and Reagent Solutions

Stock glucose solution was made by dissolving 4 g of glucose in 100 mL of distilled water. To prepare the standard working solutions, 1, 2, 3, 4 and 5 mL aliquots of the stock glucose solution were sepa-rately pipetted out into different 100 mL volumetric flasks and subsequently diluted with distilled water to the mark resulting working standard glucose solu-tions of 0.04, 0.08, 0.12, 0.16, and 0.2 g/mL, respec-tively. To determine the calibration curve for standard glu-cose, 1 mL of each of the standard solutions were pi-petted out and taken into a separate test tube. 1 mL of 5% aqueous solution of phenol reagent and 5 mL of 96% sulfuric acid were added. Then, the amount of total reduced sugar content present in the sample solution was calculated using the calibration curve and expressed as gram glucose equivalents (GE) per 50 g of sample (Miliauskas, 2004; Albalasmeh et al., 2013).

Fermentation The fermentation studies were carried out using Saccharomyces cerevisiae in the hydrolysates ob-tained from pretreated and acid hydrolyzed FMS. The specific gravity of the filtered hydrolysates was measured through hydrometer and a separate set of

4 Teshager Mekonnen et al.fermentation experiment was carried out using the pretreated hydrolysates (Galbe and Zacchi, 2002). The pH of the fermentation medium was varied to 4, 4.5, 5, 5.5, 6.0, and 6.5 by adding required amount of 4 M NaOH and 2.5 M HCl. Besides, the yeast was added at a concentration of 2, 3, 4, 5 and 6 g/L, fermentation incubation time was conducted at 2, 3, 4, 5, 6 and 7 days and the temperature was set at 25, 27.5, 30, 32.5, 37.5 and 40 oC to investigate the op-timum conditions. Based on the density of alcohol distillate at 20 oC, the ethanol yield was determined and expressed in weight % (w/v) by Hydrometer and Pycnometer (Park, 2000; Igwe et al., 2012).

The mouths of the flasks were tightly sealed with aluminum foil to maintain anaerobic condition and an outlet was provided to release CO2. The other end of the outlet was dipped in lime water to con-firm the release of CO2 as it turns lime water milky. Confirmatory tests were carried out to ascertain that the distillate was actually bioethanol as it changes to blue green color in the presence of Jones reagent (K2CrO4 + H2SO4) (Mandal and Kathale, 2012). Af-ter fermentation, separation was made using distilla-tion set up at a temperature of 85 oC for 3 hrs (Faga et al., 2010). Consequently, the yield was calculated using both Hydrometer and Pycnometer measure-ments using Eqns. 2 and 3, respectively (Park, 2000; Hadeel et al., 2011; Igwe et al., 2012).

Ethanol %( w/v) = 126.58 )(OSG

FSGOSG −

(2)

Where: 126.58 is obtained from (Specific gravity of water / Specific gravity of pure ethanol) multiplied by 100%, OSG and FSG are original specific grav-ity (specific gravity before fermentation) and final specific gravity (specific gravity after fermentation), respectively. Specific gravity of sample = ( )

( )2 1

3 1

x xx x−−

(3)

Where: x1, x

2 and x

3are weight (g) of empty pycnom-

eter, weight (g) of pycnometer + sample and weight (g) of pycnometer + water, respectively.

Metal Analysis

Since the quality of the fuel used affects the engine life and the degree of pollution of the environment, the concentration of metals such as Fe, Mg, Ca, Pb, and Cr in the biofuel was determined using the In-ductively Coupled Plasma-Optical Emission (ICP-OES) (Rocha et al., 2010; Hossain et al., 2011).

Data Analysis

An Origin Pro8 software and Microsoft excel 2007 were used for the analysis of data collected.

RESULTS AND DISCUSSION

Effects of Different Parameters on Hydrolysis

The Effect of biomass concentration on Hydrolysis

The effect of substrate concentration was investi-gated at 30 oC for 48 hrs. The highest sugar content (about 67.04 and 67.52 %w/w by phenol-sulfuric acid and Fehling method, respectively) was ob-tained at 10% biomass concentration. As presented in Table 1, the sugar content increased with increas-ing substrate concentration.

Effect of acid concentration on Hydrolysis

From Table 2, the maximum sugar content of 68.72 and 70.65% by phenol-sulfuric acid and Fehling method, respectively, was produced using 2% acid hydrolysate of FMS with minimum yield at 0% acid concentration (27.64%). This shows that 2 % sulfuric acid hydrolysis is more effective in simple sugar production as compared to 1, 3, and 4 % sulfuric acid hydrolysis. The result showed that the amount of sugar obtained increases as the acid concentration increases from 0-2 % and decreases as the acid concentration increases from 2-4 %.


Table 1. Total sugar content (% w/w) determined at various biomass concentration, 2 % H2SO4, 2

days and 30 oC.

Substrate concentration (% w/v) Amount of sugar content (% w/w)Phenol-sulfuric acid method Fehling method

6.25 38.53±0.00* 41.70±1.20*

7.14 43.53±1.92* 45.02±1.32*

8.33 56.99±2.90* 56.71±2.10*

10.00 67.04±2.93* 67.52±2.59*

12.50 Out of range Very high (V**<15 mL)

16.61 Out of range Very high (V**<15 mL)

*= Standard deviation

Where; V** volume used in the titration (titrate value) (mL).

Table 2. Total sugar content (%w/w) determination of FMS using the two methods at different acid concentration (H2SO4) hydrolysates of 10 %w/v biomass concentration, two days, and 25 oC.

Acid concentration (% v/v) Amount of sugar content (% w/w)

Phenol-sulfuric acid method Fehling method

0 25.14±0.00* 27.64±0.22*

1 58.66±2.70* 59.63±1.23*

2 68.72±2.91* 70.65±1.91*

3 53.93±2.70* 55.317±1.17*

4 35.20±0.00* 36.72±0.51*



The decrement in reduced sugar content with in-creasing acid concentration from 2-4 % may be due to degradation of monomeric sugars (xylose, glu-cose) to furfural and HMF. Besides, it may be de-rived from dehydrating or oxidizing by sulfuric acid on glucose or it could be attributed from the conver-sion of glucose to levulinic and formic acid which leads to decrease in glucose yield. But, further anal-ysis needs to be undertaken to confirm the formation and concentration of those expected products.

According to Fadel (2000), when acid concentration is higher than 6 %, not only lower glucose concen-tration was obtained but an increment in inhibitor concentration was seen. 5-HMF (5-hydroxymethyl-furfural) was not detected when using 2 % sulphuric acid. At 6% acid concentration, the concentrations of inhibitors (5-HMF and furfural) were observed. When acid concentration was raised to 10 %, the concentration of HMF and furfural became higher (Fadel, 2000). In this study the maximum reduced sugar from finger millet straw was achieved at 2 % sulfuric acid, which is an optimum condition in acid concentration.

Effect of Hydrolysis Temperature

Temperature is one of the major constraints that de-termine the sugar yield because temperature exerts a profound effect on conversion of cellulose or hemi-cellulose to simple sugars. To know the optimum temperature for sugar production, the hydrolysis media were kept at 25, 30, 35, 40 and 45oC (Table 3).The sugar yield increase due to increasing in tem-perature from 25oC to 35oC and maximum at 35oC. Beyond this temperature the sugar content was de-creased significantly. The maximum sugar yield at 35o C was 68.72 and 70.65 % by phenol-sulfuric acid and Fehling method, respectively. Because, at low temperatures (25oC), the reaction was reduced and it slows the rate of conversion of substrate into reduced sugar. A reason behind significant lower production of sugar at high temperature is degra-dation of sugar in to unwanted materials. Overall, these results indicate that extreme temperature had an unfavorable effect on sugar conversion of FMS due to formation of 5-HMS and furfural that are toxic for S. cereviciae in fermentation (Nutawan et al., 2010; Yeshitila Asteraye et al., 2013; Sahu, 2014).

Table 3. Determination of sugar content (% w/w) of FMS hydrolysate using the two methods at different temperature of 10 % w/v biomass concentration, two days and 2 % H2SO4.

Temperature (oC)Amount of total sugar content (% w/w)Phenol-sulfuric acid method Fehling method

25 57.01±2.71* 58.12±1.15*

30 62.01±2.91* 62.05±1.34*

35 68.72±2.91* 70.65 ±1.74*

40 58.66±2.90* 58.85±0.00*

45 51.93±0.00* 52.17±0.93*



Effect of Hydrolysis Time

Based on the above optimizations for hydrolysis, 10 % biomass concentration, 2 % acid concentration and 35oC were selected as optimized conditions for hy-drolysis. Prolonging the hydrolysis time significantly in-creased sugar concentration and then started to de-cline after 4 days hydrolysis. Table 4 showed that at 1, 2, 3, 4, and 5 days hydrolysis of FMS, 48.56 and 49.9, 61.89 and 62.05, 72.08 and 73.98, 79.04 and 82.01, 67.04 and 67.53 % of sugar content were ob-tained by phenol-sulfuric acid and Fehling method, respectively. The maximum sugar content, 79.04 and 82.01 %, were achieved at 4 days hydrolysis time for both phenol-sulfuric acid and Fehling methods, re-spectively. However, as hydrolysis time goes beyond 4 days it resulted in decreasing sugar content. The reason for this could be that longer residence time makes the sugars degraded to form inhibitors (fur-fural and HMF) (Nutawan et al., 2010). FMS has a maximum reduced sugar of 79.04-82.01% which is more comparable as compared to coffee husk (Sahu, 2014), olive-tree biomass (Kumar et al., 2009), sweet potato (Kumar et al., 2014), etc having 90%, 83%, and 78.19%, respectively.

Table 4. Total sugar content (%w/w) determination using the two methods at different hydrolysis time of 10 % w/v biomass concentration, 2% H2SO4 and 35oC reaction temperature.

Time (days)Amount of sugar content (%w/w)

Phenol-sulfuric acid method Fehling method1 48.57±0.00* 49.90±0.91*

2 61.89±2.89* 62.05±1.47*

3 72.08±2.91* 73.98±2.10*

4 79.04±2.92* 82.01±2.31*

5 67.04±2.91* 67.53±1.74*


Therefore, 2% sulfuric acid, 10% biomass concentra-tion, 35 oC, and 4 days residence time were selected as the optimum conditions in hydrolysis of FMS for bioethanol production.

Effects of Different Parameters on Fermentation

There are many parameters which should be consid-ered in fermentation process such as; temperature, reaction time, amount of yeast added, and pH. The effects of those parameters on bioethanol production are important for the successful progress of the fer-mentation. Considering all the mentioned parameters, the experimental outcomes of those particular results were measured their density using Hydrometer and Pycnometer to determine alcoholic content of the produced bioethanol.

Effect of pH on Fermentation

The effect of pH on ethanol production was studied by conducting from pH 4.0 to 6.5 for yeast strains (S. cerevisiae) by keeping initial substrate concentration (4 g/L), initial temperature (30 oC) and 3 days of fer-mentation period (Table 5).


As shown in Table 5 the maximum ethanol con-centration 6.08 and 6.58% by Hydrometer and Pycnometer measurement was achieved, respec-tively using S. cerevisiae culture grown at pH 6.0 and then decreased marginally above this value. Control of pH during ethanol fermentation is im-portant for two reasons: (1) the growth of harm-ful bacteria is retarded by acidic solution. (2) Yeast grows well in acidic conditions (Tahir et al., 2010; Tahir and Sarwar, 2012).

The ethanol yield increased significantly from pH 4.0 to 6.0 beyond this level there is a decre-ment in ethanol yield. High ethanol production was achieved by using initial pH 5.0 to 6.0 (Fadel, 2000). Osman et al., (2011) tested wide initial pH range and confirmed that at pH 3.0 no growth was observed and no ethanol was produced, while pH 6.0 was the optimum for ethanol production. Sim-

ilar results were obtained when Ziziphus mauriti-ana fruit pulp, potato (Kufri Bahar), and mahula (Madhuca latifolia L.) were used as a substrate (Akponah and Akpomie, 2011; Duhan et al., 2013), respectively.

Effect of Yeast on Ethanol Production Effect of yeast extract was studied by varying its concentration from 2 to 6 g/L keeping rest of the parameters at their optimal conditions. The effect of yeast extract at different concentration is shown in Table 6.

Table 6 indicates that as the concentration of yeast extract increased from 2 to 4 g/L, ethanol produc-tion was also increased from 3.92 and 4.02 to 6.26 and 6.66% by hydrometer and Pycnometer measure-ment, respectively, however, above this concentra-tion, ethanol production was decreased.

Table 5. Yield of ethanol at 30 0C, 4 g/L yeast concentration and for 3 days, but at various pH

pH

Specific Gravity

Yield of ethanol (%w/v )Hydrometer reading

Pycnometer

Reading Hydrometer value Pycnometer valueBefore fermentation

After

fermentation4.0

1.030±0.016* 0.996±0.010 0.9935±0.0004* 4.18 4.544.5

1.032±0.010* 0.995±0.020* 0.9929±0.0007* 4.54 4.995.0

1.038±0.017* 0.995±0.032* 0.9921±0.0007* 5.24 5.595.5

1.039±0.051* 0.994±0.026* 0.9916±0.0005* 5.48 5.976.0

1.040±0.037* 0.990±0.036* 0.9908±0.0003* 6.08 6.586.5

1.037±0.045* 0.996±0.044* 0.9923±0.0004* 5.00 5.44



Table 6. Ethanol yield at different amount of yeast extract, 30 oC, 3 days and pH, 6.

Yeast extract (g/L)

Specific GravityYield of ethanol (% w/v)

Hydrometer reading

PycnometerReading

Before fermentation

After fermentation

HydrometerValue

Pycnometer Value

2 1.031±0.004* 0.999±0.000* 0.9942±0.0006* 3.92 4.023 1.031±0.004* 0.991±0.003* 0.9928±0.0007* 4.91 5.064 1.031±0.004* 0.980±0.008* 0.9907±0.0011* 6.26 6.665 1.031±0.004* 0.994±0.004* 0.9918±0.0007* 5.48 5.826 1.031±0.004* 0.998±0.002* 0.9936±0.0005* 4.05 4.47


Many scholars have studied the effect of yeast ex-tract concentrations on sugar consumption for etha-nol production and maximum ethanol was achieved from sweet sorghum juice at 9.0 g/L of yeast extract (S. cerevisiae NP 01) (Nuanpeng et al., 2012), at 2.0 g/L yeast for S. cerevisiae MTCC-170 when potato (Kufri Bahar) was used as a substrates (Duhan et al., 2013).

Effect of Temperature on Ethanol production

Too high temperature destroys yeast, and yeast activ-ity slows down at lower temperature (Yeshitila Aster-aye et al., 2013). Thus, keeping a specific range of temperature is required. In this study ethanol fermen-tation was conducted at temperature range between 25-40 °C for optimizations.

From Table 7, the ethanol yield increases as the tem-perature increases from 25 to 32.5 oC. Beyond this level the ethanol content decreases significantly. The maximum ethanol yield was achieved at 32.5 oC with 6.70 and 7.12% by Hydrometer and Pycnometer measurements, respectively. At low temperatures, the yeast activity suppress-es and the yield slows down. Further, the increasing

temperature reduced the percentage of ethanol pro-duction and it is mainly due to denaturation of the yeast cells (Periyasamy et al., 2009). Duhan et al. (2013) studied the effects of temperature on bioeth-anol yield and observed that maximum bioethanol was produced at 35 oC. Temperatures between 30-35 oC have been usually employed for culturing of yeast and temperature above 35 oC has been found inhibi-tory to ethanol fermentation due to yeast growth in-hibition at higher temperatures (Tahir et al., 2010). This study is in good agreement with previously re-ported works. The maximum ethanol content was recorded at 30 oC (Rani et al., 2010), 32 oC (Asli, 2010), and 28-30 oC (Osman et al., 2011) temperatures. Therefore, those observations are almost similar to present work which yields maximum ethanol at 32.5 oC.

Effect of Fermentation Time on Ethanol

production

The fermentation was carried out at different time periods (2, 3, 4, 5, 6 and 7 days) and the results are shown in Table 8. From Table 8, maximum ethanol production was ob-served after 4 days fermentation (6.92 and 7.28 %


Table 7. Ethanol yield at pH 6, 3 days, 4 g/L yeast extract and different fermentation temperature.

Temperature (oC)

Specific GravityYield of ethanol (%w/v ) Hydrometer reading

PycnometerReading

Before fermen-tation

After fermenta-tion Hydrometer

ValuePycnometer Value

25 1.031±0.007* 0.997±0.002* 0.9938±0.001* 4.17 4.3227.5 1.033±0.005* 0.995±0.005* 0.9928±0.001* 4.65 5.0630 1.037±0.007* 0.988±0.007* 0.9911±0.002* 5.98 6.35

32.5 1.039±0.010* 0.984±0.004* 0.9907±0.004* 6.70 7.12

35 1.038±0.008* 0.992±0.011* 0.9916±0.001* 5.60 5.97

37.5 1.032±0.015* 0.993±0.003* 0.9929±0.003* 4.53 4.9940 1.030±0.009* 0.998±0.001* 0.9939±0.001* 3.93 4.24


from Hydrometer and Pycnometer measurements, respectively). Further increase in time period result-ed in decreasing of ethanol production. The differ-ence of alcoholic content measured using Hydrome-ter and Pycnometer are not much significant. The concentration of bioethanol increased with in-creasing fermentation time, and decreased in farther increment of fermentation time. From Table 8, the lowest concentration of bioethanol production (4.67 and 4.84 %) through Hydrometer and Pycnometer

measurments were obtained at fermentation time of 7 days, respectively. Beyond 4 days of fermentation time the bioethanol yield started to level off. This might be due to the consumption of sugar by the mi-croorganisms for ethanol production or the hydroly-sate does contain significant levels of metabolic in-hibitors (e.g., furfural and HMF) that can interfere with fermentation (Weil et al., 2012). At this point it is worthwhile to mention that the concentration of ethanol obtained by the hydroly-

Table 8. Ethanol yield at 32.5oC, pH 6.0, 4 g/L yeast extract, and different fermentation time

Time(day)

Specific Gravity

Yield of ethanol (%w/v) Hydrometer reading

PycnometerReading

Before fermen-tation

After fermenta-tion Hydrometer

valuePycnometer Value

2 1.035±0.005* 0.996±0.005* 0.9930±0.000* 4.76 4.913 1.036±0.006* 0.995±0.001* 0.9925±0.0013* 5.00 5.294 1.042±0.011* 0.985±0.012* 0.9899±0.0017* 6.92 7.28

5 1.040±0.009* 0.989±0.006* 0.9910±0.0011* 6.21 6.43

6 1.039±0.006* 0.992±0.010* 0.9916±0.0015* 5.72 5.97

7 1.033±0.014* 0.995±0.005* 0.9931±0.0018* 4.65 4.84



sis of the FMS using optimum conditions (6.92 and 7.28% from Hydrometer and Pycnometer measure-ments, respectively) was highly satisfactory com-pared to the maximum amount of ethanol obtained from acid hydrolysis of groundnut hulls (6.2%) and 5.5% from rise husks (Ali et al., 2011), the enzymatic fermentation of mango juice (7-8.5%) depending on the type of mango species) (Reddy, 2007).The result revealed that ethanol obtained from FMS is a promising substituent for other agricultural prod-ucts such as mango juice, cassava and corn. FMS is not edible material by human being and hence it can avert food crisis by doing away with food crops for bioethanol production.

Metal Analysis

The concentration of the metals in finger millet straw was obtained as 0.32, 0.82, 1.2, 0.38, and 0.45 mg/L for Cr, Fe, Mg, Pb, and Ca, respectively. From the re-sults of the elemental analysis, the concentration of metals in the produced bioethanol from FMS rang-es from 0.32 to 1.2 mg/L. Bioethanol obtained from FMS had smaller value of chromium and lead (0.32 and 0.38 mg/L, respectively) as compared to the other metal concentrations. Therefore, the produced bioethanol is good for engine use and it is an envi-ronmentally friendly energy source. Generally, most of the element concentrations followed the ASTM standard that is better for engine use (Iqbal et al., 2010; Rocha et al., 2010; Hossain et al., 2011).

CONCLUSION

The optimizations showed the highest bioethanol concentration was observed at dilute sulfuric acid hy-drolysis and fermentation time of 4 days held at 32.5 oC with S. cerevisiae. Both phenol-sulfuric acid and Fehling method in the hydrolysis step, and the Hy-drometer and Pycnometer in the fermentation have comparable values. The bioethanol obtained by dilute acid hydrolysis of FMS (7.28%) was highly satisfac-tory and hence, it is promising lignocellulosic feed-

stock for bioethanol production as compared to food crops such as corn, sugarcane, etc as it is not disturb the food chain of mankind.

REFERENCES

Adane Muche and Sahu, O. (2014). Biofuels from biomass in rural area. Journal of Biotechnology, Bioinformatics and Bioengineering 1: 9-13.

Akponah, E and Akpomie, O.O. (2012). Optimization of bio-ethanol production from cassava effluent using Saccharomyces cerevisiae. African Journal of Biotechnology 11: 8110-8116.

Albalasmeh, A.A., Berhe, A.A and Ghezzehei, T.A. (2013). A new method for rapid determination of carbohydrate and total carbon concentrations using UV spectrophotometry. Carbohydrate Polymers 97: 253– 261.

Ali, N.M., Mohd, K. M and Mohiuddi, M. (2011). Ethanol fuel production through microbial extracellular enzymatic hydrolysis and fermentation from renewable agro-based

cellulosic wastes. International Journal of Pharma and Bio-Sciences 2: 321-331.

Asli, M.S. (2010). A study on some efficient parameters in batch fermentation of ethanol using Saccharomyces cerevisiae SC1 extracted from fermented siahe sardasht pomace. African Journal of Biotechnology 9: 2906-2912.

Dubois, M., Gilles, K.A., Hamilton, J.K., Rebers, P.A and Smith, F. (1956). Calorimetric method for determination of sugar and related substances. Analytical Chemistry 28: 350-356.

Duhan, J.S., Kumar, A and Tanwar, S. K. (2013). Bioethanol production from starchy part of tuberous plant (potato) using Saccharomyces cerevisiae MTCC-170. African Journal of Microbiology Research 7: 5253-5260.


Erdei, B., Barta, Z., Sipos, B., Réczey, K., Galbe, M and Zacchi, G. (2010). Ethanol production from mixtures of wheat straw and wheat meal. Biotechnology for Biofuels 13: 1-9.

Fadel, M. (2000). Alcohol production from potato industry starchy waste. Egyptian Journal of Microbiology 35: 273-287.

Faga, B.A., Wilkins, M.R and Banat, I.M. (2010). Ethanol production through simultaneous saccharification and fermentation of switch grass using Saccharomyces cerevisiae D5A and thermo tolerant Kluyveromyces marxianus IMB strains. Bioresources Technology 101: 2273-2279.

Molla Fentie, (2012). Participatory evaluation and selection of improved finger millet varieties in north western Ethiopia. International Research Journal of Plant Science 3: 141-146.

Galbe, M and Zacchi, G. (2002). A review of the production of ethanol from softwood. Applied Microbiology and Biotechnology 59: 618–628.

Hadeel, A., Hossain, A.B.M.S., Latifa, K., ALNaqeb, H., Abear, J and Norah, A. (2011). Bioethanol fuel production from rambutan fruit biomass as reducing agent of global warming and greenhouse gases. African Journal of Biotechnology 10: 10157-10165.

Hossain, A.B.M.S., Ahmed, S.A., Alshammari, M.A., Adnan, A.M.F., Annuar, M.S.M., Mustafa, H and Norah, H. (2011). Bioethanol fuel production from rotten banana as an environmental waste management and sustainable energy. African Journal of Microbiology Research 5: 586-598.

Igwe, C. J., Agbaeze, E.K., Obike, I.A., Christopher, U and Sonde, U.C. (2012). Extraction, characterization and determination of ethanol

fuel composite qualities of Saccharum officinarum, Pennisetum purpureum and Costus afer. Asian Journal of Plant Science and Research 2: 643-649.

Iqbal, J., William, A., Shelly, C and Chandra, S. (2010). Metals determination in biodiesel (B100) by ICP-OES with microwave assisted acid digestion. The Open Analytical Chemistry Journal 4: 18-26.

Kumar, P., Barrett, M.D., Delwiche, J.M and Stroeve, P. (2009). Methods for pretreatment of lignocellulosic biomass for efficient hydrolysis and biofuel production. Journal of Industrial and Engineering Chemistry Research 48: 3713-3729.

Kumar, A., Duhan, S.J and Gahlawat, K.S. (2014). Production of ethanol from tuberous plant (sweet potato) using S. cerevisiae. African Journal of Biotechnology 13: 2874-2883.

Mandal, P and Kathale, N. (2012). Production of ethanol from Mahua flower (Madhuca Latifolia using Saccharomyces cerevisiae – 3044 and study of parameters while fermentation. National Monthly Refereed Journal of Research in Science and Technology 1: 6-10.

Miliauskas, G., Venskutonis, P.R and Van Beek, T.A. (2004). Screening of radical scavenging activity of some medicinal plants and aromatic plant extract. Food Chemistry 85: 231-237.

Mishra, S.M., Chandrashekhar, B., Chatterjee, T and Singh, K. (2011). Production of bioethanol from Jatropha oil seed cakes via dilute acid hydrolysis and fermentation by Saccharomyces cerevisiae. International Journal of Biotechnology Applications 3: 41-47.

Nuanpeng, S., Laopaiboon, L., Srinophakun, P., Klanrit, P., Jaisi, P and Laopaiboon, P. (2012).


Ethanol production from sweet sorghum juice under very high gravity conditions: batch, repeated-batch and scale up fermentation. Process Biotechnology 14: 4172-4186.

Nutawan, Y., Phattayawadee, P., Pattranit, T and Mohammad, N. (2010). Bioethanol production from rice straw. Energy Research Journal 1: 26-31.

Osman, M.E., Khattab, O.H., Hammad, I.A and El-Hussieny, N.I. (2011). Optimization of biofuel production by Saccharomyces cerevisiae isolated from sugar cane bagasse. Journal of American Science 7: 485-492.

Park, L.S. (2000). How do you take a hydrometer reading and what is it used for anyway? Midwest Home brewing and Winemaking Supplies. www.midwestsupplies.com. Accessed on July 12, 2014.

Periyasamy, S., Venkatachalams, S., Ramasamy, S and Srinivasan, V. (2009). Production of bioethanol from sugar molasses using Saccharomyces cerevisiae. Modern Applied Science 3: 32-36.

Rani, P., Sharma, S., Garg, F.C., Raj, K and Wati, L. (2010). Ethanol production from potato flour by Saccharomyces cerevisiae. Indian Journal of Science and Technology 3: 733-736.

Reddy, L.V and Reddy, V.S. (2007). Production of ethanol from mango (Mangifera indica L.) fruit juice fermentation. Journal of Microbiology 2: 763-769.

Rocha, S.M., Mesko, F.M., Silva, F.M., Sena, C.R and Lindomar, A. (2010). Determination of Cu and Fe in fuel ethanol by ICP OES using direct sample introduction by an ultrasonic nebulizer and membrane desolvator. Journal of Analytical Atomic Spectrometry 26: 456–461.

Sahu, O. (2014). Bioethanol production by coffee husk for rural area. Advanced Research Journal of Biochemistry and Biotechnology 1: 1-5.

Singh, p and Raghuvanshi, S.R. (2012). Analysis of stability and adaptability of late duration of finger millet (Eleusine coracana) genotypes and for food and nutritional security. African Journal of Food Science 6: 77-84.

Tahir, A., Aftab, M and Farasat, T. (2010). Effect of cultural conditions on ethanol production by locally isolated Saccharomyces cerevisiae Bio-07. Journal of Applied Pharmacy 3: 72-78.

Tahir, A and Sarwar, S. (2012). Effect of cultural condition on production of ethanol from rotten apple waste by S. cerevisiae straining. Canadian Journal of Applied Sciences 2: 187-195.

Yeshitila Asteraye, Wu, C-H., Huynh, L.H., Ismadji, S and Ju, Y-H. (20013). Bioethanol production from Yarrowia lipolytica Po1g biomass. Bioresource Technology 145: 210–216.

Weil, J., Dien, B., Bothast, R., Hendrickson, R., Mosier, N and Ladisch, M. (2012). Removal of fermentation inhibitors formed during pretreatment of biomass by polymeric adsorbents. Industrial and Engineering Chemistry Research 41: 6132–6138.

Zhao, J and Xia, L. (2010). Ethanol production from corn stover hemicellulosic hydrolysate using immobilized recombinant yeast cells. Biochemical Engineering Journal 49: 28–32.

Ethiop. J. Sci. & Technol. 8(1) 15-26, 2015 15


Aregawi Teklay1 and Meareg Amare1*

1 Department of Chemistry, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia

ABSTRACT

The main aim of this study was to assess the level of water quality of Lake Haiq, Ethiopia with respect to selected physical parameters and heavy metals. Parameters such as temperature, pH, turbidity, electrical conductivity and total dissolved solids were measured in situ. While total alkalinity, chloride, ammonia, nitrate and sulphate were investi-gated using standard analytical procedures. the level of the studied heavy metals (Pb, Cd, Cu and Zn) was determined using the inductively coupled plasma-optical emission spectrometry. Sulphate, nitrate, ammonia, chloride, and total dissolved solids were within the recommended levels for a drinking water by the WHO guidelines. In contrast, the total alkalinity, turbidity and pH values were above the recommended limits, indicating the inconveniency to use the water for drinking. While Cd in the water sample was under the method detection limit (< 0.0039 mg/L), the levels of Cu, and Zn were found to be under the respective permitted limits. However, the level of lead throughout the sampling sites ranged from 0.064 to 0.108, which is 6 to 10 times higher than the permitted level in drinking water confirming the awkwardness of using the lake water for drinking. A detailed study of the catchment on the possible sources of pollution is recommended so that appropriate control measures could be taken by the governmental bodies.

Key words/phrases: Heavy metals, Lake Haiq, South Wollo, Water pollutionDOI: http://dx.doi.org/10.4314/ejst.v8i1.2

INTRODUCTION

Water is an essential component for survival of life on earth, which contains minerals, important for hu-mans as well as for earth and aquatic life (Arian et al., 2008). Freshwater accounts 3% of the total water on the earth of which only a small percentage (0.01%) is available for human use (Arumugam et al., 2013). Even this small portion of freshwater is under im-mense stress. Rapid population growth, urbanization, industrialization, use of fertilizers in the agriculture and other human activities are some of the causes for the pollution of the freshwater with different contami-nants (Arian et al., 2008; Pati et al., 2012; Arumugam et al., 2013).Water pollution is the degradation of the quality of water rendering its suitability for its intended pur-pose. Water pollutants can be broadly classified as

organic, inorganic, suspended solids and sediments, heavy metals, radioactive materials and heat (Zen-ebe Yirgu, 2011). Heavy metals are among the most harmful water pollutants due to their non-biodegrad-ability, long biological half-life and their potential to accumulate in aquatic ecosystems (Arian et al., 2008; Moore et al., 2009; Benzer et al., 2013). Though aquatic pollution due to heavy metals is less visible in contrast to others, its effects on the ecosystem and humans can be intensive and very extensive (Arian et al., 2008; Zenebe Yirgu, 2011).Toxicity level of heavy metals depends on the type of metal, its biological role, and the type of organisms that are exposed to it. The heavy metals linked most often to human poisoning are lead, mercury, arsenic and cadmium. Others, including copper, zinc, and tri-valent chromium are actually required by the body in

*Corresponding author: [email protected]© This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/CC BY4.0)

16 Aregawi Teklay and Meareg Amare

small amounts and hence are essential, but can also be toxic in larger doses (Tolera Seda, 2007; Verma and Dwivedi, 2013). The presence of these metals in the aquatic ecosystem while has far-reaching conse-quences on the biota and man; their toxic effects on man are known to cause damaged or reduced mental and central nervous function. Abnormal blood com-position, and damaged lungs, kidneys, liver, and other vital human organs are also known to be among the consequences of heavy metal toxicity (WHO,1984; Igbinosa et al., 2012). This study focuses on Lake Haiq, which is one of the fresh and closed highland lakes of Ethiopia used by the local inhabitants for different purposes including fishing, recreation, irrigation and drinking. Human activities such as agricultural practice, deforestation, discharging of domestic sewage, and waste dispos-al from the town around it (Haiq) including a garage near the lake may cause the deterioration of the quali-ty of the water system of Lake Haiq. Moreover, since the lake is located at a short distance from the main road of Addis Ababa to Tigray, it could also be pollut-ed by particulate matters emitted by vehicles and mo-tors (Shilpa et al., 2011; Igbinosa et al., 2012). The main inflow river, Ancherkah, may also introduce ag-ricultural runoff worsening the pollution. All these to-gether may cause the deterioration of physico-chem-ical water quality parameters and accumulation of heavy metals in the lake. To the best of our knowledge, assessment of the phys-ico-chemical parameters and level of heavy met-als in Lake Haiq has not been reported. Thus, the purpose of this study was to assess the level of se-lected heavy metals (Pb, Cd, Cu, and Zn) and phys-ico-chemical parameters (temperature, pH, turbidi-ty, electrical conductivity (EC), total dissolved solid (TDS), total alkalinity (TA), chloride, ammonia, ni-trate and sulphate) of Lake Haiq, Ethiopia. While standard methods (GOI and GON, 1999) were used for the analysis of the physico-chemical param-eters, Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), which is the most powerful

with high selectivity, sensitivity, precision, and accu-racy (Boevski and Daskalove, 2007) was used for the determination of the studied heavy metals.

EXPERIMENTAL

Description of the study area

The study was conducted in Lake Haiq, which is one of the highland lakes of Ethiopia located in Northern part of Ethiopia, Amhara Regional State, South Wol-lo Administrative Zone (Figure 1), at 11o15’ N lati-tude, 39o 57’ E longitude, and at an altitude of 2,030 m above sea level (Betel Assefa, 2010). It is a crater lake with surface area, maximum depth and mean depth of 23.2 km2, 88 m and 37.4 m, respectively (Betel Assefa, 2010). The only stream of any size en-tering the lake is the Ankarka River, which flows to its south east corner. The lake has no visible outlet. A small town called Haiq is located at the southern shore of the lake. The water level of the lake fluctu-ates with variability of rainfall, its maximum volume being during the rainy season. The area is character-ized by a sub-humid tropical climate with an average annual rainfall of 1211.4 mm and a mean annual tem-perature of around 25.9 oC (Betel Assefa, 2010).

Selection of sampling sites and description

Six sampling sites; labeled as site-1 to site-6 (Figure 1) covering the whole area of the lake were selected and the water samples were collected just from the surface of the lake. Sampling site -1 is located along Haiq Estifanos Mon-astery, which serves as holy site for Orthodox Chris-tian pilgrims. Wastes released from habitats and cattle watering at the site may influence the quality of the lake. This site which is also close to the highway may receive particulate pollutants related to motor ve-hicles.Sampling site-2 is situated to the opposite of Haiq Es-tifanos Monastery. It is the site of minimally impacted physical habitat, low human population pressure and


no known discharge and hence used as reference site. Sampling site-3 is the site along which the lake is used by the community for irrigation, fetching water for drinking, bathing, washing clothes, cattle drinking etc. Sampling Site-4 is near the entry of the Ankarka Riv-er. This river loaded with industrial waste and dust materials may have the chance of altering the quality of the lake water. Sampling site-5 is located on the side of the Logo of Haiq Logi recreation. This site does not have point source of pollution but there may be non-point source of pollution from the agricultural land and soil of the area.Sampling site-6 is located near the Haiq town and is commonly used for recreational purpose. Since there

is population pressure along this site, it was found congested with non-biodegradable use throw plastic wastes. This site is the area where the lake receives effluent of the Hotels as well as urban runoff.

Instruments and apparatus

pH meter (Wagtech pH meter, W-30200), turbid-ity meter (HACH2100AN), electrical conductivi-ty (Wagtech EC/TDS meter, W-30210), Photometer (Wagtech 500, palintest Ltd, England), refrigera-tor (FR 1082), digital electrical balance (PW 124), Ice box (Lec technical), Global positioning system (GPS) (Garmin 12 Channel GPS, USA), and Induc-tively Coupled Plasma-Optical Emission Spectrome-try (ICP-OES) (ULTIMA 2, HORIBA scientific) were used in this study.

Figure 1. The map of the study area with sampling sites marked as S1through S6.


Chemicals and Reagents

HNO3 (69-72%), and HCl (35-38%) (both from Fish-er Scientific, UK), AgNO3, Pb(NO3)2, Cd(NO3)2.4H2O, Cu(NO3)2.3H2O, and Zn(NO3)2.6H2O (all from Blu-lux Laboratories Ltd,) were used. All chemicals and reagents used were of analytical grade and were used without further purification. Freshly prepared distilled water and deionized water were used throughout the experiment for preparing standard solutions, dilution and rinsing apparatus.

Procedures

Sample collection and preservation

Water samples were collected from the surface of the lake in April, 2014 just before noon. The samples were collected from the six sampling sites illustrated under sampling site description using one liter-capac-ity cleaned polyethylene bottles. A composite water sample was obtained from each sampling site by mix-ing the grab samples collected from four spots of the same depth (surface) at each sampling site. Hence, water samples from the six sampling sites were col-lected separately, and preserved in ice box to mini-mize physico-chemical changes. Each one L composite water sample collected from each sampling site was then divided into two equal portions; a portion for the physico-chemical analy-sis and the other for the analysis of dissolved heavy metals. One portion was first filtered using Whatman No 1filter paper in to a 500 mL pre-cleaned polyeth-ylene bottle, and acidified by adding two mL of con-centrated HNO3 to minimize precipitation and adsorp-tion on the container walls (APHA, 1998) and was then stored in a refrigerator until analysis. The other portion of the water sample was used for the determi-nation of physico-chemical parameters without any pre-treatment.

Analysis of physico-chemical parameters

Temperature, pH, turbidity, electrical conductivity (EC) and total dissolved solids (TDS) were measured

in the field (in situ). Temperature readings were made on site by directly immersing the probe in to the sur-face water. The pH of water samples were measured at each sampling site by inserting the pH meter probe (calibrated using pH 4.00, 7.00, and 10.00 standard buffer solutions prior to measurement) in to the water immediately after collection. Turbidity, EC and TDS readings were also taken at the same time as pH.

Total alkalinity (TA) was determined by a standard method (titration) using 0.1 N HCl as a titrant, and methyl-orange and phenolphthalein as endpoint indicators. Similarly, chloride was determined by titration using silver nitrate as titrant, and potassium chromate (K2CrO4) solution as an indicator following a standard analytical procedure for water analysis (GOI and GON, 1999). Ammonia, nitrate and sulphate were determined using palintest photometer following standard procedures (Osei and Jackson, 2008).

Heavy metal analysis

To prepare stock solutions of Pb, Cd, Cu, and Zn each of 1000 mg/L concentration, an appropriate amounts of Pb(NO3)2, Cd(NO3)2.4H2O, Cu(NO3)2.3H2O, and Zn(NO3)2.6H2O, respectively were taken. For each metal, the required amount of the corresponding salt was put in to a 1000 mL volumetric flask to which a 4 mL (1:1) HNO3 was added. After shaking, the solution, it was then diluted up to the mark with deionized water. Intermediate standard solutions of 10 mg/L concentration were prepared from the respective stock solution by dilution from which, standard working solutions of series concentrations were freshly prepared (Table 3). Following the analysis of the series concentrations for each metal, reagent blank and water samples were aspirated in to the ICP-OES where triplicate measurements for each were made. The ICP-OES operating conditions and emission wave length for each studied metal are shown in Table 1.


Statistical Analysis of data

To check whether the mean values of the investigated parameters between sampling sites are significantly different or not, statistical analysis of data (one way ANOVA) using SPSS version 20 software was em-ployed. Difference in mean values were accepted as being statistically significant if p < 0.05.

RESULTS AND DISSCUSION

Physico-chemical analysisThe average results for the investigated physi-co-chemical parameters are summarized in Table 2. As shown in the table, the temperature of the lake varied between 22.84 ± 1.47 to 29.25 ± 0.89 °C with a mean value of 26.46 ± 2.34 °C. The variation may be ascribed to the sampling time temperature differ-ence at the sampling sites.

The pH value of the surface water of Lake Haiq ranged from 8.71 ± 0.13 to 8.90 ± 0.08, the mean value being 8.83 ± 0.07. The pH value showed slight alkalinity compared to the allowed WHO guideline value (Table 2). The temperature and pH variation showed the same trend through the sam-pling sites (Figure 2). While turbidity is a measure of cloudiness of wa-ter, high turbidity may indicate the presence of or-ganisms including bacteria, viruses, and parasites that can cause symptoms such as nausea, cramps, diarrhea, and associated headaches (Akoto and Adiyiah, 2007). In the present investigation, sam-pling sites S3, S5, and S6 showed higher turbidity values than the recommended limit by WHO stan-dard, which may be due to contamination of the water by wastewater, garbage, mass bathing, of-fering foods, flowers, garlands and other religious matters.

Table 1. ICP-OES operational parameters and emis-sion wave length of the studied heavy metals

Operational parameter Value

Plasma power (RF) 1450 wPlasma gas (Ar) flow 15 L/minAuxiliary Ar flow 0.5 L/minnebulizer flow rate 0.51 L/minNebulizer pressure 1.77 barPump speed 20 rpm

Sheath flow 0.2 L/min

Studied metalEmission wave length (nm)

Pb 220.4

Cd 226.5

Cu 327.4

Zn 213.9

S1 S2 S3 S4 S5 S622

23

24

25

26

27

28

29

30

Sampling sites

Tem

pera

ture

/ o

C

8.70

8.75

8.80

8.85

8.90

pH

Figure 2. Trends of Temperature and pH with sam-pling sites.


Tabl

e 2.

Val

ues*

of t

he se

lect

ed p

hysi

co-c

hem

ical

par

amet

ers a

t sam

plin

g si

te (S

1-S6

) of L

ake

Hai

q an

d co

mpa

rison

s with

WH

O st

anda

rd g

uide

line

Para

met

erS1

S2S3

S4S5

S6W

HO

, 200

4

Tem

pera

ture

(0 C)

22.8

4 ±

1.41

24.5

4 ±

0.48

27.1

5 ±

1.20

27.7

0 ±

1.13

29.2

5 ±

0.89

27.2

9 ±

0.23

N

L

pH8.

71 ±

0.1

38.

78 ±

0.0

68.

84 ±

0.0

48.

87 ±

0.0

68.

90 ±

0.0

88.

89 ±

0.0

8

6.5-

8.5

Turb

idity

(NTU

)3.

69 ±

0.2

11.

26 ±

0.0

466

.10

± 1.

842.

13 ±

0.1

615

.97

± 0.

3747

.35

± 2.

76

<5

EC(μ

S/cm

)92

4.00

± 1

5.56

924.

50 ±

9.1

992

6.50

± 1

2.02

920.

50 ±

16.

2691

6.00

± 2

9.69

921.

50 ±

17.

68

NL

TDS

(mg/

L )

462.

00 ±

7.0

746

1.50

± 6

.36

463.

00 ±

50.

6646

1.50

± 7

.78

458.

00 ±

14.

1446

2.00

± 7

.07

1000

mg/

L

T-A

lk (m

g/L)

430.

79 ±

3.5

934

9.53

± 4

.06

494.

65 ±

7.5

757

3.56

± 9

.75

627.

57 ±

8.1

555

7.74

± 1

2.61

200

mg/

L

Chl

orid

e (m

g/L)

44.8

6 ±

1.47

46.1

4 ±

2.12

48.2

3 ±

0.84

47.6

5 ±

1.41

47.2

7 ±

1.58

49.1

4 ±

0.72

250

mg/

L

Am

mon

ia (m

g/L)

0.26

± 0

.04

0.33

± 0

.03

0.37

± 0

.02

0.16

± 0

.02

1.35

± 0

.21

0.14

± 0

.02

3 m

g/L

Nitr

ate

(mg/

L)0.

21 ±

0.0

20.

04 ±

0.0

20.

28 ±

0.0

30.

05 ±

0.0

10.

04 ±

0.0

10.

29 ±

0.0

150

mg/

L

Sulp

hate

(mg/

L)2.

72 ±

0.1

62.

36 ±

0.0

62.

49 ±

0.2

65.

20 ±

0.8

54.

55 ±

1.2

82.

18 ±

0.2

5/L

*mea

n ±

RSD

for t

riplic

ate

mea

sure

men

ts; N

L =N

ot c

ited

in li

tera

ture


Electrical conductivity (EC) is an estimate of total dissolved salts in water. EC values between 2,500 and 10,000 µS cm-1 is not recommended for human consumption and normally not suitable for irrigation except for very salt tolerant crops with special man-agement techniques (Yenkie et al., 2010). The elec-trical conductivity of the water sample collected from Lake Haiq varied between 916.00 ± 29.69 and 926.50 ± 12.02 μS/cm, the highest value being for the water sampled at S3 which may be attributed to the geology along agricultural land and discharges of domestic ef-fluents. Total dissolved solids (TDS) is an important indica-tor of the suitability of water for drinking, recreation-al, irrigation and industrial use. TDS includes those materials dissolved in the water including bicarbon-ate, sulphate, phosphate, nitrate, calcium, magne-sium, sodium, organic ions, and other ions which are important in sustaining the aquatic life (Zenebe Yirgu, 2011). As can be observed from Table 2, the TDS values ranged from 458.00 ± 14.14 to 463.00 ± 5.66 mg/L indicating TDS throughout the lake is un-der WHO’s maximum allowable concentration (1,000 mg/L). Alkalinity is an estimate of the ability of water to re-sist change in pH upon addition of acids. The various ionic species that contribute to alkalinity include bi-carbonate, hydroxide, phosphate, borate and organ-ic acids (Yadaw et al., 2012). Total alkalinity values for the investigated samples were found to be in the range 349.53 ± 4.06 to 627.57 ± 8.15 mg/L (Table 2). The total alkalinity of water collected from most of the sampling sites was higher than the permitted val-ue by WHO guidelines (200 mg/L) which could be ascribed to higher carbon dioxide concentration and release of bicarbonates by sediments. Higher level of chloride in natural water is a definite indication of domestic sewage. The ecological signif-icance lies in its potential to regulate salinity of water and exert consequent osmotic stress on biotic com-munities (Shinde et al., 2011). In the present analy-sis, chloride concentration in the water samples was

found in the range 44.86 ± 1.47 to 49.14 ± 0.72 mg/L showing that all the values are under the maximum allowed concentration by WHO guidelines .Relative to the other sampling sites, higher chloride concentra-tion at S6 (Table 2) could be due to large discharges of domestic sewage near the sampling site. In the present investigation, the level of NH3 in all sampling sites ranged from 0.14 ± 0.02 to 1.35 ± 0.21 mg/L which still is within the recommended level of ammonia in drinking water by the WHO (3 mg/L). Higher ammonia concentration at S5 (Table 2) might be due to fertilizer runoff and sewage releases into the Lake water. The average nitrate concentration in the water samples collected from Lake Haiq ranged from 0.04 ± 0.01 to 0.29 ± 0.01 mg/L indicating the level is still under the permissible limit of WHO. Low nitrate level is an indication of the absence of nitrogen fixing bacteria responsible for convertion of ammonia in to nitrate. The relatively higher nitrate concentration at sampling site S6 (Table 2) might be due to the use of Nitrogen fertilizers in the agricultural fields along the shore and effluents discharged from domestic wastes.Sulphate occurs naturally in water as a result of leaching from gypsum and other common minerals. Discharge of industrial wastes and domestic sewage tend to increase its concentration (Gopalkrushna, 2011). In the present investigation, the levels of Sulphate (SO4

2-) in the Lake ranged from 2.18 ± 0.25 to 5.20 ± 0.85 mg/L all of which being much lower than the WHO recommended value. A relatively higher SO4

2- level at sampling site S4 (Table 2) might be due to geological origin through weathering of sulfur containing rocks.

Heavy metal analysis

Calibration curve and method Detection Limit

Five working standard solutions for each analyzed metal were prepared freshly from the respective intermediate standard solution as discussed under the experimental section. Table 3 presents the list of


working standard solutions, corresponding regression equation, correlation coefficient and calculated LOD (s = 3σ for n = 3) (Peter and Kirk, 2002) for each analyzed metal.

ICP-OES determination of the concentration of heavy metals in water samples

ICP-OES was used for the determination of the lev-el of the studied heavy metals in the water samples collected from Lake Haiq. After measuring the ICP-

Table 3. Summary of the working standard concentrations, linear regression equation, correlation coefficient and method LOD for each studied heavy metals using ICP-OES method

Metal Conc. of standard solutions (mg/L)

Linear regression equation R2 LOD (mg/L)

Pb 0.05, 0.1, 0.2, 0.4, 0.8 I = 173,468.79 + 6,114.305C 0.99967 0.0039

Cd 0.005,0.01,0.02,0.04, 0.08 I = 278440.53 + 242,449.18C 0.99565 0.00073Cu 0.25, 0.5,1, 2, 4 I = 1,045,430+ 5,072.66C 0.99483 0.00051

Zn 0.25,0.5,1, 2, 4 I = 30,518.65 + 384,109.72C 0.99992 0.0012

0.0 0.2 0.4 0.6 0.8

174000

176000

178000

inte

nsity

[Pb] (mg/L)

Y=173468.79 + 6114.305CR2= 0.99967

(a)

0.00 0.02 0.04 0.06 0.08

280000

285000

290000

295000

300000in

tens

ity

[Cd] (mg/L)

Y=278440.53 + 242449.18CR2= 0.99565

(b)

0 2 41045000

1050000

1055000

1060000

1065000

inte

nsity

[Cu] (mg/L)

Y=1045430 + 5072.66CR2= 0.99483

(c)

0 1 2 3 40

300000

600000

900000

1200000

1500000

inte

nsity

[Zn] (mg/L)

Y=30518.65 + 384109.72CR2=0.99992

(d)

Figure 3. ICP-OES calibration curves for (a) Pb, (b) Cd, (c) Cu and (d) Zn standards of different


OES signal for each metal in the collected samples, the corresponding concentrations were calculated by inserting the signals in to the respective regression equation. Table 4 presents the summary of the lev-els of the studied heavy metals in the. As can be seen from the table, cadmium concentration was below the method detection limit while the rest were above the respective method detection limits. The levels of Cu and Zn were found to be under the respective allowed levels by WHO guidelines show-ing that there is no risk of pollution due to these met-als. On the contrary, Pb which has been found to be responsible for quite a number of ailments in humans such as chronic neurological disorders especially in fetuses and children was found at a level higher than the permitted level by WHO guidelines. The concen-

tration of lead at all the sampling sites was found to be six to ten times higher than the permitted level by the WHO guidelines. As presented by Ogbonna et al. (2011), automobile exhaust fumes have been reported to account for about 50% of the total inorganic Pb ab-sorbed by human beings. Other inputs of Pb into the environment are from used dry-cell batteries, from sewage effluent, runoff of wastes and atmospheric deposition. In this regard, the highest value of lead detected at sampling site S6 could partly be account-ed for the closeness of the site to the high way (Addis Ababa-Tigray) from where vehicle emission may pol-lute the water system and partly for the garage located near the site from where lead from car batteries could leak in to the water system.

Table 4. Level of the studied heavy metals in Lake Haiq by sampling site

Sampling site Metal concentration* in mg/L

Pb Cd Cu ZnS1 0.093±0.004 BDL 0.540 ± 0.320 0.160 ± 0.002S2 0.064±0.008 BDL 0.260 ±0.072 0.160 ±0.001S3 0.082±0.015 BDL 0.360±0.140 0.150 ±0.003S4 0.069±0.007 BDL 0.450±0.109 0.160 ±0.001S5 0.097±0.006 BDL 1.940±0.398 0.160±0.001S6 0.108±0.001 BDL 1.440±0.120 0.150 ±0.002Allowed limit** 0.01 0.003 2 3

*mean ± RSD for n = 3; BDL = below instrument detection limit, **= WHO ( 2004)

Table 5. Comparison of the level of Pb detected in the water of Lake Haiq (mg/L) against its level in re-ported works.Name of the Lake Country Pb ReferenceMahrulu Iran 0.0050 Moore et al. (2009)

Muhazi Rwanda 0.2920 Nhapia et al. (2012)

Baringo Kenya 0.0430 Ochieng et al. (2007)

Manzala Egypt 0.0990 Saeed and Shaker (2008)

Hawassa Ethiopia 0.0030 Zenebe Yirgu (2011)

Koka Ethiopia 0.0006 Larissa et al. (2012)

Haiq Ethiopia 0.0860 This study


Comparison of the level of Pb in the studied Lake with its values in Lakes of reported works is present-ed in Table 5. As shown in the table, the level of Pb in Lake Haiq was higher than its value in the two Ethi-opian Lakes (Hawassa and Koka). Moreover, it was higher than its values in Lakes Baringo (Kenya) and Mahrulu (Iran), but lower than (ahazi (Rwanda) and Manzala (Egypt) lakes.

One way ANOVA analysis

As described under the experimental section, the p-values for all the studied parameters were calculat-ed using the SPSS version 20 software. According to the result, the calculated values of temperature (p = 0.006), turbidity (p = 0.000), TA (p = 0.000), NH3 (p = 0.00), NO3

- (p = 0.00), SO42- ( p = 0.013), Pb (p =

0.000), and Cu (p = 0.000) at different sampling sites showed statistically significant difference and hence a pooled mean cannot be used as a representative of the values of each parameter at the six sampling sites. The significant variation among sampling sites could be due to the difference in sampling time, anthropo-genic influences and geographical location. In contrast, the difference between the results ob-tained at the six sampling sites for pH (p = 0.409), EC (p = 0.992), TDS (p = 0.993), Cl- (p = 0.176), and Zn (p = 0.704) were not statistically significantly differ-ent. In such a case, a pooled mean can be used as a representative value of the studied parameter for its values at the six sampling sites.

CONCULUSION

This study revealed that EC, TDS, chloride, ammo-nia, nitrate, and sulphate in Lake Haiq were within the WHO recommended limit whereas pH, turbidity and TA were above the recommended limit. One way ANOVA analysis showed that results for some stud-ied parameters differ significantly among sampling sites, which might be ascribed to the different sourc-es of pollution and sampling time. Among the stud-ied heavy metals, Pb, Cu and Zn were detected in all sampling sites while Cd was not detected at any sam-

pling site. Amongst the assessed heavy metals, lead was found to be in a level above the WHO recom-mended limit for drinking water. The potential cause for high level of Pb in the lake water may be the in-tense emission by vehicles along the nearby highway (Addis Ababa to Tigray) and discharges from the ga-rage around the lake.Hence, the presence of heavy metals in a detectable amounts in a water system used for fishing appended on the bio-accumulating property of the metals obvi-ously leads to the entrance of the metals in to the food chain. Periodical monitoring of the lake water quality is thus required to assess the level of the heavy metals and physico-chemical parameters. Since leather facto-ry is present around the lake, the level of chromium in the lake should also be investigated before it causes health problems on the bio-system.

REFERENCES

Akoto, O and Adiyiah, J. (2007). Chemical analysis of drinking water from some communities in the brong ahafo region. International Journal of Environmental Science and Technology 4(2): 211-214.

APHA (1998). American Public Health Association, Standard Methods for the Examination of Water and Wastewater, 20th ed., Washington D.C.

Arian M.B., T.G. Kazi, M.K. Jamali, H.I. Afridi, J.A. Baig, Jablini, N and Shah, A.Q. (2008). Evaluation of physico-chemical parameters of Manchar Lake water and their comparison with other global published values. Pakistan Journal of Analytical and Environmental; Chemistry 9(2):101-109.

Arumugam, G., Elizabeth, S.H., Thirumagal, J., Islam, N.N and Panneerselvam, A. (2013). Evaluation of water quality of Pulliy Yakanu Lake with reference to physico-chemical aspects at Vellore District. World Journal of Pharmacy and Pharmaceutical Sciences 2(5): 3641-3649.

Benzer, S., Arslan, N and Yılmaz, M. (2013).


Concentrations of metals in water, sediment and tissues of Cyprinus carpio from Mogan Lake (Turkey). Iranian Journal of Fisheries Sciences 12(1): 45-55.

Betel Assefa. (2010). Studies on Benthic Macroinvertebrates of Lake Haiq, Ethiopia. M.Sc. Thesis, Addis Ababa University, Ethiopia.

Boevski, I and Daskalova, N. (2007). Determination of toxic and heavy metals in surface river water samples using Inductively coupled plasma Atomic Emission spectrometry (ICP-AES). Journal of the University of Chemical Technology and Metallurgy 42(4): 419-426.

Government of India (GOI) and Government of The Netherland (GON). (1999). Technical Assistance hydrology project. Standard Analytical Procedures for Water Analysis.

Gopalkrushna, M. (2011). Determination of Physico-Chemical parameters of Surface Water Samples in and around Akot City. International Journal of Research in Chemistry and Environment 1(2): 183-187.

Igbinosa, E.O., Odjadjare E.E., Ajuzie, C.U and Adewole, E.M. (2012). Assessment of physico-chemical qualities, heavy metal concentrations and bacterial pathogens in Shanomi Creek in the Niger Delta, Nigeria. African Journal of Environmental Science and Technology 6(11): 419-424.

Larissa, D., Mesfin, M and Elias, D. (2013). Assessment of heavy metals in water samples and tissues of edible fish species from Hawassa and Koka Rift Valley lakes, Ethiopian Environment Monitoring and Assessment 185(4): 3117-3131.

Moore, F., Orghani, G and Qishla, A. (2009). Assessment of Heavy metal contamination in water and surface sediments of the Maharlu saline Lake, Swiran. Iranian Journal of Science and Technology, Transaction A 33(1): 44-54.

Nhapia, I., Walib, U.G., Usanzinezab, D and Kashaigilic, N. (2012). Distribution of Heavy Metals in Lake Muhazi, Rwanda. The Open Environmental Engineering Journal 5: 96-102.

Ochieng, E,Z., Lalah, J.O and Wandiga, S.O. (2007). Analysis of Heavy metals in water and surface sediment in five Rift Valley lakes in Kenya for assessment of recent increase in anthropogenic activities. Bulletin of Environmental Contamination and Toxicology 79: 570-576.

Ogbonna, O., LJimoh, W., Awagu, E.F and Bamishaiye, E.I. (2011). Determination of some trace elements in water samples within kano metropolis. Advances in Applied Science Research 2(2): 62-68.

Osei, A and Jackson, A. (2008). Dissolved nitrogen in drinking water resources of farming communities in Ghana. African Journal of Environmental Science and Technology 2(2):031-035.

Patil, P.N., Sawant, D.V and Deshmukh, R.N. (2012). Physico-chemical parameters for testing of water – A review. International Journal of Environmental Sciences 3(3): 1194-1207.

Peter, E.J and Kirk, C. (2002). Advances in the determination of inorganic ions in potable waters by ion chromatography. Journal of Environmental Monitoring 4(1): 10-15.

Saeed, S.M and Shaker, I.M. (2008). Assessment of heavy metals pollution in water and sediments and their effect on Oreochromis Niloticus in Northern Delta Lakes, Egypt. 8th International Symposium on Tilapia in Aquaculture.

Shilpa, P., Goroba, C.S., Suhas, J.A and Raut, P.D. (2011). Study of physico-chemical and biological characteristics of lakes from Shivaji University Campus, Kolhapur, Maharashtra. Advances in Applied Science Research 2(6): 505-519.

Shinde, S.E., Pathan, T.S and Raut, D.L. (2011). Sonawane, Studies on the Physico-chemical


Parameters and Correlation Coefficient of Harsool-savangi Dam, District Aurangabad. India. Middle-East Journal of Scientific Research 8(3): 544-554.

Tolera, S. (2007). Determination of common ions and heavy metals in bottled mineral water consumed in Addis Ababa (Ethiopia). M.Sc. Thesis, Addis Ababa University, Ethiopia.

Verma, R and Dwivedi, P. (2013). Heavy metal water pollution- A case study. Recent Research in Science and Technology 5(5): 98-99.

WHO (1984). Guideline for Drinking Water Quality. Health Criteria and Supporting Information 2: 63-315.

WHO (2004). Guidelines for drinking-water quality, 3rd ed. WHO, Geneva, Switzerland.

Yadaw, K.K., Kumer, V., Aryal, S and Singh, D. (2012). Physico-chemical analysis of selected ground water samples of Agra city, India. Recent Research in Science and Technology 4(11): 51-54.

Yenkie, M.K., Battalwar, D.G., Gandhare, N.V and Dhanorkar, D.B. (2010). A study and interpretation of physico-chemical characteristic of Lake water quality in Nagpurcity (India). Rasayan 3(4): 800-810.

Zenebe Yifru. (2011). Accumulation of certain heavy metals in Nile Tilapia (Oreochromis niloticles) fish species relative to heavy metal concentrations in the water of Lake Hawassa. M.Sc. Thesis, Addis Ababa University, Ethiopia.



Maritu Admassu1, Gebremariam Yemane1, Mulugeta Kibret1*, Bayeh Abera2, Endalkachew Nibret1 and Melaku Adal1

Department of Biology, Science College*, Bahir Dar University, P. O. Box 79, Bahir Dar, Ethiopia

2Department of Microbiology, Parasitology and Immunology, College of Medicine and Health Sciences Bahir Dar University, P. O. Box 79, Bahir Dar, Ethiopia

ABSTRACTDiarrheal diseases remain the major cause of morbidity and mortality in children under five years of age. Salmonella and Shigella species are among the leading causes of diarrhea in children. The aim of this study was to determine the prevalence and antimicrobial profiles of Salmonella and Shigella spp. in children less than five years of age with acute diarrhea. A cross sectional study was conducted among 422 children with diarrhea from December 2011 to February 2012. A structured questionnaire was used to collect socio-demographic and clinical data. Identification of Salmonel-la and Shigella species and antimicrobial susceptibility tests was done following standard microbiology procedures. The overall prevalence of Salmonella and Shigella was 7.8% and 9.5 %, respectively. The isolation rates of S. flexin-eri, S.dysenteri and S. boydii were 18 (45 %), 12 (30 %) and10 (25 %), respectively. The prevalence of Salmonella and Shigella were not statistically significant along age groups and gender of the children. Most of Salmonella and Shigel-la were resistant to ampicillin (> 88.7%) and cotrimoxazole (50%).In contrast, 83.3-89.9% of isolates showed suscep-tible to norfloxacin, ciprofloxacin and gentamicin. More than 90% of the Salmonella and 80% Shigella spp. were mul-tiple drug resistant. High prevalence of Shigella and Salmonella linked with high levels of antimicrobial resistance is a major public health concern in the study area. Continuous surveillance of antimicrobial susceptibility should be done. Ciprofloxacin, norfloxacin and gentamicin appeared to be drugs of choice for empirical treatment of these infections.Keywords: Shigella, Salmonella antimicrobial susceptibility, diarrhea, EthiopiaDOI: http://dx.doi.org/10.4314/ejst.v8i1.3

unhygienic environment, and the number and severity of diarrheal episodes especially for under five children (Keusch et al., 2006) . Globally, it is estimated that shigellosis causes about 1, 1 million deaths per year, two-thirds of the patients being children under 5 years of age (Ranjbar et al., 2008). Despite scientific advances over the past century, the epidemiological characteristics, virulence and ability to develop drug resistance has led to renewed and increased incidence of Shigella (Ranjibar et al., 2008). Salmonellosis represents a major communicable worldwide disease problem (Laurent et al., 2005). Salmonellae may be present in all kinds of food grown in faecally polluted

INTRODUCTION

Diarrhea is the leading causes of death among children under the age of five, particularly in the developing world (WHO, 2013), Elias, 2008). Diarrhea kills more young children than AIDS, malaria and measles combined (UNICEF/WHO, 2009). In developing countries, acute bacterial diarrhea is frequently disabling, recurrent, and significantly contributes to malnutrition and death (Rodríguez et al., 2011). In Ethiopia, diarrhea accounts to about 20% of death in 0-5 years age (FMoH, 2011). Diarrheal disease affects rich and poor, old and young, and those in developed and developing countries alike, yet a strong relationship exists between poverty, an

*Corresponding author:[email protected]© This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/CC BY4.0)

28 Maritu Admassu et al.

environments, and are commonly isolated from poultry and livestock and foods prepared from them. The threat to human health posed by antibiotic resistance is of growing concern.

Studies from developed and developing countries showed that multidrug resistant Salmonella and Shi-gella was increasing from time to time (Kariuki et al., 2006; Gizachew Yismaw et al., 2006). Pathogen occurrence and susceptibility profiles show substan-tial geographic variations as well as significant dif-ferences in various populations and environments (Leegaard et al., 2000; Nagal et al., 2006). However, few studies have been conducted on the prevalence of these pathogens in Ethiopia (Moges Tiruneh, 2009; Getachew Debas et al., 2011). Thus, updated data on Salmonella and Shigella at the local level is required for appropriate recommendations for optimal empir-ical therapy. Therefore, this study was conducted to determine the prevalence and antibiogram of Salmo-nella and Shigella spp. in under five children in Bahir Dar Town, Ethiopia.

METHODS

Study area

The study was conducted in Bahir-Dar which is the capital of Amhara National Regional State, located northwestern Ethiopia approximately 578 km from Addis Ababa. The town has latitude and longitude of 11o36’N 37o23’E and an elevation of 1840 me-ters above sea level. The town has a total population of 256,999 (CSA, 2011). This study was specifically conducted at Arsema and Universal Pediatric clinics.

Study design and period

A cross-sectional study was conducted in two pediat-ric clinics in Bahir Dar between December 2011 and February 2012. Children under five years of age at-tending pediatric clinics were considered in the study.

Diarrhea was defined as the passage of 3 or more liq-uid stools in a 24 hour period.

Study population, sample size and sampling

All children under five years of age who visited Ar-sema and Universal Pediatric clinics with acute diar-rhea and whose caretakers were willing to participate in the study were included in the study. A minimum sample size of 384 was calculated using single popu-lation proportion formula, assuming 95% confidence interval, 50% prevalence and margical error of 5% (Daniel, 1999). A 10% contingency (38) was added and a sample size of 422 was obtained. The study par-ticipants were selected using systematic random sam-pling method. Considering average monthly diarrhea cases of 230 in each clinic, the estimated total number of diarrheal cases, N, for the study period was, 4*460 = 1840. To obtain a sample size of 422, the selection interval, K, was calculated using the following formu-la, K = N/n = 1840/422. Hence, it was decided to in-clude every fourth case in the sample.

Inclusion criteria

Children aged less than five years with acute diarrhea who visited Arsema and Universal pediatric clinics whose caretakers were willing to participate in the study were included.

Exclusion criteria

Children aged greater or equal to 5 years and children who were on antibiotic therapy for two weeks. , Chil-dren having diarrhea of more than 14 days and those whose caretakers did not agree to give samples were excluded from this study.

Sample Collection

A total of 422 stool samples were collected with ster-ile plastic containers by experienced laboratory tech-nicians. The specimens were transported in ice-box to the Microbiology Laboratory of Bahir Dar University and analyzed for detection of Salmonella and Shigella


spp. A pre-tested structured questionnaire was used to collect socio-demographic characteristics and clinical data of the children.Culture and identification

Stool samples were enriched in Selenit F broth for 8 hours prior to inoculating into MacConkey agar and Xylose-lysine deoxycho late (XLD) agar (Oxoid, En-gland). The plates were incubated under aerobic at-mosphere at 37oC and examined after 24 hours. Typ-ical colorless colonies on MacConkey agar and pink to red colonies on XLD agar were picked and further identified through a series of biochemical tests as per standard method (Collins and Lyne, 2004; Chees-brough, 2006). Serological identification of Shigel-la species was performed by slide agglutination test (Cheesbrough, 2006). The antisera used for Shigella serotypes identification were: S. boydii polyvalent 1(1-6), S boydii polyvalent 2(7-11), S. boydii poly-valent 3(12-15), S. dysenteriae polyvalent (1-10), S. flexineri polyvalent (1-6, X & Y), and S. sonnei (phase 1 and 2) (Oxoid, England).

Antimicrobial susceptibility testing

In Vitro antimicrobial susceptibility testing was performed for 33 Salmonella and 40 Shigella isolates on Mueller-Hinton Agar (Oxoid, England) using disk diffusion technique (Bauer et al., 1966). The antimicrobials tested were: ampicillin (10μg), amoxicillin/clavulanic acid (20/10μg), tetracycline (30μg), gentamicin (10μg), chloramphenicol (30μg), norfloxacin (10μg), ciprofloxacin (5μg), Trimethoprim-sulfamethoxazole (1.25/23.75 μg) and ceftizoxime (30 µg) (Oxoid, UK). Morphologically identical 4-6 bacterial colonies from overnight culture were suspended in 5ml nutrient broth and incubated for 4 hours at 37oC. Turbidity of the broth culture was equilibrated to match 0.5 McFarland standards. The surface of Mueller Hinton agar plate was evenly inoculated with the culture using a sterile cotton swab. The antibiotic discs were applied on the surface of the

inoculated agar. After 18-24 hours of incubation, the diameter of growth inhibition around the discs were measured and interpreted as sensitive, intermediate or resistant according to Clinical and Laboratory Standards Institute (CLSI, 2011). Reference strain of E. coli ATCC 25922 was used as quality control for antimicrobial susceptibility tests.

Data analysis

Data were analyzed using the Statistical Package for Social Sciences version 20 software (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Armonk, NY: IBM Corp). Association between so-cio-demographic characteristics of the study partici-pants, some clinical data and prevalence of Salmonel-la and Shigella analyzed using chi-square test, and a p-value of less than 0.05 was considered statistically significant.

Ethical consideration

The study was ethically approved by the Institutional Ethics Review Board of Bahir Dar University. Written consent was obtained from parents/guardians of the child before enrolment into the study.


A total of 422 children (239 males and 183 females) aged 0-59 months with acute diarrhea were enrolled in the study. Of the 76 children below six months, 32 were exclusively breastfed. Two hundred and eighty one (66.6 %) children exhibited non bloody diarrhea followed by watery diarrhea in 134 (31.8%) while 7 (1.6%) had bloody diarrhea (Table 1).

Both Salmonella (2.6%) and Shigella (2.9%) were highly prevalent in children of age category 6-11 months. However, the differences were not statisti-cally significant along age groups and gender of the children (Table 1). Non-bloody mucoid was the most


common form of diarrhea observed in 281 (66.6%) children, of whom 25 (5.9%) were positive for Shi-gella. Statistically significant association was found between appearance of diarrhea and prevalence of Shigella (p=0.01). This result is in line with the find-ing of Jafari et al. (2008).

There was statistically significant association between breast feeding practice of the mothers (p=0.02) and nutritional status of the children (p = 0.02) and the prevalence of Salmonella. This finding is in agreement with the findings of Rowe et al. 2004 and Vesta et al. (2010). Most mothers nowadays are advised to feed their babies with breast-milk only and not give their babies supplementary food items until the age of 6 months (Abdullahi, 2010). As shown in Table 2, the prevalence of Salmonella species was 33 (7.8%). This study is in agreement

with the study conducted in Ethiopia (Getnet Beyene et al., 2011) however it is higher than the prevalence of Salmonella species in Saudi Arabia (3.3%) (Johargy et al., 2010) but it is lower than 15.4% prevalence in Jimma, Ethiopia (Abebe Mache et al., 1997). Nagal et al. (2006) found out that the isolation rate of Salmonella species varies from time to time and from place to place and show seasonal variation Among a total of 40 Shigella spp. isolated, 18 (45%), 12 (30 %) and 10 (25%) were S. flexineri, S. dysenteri and S. boydii, respectively (Table 2). This result is in line with the finding reported from Central African Republic (Manirakiza et al., 2010). This result is lower compared to that reported from Ethiopia (Moges Tiruneh, 2009) and Botswana (Urio et al., 2011), however the prevalence recorded in this study is higher than the prevalence reported from Cameroon (Yongsi, 2008) and Tanzania (Moyo et al., 2011).

able1. Socio-demographic characteristics and clinical data of the study subjects and prevalence of Shigella, spp. and Salmonella at Bahir Dar Health, Ethiopia, 2012 (n= 244)

Variables Salmonella Shigella

Positive No (%)

Negative No (%)

P valuePositive No (%)

Negative No (%)

P- valueAge (months )

0-5 8 (1.9) 53 (12.6) 0.2 7 (1.7) 54 (12.8) 0.176-11 12 (2.9) 106 (25.1) 11 (2.6) 107 (25.4)12-23 8 (1.9) 133 (31.5) 5 (1.2) 103 (24.4)24-35 3 (0.7) 52 (12.3) 2 (0.5) 31 (7.3)36-47 0 (0.0) 28 (6.6) 4 (0.9) 30 ( 7.1)48-59 2 (0.5) 17 (4.0) 11 (2.6) 57 (13.5)

SexMale 15 (3.5) 224 (53.1) 0.2 16 (3.8) 167 (39.6) 0.65Female 18 (4.3) 165 (39.1) 24 (5.7) 215 (50.9)

Exclusive breast Feeding (0-6 months)Yes 1 (1.3) 31 (41.9) 0.02 8 (10.7) 67 (89.3) 0.72No 9 (12.2) 35 (44.6) 0 (0.0) 1 (1.3)

Nutritional status Normal 1 (0.2) 69 (16.4) 0.02 32 (7.6) 317 (75.1) 0.63Malnourished 32 (7.6) 320 (75.8) 8 (1.9) 65 (15.4)

Type of diarrhea

Watery 6 (1.4) 128 (30.4) 0.27 13 (3.1) 121 (28.7) 0.01

Bloody 0 (0.0) 1 (0.2) 1 (0.2) -

Non bloody 27 (6.4) 254 (60.2) 25 (5.9) 256 (60.7)

Bloody mucoid 0 (0.0) 6 (1.4) 1 ( 0.2) 5 (1.2)


The antimicrobial susceptibility results of Salmonella isolates are shown in Figure 1. The highest resistance was documented for ampicillin (93.9%) followed by amoxicillin-clavulanic acid (75.8%) which is in agreement with reports from Harrar, Ethiopia (Ayalu Reda et al., 2011). This increased resistance to anti-

biotics might be due to the unwise use of antibiotics in many developing countries such as Ethiopia, which would have led to an increased antibiotic resistance and in turn reduced therapeutic efficacy in these coun-tries (Daniel Asrat, 2008). On the other hand, in this study, the isolated Salmonella species tested showed susceptibility to ciprofloxacin and norfloxacin, (93.9% each) followed by gentamicin (87.9%). This result is in agreement with different studies reported from Ethiopia (Ayalu Reda et al., 2011; Daniel Asrat, 2008; Assefa et al., 1997), England (Hopkins et al., 2006), France (Wiell et al., 2006) and China (Cui et al., 2009). The antimicrobial resistance profiles of Shigella spp. is shown in Table 3. Shigella isolates showed high resistance rates to ampicllin, amoxycillin-clavulanic acid and tetracycline. However, Shigella isolates were highly sensitive to ciprofloxacin, norfloxacin, and gentamicin. The most predominant isolates of Shigel-

Table 2. Proportion of Shigella serotypes isolated from under five children with acute diarrhea, Bahir Dar, 2012.

Bacterial Isolates Number (%)

Shigella spp. 40 (9.5%S. flexineri 18 (45 %)S.dysenteri 12 (30 %)S. boydii 10 (25 %)

Salmonella spp. 33 (7.8 %)

AMP;-Ampicillin; AMC- amoxicillin/clavulanic acid;SXT- Trimethoprim-sulfamethoxazole TE-tetracycline; C- chloramphenicol; ZOX- ceftizoxime; CN- gentamicin; CIP- ciprofloxacin; and NOR- norfloxacin.


la spp. S. flexneri, were more resistant to amoxycil-lin-clavulanic acid and tetracycline than S. dysentery and S. boydii and these finding concords with results reported in Ethiopia (Belay Roma et al., 2000). More than 90.9% of Salmonella isolates were multidrug-resistant (Table 4). The multiple antimicrobial resistances of Salmonella isolates in the present study are higher compared to the reports from other (Fadlalla et al., 2012; Okeke et al., 2005).

This might be because multiple antibiotic resistant Salmonella species increase from time to time (Nagal et al., 2006) and differ from place to place (Kariuki et al., 2006). Over 80% of the isolates were resistant to two or more antimicrobials and none of the strains were sensitive to all antimicrobials tested (Table 4). High resistance rates to ampicillin, amoxycillin-clavulanic acid and tetracycline are supported by a number of studies in Ethiopia (Gizachew Yismaw

Table 3: Antimicrobial resistance profiles of Shigella isolates from under five children with acute diarrhea for commonly used antibiotics.

Antimicrobials tested S.flexineri (n=18) S.dysenteri (n=12)

S.boydii (n=10)

Ampicillin 88.7 91.7 90.0Amoxycillin-clavulanic acid 77.7 58.3 50.0Chloramphenicol 61.1 16.7 10.0Ceftizoxime 33.3 25.0 20.0Ciprofloxacin 11.1 0.0 10.0Trimethoprim-sulfamethoxazole 72.2 33.3 20.0Gentamicin 16.7 8.3 0Norfloxacin 16.7 8.3 10.0Tetracycline 88.9 25.0 50.0

Table 4: Multiple antimicrobial resistance patterns of bacterial isolates

Antibiogram Bacteria

Salmonella spp. Shigella spp.

R0 6.1 0.0R1 3.0 12.5R2 33.3 17.5R3 12.1 17.5R4 18.2 7.5R5 9.1 22.5R6 12.1 15R7 6.1 0.0

R9 0.0 2.5

R0 = susceptible to all; R1, R2, R3, R4, R5, R6, and R7, R9 resistant to 1, 2, 3, 4, 5, 6, 7 and 9 antimicrobials tested respectively.


et al., 2006; Moges Tiruneh, 2009) and reported in other parts of the world (Mandomando et al., 2009; Kavaliotis et al., 2002; Peridano et al., 2006).

CONCLUSION

The prevalence of Shigella and Salmonella were high in the study area. High prevalence and alarming rate of resistance to commonly prescribed antimicro-bials is a serious public health problem in the study area. Exclusive breastfeeding and improved nutrition should be promoted. Continuous surveillance of an-timicrobial susceptibility patterns should be done for choosing antimicrobials for empirical treatment. Cip-rofloxacin, norfloxacin and gentamicin can be drugs of choice for empirical treatment of infections caused by these pathogens. This study has limitation because examination of stool alone does not ensure diagnos-ing typhoid fever caused by Salmonella.

ACKNOWLEDGMENTS

We would like to thank Bahir University for the fi-nancial support, through the president’s fund (Grant No. BDU/RCS/Sc/03/02). We would also like to thank the parents/guardians of the children who par-ticipated in this study, whose cooperation rendered this study possible. We acknowledge the technical support of the staff of the Bahir Dar Regional Health Research laboratory centre.

REFERENCES

Abdullahi, M. (2010). Incidence and antimicrobial susceptibility pattern of Salmonella species in children attending some hospitals in Nano Metropolis, Kanostate-Nigeria. Bayero Journal of Pure and Applied Sciences 3: 202-206.

Abebe Mache, Yohannes Mengistu and Cowley, S. (1997). Salmonella serogroups identified from adult diarrheal out-patients in Addis Ababa,

Ethiopia: antibiotic resistance and plasmid resistance analysis. East African Medical Journal 74:183-186.

Aseffa, A., Gedlu, E and Asmelash, T. (1997). Antibiotic resistance of prevalent Salmonella and Shigella strains in northwest Ethiopia. East African Medical Journal 74: 708-713.

Ayalu Reda, Berhanu Seyoum, Jemal Yimam, Gizachew Andualem, Sisay Fiseha and Jean-Michel, V. (2011). Antibiotic susceptibility patterns of Salmonella and Shigella isolates in Harar, Eastern Ethiopia. Journal of Infectious Diseases and Immunity 3(8):134-139.

Bauer, A. W., Kirby, W. M., Sherris, J. C.and Turck, M. (1966). Antibiotic susceptibility testing by a standardized single disk method American Journal of Clinical Pathology 45(4): 493-496.

Belay Roma, Solomon Worku, Shiferaw T/Mariam and Nina, L. (2000). Antimicrobial susceptibility patterns of Shigella isolates in Awassa. Ethiopian Journal of Health Development 14: 149-154.

Cheesbrough, M. (2006). Direct Laboratory Practice in Tropical countries. Part 2. Cambridge University Press.

Clinical Laboratory Standards Institute. (2011). Performance Standards for Antimicrobial Susceptibility Testing; Twenty Information Supplement. CLSI document M100-S21, Clinical and Laboratory Standards Institute, Wayne Pennsylvania.

Collins, C. H and Lyne, M. (2004). Microbiological Methods. 8th edition. Oxford University Press Inc. London..

Cui, S., Li, Z.J., Sun, C., Hu, S., Jin, F., Li, Y., Guo, L and Ma, Y. ( 2009). Characterization of Salmonella enterica isolates from infants and toddlers in Wuhan, China. Journal of Antimicrobial Chemotherapy 63:87–94.


Danile Asrat. (2008). Shigella and Salmonella serogroups and their antibiotic susceptibility patterns in Ethiopia. East Mediterranean Health Journal 14: 760-767.

Daniel, W.W. (1999). Biostatistics: A Foundation for Analysis in the Health Sciences.7th edition. New York: John Wiley and Sons.

Elias, C.J. (2008). Diarrhea Disease: Emergency and Humanitarian Action (EHA). World Health

Organization Week 22, Geneva.

Fadlalla, T., Mohamed, E., Ahmed, G and Mohamed, T. (2012). Antimicrobial susceptibility of Salmonella serotypes isolated from human and animals in Sudan. Journal of Public Health and Epidemiology 4:19-23.

FMoH (2011). The transformational path to achieving universal access to hygiene and sanitation. Ministry of Health Ethiopia.

Getachew Debas, Mulugeta Kibret, Fantahun Biadglegne and Bayeh Abera. (2011). Prevalence and antimicrobial susceptibility patterns of Shigella spp. at Felege Hiwot Referral Hospital, Northwest Ethiopia. Ethiopian Medical Journal 49:249-255.

Getnet Beyene, Nair S., Daniel Asrat, Yohannes Mengistu, Engers, H and Wain, J. (2011). Multidrug-resistant Salmonella Concord is a major cause of salmonellosis in children in Ethiopia. Journal of Infections in Developing Countries 5:23-33.

Gizachew Yismaw, Challa Negeri and Afework Kassau. (2006). A five year antimicrobial resistance observed in Shigella species isolated from stool sample at the Gondar University Hospital, North west Ethiopia. Ethiopian Journal of Health Development 20:194-196.

Hopkins, K.L., Davies, R.H and Threlfull, E.J. (2006). Mechanisms of quinolone resistance in Escherichia coli and Salmonella: recent developments. International Journal of Antimicrobial Agents

25:358–373.

Jafari, F., Garcia-Gil, L.J., Salmanzadeh-Ahrabi, S., Shokrzadeh, S., Aslani, M., Pourhoseingholi, M,A., Derakhshan, F and Zali, Z.R. (2008). Diagnosis and prevalence of enteropathogenic bacteria in children less than 5 years of age with acute diarrhea in Tehran children’s hospital. Journal of Infection 58:21-17.

Johargy, A., Ghazi, H and Umenah, A. (2010). Frequency of viral, bacterial and parasitic enteropathogens among young children with acute diarrhea in Saudi Arabia. Journal of Pakistan Medical Association 60: 456-59.

Kariuki, S., Revathi, G., Kariuki, N., Kiiru, J., Mwituria, J., Muyodi, J., Githinji, W., Kagendo, D., Munyalo, A and Anthony, C. (2006). Invasive multidrug-resistant non-typhoidal Salmonella infections in Africa: zoonotic or anthroponotic transmission? Journal of Medical Microbiology 55: 585–591.

Kavaliotis, J., Karyda, S., Konstantoula, T., Kansouzidou, A and, Tsagaropoulou, H. (2002). Shigellosis of childhood in northern Greece: epidemiological, clinical and laboratory data of hospitalized patients during the period 1971-1996. Scandinavian Journal of Infectious Diseases 32: 207-211.

Keusch, G.T., Fontaine, O., Bhargava, A., Pinto, C.B., Bhutta Z.A., Gotuzzo, E., Rivera, J., Chow, J, Shahid-Salles, S.A and Laxminarayan, R. (2006). Diarrheal Diseases. Disease Control Priorities Project 31-49.

Laurent, P., Visser, M and Fesselet, J.F. (2005). Household drinking water systems and their impact on people with weakened immunity. MSF-Holland Public Health Department 1- 72.

Leegaard, T.M., Caugant, D.A., Froholm, L.O and Hoiby, E.A. (2000). Apparent difference in antimicrobial susceptibility as a consequence of national guidelines. Clinical Microbiology and


Infections 6:290.

Mandomando, I., Jaintilal, D., Pons, M.J., Valles, X., Espasa, M., Mensa, L., Sigallque, B., Alonso, P.L and Ruiz, J. (2009). Antimicrobial and mechanism of resistance in Shigella and Salmonella isolated from children under five years of age with diarrhea in rural Mozambique. Antimicrobial Agents and Chemotherapy 53: 2450-2454.

Manirakiza, A., Megne-Boudjeka, P., Bobossi-Serengbe, G., Bercion, R and Faou, A.L.(2010). Prevalence of shigellosis diarrhea in a pediatric population: hospital based survey in Bangui, Central African Republic. Journal of Infections in Developing Countries 4: 655- 657.

Moges Tiruneh. (2009). Serodiversity and antimicrobial resistance pattern of Shigella isolated at Gondar University Teaching Hospital. Japanese Journal of Infectious Diseases 62:93-97.

Moyo, S.J., Gro, N., Matee, M.I., Kitundu, J., Myrmel, H., Mylvaganam, H., Samuel, Y., Maselle, S.Y. and Langeland, N. (2011). Age specific aetiological agents of diarrhoea in hospitalized children aged less than five years in Dares Salaam, Tanzania. MBC Pediatrics 11: 1-6.

Nagal, K., Mandial, K., Katoch, C and Chahota, R. (2006). Occurrence of Salmonella enteric subspecies enteric serovar Berta (Salmonella Berta) in bovine calves, in Himachal Pradesh, India. Veterinary Archives 76:153-157.

Okeke, I.N., Laxminarayan, R., Bhutta, Z.A., Duse, A.G., Jenkins, P., O’Brien, T.F., Pablos-Mendez. A and Klugman, K.P. (2005). Antimicrobial resistance in developing countries. Part I: recent trends and current status. Lancet Infectious Diseases 5:481-493.

Peridano, G., Souza, F and Rodriques, S. (2006). Frequency of serovars and antimicrobial resistance in Shigella spp. from Brazil. Memorias do Instituto Oswald Cruz 100:245-250.

Ranjbar, R., Dallal, M.M.S and Pourshafie, M.R. (2008). Epidemiology of shigellosis with special reference to hospital distribution of Shigella strains in Tehran. Iranian Journal of Clinical and Infectious Diseases 3: 35-38.

Rodríguez, L., Cervantes E and Rocío Ortiz, R. (2011). Malnutrition and Gastrointestinal and Respiratory Infections in Children: A Public Health Problem. International Journal of Environmental Research and Public Health 8: 1174–1205.

Rowe, Y., Rocourt, R., Shiferaw, B., Kassenborg, D., Segler, D., Marcus, R., Daily, J., Hardnett, P and Slutsker, L. (2004). Breast-feeding decreases the risk of sporadic salmonellosis among infants in food net sites. Clinical and Infectious Diseases 38::S262–S267.

United Nations Children’s Fund /World Health Organization. (2009). Why children are still dying and what can be done? Geneva, UNCEFE/WHO.

Urio, E.M., Collison, E.K., Gashe, B.A., Sebunya, T.K and Mpuchane, S. (2011). Shigella and Salmonella strains isolated from children under 5 years in Gaborone, Botswana, and their antibiotic susceptibility patterns. Journal of Tropical Medicine and International Health 6: 55-59.

Vesta, R., Joselito, H., Manjari, Q., Marcelino, E., Brian, J and Cesar, M. (2010). Effect of rotavirus vaccination on death from childhood diarrhea in Mexico. New England Journal of Medicine 362:299-305.

Wiell, F.X., Bertrand, S., Guesnier, F., Baucheros, S., Grimont, A.D and Cloechaor, A. (2006). Ciprofloxacin Resistant Salmonella Kentucky in Travelers. Emerging Infectious Diseases 12:1611-1612.

Yongsi, H.B.N. (2008). Pathogenic microorganisms associated with childhood diarrhea in low-and-middle income countries. International Journal of Environment Research and Public Health 5: 213-229.



Gizachew Teshome1*, Abebe Getahun2, Minwyelet Mingist3 and Wassie Anteneh4

1Semera University, College of Natural Sciences, Department of Biology, P.O.Box 135, Semera, Ethiopia2Addis Ababa University, College of Natural Sciences, Department of Zoological Sciences P.O.Box 1176, Addis Ababa,

Ethiopia3Bahir Dar University, College of Agriculture and Environmental Science, Department of Fisheries, Wetlands and Wildlife

Management, P.O.Box 79, Bahir Dar, Ethiopia4Bahir Dar University, College of Science, Department of Biology, P.O.Box 79, Bahir Dar, Ethiopia

ABSTRACTSpawning migration of Labeobarbus species was studied from August to December 2013 in some tributary rivers (Qimon, Guanta, Shini, and Chibirna) of Lake Tana. Fish specimens and physico-chemical parameters were measured bimonthly in August and September but monthly from October to December. Adult fish spec-imens were caught using 6 and 8 cm stretched mesh size monofilament gillnets and 6, 8, 10 and 12 cm mesh size multifilament gillnets. A total of 933 adult Labeobarbus specimens were collected during the study peri-od. Labeobarbus intermedius was the most abundant species followed by L. brevicephalus. The peak spawn-ing season of L. intermedius was from fourth week of August to end of September and for L. brevicephalus it was from fourth week of August to beginning of October. Both species were found to spawn in all sam-pling rivers. However L. truttiformis spawned only at Guanta and Qimon Rivers during August while L. nedgia in Shini and Chibirna Rivers at the end of September. This implies the presence of micro-spatial seg-regation among species. Pair-wise comparison of Labeobarbus species showed temporal segregation in all sampling months, except L. brevicephalus and L. nedgia. The present findings showed that small tributary rivers and streams are the main spawning grounds for Labeobarbus species of Lake Tana. Therefore, main spawning grounds or routes should be protected from the deleterious effects of anthropogenic activities like illegal fishing, irrigation and sand mining for wise use of the fish resources.Key words: Fecundity, Fish, Migration, River, SpawningDOI: http://dx.doi.org/10.4314/ejst.v8i1.4

The reproductive biology Labeobarbus species of Lake Tana is more or less similar to Barbus holubi, B.

kimberleyensis, Labeo capensis and L. umbratus which are found in a large reservoir on the Orange River, South Africa (Tomasson et al., 1984). In continuous-ly flowing regulated rivers, time of spawning is gov-erned by water temperatures and fish have moderate fecundity; large eggs, incubation time of several days and the larvae are initially immobile with large yolk sacs (Tomasson et al., 1984).

The single annual upstream migration reproductive strategy of Lake Tana Labeobarbus species make them highly vulnerable as fishers target the spawning aggregations (Skelton et al., 1991). The most plau-sible explanation for the decline of the Labeobar-

INTRODUCTION

Ethiopia is rich in its fish fauna, having a diversified species in the inland water bodies (Abebe Getahun, 2002). The three major fish families (Cichlidae, Cla-riidae and Cyprinidae) are widely distributed through-out the country. In Lake Tana, Cyprinidae is the larg-est family and is represented by four genera. These are the genera Barbus represented by three species: B. humilis, B. pleurograma, and B. tanapelagious (Esh-ete Dejen et al., 2003), Varicorhinus represented by one specie: V. beso, Garra represented by four spe-cies: G. dembecha, G. tana, G. regressus and G. small mouth (Akewake Geremew, 2007) and Labeobarbus, which is the most abundant genus of the family and consists of 17 species forming a unique species flock in Lake Tana (Nagelkerke, 1997).


38 Gizachew Teshome et al.

bus stock in Lake Tana is thought to be recruitment overfishing by the commercial gillnet fishery that targets the riverine spawners (de Graaf et al., 2004) and poisoning of the spawning stock in rivers using the crushed seeds of Birbira (Milletia ferruginea) (Nagelkerke and Sibbing, 1996).

Many recent studies have shown that Lake Tana tribu-tary rivers are severely degraded by different develop-mental activities like irrigation and sand mining. In so far as we know, there is no any study dealing with the importance of the present small tributary rivers and streams of Lake Tana as the main spawning grounds for some endemic Labeobarbus species. Thus, this study aims at investigating the spawning migration fish species to these unexplored tributary rivers of Lake Tana.

MATERIALS AND METHODS

Description of the study area

Lake Tana is the largest lake and situated in the north-west of Ethiopia, at an altitude of 1830 m with a surface area of 3200 km2 and a watershed of 16500 km2 (Rzóska, 1976). More than 60 small season-al tributaries and seven permanent rivers (Gumara, Ribb, Megech, Gilgel Abbay, Gelda, Arno-Garno and Dirma) feed the lake. Gumara is one of the largest pe-rennial rivers flowing into Lake Tana and arises from the western side of Gunna Mountain peaks. It has a number of tributary rivers and streams namely: Duka-lit, Kizin, Wonzema, Bawaza and Guanta.

A number of tributary rivers join the main Ribb Riv-er such as Chibirna, Shini, Kirarign, Keha, Barya, Hamus and Melo Rivers. Shini River is located at 12°05.906’N and 37°45.702’E, and Chibirna River is also located at 12°04.402’N and 37°44.075’E. In Shi-ni River, sand mining is common activity and there is also small scale irrigation. Shini and Chibirna Rivers confluence at Bura Egziabher Kebele before entering

to Ribb River. Both Rivers dry out during the dry sea-son due to excessive withdrawal of water for small ir-rigation.

Qimon River is located in northeastern part of Lake Tana at 12°11.000’N and 37°34.587’E. This river is starting from Kulkual Ber watershed in Gondar Zuria district and the upstream part of the river watershed is covered by dense forest, gravel beds. Qimon River is characterized by dense forest in the upper part of the river and the mouth of this river is also densely covered by shrubs, grass, and water hyacinth. Guanta River is characterized by dense vegetation. Figure 1 shows the specific sampling sites at the tributary riv-ers of Lake Tana.

Field sampling and type of data

Abiotic parameters

Physico-chemical parameters were measured in all sampling sites of spawning rivers. Water quality pa-rameters like conductivity, pH, temperature and total dissolved solids (TDS) were measured using Cond/TDS meter (Wagtech Int., Model CE 470 Cond Me-ter 01189) and dissolved oxygen using oxygen meter (Wagtech Int., Model CO-411). Water transparency (secchi depth) of the rivers was estimated with a stan-dard Secchi Disc of 25 cm diameter.

Gillnet setting and species identification

Sampling sites were selected based on the suitability for gillnet setting and human interference. In all sam-pling sites of the tributary rivers of Lake Tana, gillnet was set during the day time and sampling of fish was conducted usually from 10:00 am to 3:00 pm. Mul-tifilament gillnets with 6, 8, 10 and 12 cm stretched mesh size and monofilament gillnets with 4 and 6 cm mesh size were used for collecting fish specimens. After capture, the collected specimens of Labeobar-


bus species were identified to species level using keys developed by Nagelkerke (1997), and Nagelkerke and Sibbing (2000). Then, the specimens’ total length, fork length, standard length and total weight were measured to the nearest 0.1 cm and 0.1 g precision for length and weight, respectively. After dissection, go-nadal maturities of each fish specimen were identified using seven point maturity scales (Nagelkerke, 1997). Gonad weight was measured to the nearest 0.1 g us-ing sensitive balance.

Relative abundance and fish composition

Index of Relative Importance (IRI) is a measure of the relative abundance or commonness of the species based on number and weight of individuals in catch-es as well as their frequency of occurrence (Kold-

ing, 1999). IRI was used to find the most important species in terms of number, weight and frequency of occurrence in the catches from the different sampling sites. IRI gives a better representation of the ecolog-ical importance of species rather than the weight, number frequency of occurrence alone (Sanyanga, 1996). Percent of IRI was calculated as follows:

X100 )%Fj j%(%Wj

)%Fi %Ni%Wi ( IRI

1s

1j∑=

=

+

+=

N

Where, %Wi and %Ni are percentage weight and number of each species of total catch, respectively; %Fi is percentage frequency of occurrence of each species in total number of settings. S is total number

Figure 1. Map of Ethiopia, Amhara, Lake Tana and sampling sites at some inflowing rivers (own map).


F = aFLb, Where F – fecundity, FL – fork length; a –constant and b- exponentF= aGWb, where GW – gonad weight

Data analysis

Data analysis was made using Statistical Package for Social Science Students (SPSS Inc., software version 20.0), SASS version 9.2 and Microsoft Excel. Phys-ico-chemical parameters of water were performed using one way ANOVA. For all ANOVA test, mean comparison was performed using post hoc (LSD) to identify the difference between each parameter among sampling sites. Presence or absence of spatial and temporal segregation patterns of Labeobarbus species in all sampling rivers was also analyzed using x2rXc contingency table.

RESULTS

Abiotic parameters

The analysis for temperature, TDS, conductivity and Secchi depth showed significant differences among the different rivers (p<0.05). Mean comparison of all physico-chemical parameters showed significant difference between Guanta River and other sampling rivers (Table 1).

of species. %Wj and %Nj are percentage weight and number of total species of total catch, respectively. %Fj is percentage frequency of occurrence of total species in total number of setting.

Gonado-Somatic Index (GSI) and fecundity

GSI is the ratio of fish gonad weight to body weight and used to determine the peak time of reproductive season of spawning migratory species. The GSI was determined using the following formula:

Eggs from the ripe female fish were preserved with 8% formalin for fecundity estimation. Three sub-sam-ples of 1 g eggs were taken from different parts of ovary and counted and the average was calculated. The total number of eggs per ovary was calculated by extrapolation from the mean calculated. The correla-tion of fecundity with fork length, total weight and ovary weight were made to determine the relation-ship of fecundity with morphometric measurements. Fecundity was determined based on fork length and gonad weight. These two formulas were indicated as follows:

Table 1. Water quality parameters in the sampling sites (Mean ± S.E).

RiverOxygen(mgl-1)

Temperature (oC)

Conductivity(µs cm-1)

Total dissolved solids(ppm)

pHSecchi depth(cm)

Chibirna 6.5± 0.44 22.1±0.63a 313.1±27.19a 214.3±19.24a 8.1±0.13 82.1±7.78a

Shini 6.5±0.54 20.8±1.02cb 239.5±10.12b 162.9±7.07b 8.2±.14 73.3±9.52ba

Overall mean

6.2±0.24 20.9±0.40 235.4±14.40 159.4±10.20 8.2±0.08 65.9±5.30

P-value 0.52 0.010 0.002 0.003 0.116 0.001The same subscript letters denotes non-significance at 0.05 using LSD mean comparison.


Species composition and relative abundance

A total of 1178 fish specimens were collected from Qimon, Guanta, Chibirna, and Shini Rivers. From the total specimens collected, 32.24% (382), 17.65% (208), 24.37% (287) and 25.52% (301) were sampled/contributed from Chibirna, Guanta, Qimon, and Shini Rivers, respectively. From the whole catch compositions, the genus Labeobarbus contributed 79.2% (933), V. beso 14.67% (172), C. gariepinus 2.4% (28), and O. niloticus 3.5% (41). From the total Labeobarbus specimens collected, L. intermedius was the most dominant specie (43%) followed by L. brevicephalus (23%), whereas the rest were few in number (Table 2).

The relative abundance and distribution of each species varied among sampling rivers across sampling months (Table 3). Most of the species were sampled in good numbers in the month of September. Among the 933 Labeobarbus specimens, 97% (905) were from the three dominant species (L. intermedius, L. brevicephalus and L. nedgia). The rest five Labeobarbus species were found rarely in all sampling rivers. In all sampling rivers, L. intermedius was the most important specie, followed by either L. brevicephalus or L. nedgia (Table 3).

Table 2. Species composition in all sampling rivers.

Species Rivers

Total PercentageChibirna Guanta Qimon Shini

L. brevicephalus 47 73 126 31 277 23.5

L. crassibarbis 0 0 2 0 2 0.2

L. dejeni 1 0 0 2 3 0.3

L. intermedius 189 81 113 124 507 43

L. megastoma 0 0 5 0 5 0.4

L. nedgia 41 0 1 79 121 10.3

L. truttiformis 0 5 7 0 12 1

L. tsanensis 0 0 6 0 6 0.5

V. beso 90 28 0 54 172 14.6

Barbus humilis 2 0 0 0 2 0.2

C. gariepinus 7 11 1 9 28 2.4

G. dembecha 2 0 0 0 2 0.2

O. niloticus 3 10 26 2 41 3.5

Total 382 208 287 301 1178 100.00


Table 3. Percentage index of relative importance (%IRI) of Labeobarbus species from all sampling rivers.

River Fish species N %N W %W F %F IRI %IRI

Qimon

L. intermedius 113 43.46 7133.4 39.54 6 100.00 8300 49.28

L. brevicephalus 126 48.46 7509.1 41.63 5 83.33 7507.2 44.57

L. nedgia 1 0.38 129.4 0.72 1 16.67 18.337 1.83

L. tsanensis 6 2.31 1255.4 6.96 2 33.33 308.96 1.82

L. truttiformis 7 2.69 1207.3 6.69 3 50.00 469 1.04

L. megastoma 5 1.92 606.9 3.36 2 33.33 175.98 0.36

L. crassibarbis 2 0.77 195.7 1.08 2 33.33 61.66 0.36

Total 260 18037.2 100.00 16958.75 100.00

Chibirna

L. intermedius 189 67.98 1683.1 20.18 6 85.71 7555.83 46.76

L. nedgia 41 14.75 3631.1 43.53 6 85.71 4994.91 30.91

L. brevicephalus 47 16.91 2741.5 32.86 5 71.43 3555.29 22.00

L. dejeni 1 0.36 286.5 3.43 1 14.28 54.18 0.33

Total 278 100.00 8342.2 100.00 100.00 16160.21 100.00

Guanta

L. intermedius 72 48.00 7239.7 47.98 6 85.71 8226.4 59.01

L. brevicephalus 73 48.67 3536.1 23.43 5 71.43 5150.1 36.94

L. truttiformis 5 3.33 2474.3 16.40 2 28.57 563.69 4.04

Total 150 100.00 15090.3 100.00 100.00 13940 100.00

Shini L. intermedius 124 52.54 11980.20 83.50 6 100.00 13603.86 71.84L. nedgia 79 33.47 365.00 2.54 5 83.33 3601.84 19.02L. brevicephalus 31 13.14 1746.80 12.17 4 66.67 1687.33 8.91L. dejeni 2 0.85 256.20 1.79 1 16.67 43.88 0.23Total 236 100 14348.2 100.00 6 266.67 18936.9 100.00


Figure 2. The proportion of maturity stages of the dominant Labeobarbus species.

Figure 3. Temporal distribution of maturity levels of Labeobarbus species in sampling rivers.

Gonado-Somatic Index (GSI) and fecundity

Gonad stage VI (running level) was the highest in levels of percent maturity for all species and at all sampling sites as compared to other stages (Figure 2). From the 933 Labeobarbus specimens sampled during peak-seasons, 64.95% were found at maturity stage of IV-VII. The rest 35.5% were immature (I-III) and the peak time of spawning was September (Fig-ure 3).

The mean value of GSI is used to indicate peak time of reproductive season for spawning migratory spe-cies. Most of the time, males of the three dominant species had similar peak time which is September while female for L. brevicephalus peaked in October which is different from L. intermedius and L. nedgia that peaked in September and August, respectively.


Fecundity rates of the migratory Labeobarbus spe-cies were determined from a total of 141 specimens which mainly included L. intermedius (59), L. brev-icephalus (67) and L. nedgia (15). Absolute (total) fecundity of the most dominant Labeobarbus species (L. intermedius, L. brevicephalus and L. nedgia) had linear relationship with their total gonad weight and fork length (Figures 4 and 5). Labeobarbus inter-medius with fork length of 15 to 28 cm (21.79±0.44 cm, mean±SE) had an absolute fecundity of 1265 to 13289 (mean fecundity of 5057), respectively and. Labeobarbus brevicephalus with fork length of 15 to

Figure 4. The relationship of absolute fecundity with gonad weight of the dominant Labeobarbus species

29 cm (19.78±0.29 cm, mean±SE) had absolute fe-cundity ranging from 1225 to 9469 (mean fecundity of 3474), respectively. Labeobarbus nedgia with fork length 18 to 37 cm (25.14±1.42 cm, mean±SE) had absolute fecundity ranging from 1448 to 14018 (mean fecundity of 5200), respectively. The highest absolute fecundity (i.e., 18688 eggs) was recorded from L. truttiformis in Guanta River which had 58.4 g gonad weight. Absolute fecundity of each dominant migratory fish species had significant differ-ence with their gonad weight ANOVA, (p<0.0001) as shown in Figure 4.


Spatial and temporal spawning segregations

Composition and distribution patterns of the three most abundant Labeobarbus species did not show significant difference in all sampling rivers except in the case of L. nedgia (one way ANOVA, p >0.05) (Table 4).

Table 4. Spatial segregation of the three most dominant Labeobarbus species in all sampling rivers (one way ANOVA).

River L. brevicephalus (N=277) L. intermedius (N=507) L. nedgia (N=121)Mean ± S.E Mean ± S.E Mean ± S.E

Chibirna 9.4 ± 4.43 37.8±6.02 8.2±2.20b

Guanta 14.6 ± 7.15 16.2±4.59 0.00Qimon 31.5 ± 9.89 28.3±14.50 .3±.25 c

Shini 7.8 ± 4.59 31.0±7.82 19.8±8.23 a

Total 15.4 ± 5.07 28.2±4.28 6.7±2.55P value 0.398 0.318 0.01

Figure 5. The relationship between absolute fecundity and fork length of L. brevicephalus and L. intermedius.


Spatial and temporal composition of the most abundant Labeobarbus species in all sampling rivers (Qimon, Guanta, Shini and Chibirna) is given in Table 5. Main breeding seasons were August, September and October and among these breeding months September was the most peak season for most of male Labeobarbus species.

Labeobarbus intermedius was the first species to mi-grate to the upstream sites of Shini River starting from the second week of August and but its catch was high-er at the end of September (fourth week). Labeobar-bus brevicephalus and L. nedgia were the last species to migrate to Shini and Chibirna Rivers. Labeobar-bus megastoma and L. truttiformis were caught rarely from Qimon and Guanta Rivers and reach peak during October and August, respectively.

Table 5. Spatial and temporal segregation of the dominant Labeobarbus species (x2rXc contingency table).

Species Spatial distribution

Chibirna Guanta Qimon Shini Total

L. brevicephalus 47a 73b 126b 31a 277

L. intermedius 189a 81b 113b 124b 507

L. nedgia 41a 0 1 79c 121

Total 277 159 247 234 917

Species Temporal distribution

August September October November December

L. brevicephalus 25a 166c 33a,b 18a 35b

L. intermedius 94a 180b 95a 64a 74a

L. nedgia 14a 606 21a 16a 10a

Total 140 408 149 101 119

Note: Same superscript letters in a row indicates non-significant difference among each other at 0.05 and Numbers indicate absolute numbers of specimens

feeding and sexual maturity were determined by abiotic factors (Murdoch and Martha, 1999). The study indicated that all studied tributary rivers had different physico-chemical parameters due to different watershed, anthropogenic activities like sand mining in upstream of the rivers, and natural environmental variability. According to Wassie Anteneh et al. (2005) and Shewit Gebremedhin et al. (2012), Megech and Arno-Garno Rivers are the

DISCUSSION

Abiotic factors and Spawning grounds

All biological functions of living organisms in aquatic ecosystem such as growth, reproduction,


main preferable sites for Labeobarbus spawning, the same to that Chibirna, Shini and Qimon Rivers were depicted in current study while Guanta River had less fish diversity as well as least number of spawning migratory species. Highly oxygenated water and gravel beds are general requirements for Labeobarbus spawning due to their critical importance in the development of eggs and larvae (Skelton et al., 1991; Rodriguez-Ruiz and Granado Lorencio, 1992; Baras et al., 1996; Baras, 1997; Abebe Getahun et al., 2008). Similar assessment was made by de Graaf (2005) in Gumara, Gilgel Abbay, Ribb and Gelda Rivers that had different physical parameters such as temperature and secchi disk, showing strong seasonal patterns. In contrast with the report of Eshete Dejen (2004), Lake Tana mean value of conductivity was (132.8 µs cm-1)

which is much lower than conductivity value of the studied tributary rivers. This might be due to the different anthropogenic activities and environmental differences in Lake Tana and its tributary rivers.

In general, the variability of physico-chemical parameters in flowing rivers had strong relations with biological functions of any aquatic organism. Recently, Lake Tana fish production is declining as compared to the last ten years due to environmental and anthropogenic factors (Dereje Tewabe, 2013). This result is in line with Jamu et al. (2003) who reported similar results on some physico-chemical parameters that showed significant predictors of the migration dynamics and reproductive status of two Barbus species from Lake Chilwa basin (Malawi).

Low abundance and diversity of migratory fish species were recorded in the present study as compared to the previous studies which might indicate the decline of the migratory fish in the lake. Overfishing at Lake Tana and also degradation of spawning grounds and routs were might be main cause of the decline of spawning migratory species.

In agreement with Dereje Tewabe (2013) the main cause for the decline of the migratory fish species is fishing pressure at spawning grounds with illegal monofilament introduction.

Gonado-Somatic Index (GSI %) and Fecundity

Previous studies on reproductive segregation of Labeobarbus species of Lake Tana including Palstra et al. (2004), de Graaf (2005), Wassie Anteneh et al. (2005) and Shewit Gebremedhin et al. (2012) have reported the spawning aggregations of ripe and running fishes. According to Wassie Anteneh et al. (2005), L. intermedius reproduce throughout the year and it is both riverine and lacustrine, whereas in the current study, L. intermedius has similar reproductive season (GSI%) with other spawning migratory Labeobarbus species. During the end of spawning season high number of spent maturity levels and low abundance of fish specimens were recorded. Similar results were reported by de Graaf et al. (2005) that stated the peak-spawning season for Labeobarbus species in Lake Tana to be from August to October.

The fecundity rate of most dominant Labeobarbus species was in line with the results made by Mohammed Omer (2010) that studied Labeobarbus species at the head of Blue Nile River. Lower fecundity value was recorded in this study (L. brevicephalus, 1225 eggs) and this absolute fecundity value was lowest as compared to other cyprinids, Barbus grypus (16000-235784 eggs), Labeo senegalensis (12948-74832 eggs) and Labeo parvus (8723-124363 eggs) (Claudine et al., 2013). This difference might be due to difference in genetic variability, food availability and other environmental conditions. The relationship of fork length and total body weight with absolute fecundity was linear for L. brevicephalus. This is in line with the report of


Dagnew Mequanent (2012). This indicates that absolute fecundity is directly related to the body size of the migratory species. According to Claudine et al. (2013) gonad weight is better correlated with reproductive capacity than fish size and fish species with these types of relationship have rapid growth and high fertility. Such characteristics are important for aquaculture development. According to Oliva Paterna et al. (2002) fast growth, high fecundity and early maturity are the characteristics of unstable environments.

CONCLUSION

Lake Tana Labeobarbus species is a unique species flock that was found highly threatened due to fishing activities and degradation of the environment. Small tributary rivers of Lake Tana (Shini, Qimon, Chibirna, and Guanta Rivers) are main spawning grounds however human interface (unregulated sand mining and irrigation) have influenced these spawning grounds. Numerous table size Labeobarbus species were caught from Shini and Chibirna Rivers during sampling seasons, but during the dry season no fish could be seen due to water withdrawal for irrigation. The communities who are living nearby the water bodies are not aware of when and where fish breed. That is why local inhabitants around Qimon and Guanta Rivers have the tradition of capturing fish for consumption and marketing during the peak spawning season and they use traditional fishing methods (using hooks and lines and monofilament gillnets). The time and sites of setting fishing gears are highly correlated with the time of segregation and aggregation of riverine migratory Labeobarbus species. Therefore, at least during peak time of spawning season, specific spawning grounds should be closed from fishing activities as well as irrigation canals have to be integrated and harmonized with river biodiversity. The community, governmental and non-governmental organizations, policy makers and fishers should be aware of the reproductive

strategy of the migratory fishes and potential human impacts on them for sustainable utilization of the resources.

ACKNOWLEDGEMENTS

We are greatly indebted to Rufford Foundation, UK and Blue Nile Water Institute, Bahir Dar University for provision of funds for this study. The kind collaboration of the staff members of Bahir Dar Fish and Other Aquatic Life Research Centre in providing materials needed for the study is very much appreciated.

REFERENCES

Abebe Getahun. (2002.) The Nile basin: Riverine fish and fisheries, Department of Biology Addis Ababa University, Ethiopia .19pp.

Abebe Getahun, Eshete Dejen and Wassie Anteneh. (2008). Fishery studies of Ribb River, Lake Tana Basin, Ethiopia. Final Report E1573, Vol. 2, Ethiopian Nile Irrigation and Drainage Project Coordination Office, Ministry of Water Resources, Ethiopia.

Akewake Geremew. (2007). Taxonomic revision, relative abundance, and aspects of the Biology of some species of the genus Garra, Hamilton 1922 (Pisces: Cyprinid) in Lake Tana, Ethiopia. MSc. Thesis, Addis Ababa University, Ethiopia.

Baras, E. (1997). Environmental determinants of residence area selection and long term utilisation in a shoaling teleost, the common barbel (Barbus barbus L.). Aquatic Living Resources 10: 195–206.

Baras, E., Philipart, J.C., and Nindaba, J. (1996). Importance of gravel bars as spawning grounds and nurseries for European running water cyprinids. 2nd International Symposium on Habitat Hydraulics, volume A: 367–378.


Claudine, T.T., Minette, T.E and Joseph, T. (2013). Reproductive Strategy of Labeobarbus batesii (Teleostei: Cyprinidae) in the Mbô Floodplain Rivers of Cameroon, International Journal of Zoology http://dx.doi.org/10.1155/2013/452329.

de Graaf, M., Machiels, A. M., Tesfaye Wudneh and Sibbing, F. A. (2004). Declining stocks of Lake Tana’s endemic Barbus species flock (Pisces; Cyprinidae): natural variation of human impact? Biological Conservation 116: 277–287.

de Graaf, M., Nentwich, E.D., Osse, J.W.M and Sibbing, F.A. (2005). Lacustrine spawning, a new reproductive strategy among ‘large’African cyprinid fishes? Journal of Fish Biology 66:214–1236.

Dereje Tewabe. (2013). Status of Lake Tana commercial fishery, Ethiopia. ABC Research Alert 1:1-16.

Eshete Dejen, Osse, J.W.M and Sibbing, F.A. (2003). Ecological position of ‘small barbs’ and their potential for fisheries: An option to reduce fishing pressure on ‘large barbs’ of Lake Tana (Ethiopia)? Aquatic Ecosystem Health and Management 6: 337-342.

Eshete Dejen, Vijverberg, J., Nagekerke, L.A.J and Sibbing, F.A. (2004). Temporal and spatial distribution of micro crustacean zooplankton in relation to turbidity and other environmental factors in large tropical lake (Lake Tana, Ethiopia). Hydrobiology 513: 39-49.

Jamu, D. M., Chimphamba, J. B and Brummett, R. E. (2003). Land use and cover changes in the Likangala catchment of the Lake Chilwa basin, Malawi: implications for managing a tropical wetland. African Journal of Aquatic Science 28: 123–135.

Kolding, J. (1999). Pass gear: A data base Package for experimental or artisanal fishery data

from passive gears. An introductory manual University of Bergen Dept. of Fisheries and Marine Biology.

Murdoch, T and Martha, C. (1999). The Stream Keeper’s Field Guide: Watershed Inventory and Stream Monitoring Methods. A Product of the Adopt-A-stream Foundation. Everett, WA, 296 pp.

Nagelkerke, L.A.J and Sibbing, F.A. (1996). Reproductive segregation among the large barbs (Barbus intermedius complex) of Lake Tana, Ethiopia. An example of intra lacustrine speciation? Journal of Fish Biology 49: 1244–1266.

Nagelkerke, L. A. J. (1997). The barbs of Lake Tana, Ethiopia: morphological diversity and its implications for taxonomy, trophic resource partitioning and fisheries. PhD Thesis, Wageningen University, The Netherlands.

Nagelkerke, L. A. J and Sibbing, F. A. (2000). The large barbs (Barbus spp., Cyprinidae, Teleostei) of Lake Tana (Ethiopia), with a description of a new species, Barbus osseeinsis. Netherlands Journal of Zoology 50: 179–214.

Palstra, A.P., de Graaf, M and Sibbing, F.A. (2004). Riverine spawning in a lacustrine cyprinid species flock, facilitated by homing? Animal Biology 54: 393–415.

Rzóska, J. (1976). Lake Tana, head water of the Blue Nile. In The Nile, Biology of an Ancient River (Rzóska, J., ed), pp. 223–232. The Hague: Dr Junk Publishers.

Rodriquez-Ruiz, A. and Granado-Lorencio, C. (1992). Spawning period and migration of three species of cyprinids in a stream with Mediterranean regimen (SW Spain). Journal of Fish Biology 41: 545–556.


Sanyanga, R.A. (1996). Variation in abundance of Synodontis zambezensis (Pisces: Mockokidae) Peters 1852, in the shore fishery of Lake Kariba. Fisheries Research 26:171-186.

Shewit Gebremedhin, Minwyelet Mingist, Abebe Getahun and Wassie Anteneh (2012). Spawning migration of Labeobarbus species of Lake Tana to Arno-Garno River, Lake Tana sub-basin, Ethiopia. SINET: Ethiopina Journal of Science 35:95–106.

Skelton, P.H., Tweddle, D. and Jackson, P. (1991). Cyprinids of Africa. In: Cyprinid Fishes, Systematics, Biology and Exploitation (Winfield, I. J. and Nelson, J. S., eds), pp. 211–233. London: Chapman and Hall.

Tómasson,T., Cambray, J.A. and Jackson, P. B. N. (1984). Reproductive biology of four large riverine fishes (Cyprinidae) in a man-made lake, Orange River, South Africa. Hydrobiologia 112: 179-195.

Wassie Anteneh (2005). The spawning migration and reproductive biology of Labeobarbus (Cyprinidae: Teleostei) of Lake Tana to Dirma and Megech Rivers Ethiopia. MSc thesis, Addis Ababa University, Addis Ababa, Ethiopia.


Second degree generalized gauss-Seidel iteration method for solving linear system of equations

V. Rajaguru1*, B.V.S. Prahasith Dasari1, Abebe Tsegaye1

Department of Electrical & Computer Engineering , Debre Berhan University, P.O. Box 445, DebreBerhan, Ethiopia

ABSTRACTThe main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load and losses so that the desired frequency and power interchange with neighbouring systems are maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus the high frequency deviation may lead to system collapse. Therefore, it is necessary to include the active controller as Superconducting Magnetic Energy Storage Units (SMES) to maintain the nominal system frequency. This paper presents the application of energy storage units in an interconnected two area hydropower system. The proposed work consist of two area interconnected hydro power system with Superconducting Magnetic Energy Storage units has been designed to improve the dynamic perfor-mance of the system and also Integral Square Error (ISE) technique is used to obtain the optimal integral gain settings. The simulation result shows that the Load Frequency Control in an interconnected hydro pow-er system with SMES units is considerably improved the system dynamics such as peak overshoot, settling time and frequency oscillation as compared to that of the system without SMES units. The system simula-tion realized by using MATLAB software.

Key words: Automatic Generation Control, Load Frequency Control, Energy Storage Units, SMES unit, Sys-tem dynamics, Hydro Power System.DOI: http://dx.doi.org/10.4314/ejst.v8i1.5

INTRODUCTION

Automatic Generation Control (AGC) or Load Fre-quency Control (LFC) is a very important factor in power system operation and control for supplying suf-ficient and reliable electrical power to consumer with good quality. Controlling the frequency has always been a major subject in electrical power system oper-ation and is becoming much more significant recent-ly with increasing size, changing structure and com-plexity in interconnected power systems. Each control area of interconnected power system must meet its own demand and its scheduled power interchange to maintain the power balance between the generation and the demand. If the power balance does not exist, then it will leads to frequency deviation which will cause unnecessary disturbances in the power system. A lot of research works has been made in this area

and some of them are as follows.A fuzzy logic controller for AGC in an interconnect-ed thermal power system including SMES units has been studied (Demiroren and Yesil, 2004). Real time simulation of AGC for interconnected power system is presented and a new control strategy for digital controller is developed (Naimul Hasan et al., 2012). PI controller design using Maximum Peak Resonance Specification (MPRS) has been implemented to main-tain frequency and the power interchange and also proved that effective and efficient method to control the overshoot, settling time and maintain the stabili-ty of the system (Prajod and Carolin, 2013). Effect of SMES unit in a restructured power system con-sidering GDB non-linearity is examined (Rajaguru et al., 2015). A simulation model for load frequency control in an interconnected hydro power systems using fuzzy PID controller is presented and proved


52 V. Rajaguru et al.

that fuzzy logic controller yields better control per-formance (Ramanand and Sankeswari, 2014). Load frequency control in an interconnected two area hy-dro-hydro system has been studied (Ramanand et al., 2013). Automatic Generation Control (AGC) of an interconnected four area hydro-thermal system using Superconducting Magnetic Energy Storage (SMES) unit is examined (Ruby meena and Senthil-kuma, 2014). Implementation of load following in multi-area hydro thermal system under restructured environment is investigated (Suresh et al., 2012). A comprehensive digital computer model of a two area interconnected power system including the GDB non-linearity, steam reheat constraints and the boil-er dynamics is developed. The improvement in AGC with the addition of a small capacity SMES unit is studied (Tripathy et al., 1992).

Even in the existence of controllers (such as Integral controller, Proportional Integral controller and Pro-portional Integral Derivative controller), the governor not able to correct the frequency variations quickly. Therefore, now a day’s along with this convention-al controller we incorporate active controllers like SMES units for better control.Wecan use optimiza-tion tools (such as Fuzzy Logic, Genetic Algorithm, Particle Swarm Optimization, etc.,) for further im-provement in the dynamic performance of the system.

MODEL OF TWO AREA INTER-CONNECTED HYDRO–HYDRO POWER SYSTEM

A two area system consists of two single area sys-tems, Connected through a power line called tie-line, is shown in the Figure1. Each area feeds itsuser pool, and the tie line allows electric power to flow between the areas. Information about the local area is found in the tie line power fluctuations. It is conveniently assumed that each control area can be represented by and equivalent turbine, generator and governor sys-tem. Figure 1, shows the block diagram representing the two area interconnected hydro power system. This model includes the conventional integral controller gains (K11, K12). Each power area has a number of generators which are closely coupled together so as to form a coherent group. Such a coherent area is called a control area in which the frequency is assumed to be same.

SMES UNIT

The Figure2, shows the basic configuration of a SMES unit in the power system. The super-conducting coil can be charged to a set value (which is less than the full charge) from the utility grid during normal operation of the grid. The DC magnetic coil is connected to the AC grid through a Power Conversion System (PCS) which includes an

Figure1. Transfer Function Model of two area interconnected hydro-hydro power system


inverter/rectifier. Once charged, the superconducting coil conducts current, which supports an electromagnetic field, with virtually no losses. The coil is maintained at extremely low temperature (below the critical temperature) by immersion in a bath of liquid helium.When there is a sudden rise in the demand of load, the stored energy is almost immediately released through the PCS to the grid as line quality AC. As the governor and other control mechanisms start work-ing to set the power system to the new equilibrium condition, the coil charges back to its initial value of current. Similar is the action during sudden release of loads. The coil immediately gets charged towards its full value, thus absorbing some portion of the excess energy in the system, and as the system returns to its steady state, the excess energy absorbed is released and the coil current attains its normal value. The op-eration of SMES units, that is, charging, discharg-ing, the steady state mode and the power modula-

Where: Ed = DC voltage applied to the inductor (KV) α = firing angle (degree)Id = current through the inductor (KA)Rc= equivalent commutating resistance (Ω)Vdo= maximum open circuit bridge voltage of each six pulse convertor at α=0 degree (KV). The inductor is initially charged to its rated current, Ido by applying a small positive voltage. Once the current has attained the rated value, it is held con-stant by reducing voltage ideally to zero since the coil is superconducting. A very small voltage may be required to overcome the commutating resis-tance.The energy stored at any instant, WL = (½)(LId

2), MJ (2)

Where, L = inductance of SMES, in Henry.

In LFC operation, the Ed is continuously controlled by the input signal to the SMES control logic. The inductor current must be restored to its nominal val-ue quickly after a system disturbance so that it can respond to the next load disturbance immediately. Thus, in order to improve the current restoration to its steady state value the inductor current deviation is used as a negative feedback signal in the SMES con-trol loop. Based on the above discussion, the convert-er voltage deviationsapplied to the inductor and in-ductor current deviations are described as follows:

(4) (S)diÄEiSL

1(S)diÄI

(3) (S)diÄIdciST1

idK(S)SMESiU

dciST1SMESK

(S)diÄE

=

+−

+=

Figure 2. Configuration of SMES unit

tion during dynamic oscillatory period are controlled by the application of the proper positive or negative voltage to the inductor. This can be achieved by con-trolling the firing angle of the converter bridges.

Neglecting the transformer and the converter losses, the DC voltage is given by

Ed = 2Vdocosα - 2IdRc (1)


Where:∆Edi (s) = Converter voltage deviation applied to inductor in SMES unitKSMES = gain of control loop SMESTdci= convertor time constant in SMES unit USMES = control signal of SMES unitKid= gain for feedback ∆Id in SMES unit∆Idi (s) = inductor current deviation inSMES unit.

The ACEi is defined as follows:

Figure 3. The block diagram of SMES unit

LS

Idoi+DIdi

ACEi = BiΔFi +ΔPtie,i(5)WhereBi = Frequency bias in area i.∆Fi = Frequency deviation in area i.∆Ptie,i = Net tie line power flow deviation in area i.

The deviation in the inductor real power of SMES unit is expressed in time domain as follows:ΔPSMESi = ΔEdiIdoi+ ΔIdiΔEdi(6) Where, ∆PSMESi = Deviation in the inductor real power of SMES unit in area i.

This value is assumed to be positive for transfer from AC grid to DC. Figure 3,shows the blockdiagram of SMES unit.

INTEGRAL CONTROLLER

The integral control composed of a frequency sensor and an integrator. The frequency sensor measures the frequency error (Δf) and this error signal is fed into the integrator. The input to the integrator is called Area Control Error (ACE). The ACE is the change in area frequency, which when used in an Integral-con-trol loop, forces the steady-state frequency error to zero.

Figure 4. Mathematical model of Integral Controller

Figure 4,shows the mathematical model of integral controller.Here,’ 1’ can be called as integral gain (KI). Thisgain can be optimized by several techniques su-chas integral square error technique, singularfrequen-


cy technique, etc.,The integrator produces a real-pow-er command signal ΔPc and is given by

(7) dt ÄfKicÄP −=

(8) dt ACEKi −=

Where, ΔPc = input of speed –changer Ki = integral gain constant.The value of Ki is so selected that the response will be damped and non-oscillator. Here, the integral control-

ler gains KI is determined by using Integral Square Error (ISE) criterion. The objective function used for this technique is

(9) )dt 21ÄPtie

t

0F

21(ÄJ +∫=

Where,Df1= change in frequency in area 1DPtie = change in tie-line power.

The optimum values of KI are given in appendix.


Figure5 (a). Load frequency control in an interconnected hydro – hydro power system without SMES units


Figure 5 (b). Load frequency control in an interconnected hydro – hydro power system with SMES units


0 50 100 150 200 250 300 350 400 450 500-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Time(s)

chan

ge in

freq

uenc

y(DE

LF1)

, Hz

FREQUENCY DEVIATION IN AREA 1

with SMESwithout

Figure 6(a). Frequency Response of Area-1

0 50 100 150 200 250 300 350 400 450 500-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Time(s)

chan

ge in

freq

uenc

y(D

ELF

2), H

z

FREQUENCY DEVIATION IN AREA 2

with SMESwithout

Figure 6(b). Frequency Response of Area-2

0 50 100 150 200 250 300 350 400 450 500-3

-2

-1

0

1

2

3x 10

-5

Time(s)

chan

ge in

tie

line

powe

r(DEL

Ptie

),PU.

MW

TIE-LINE POWER DEVIATION

with SMESwithout

Figure 6 (c). Tie line power deviation of area-1 &2


The figure 5 (a & b), shows the simulation diagram of Automatic Generation Control or Load Frequency Control in an interconnected hydro – hydro power system with & without SMES unit.

Figure 6 (a, b, & c), shows the simulation results of two area inter-connected hydro - hydro power system with SMES unit and also for without SMES unit con-sidering Integral controller. Figure 6 (a and b), shows the frequency response of area-1 (i.e. ∆f1) and area-2 (i.e. ∆f2) for the system with & without SMES unit. And the figure 6(c) shows the tie line power devia-tion (∆ptie) for the system with and without the SMES units. Thus, from the Simulation Results, it is clear that the dynamic performance (such as frequency os-cillation, peak overshoot and settling time) of the hy-dro power system is significantly improved than that of the system without SMES unit.

CONCLUSIONS

Automatic Generation Control provides a relatively simple, yet extremely effective method of adjusting generation to minimize frequency deviation and regu-late the tie-line power flows. In this paper, application of energy storage units in an interconnected hydro power system is proposed. The power system model consists of identical hydro units (two units per area) with and without SMES units are considered for this study. In addition to this, Integral Square Error (ISE) technique is used to obtain the optimum conventional integral controller gains. The simulation results show that the dynamic performance of the system is signifi-cantly improved in terms of frequency oscillations, peak overshoot and settling time when the SMES units are included in a two area interconnected hydro – hydro power system.

ACKNOWLEDGEMENTS

The authors wish to thank the authorities of Debre Berhan University, Debre Berhan, Ethiopia for the facilities provided to prepare this paper and also we would like to thank all the faculty of Electrical and Computer Engineering department for his valuable suggestion and continuous support.

REFERENCES

Demiroren A and Yesil E. (2004). Automatic Generation Control with Fuzzy Logic Controllers in the Power System including SMES units. Electrical Power and Energy Systems 26(1):291–305.

Naimul Hasan and Ibraheem, Shuaib Farooq. (2012). Real time simulation of Automatic Generation Control for interconnected power system. International Journal of Electrical Engineering and Informatics 4(1):40-51.

Prajod, V.S and Carolin, M. (2013). Design of PI controller using MPRS method for Automatic Generation Control of hydropower system. International Journal of Theoretical and Applied Research in Mechanical Engineering 2(1):1-7.

Rajaguru, V., Sathya, R and Shanmugam, V. (2015). Effect of SMES unit in a Restructured Power System Considering GDB Non-Linearity. International Journal of Engineering Research and Technology 4(2): 1-12.

Ramanand Kashyap and Sankeswari, S.S. (2014). A simulation model for LFC using fuzzy PID with interconnected hydro power systems. International Journal of Current Engineering and Technology 3:183-186.

Ramanand Kashyap, Sankeswari, S.S and, Patil, B.A. (2013). Load Frequency Control using fuzzy PI controller generation of interconnected hydropower system. International Journal of Emerging Technology and Advanced Engineering


3 (9):655-659.

Ruby, A and Senthilkumar, S. (2014). Load Frequency Stabilization of four area hydro thermal system using Superconducting Magnetic Energy Storage System. International Journal of Engineering and Technology 6(3):1564-1572.

Suresh Babu, A, Ch. Saibabu, Sivanagaraju.S. (2012). Implementation of load following in multiarea hydrothermal system under restructured environment. International Journal of Engineering Sciences and Emerging Technologies 3(1):13-21.

Tripathy, SC, Balasubramanian, R and Chanramohanan, Nair P.S. (1992). Effect of Superconducting Magnetic Energy Storage on Automatic Generation Control considering governor dead band and boiler dynamics. IEEE Transaction on Power Systems 3(7): 1266–1273.

APPENDIXA.1 Data for the two-area interconnected hydro-hydro

power system without SMES unit.

T1= 41.6 s, T2 = 0.513 s, TR = 5 s, TW = 1 s, H = 5 sec, D = 8.33*10-3 Pu. MW/Hz, KI=0.02, B = 0.425 Pu.MW/Hz, R = 2.4 Hz/Pu.MW.

A.2 Data for the two-area interconnected hydro-hydro power system with SMES unit.

T1= 41.6 s, T2 = 0.513 s, TR = 5 s, TW = 1 s, H = 5 sec, D = 8.33*10-3 Pu. MW/Hz, KI=0.04, B = 0.425 Pu.MW/Hz, R = 2.4 Hz/Pu.MW.

A.3 Data for SMES block

L =2.65 H, Tdc= 0.03 s, KSMES = 70 KV/unit MW,

Kdi= 0.2 KV/KA, Ido = 4.5KA.

Author Guidelines

Manuscript Submission: Manuscripts should be submitted to the Editorial office of EJST e-mail: [email protected] letter signed by all authors declaring that the manuscript has not been published or submitted for publication elsewhere should accompany the manuscript. Authors should state that they have agreed to its submission and are responsible for its contents. This statement should preferably be signed by all au-thors. The cover letter should also indicate the name and ad-dress of the corresponding author.

Article content and formatManuscripts should be written in English and typed one and half-spaced, on A4size pages, with margins of 1.75 cm on each side of the paper (top, bottom, left, and right sides). A font size of 12 points (Times New Roman) should be used throughout. The major headings of the manuscript such as ABSTRACT IN-TRODUCTION, MATERIALS AND METHODS, RESULTS AND DISCUSSION, CONCLUSION, ACKNOWLEDGE-MENTS, and REFERENCES should be left aligned and writ-ten in UPPERCASE letters. The title of the manuscript should be centered at the top of page and written in sentence case with bold face font. All pages in papers must be numbered consecutively. The main text should be typed flush left with no indents and double line spaced. Insert one return between paragraphs, and a double re-turn between paper title, and authors’ names and addresses on the first page.ABSTRACT The abstract of the manuscript should not exceed 250 words.

REFERENCES References should be cited in the text as follows:1. Single author: the author’s name (without initials, unless there is ambiguity) and the year of publication e.g. (Williams, 2013). Except Ethiopian names which should be cited in full i.e. the first name followed by father’s name.2. Two authors: both authors’ names and the year of publica-tion e.g. (Williams and David, 2013).3. Three or more authors: first author’s name followed by ‘et al.’ and the year of publication e.g. Williams et al., 2013). 4. Citations may be made directly or in parenthesis. Groups of references should be listed first chronologically then, alphabet-ically. Example (Girma Adugna et al., 2000; Girma Adugna et al., 2001; CABI, 2003; Girma Adugna, 2004; Oduor et al., 2004).More than one reference from the same author(s) in the same year must be identified by the letters ‘a’, ‘b’, ‘c’, etc., placed after the year of publication. Examples: ‘as demonstrated (Al-lan, 2000a; Allan, 2000b, Allan, 1999; Allan and Jones, 1999). Kramer et al. (2010) have recently shown ....’ According to Berdy (1995), around 11,900 antibiotics had been discovered by 1994 …….

Reference styleThe references should be arranged alphabetically by author’s last name then chronologically per author. Publications by the same author(s) in the same year should be listed by year fol-lowed by the letters a. b. c. etc. (e.g. 2002a. 2002b, 2002c.).

Journal article Author (s), year of publication (in parenthesis), title of the arti-cle (sentence case), full name of journal (in italic font) , vol-ume (bold), number (in parenthesis), and page numbers in full.

Examples: Shukla, G.K. (1972). Some statistical aspects of partitioning genotype-environmentalcomponents of variability. Heredity 29: 237-245.

For a bookAuthor (s), year of publication (in parenthesis), title of the book (bold), publisher and place of publication.ExampleSteel, R. G and Torrie, J. H. (1980). Principles and Proce-dures of Statistics. McGraw-Hill, New York.Pages in a publication prepared by one or more editors Author (s), year of publication (in parenthesis), title of the pub-lication, name of the publication (bold), pages, Editors (in pa-renthesis), publisher, place of publication.ExampleLechevalier, M.P and Lechevalier, H.A. (1980). The chemo-taxonomy of Actinomycetes.In: Actinomycete Taxonomy, Society for Industrial Micro-biology, pp. 227-291 (Dietz, X. and Thayer, Y., eds.). Arling-ton, VA.

ProceedingsAuthor (s), year of publication (in parenthesis), Title of the publication, Name of the proceeding (Bold font) , pages, Place (City/town, Country) Example Eshetu Derso, Teame Geberzgi and Girma Adugna (2000). Significance of minor diseasesof Coffea arabica in Ethiopia. In: Proceedings of the Work-shop on Control ofCoffee Berry Disease (CBD) in Ethiopia, pp. 35-46, Addis Ababa, Ethiopia.

For a ThesisAuthor (s), year of publication (in parenthesis), title of the the-sis, type (MSc or PhD), University, Country. ExampleAlberts, M.J.A. (2004). A comparison of statistical methods to describe genotype xenvironment interaction and yield stability in multi-location maize trials. M. Sc. Thesis, University of the Free State, South Africa.

Web references Toni, R.L and Culvert, L.L. (2003). Safer Hospital Stay and Reducing Hospital-Born Infections. Health Scout News. http://www.healthscout.com, (accessed January 9, 2010).

Tables, figures and scientific measurements Tables and figures should be of reproducible quality, include comprehensive captions and not duplicate material presented in the text. Tables should bear Arabic numerals and be referred to in the text by their numbers e.g. Table 1. Each table must be typed on a separate sheet and should be placed at the end of the manuscript. Vertical lines should not be used to separate col-umns; instead extra space should be left between the columns. Footnotes in tables should be indicated by superscript letters beginning with ‘a’ in each table. Descriptive material not des-ignated as a footnote may be placed under a table as a NOTE.All illustrations should be given separately. They should be numbered as figures in sequence with Arabic numerals e.g. Figure 1. Each figure should have a descriptive legend. All illustrations (tables and figures) must be cited consecutive-ly in the text.

ETHIOPIAN JOURNAL OF SCIENCE AND TECHNOLOGY VOLUME 8, NUMBER 1 (JUNE, 2015)

CONTENTS


Bioethanol production from finger millet (Eleusine coracana) straw Teshager Mekonnen Tekaligne, Abebe Reda Woldu, Yeshitila Asteraye Tsigie

1


Aregawi Teklay and Meareg Amare

15


Maritu Admassu, Gebremariam Yemane, Mulugeta Kibret, Bayeh Abera, Endalkachew Nibret and Melaku Adal

27


Gizachew Teshome, Abebe Getahun, Minwyelet Mingist and Wassie Anteneh

37

Application of energy storage units in an interconnected hydro power system

V. Rajaguru, B.V.S. Prahasith Dasari, Abebe Tsegaye

51

Author Guidelines

ETHIOPIAN JOURNAL OF SCIENCE AND TECHNOLOGY VOLUME …

Documents

Transcript of ETHIOPIAN JOURNAL OF SCIENCE AND TECHNOLOGY VOLUME …