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Sacha Journal of Environmental Studies, Volume 1 Number 1 April 2011; pp. 1-30

Effluent Discharge by Mumias Sugar Company in Kenya: An Empirical Investigation of the Pollution of River Nzoia Akali N. Moses1; Nyongesa N. Destaings2; Neyole E.Masinde3; and, Miima, J. B4

1 2

Kenya Institute of Management (KIM), Kenya Department of Economics and Business Studies, Maseno University, Kenya; 3 Centre of Disaster Management and Humantarian Assistance, Masinde Muliro University of Science and Technology, Kenya 4 School of Disaster Management, Masinde Muliro University of Science and Technology, Kenya ABSTRACT Most industries in Western Kenya obtain water for industrial and domestic purposes from River Nzoia. Effluents from sugar milling activities by Mumias Sugar Factory are treated through a system of six ponds before being discharged into River Nzoia. There is likelihood that the river is polluted. This study sought to analyze pollution loading due to wastewater discharge by Mumias Sugar Company into the Nzoia River. Samples were examined for six water quality parameters: Temperature, pH, Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Dissolved Solids (TDS) and Total Suspended Solids (TSS). Temperature, pH, and TDS were measured in situ. The Findings indicated that the measured values were higher than permissible limits by NEMA and the World Health Organisation (WHO). The results also depicted significant loading of pollution parameters in waste water from the sugar company into River Nzoia. Keywords: Pollution, Mumias Sugar Company, River Nzoia, Kenya

INTRODUCTION Water comprises over 70% of the Earth's surface; water is undoubtedly the most precious natural resource that exists on the planet, without which life would be non-existent. It is essential for everything to grow and prosper. Despite this, human-made activities worldwide pollute water bodies. Estimates suggest that nearly 1.5 billion people lack safe drinking water globally and that at least 5 million deaths per year can be attributed to waterborne diseases (Onsdorff, 1996). Water bodies have long been considered limitless dumping ground for wastes such as industrial effluents, raw sewage, garbage, and oil spills. Water quality is closely linked to water use and to the state of economic development in industrialized countries. Most industrial activities entail water intensive processes and consequently discharge effluent into the environment in large amounts. The effluent contains chemical contaminants known to cause degradation of the receiving aquatic environment. Mumias Sugar Company is a consumer of large quantities of water, discharging wastewater containing high levels of oil, suspended solids, organic matter, and chemicals. Location of such a facility is influenced by a


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reliable water source, both as a primary requirement for milling and as a recipient of the resultant effluent. Most chemicals used in sugar processing are toxic, if not well handled might eventually find their way into the wastewater stream thus degrading quality of the receiving water. Bacterial contamination of surface water caused serious health problems in major cities throughout the mid 1800's (USEPA, 2002). By the turn of the century, cities in Europe and North America began building sewer networks to channel domestic wastes downstream of water intakes. Since World War II and the birth of the "chemical age", water quality has been heavily impacted worldwide by industrial and agricultural chemicals (Mackenzie, 1996). Newly industrialized countries such as China, India, Thailand, Brazil and Mexico all face these issues simultaneously. Many rivers in developing countries also face similar challenges. They receive pollutants from household and industrial discharges as well as diffuse sources generated by agricultural and urban run-off. River Nzoia, which lies in the western region of Kenya, is not an exception. It measures about 334 km long with a catchment area of about 12,900 km2 (NRBMI, 2006). The River is the main source of water for industrial and agricultural establishments within Nzoia basin. As the River flows down to Lake Victoria, it traverses three fundamental zones namely; upper, middle, and lower catchment. These zones have varied activities both human and economic, from which the surrounding populations earn a living. Each zone impacts differently on the catchment in terms of water quality. The upper catchment which covers Marakwet District, and Trans-Nzoia District amongst, is mainly dominated by agricultural activities. Pollution threats in this region are agrochemical based. Middle catchment harbours major urban centres such as Eldoret, Kapsabet, Webuye, Bungoma, Kitale, Mumias, and Kakamega. Around these town municipalities, the river receives effluents from the factories in the area which degrades water quality(Achoka, 1998). The lower zone covers areas towards Lake Victoria, which is dominated by rice cultivation such as Bunyala irrigation scheme. The river absorbs agrochemical related pollutants due to the use of nitrogen and phosphorous fertilizers meant to enhance farming activities. Therefore, the main challenges in the basin include soil erosion, sedimentation, deforestation, flooding, wetland degradation, pollution and solid waste, river bank cultivation, sand harvesting, brick making, human-wildlife conflict and poorly developed infrastructure, as noted by Wasike (1996). Stakeholders in the basin are increasingly noticing declining water quantity and quality due to environmental degradation (NRBMI, 2006). Degradation, unsustainable development, and inadequate management of River Nzoia water resources threaten the social and economic livelihood of Kenyans especially the poor. Water quality parameters from the factories' lagoons vary widely. Achoka (1998) observed that, increased load on effluent treatment system has significantly reduced the efficacy of aerated lagoons within the basin and hence inadequately treated industrial effluents have been reported in River Nzoia. This is augmented by Omuterema (2005), who asserts that "There is stagnation in technology for effective adoption of modern Environmental Management System (EMS) in waste management at Mumias Sugar Factory". Mumias Sugar Company abstracts water from River Nzoia through a pump and pipeline to a treatment plant within the factory premises. There is no metering system for the respective water consumption points, but it was estimated that the factory utilizes an average of 5,000m3 and 4,000m3 for domestic and other non-production purposes per day (ECMC, 2004). Water abstracted is of poor quality due to pollution from urban centres upstream, such as Webuye, whose wastewater is discharged into the river. Other pollutants originate from agro-chemical


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applications in the sugarcane plantations. Surface runoff also contributes to pollution of the river through washing down chemical pollutants and suspended solids from agricultural farms located within the river catchment areas. The management on the other hand has not put in place mechanisms for measuring quantity of industrial effluent that leaves the company through its treatment line. However, the total treated effluent flowing back to the river is estimated at 0.0361m3/s on daily basis (ECMC, 2004). The rest of the factory effluent is channelled to a set of six stabilization ponds located about 500 metres away from River Nzoia. The effluent discharging from the sixth pond into the river could be containing pollutants thus impacting on the downstream consumers and ecosystems. Boilers' ash, solid materials from the cane yard and cane crushing areas as well as the bagasse holding yard is washed down with water along with surface runoff into a drainage system. The water enters River Nzoia about five metres downstream of the water intake point still carrying loads of ash and other solids, visibly black. There are two water pollution control facilities installed at the site; process wastewater treatment plant, and three sewage treatment systems. Key point sources of pollution include oil storage and diffuser tanks. These areas are characterized with high water spillage. Lack of efficient wastewater treatment plant operations and management guidelines are a precursor to the reduced water quality of River Nzoia. There is need for a holistic approach to analyze pollution loading in River Nzoia, to assess the potential environmental effects of industrial wastewater discharge into the River. In order to control water pollution, it is necessary to analyze water quality parameters since it can help to establish the efficacy of wastewater treatment plants, monitor and control water quality degradation. Indeed, the determination of River Nzoia water pollution loads caused by Mumias Sugar Company is a prerequisite for rational decision-making and management of the River and its public utility. The broad objective of the paper is to determine the effect of the waste water disposed of by Mumias Sugar Company on the water quality of River Nzoia, Western Kenya, specifically the paper seeks to determine consistency of pollution loads in effluents from Mumias Sugar Company, based on Temperature, pH, BOD, COD, TDS, and TSS;variation in pollution loads with distance downstream from Effluent Discharge Point; compare actual wastewater pollution loads with the expected national and international standards. MATERIALS AND METHODS (a) Conceptual Framework

The study was guided by Thomann's model of water quality engineering. Thomann and Mueller (1987) observe that water quality management problems can be controlled by means of assignment of allowable discharges to a water body, so that designated water use and quality standards are met. There are several components to the problem namely; determination of desired water use standards, water quality standards, and comparison between desired and the actual quality, sources of pollution and control measures. Working from the premise that uncontrolled discharge of wastewater into a river system lowers its water quality, the model was modified and adapted for this study as illustrated in Figure 1.1.


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Figure 1.1: Conceptual framework (Adapted from Thorman and Mueller, 1987) Available water for use. Community

Public health and ecological water quality standards Environmental Engineering controls (Treatment Plant)

Mumias Sugar Processing factory.

Water quality concentration. Actual vs. Desired.

GIS platform.


Study Area

The study made use of primary data generated out of field measurements and experiments performed in the laboratory. Geographically referenced coordinates of sampling sites were recorded by help of hand-held GPS receiver. ArcView GIS was used to generate an overlay of the study area as shown in Figure 2.1.


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Figure 2.1: Study Area

The focal point of this study was Mumias Sugar Company, located in Mumias District in Western Province. The company occupies 4,295 hectares of land (ECMC, 2004). Cultivation and milling sugarcane are the main activities undertaken by the company alongside power generation and wastewater treatment as supporting activities. Mumias District with population of 168,743 has a varying topography with a few hills and valleys dissected by a number of streams. The economy of the region is largely rural and more than 90 per cent of the population earns its living from subsistence agriculture and livestock. The farms are privately owned and on average 1-3 hectares. However, large commercial farms with an average of 50-100 hectares or more characterize some districts within the basin, such as Trans Nzoia and Uasin Gishu. The main food crops include maize, sorgum, millet bananas, groundnuts, beans, potatoes, and cassava while the cash crops consist of coffee, sugarcane, tea, rice, sunflower and horticultural crops. Dairy farming is also practiced together with traditional livestock keeping. The River Basin is of great economic importance at local as well as national levels especially in such sectors as agriculture, tourism, fishing, forestry, mining and transport (GoK, 2002).


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Study Design

The factory water abstraction point was located as a remote point, from which ground information was collected. Points picked along the main wastewater treatment line were, factory raw waste, out pond three (3), and out pond six (6)-effluent discharge point (EDP). Other points were drainage channel outlet and three in-stream points after the EDP at an interval of 100m. Sampling was carried out twice every week, for a period between 16th July and 31st August 2007. Samples were collected in plastic containers thoroughly pre-washed in the laboratory before taking them for sampling. Composite grab sampling by a hand-dip method was adopted insitu. Sampling containers were grasped near the base on the downstream side, plunged into wastewater/water and opened downward below the surface. The containers were allowed to fill with the opening pointed upward into the current, capped securely and transported to the laboratory for physical-chemical analysis. (d) Field Methods

Temperature, pH and TDS were measured insitu by use of thermometer, pH meter, and TDS/Conductivity meter respectively. Laboratory analyses were carried out at Mumias Sugar Factory production laboratory and the Ministry of Water and Irrigation laboratory at Kakamega. The procedures used were as described in the standard methods (APHA, 1998). All through the study, samples were collected and taken to the laboratory within two hours of sampling. Two replicates of each sample were separately subjected to tests for BOD5, COD and TSS loading. Mean values for each pair of sub-sets were recorded for every sample. Materials for chemical analysis included apparatus and reagents approved by standard methods (Kulkarni, 1993 and APHA, 1998).Temperature was measured by use of a laboratory thermometer with glass enclosed scale and range -10 to +110 0C in 10C divisions, 30cm long and diameter of 7mm.The pH was determined by means of electrometric measurement, where activity of hydrogen ions was measured by use of a standard hydrogen electrode and a reference electrode. The following is a description of the portable meter used; Beckman 12600, model N-1, battery­operated of range 0-14, with acidic aqueous solution enclosed by a glass membrane that allows migration of H+ ions, and distilled water. At each sampling site, the meter was turned on and its probe placed into wastewater/water column and the value recorded. Distilled water was always used to rinse the probe after each measurement. (e) Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD

Biochemical Oxygen Demand (BOD) test was based on determination of Dissolved Oxygen (DO) prior to and after a 5-day period of incubation at 20 0C i.e. BOD5 test (APHA, 1998). Standardized laboratory procedures were used to determine relative oxygen requirements of wastewater. Samples were diluted with water containing phosphate buffer, manganese sulphate, calcium chloride and ferric chloride. Measurements for DO of the dilution water before (Blank) and after incubation were done. The following apparatus and reagents were used during the test; Air incubator controlled at 20 ± 1 oC, Burette, Pipette, and measuring cylinder. Phosphate buffer solution, Magnesium sulfate solution, Calcium chloride solution, Ferric chloride solution, Acid and alkali solutions,


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Alkali, Sodium sulfite solution, Nitrification inhibitor, 2-chloro-6-(trichloromethyl) Pyridine, Glucose-glutamic acid solution, Ammonium chloride solution, and Dilution water. Compressed air was bubbled in 5 litres of pure water in a glass container for two days until saturation point. Equal amounts (1ml) of each of manganese sulphate, phosphate buffer, ferric chloride, and calcium chloride solution was added to the saturated water for each litre of distilled water. Each sample was neutralized to pH 7. Two blank, BOD bottles were prepared for determination of initial DO. Diluted samples were added and the two blanks maintained. Four bottles were kept in an incubator at 20 0 C for five days. A blank and one sample bottles were taken for determination of initial DO contents. After incubation for 5-days, DO was determined again and BOD5 calculated from the relation: 1 BOD5 D0 D5 C0 C5 P Where, Do represents DO of sample on 0th day D5 represents DO of sample on 5th day C0 represents DO of Blank on 0th day C5 represents DO of Blank on 5th day and P is the percentage dilution Chemical Oxygen Demand (COD) loading was determined by use of Open Reflux Method. A sample was refluxed in strongly acid solution with excess of potassium dichromate (K2Cr2O7). After digestion, the remaining unreduced K2Cr2O7 was titrated with ferrous ammonium sulfate (FAS) to determine the amount of K2Cr2O7 consumed and the oxidizable matter calculated in terms of oxygen equivalent (APHA, 1998).The following reflux apparatus and reagents were used; 500ml Erlenmeyer flasks with ground-glass 24/40 neck and 300mm jacket Lie big, and a hot plate with power to produce 1.4 W/cm2 of heating surface, Blender, Pipette, Standard potassium dichromate solution 0.04167M, Sulphuric acid reagent, Ferroin indicator solution, and Sulfamic acid. A sample size of 50ml was pipetted into refluxing flask and to it, 1g HgSO4 was added, several glass beads, 5mL sulfuric acid reagent, with mixing to dissolve HgSO4 , and 25mL 0.04167M K2Cr2O7 solution. The flask was attached to condenser and cooling water turned on. Remaining sulphuric acid reagent (70mL) was added through open end of condenser and continued swirling and thoroughly mixing while adding sulphuric acid reagent as heat was applied and refluxed for 2 hrs. The condenser was cooled, washed down, disconnected and the mixture diluted to twice its volume with distilled water. It was then cooled to room temperature and excess K2Cr2O7 titrated with FAS, using 0.10 to 0.15 mL ferroin indicator. This was titrated with N/10 Ferrous Ammonium Sulphate solution, till the colour changed from green to wine red and the end point noted. Same procedure was performed with `Blank' using distilled water. COD was calculated by means of applying the equation:

COD(mgO2 / l )

( A B) M 8000) mLofsample

Where: A represents mL FAS used for blank, B represents mL FAS used for sample, M is the molarity of FAS, and 8000 is the milli-equivalent weight of oxygen X 1000 mg/L.


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Total Dissolved Solids (TDS) was measured insitu by use of a TDS/ Conductivity meter with the following attributes; Range of 0-8560 (mg/L), Digital calibration by push button, Detachable platinum Probe, Waterproof housing, and 4.5V Power source. Total Suspended Solids (TSS) in wastewater samples was determined through laboratory experiments. It involved filtering of the samples through a weighed filter paper and evaporating the liquid in an oven. A residue retained on the filter was dried to a constant weight at a temperature of 105°C. Apparatus used were; Vacuum pump with attached funnel, Whatman filter papers No. 44, Kiesselghur, dessicator, electronic weighing machine and Utility thelco model 18, double walled oven measuring temperature up to 200 ±10C, 110volts, 60 cycles, 1160 watts, and oven chamber of 48cm wide by 36cm long by 48cm high. Dried known mass of Kiesselghur was added to 50g of wastewater sample in a beaker, thoroughly mixed and boiled on a hot plate before filtering through a dried and weighed filter paper on the vacuum pump. Resulting cake and the beaker were washed and dried in the oven at 105 0C for 4hrs and cooled in a desiccator for 1hr to room temperature and weighed. Thus quantity of TSS was calculated from the relation in equation: 100 S TSS Weightofsa mple (Volume ) Where: P is the weight of filter paper Y is the weight of paper and residue X represents the weight of Kiesselghur and is defined by the expression S (Y P X ) Where S represents the percentage weight of suspended solids RESULTS (a) Temperature and pH fluctuations

From the tests conducted, the results indicated that there was no significant difference in temperature upstream and downstream. This implies that on many occasions, wastewater from the factory had no considerable impact on the temperature of river water downstream. After wastewater treatment, the effluent was discharged into the river with temperature mean value of 26.9 0C and at spray pond and meander field discharge point was 36.4oC (see figure 3.1). Therefore, the impacts could be localized at these points which had a temperature gradient of 4 to 6 0C/m compared to the river water. Through the pond system, temperature values reduced considerably and while in-stream, the value dropped further and stabilized at about 220 C.


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Figure 3.1 Temperature variations base on sampling sites

70 60


50 40 30 20 10 0

Upstream MF& S pond XProcess OutPond.3 OutPond.6 D/S-Pt.1 D/S-Pt.2 D/S-Pt.3

Standard Temp. Actual Temp


The results indicated that there was no significant variation in daily pH values downstream and upstream. It was however observed that effluent from the factory was highly alkaline. This implied that the treatment process tends to lower the pH to slightly acidic levels with a mean value of 5.5. This lies below the recommended value by both NEMA and WHO. Therefore, the discharge was more acidic as it got into the river; which was likely to increase acidity of the receiving river water and thus affect the aquatic environment. (b) BOD5 variation

Concerning the daily mean variations in the BOD5 loading through the sampling sites; the loading decreased downstream after the effluent discharge point. This is an indicator that effluent from the factory had significant impact on this water quality parameter (see table 3.1). Table 3.1: Daily BOD5, variation in mgO2/L UpMF& X-Process OutOutD/S-Pt.1 D/S-Pt.2 D/S-Pt.3

stream S pond Mean 1.272 468.06 SD 0.0 ±1.1 2662.79 ±27.6

Pond.3 Pond.6 3608 ±43.1 3304.64 ±6.9 1.784 ±0.2 1.350 ±0.2 0.375 0.0


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Compliance of BOD5 loading with standards was investigated; the results in table 3.2 indicated that the mean BOD5 value decreased downstream after the effluent discharge point. Table 3.2: BOD5 variation downstream Distance Downstream (m) 100 200 300 Mean BOD5 (mg/L) 1.784 1.350 0.375 SD ±0.2 ±0.2 0.0

As compared to the NEMA standard (maximum permissible BOD5 of 30mg/l), there was no significant pollution effect as a consequence of BOD5 downstream. Therefore, it was valid to suffice that there was a significant relationship between BOD5 loading and distance downstream after the effluent discharge point. Pollution loading due to BOD5 reduced downstream and stabilized at about 300m downstream as was depicted by the standard deviation at this point. It is an indicator that there was a strong inverse relationship between BOD5 and distance downstream. Table 3.3: Mean daily COD variation UpMF&S XProcess OutPond.3 OutPond.6 D/SPt.1 D/SPt.2 D/SPt.3

stream pond

Mean 84.2 SD ±1.00

640.4 ±1.8

5562.3 ±50.4

28793.6 11157.9 ±83.9 ±52.1

94.5 ±1.5

88.0 ±7.8

90.7 ±5.9

Conditions upstream and downstream point to fluctuations in the COD values on daily basis. Upstream COD loading level was 84.2 mgO2/L while 300m downstream was 90.7 mgO2/L. It follows that the impact of the effluent on the river water quality needs further investigation. After industrial processes; the wastewater was discharged back into the river at 11157.9 mgO2/L and 640.7 mgO2/L for out pond six and meander fields respectively. Loading level of COD increased from the X-process and reached its peak through the treatment plant in pond three. It then reduced in pond six and reached the NEMA set standard 100m downstream from the point of discharge into the river. In the meander field and spray pond, the COD value was above the set NEMA and WHO standard (50 and 150mg/L respectively). Figure 3.2 depicts an R2 value of 0.336 indicating that there was no significant trend in the variation of COD downstream. Generally the COD decreased downstream. Error bars were set at 10%.


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Figure 3.2: COD variation downstream



COD 92.00

y = -1.907x + 94.866 R2 = 0.336

90.00 88.00 86.00 0.5 1 1.5 2 2.5





In stream Sampling Points (x10 COD

Linear (COD)

Table 3.4 below shows mean values of measured COD values and the set NEMA standard; the measured COD values were found to be higher than the recommended maximum permissible value by NEMA of 50mg/L. Table 3.4: Mean COD values down stream Distance Downstream (m) Mean COD value (mg/L) 100 200 300 (c) TDS variation 94.5 88.0 90.7 SD ±1.5 ±7.8 ±5.9

The results shows that although there were fluctuations in daily TDS values downstream and upstream, there was a marked decrease in TDS values from the point of effluent discharge downstream. This showed that effluent discharge into the river water had a significant impact on its solubility characteristics. Several of the dissolved substances precipitate on merging with the factory effluent. Figure 3.3 below shows TDS trend in relation to sampling points. The highest recorded value was 1,538.5 mg/L at the out let pond six and 100.39mg/L at the discharge for wastewater from meander fields and the spray pond (see Table 3.1). TDS load from the factory raw waste showed an increase in its value up to the discharge into the environment via the series configuration of six ponds. The value increased from 542.47 to 1538.5 mg/L, at X-process and outlet pond six discharge respectively. The increase could be due to additional inorganic


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fertilizers because of surface runoffs from the surrounding sugar cane plantations. In addition, it could be linked to the possible poor ion adsorption by the bed of the lagoons and mineralizing process as wastewater discharged through the factory treatment system. Figure 3.3 depicts that TDS value at the effluent discharge point (out of pond six) was higher than the NEMA but within allowable limits at other sampling points. This is an indicator that TDS loading was not a significant pollution hazard in the River Nzoia catchment. Figure 3.3: TDS variation based on sampling sites

180 160 140


120 100 80 60 40 20 0 Up stream MF& pond XProcess Out Pond. Out Pond. D/SD/SD/S-

NEMA Value Actual Value WHO Value


Figure 3.4 below indicates that there was a significant relationship between TDS values and distance downstream. The R2 value of 0.7804 implies that the TDS value decreased significantly downstream. The error bars were set at 5% and from the figure it is clear that the equation for the prediction of TDS value downstream lies within this percentage error. Table 3.5 shows variation of TDS downstream the river after the effluent discharge point.


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Figure 3.4: Variation of TDS downstream (Error bars 5%)



80.0 0 60.0 0 40.0 0 20.0 0 0.0 0 0.5

y = -6.65x + 74.193 R 2 = 0.7804









In stream Sampling Points (x10

Table 3.5: TDS mean values downstream Distance downstream (m) 100 200 300 Mean TDS values (mg/L) 69.58 56.82 56.28 SD ±0.2 ±0.2 ±0.1


TSS variation

Daily TSS values upstream fluctuate between 100 and 600mg/L and between 300 and 1100mg/L at the effluent discharge point. TSS values increase downstream after the effluent discharge point. In reference to Table 3.10, TSS mean value was 1398.4 mg/L at the discharge for wastewater from the by-pass channel. It also shows that TSS values in all the sampled sites were higher than the NEMA and WHO allowable standards of 30 and 40mg/L respectively. It follows that TSS is a significant parameter contributing to the pollution of the river Nzoia by effluent from the factory. Figure 3.5 with R2 value of 0.7006 is an indicator that there was a significant relationship between TSS loading and the distance downstream of the discharge point.


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Figure 3.5: Downstream variation in TSS (error bars 2%)


490.0 480.0 470.0 460.0 450.0 440.0 430.0 420.0 410.0 400.0

y = 29.714x + 381.43 R 2 = 0.7006

TSS, (mg/L)


2 1.5 2.5 2 In stream Sampling Points (x10 metres)




There was a general increase in the mean values of TSS downstream (see Table 3.6 below). This was confirmed by the positive gradient in figure 3.6. Table 3.6: Variation of mean TSS downstream Distance downstream (m) Mean TSS values (mg/L) 100 200 300 422.36 418.43 481.79 SD ±216.4 ±230.3 ±234.1


Summary of study parameters

Table 3.7 indicates that all the parameters of the effluent measured vary significantly from the NEMA standards. Temperature variation of 50C was observed. The temperature however stabilized 100m downstream, an indicator that presently it is not a significant risk as in Table 3.10. The pH also stabilized downstream.


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Table 3.7: Measured values up-stream, meander field, X-process, Out of pond six, and NEMA standards. Parameter


Up-stream MF&S-ponds X-Process Out-pond-six NEMA standards

Temp( C) PH

22.0 7.5

36.4 6.7 468.1 640.7 100.4 1398.4

60.4 9.6 2663.0 5562.0 542.5 220.0

27.0 5.5 3304.6 11158.0 1538.5 805.1

3 6.5 - 8.5 30 50 1200 30

BOD5 (mgO2/L) 1.27 COD (mgO2/L) TDS (mg/L) TSS (mg/L) 84.2 55.8 248.0

From Table 3.7 above it follows that the effluent from the factory had significant impact on the COD. These parameters were therefore investigated further in the following sections. Temperature and pH decreased through the effluent treatment plant while BOD5, COD, TDS and TSS increased (see Table 3.8). Table 3.8: Mean Variation of Parameters through the wastewater treatment system Parameter








Temp ( C) PH BOD5 (mgO2/L) COD (mgO2/L) TDS (mg/L) TSS (mg/L)

60.5 9.6 2663.0 5562.0 542.5 220.0

±9.9 ±2.3 ±27.6 ±50.4

28.0 4.5 3608.6 28794.0

±0.8 ±0.9 ±43.1 ±83.9 ±54.7

27.0 5.5 3304.6

±0.5 ±0.6 ±6.9

11158.0 ±52.1 1538.5 ±230.3 ±320.4

±432.3 601.2 ±121.3 559.2

±220.2 805.1

Study findings revealed that the effluent receiving river water was polluted and was not fit for human consumption (see Table 3.9). The water therefore requires treatment in order to use for drinking.


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Table 3.9: Variation of parameters from the effluent discharge point downstream in comparison to EPA standards on potable water

Parameter Temp (0C) PH BOD5 (mgO2/L) COD (mgO2/L) TDS (mg/L) TSS (mg/L)

Out-Pond.6 D/S-Pt.1 D/S-Pt.2 D/S-Pt.3 EPA standard for potable water. 27.0 5.5 3304.6 11158.0 1538.5 805.1 22.0 7.4 1.8 94.5 69.6 422.4 22.0 7.5 1.4 88.0 56.8 418.4 22.0 7.5 0.4 90.7 56.3 481.8 6.5-8.5 5*(minimum) 500(limit) 30*

Mean parameter values in relation to the sampling points are given in Table 3.10 below.

Table 3.10: Table showing parameter mean values

Parameter UpStream 22.0 7.5 1.3 84.2 55.8 248 MF& S Xpond Process 36.4 60.4 6.7 9.6 468.1 2663.0 640.7 100.4 1398.4 5562.0 542.5 220. 0 OutPond.3 27.7 4.5 3608.6 28794.0 601.2 559.2 OutPond.6 26.9 5.5 3304.6 11158 1538.5 805.1 D/SPt.1 22 7.4 1.8 94.5 69.6 422.4 D/SPt.2 22 7.5 1.4 88 56.8 418.4 D/SPt.3 22 7.5 0.4 90.7 56.3 481.8

Temp (0C) Ph BOD5 (mgO2/L) COD (mgO2/L) TDS (mg/L) TSS (mg/L)

Attribute data was tabulated on a GIS platform for all the sampling sites to enhance presentations and display of the study results. Figures 3.6 and 3.7 shows a typical display of wastewater parameter loading at two separate effluent discharge points.


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Figure 3.6: Attribute table showing wastewater parameter loading levels at out-pond six

Figure 3.7: Attribute table showing wastewater parameter loading levels at discharge point from the meander fields and spray pond


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TESTING OF HYPOTHESIS (a) TSS Effluent stream entry point

The chi-square test was carried out to test for consistency of the treatment plant based on TSS loading at the effluent discharge point (see Table 3.11). Table 3.11: TSS Chi­square test Parameter Chi-Square(a) d.f Asymp. Sig. TSS 14.286 8 0.075

The probability of making type I error (rejecting a hypothesis when actually it is true) by accepting the fact that these values are not independent is 7.5%. At 92.5% confidence interval it is proper to conclude that the samples were from the same source (Mugenda & Mugenda, 1999). The test was done at 97.5% confidence interval with alpha (critical asymptotic significance level) being set at 0.025 for the purpose of acceptance or rejection of the hypothesis. The discharge levels are consistent but not within 97.5% confidence interval range. Levels of compliance with the NEMA recommended standards were tested by way of t-test analysis (see Table 3.12). Table 3.12: Paired samples t-test

Paired Differences 97.5% Confidence interval of Difference Mean TSS ­ NEMA 775.07 Standard Std. Error Deviation Mean lower upper t df Sig. 2-tailed




991.92 9.05



The t-statistic of +9.05 is extremely higher than the significant t value of 0.00. This is an indicator that there is a significant variation between the mean values of TSS and NEMA standard, which implies that the actual measured values are higher than the NEMA standard. Therefore, the effluent cause water pollution since it does not meet effluent discharge standards into the environment.


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TDS Effluent discharge point

Consistency of the treatment plant based on TDS pollution loading at the effluent discharge point was carried out by chi-square test. The TDS values seem not to be sampled from one source with the same distribution since the probability of making an error by rejecting the fact that the values are independent is high i.e. 97.4%.(see Table 3.13). Table 3.13: TDS Chi-square test Parameter Chi-Square(a) Df Asymp. Sig. TDS 3.286 10 0.974

The null hypothesis that the TDS values measured are independent is therefore accepted. This depicts significant variation in daily TDS values from the wastewater treatment plant: Asymptotic Significance of 0.974 is more than alpha set for this study of 0.025. Consequently it suffices to conclude that the measured TDS values are independent from each other. Paired samples t-test was carried out to test for the treatment plant compliance with NEMA standards based on TDS loading at the effluent discharge point (see Table 3.14). A tstatistic of +5.5 which is higher than the significant t value of 0.00, points to the fact that there is a significant variation between the mean values of TDS and NEMA standard. The effluent thus causes water pollution since it does not meet effluent discharge standards. Table 3.14: Paired samples t-test Sig. Paired Differences Std. Mean Deviation TDS NEMA Standard 338.5 230.28 61.55 182.63 494.37 5.50 13 0.00 Std. Error Mean 97.5% Confidence Interval of the Difference Lower Upper t Df (2-tailed)


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BOD5 at effluent discharge point

Table 3.15 below shows test for compliance with the NEMA recommended standard based on BOD5 loading levels at the effluent discharge point. Table 3.15: t-test BOD5 measured BOD5 (NEMA) Mean Variance Observations 3304.64 47.93 14 30 0 14

Hypothesized Mean Difference 0 Df t Stat P(T t) one-tail t Critical one-tail 13 1769.62 1.13178E-36 2.16

A t-statistic of 1.132 E-36 is far much below the t-critical value of 2.16 (see Table 3.15 above). This is an indicator that there is significant variation between the measured BOD5 value and the expected NEMA standard. The effluent therefore does not meet expected standards. (d) Variation of pollution loading downstream

Table 3.16 given below shows average pH values for the three sampling points (1, 2 and 3) of 7.42, 7.47 and 7.45 respectively. This is a neutral range pH with a tendency towards alkalinity. Table 3.16: pH variation downstream SUMMARY Groups D/S-Pt.1 D/S-Pt.2 D/S-Pt.3 Count 14 14 14 Sum 103.90 104.51 104.36 Average 7.42 7.46 7.45 Variance 0.04 0.05 0.07

Table 3.17 is a summary of statistical parameters describing analysis of variance for pH downstream after the effluent discharge point.


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Table 3.17: Analysis of variance of pH downstream ANOVA Source of Variation SS Between Groups Within Groups 0.01 2.26 df 2 39 MS 0.0072 0.058 F 0.124 P-value 0.88 F crit 4.06

The F value of 0.124 is less than Fcrit of 4.06 (see Table 3.17 above). This depicts that pH variation downstream is not significant. The P-value of 0.88 is much greater than alpha of 0.025 an indicator that the relationship between pH values downstream is not by chance. There was a general decrease in BOD5 loading levels downstream from 1.78 to 0.37mg/L 300m after the EDP (see Table 3.18 below). Table 3.18: BOD5 variation downstream SUMMARY Groups D/S-Pt.1 D/S-Pt.2 D/S-Pt.3 Count 14 14 14 Sum 24.98 18.9 5.26 Average 1.78 1.35 0.37 Variance 0.02 0.04 0.002

F value of 300.7 is far greater than Fcrit value of 4.06 (see Table 3.19 below). This is an indicator that there is significant variation in BOD5 downstream. At 97.5% confidence interval it suffices to conclude that BOD5 varies significantly downstream. Table 3.19: Analysis of variance of BOD5 downstream ANOVA Source of variation Between groups Within groups SS 14.56 0.94 Df 2 39 MS 7.28 0.02 F 300.6 P-Value 2E-24 Fcrit. 4.06

Table 3.20 shown below gives a summary of ANOVA statistic on variation of COD downstream. Sampling point 1 had mean COD value of 94.50, point 2 had 87.95 and point 3 had 90.69 mg/L.


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Table 3.20: COD variation downstream SUMMARY Groups D/S-Pt.1 D/S-Pt.2 D/S-Pt.3 Count 14 14 14 Sum 1323.1 1231.4 1269.7 Average 94.50 87.95 90.69 Variance 2.35 60.48 34.75

F value of 4.65 is close to Fcrit value of 4.06 (see Table 3.21 below). There is therefore a slight variation in COD concentration downstream. The P-value of 0.015 is lower than alpha value of 0.025 a proof that there is no significant variation among COD values downstream. Table 3.21 Analysis of variance of COD downstream ANOVA Source of Variation SS Between Groups Within Groups Total 303.03 1268.85 1571.88 df 2 39 41 MS 151.51 32.53 F 4.65 P-value F crit 0.015 4.06

Pollution loading levels based on TDS after the discharge point were summarized in table form (see Table 3.22). Table 3.22: TDS variation downstream SUMMARY Groups D/S-Pt.1 D/S-Pt.2 D/S-Pt.3 Count 14 14 14 Sum 974.12 795.51 787.92 Average 69.58 56.82 56.28 Variance 1004.83 267.79 247.38

Variance in the measured values of TDS decreased downstream from sampling point one to three. F value of 1.57 is less than Fcrit of 4.06. The P-value was 0.22 bigger than alpha value of


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0.025, which indicates that the variation downstream was not significant for TDS as depicted (see Table 3.23). Table 3.23: Analysis of variance of TDS downstream ANOVA Source of Variation SS Between Groups Within Groups Total 1586.41 df 2 MS 793.20 506.67 F 1.56 P-value 0.22 F crit 4.06

19760.21 39 21346.63 41

Mean TSS values for the three sampling points were 422.36, 418.43 and 481.79mg/L respectively for sampling points 1,2 and 3 (see Table 3.24). Table 3.24: TSS variation downstream SUMMARY Groups D/S-Pt.1 D/S-Pt.2 D/S-Pt.3 Count 14 14 14 Sum 5913 5858 6745 Average 422.36 418.43 481.79 Variance 46833.02 53055.03 54819.41

There was observed a general increase in variance downstream through the in-stream sampling points. More statistical units were summarized in Table 3.25 below. It gives F value of 0.34 which is less than Fcrit value of 4.06. P-value of 0.71 is higher than alpha of 0.025. These statistics show that there is a relationship in TSS values downstream. Table 3.25: Analysis of variance of TSS downstream ANOVA Source of Variation Between Groups Within Groups Total SS df MS 17643.07 51569.15 F 0.34 P-value 0.71 F crit 4.06

35286.14 2 2011197 39 2046483 41


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DISCUSSIONS (a) Consistency of pollution loading in the factory effluent and Compliance with NEMA/WHO standards

The mean temperature values for various sampling sites were recorded. There was significant drop in temperature for wastewater flowing through the pond system. Mean temperature value of 60.43 0C was recorded at the factory raw wastewater and 270C at the discharge point into River Nzoia. This translated into 55.32 % temperature reduction and hence explained the effectiveness of the treatment lagoons in temperature diminution before the effluent was discharged into the environment. The highest recorded mean value (60.430C) could be attributed to hot wastewater from the mill house and the process unit that housed evaporators, juice heater, and the vacuum pan. The lowest recorded value was 220C. This was the mean temperature value for River Nzoia water, upstream of the abstraction point and downstream the discharge point. At the effluent discharge point, temperature mean value of 27 0C above allowable limit of 26.860C was registered. Effluent through meander fields joining leakages from the spray pond was noted to overflow with relatively high velocity through a by-pass channel into River Nzoia at a mean temperature of 36.43 0C. Such high temperature value above the allowable minimum for wastewater disposal could deplete Dissolved Oxygen (DO) levels in the receiving river system. As a consequence, it could impact on metabolic & reproductive activities of the aquatic environment and speed up rates of photosynthesis and decomposition. It could also provide the right environment to enhance faster growth of pathogens, which may increase susceptibility of aquatic organisms to disease. According to Chapman (1992), temperature affects physicochemical and biological processes in water bodies and therefore controls the structure and functioning of aquatic ecosystems in a significant way. Ground information from the upstream indicated that the water acidity attributes observed were within the recommended standards by both NEMA and WHO i.e. between 6.5 and 8.5. The mean pH value of the factory raw waste recorded was 9.6. This value was always raised through addition of lime to untreated wastewater, a process called lime stabilization. High pH environment inhibits survival of micro-organisms and thus eliminates the risk of sludge putrefaction and odour creation. Variation in the mean pH values within sampling points showed significant increase in acidity up to 4.5 at the out let of pond three along the treatment line. Momanyi (2002) found that pH value showed considerable decrease as wastewater flowed from one pond to another. The decrease in the pH value could be ascribed to micro-biological decomposition of wastes and mineralization process in the lagoons. The pH mean value of the effluent at the discharge point was 5.5. This implied a more acidic discharge into River Nzoia than the recommended minimum standard by both NEMA and WHO. Variations in BOD5 mean values for all sampling sites were tabulated. Raw water at abstraction point had a mean BOD5 of 1.27mg/L. There was a slight drop in BOD5 concentration by 8.42% for effluent that flowed through the lagoons between ponds three and six . The reduction in the loading level could be due to biological decomposition of organic matter during the time wastewater was retained in the aerated lagoons. This allowed the dissolved solids to settle down hence decrease in BOD5. The BOD5 mean loading value of 3,304.6mg/L at the


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discharge point was significantly higher than the recommended value of 30 or 40mg/L by NEMA or WHO respectively, for wastewater discharge into public courses. According to Keya et al. (2006), sugar manufacturing effluents typically have BOD of 1,700 ­ 6,600 mg/L in untreated effluent from cane processing. The boiler house sporadically discharges wastewater from the carbon dioxide scrubbing unit, equipment leakage and floor washings which comprises of the most highly polluted component of the combined sugar mill effluents because of its very high BOD (ETPI, 2001). The BOD5 loading considerably varied from one stage of treatment to another with values being offloaded into River Nzoia higher than the standards. The effluent gets into the environment through two channels i.e. meander fields and the out let pond six whose respective mean values were 4,68.057 and 3,304.64mg/L. According to Tyagi and Mehra (1990), such high values could deplete available oxygen; create septic conditions, generate foul-smelling hydrogen sulfide, which in turn can precipitate iron and any dissolved salts, turning the water black and highly toxic for aquatic life in the receiving river system. Chemical Oxygen Demand (COD) loading variation from the raw waste through the treatment line up to the discharge point was summarized in a table form. It was shown that COD loading in the wastewater into the river had a high value of 11,158mg/L than the allowable loading of 50mg/L (see Table 4.7). This may have led to increased demand for oxygen by the receiving water in order to support life. The highest and lowest mean values of 28,793.57mg/L and 84.2mg/L were reported at the out-pond three and upstream before the abstraction point, respectively. Comparison of the COD mean level at EDP and respective NEMA/WHO (50/150mg/L) standards, revealed that the COD obtained value of 11,210mg/L was higher than these values for wastewater discharge into public courses. The highest and lowest TDS concentration values were recorded at the discharge point and upstream, before the abstraction point. The respective values were 1,538.50mg/L and 55.83mg/L. Therefore, TDS loading levels were higher than the allowable limit of 1,500mgL by WHO for effluent discharge into the environment. Discharge of wastewater with a high TDS level would have adverse impact on aquatic life, render the receiving water unfit for drinking, reduce crop yields if used for irrigation, and exacerbate corrosion in water systems and pipe (ETPI, 2001). Total suspended solids (TSS) mean loading value recorded at the discharge point was 805.07mg/L. This was higher than allowable limits by both NEMA and WHO of 30 and 40mg/L respectively, for wastewater discharge into a public river course. Effluent through the by-pass channel into River Nzoia registered TSS mean loading value of 1398.36mg/L .These high TSS loading values could be due to lofty solids and organic matter in the x-factory effluent and high amounts of infiltrable bagasse that was released from the factory. Further contributory sources could be spillages of molasses into the effluent and refuse from the cane yard section of the factory. (b) In-stream variation in pollution loading

The mean temperature both up-stream and down-stream was 220C. In a similar study by Achoka (1998), temperature mean values at water intake and effluent discharge points (EDPs) were 210C and 27.1 0C respectively. Figure 4.1 illustrates variation of temperature from the remote point upstream to downstream point three. Through the pond system, temperature values reduced considerably and while in-stream, the value dropped further to stabilize at about 220 C.


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Findings of related study by Karuri (1995), temperature value of 23.40C was recorded, one kilometer after the effluent discharge point of Njoro treatment works. This is further augmented by temperature mean value of 22.60C for river water samples from downstream of Eldoret West treatment works along Endaragwa, Endaragweti, Sambul and Sosiani rivers in Uasin Gishu district (Odongo, 1991). However, effluents from the meander fields were discharged through a by-pass channel into River Nzoia before any kind of treatment at a mean temperature value of 36.4 0C. This subjected the receiving water to thermal pollution and hence could lead to significant changes in fauna and flora of the river. Temperature changes even as low as 0.80C have been found to be harmful to and even lethal to some organisms in a very short period of time (WHO, 1984) Respective mean values for pH upstream and downstream were 7.5 and 7.4, which were within the allowable limits (6.5 ­ 8.5). This was possibly due to dilution and self buffering effect by large amounts of water in the river course. The values did not vary significantly with a range of 6.63 ­ 8.81 as determined by Davies (1996) for River Nzoia in the period of June-July 1993. The BOD5 mean values obtained along River Nzoia at upstream and three points downstream after the effluent discharge point were found to range from 0.38 mg/L to 1.78mg/L. In a related study by Davies (1996), the values were found to fall between 3mg/L and 20mg/L. In addition, Khazenzi (1996) showed that BOD5 values in River Sosiani downstream the discharge point ranged between 10 and 25mg/L at various sites. Nakuru by Karuri (1995) investigated Njoro River and found values between 104mg/L and 180mg/L after treatment by stabilization ponds and conventional treatment respectively. The River Njoro that receives the effluent (in-stream) was 30mg/L at a point 1Km from the effluent discharge point. Such variations in the parameter loading could be accredited to differences in dilution factors within the river courses and given that the studies were undertake at different seasons of the year. Even though BOD5 loading value of 3,304.6mg/L at the effluent discharge point was well above the recommended values, the concentration downstream was insignificant by all standards. Chapman (1992) and Tyagi and Mehra (1990) contend that unpolluted waters typically have BOD values of 2mg/L or less., whereas those receiving waters may have values upto 10mg/L or more, particularly near the point of wastewater discharge. Respective Chemical Oxygen Demand (COD) mean values of 84.2mg/L and 90.69mg/L were obtained at the water abstraction point and 300m downstream the effluent discharge point (see Table 3.10). However, Davies (1996) reported an in-stream mean value of 60.3mg/L for River Nzoia. These loading values could be due to non-point sources of pollution and discharges through the by-pass channel, all of which were not being monitored. Such in-stream loading values serve as a pointer to the fact that River Nzoia is under the threat of pollution based on COD. The concentration of COD observed in surface water range from 20mg/L or less in unpolluted waters to greater than 200mg/L on waters receiving effluents(Chapman,1992, WHO/UNEP,1996). One-way ANOVA revealed insignificant variation in COD level downstream with F value of 4.65 that was comparable to Fcrit. of 4.06. A P-value of 0.015 was lower than alpha of 0.025, a further proof that the COD loading downstream up to 300m was invariant (see Table 3.21). However, the loading was by far lower than the value at the EDP (11,210mg/L).This shows 99.2% reduction in the parameter concentration which could be ascribed to turbulence and self-purification of the River Nzoia downstream. Usually there is high aeration during high turbulence. At the water intake point, TDS mean value of 55.8mg/L was recorded. After the effluent discharge point (EDP), the values were found to range between 56.3mg/L and 69.6mg/L.


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Wastewater through the by-pass channel registered a mean value of 100.4mg/L at the discharge point (see Table 3.10). Contrary to these findings, Davies (1996) registered a mean value of 556mg/L for the same river. Inconsistency within the loading values could be accredited to seasonality differences experienced within Nzoia catchment and possible disparity due to contributions from non-point sources. At the water abstraction point, the value of TSS was 248mg/L comparable to 260mg/L (Achoka, 1998). Ndagijimana (1998) reported that TSS upstream of Sosiani ranged between 20mg/L and 180mg/L and 40mg/L to 100mg/L downstream after the discharge point. In-stream loading values along River Nzoia after the EDP were between a mean of 418mg/L to 481mg/L (see Table 3.8). The high TSS in-stream loading value was attributed to factors such as high flow rates, where fast running water carried more particles and larger-sized sediment. Heavy rains occasionally experienced in the upper catchment during the study period could pick up sand, silt, clay, and organic particles from the land and carry it into the river. This also led to increased amount of water flowing down the river channel and hence high flow rate. The increasing speed of the water current enhances re-suspension of particulate matter from the river's bottom sediments which may have led to high TSS loading. Soil erosion, decaying plants and animals release suspended organic particles that contribute to TSS. Occasionally, during the study period, floods were experienced and may have brought along inorganic and organic particles from the land surface, and contributed this to the river, leading to increased TSS levels. According to Hatcher (1989), apart from natural events, sediment loading can also be increased as a result of human disturbances. Therefore, human activities such as swimming, fishing, and sand harvesting which are prevalent along the river could also be liable for the high TSS loading in-stream values. High TSS concentration reduce light penetration and, as a result, hamper plant production in the receiving water body by increasing turbidity and can also clog fish gills. It was observed that individual parameter loading values were low at the remote point. Sampling sites; X-process, out-pond three and six showed increased parameter loading values. In-stream points; downstream one, two, and three depicted exponential reduction in the parameter concentration with distance. The decreasing trend in the pollution loading with distance downstream was due to the massive river water dilution factor from which self replenishing capacity of the river can be explained. This was found to be in tandem with research findings by Kebeney (1997). In his study, he contends that, "it is expected that, as the river flows downstream; the concentration of parameters should decrease, unless there are other factors contributing to the change in concentration". (c) Health and environmental impacts of observed pollution load

High pollutant loads of sampled effluents, in quantities exceeding NEMA and WHO limits in the study parameter levels, coupled with the large-scale sugar production by the factory, signifies that there is a likelihood of alarming levels of pollutants being discharged into River Nzoia. The nature of sugar mill effluent poses danger to natural ecology of the receiving aquatic environment, with more muted impacts also likely, for fertility of the agricultural land irrigated and the populations exposed through using the effluent receiving waters. Health and ecological effects of the study parameters have been discussed in the preceding pages. The most notable revelation of the chemical analysis is the presence of extremely high BOD5 and COD levels at


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pond six discharge points and it's followed by substantial levels of temperature, TDS, and TSS for discharge via the by-pass channel. This indicates acute adversity for the survival of aquatic life through diminished oxygen supply and production of toxic hydrogen sulfide. The resultant foul-smelling septic conditions of wastewater from the factory lagoons would also render the water body a nuisance for local residents and spoil any aesthetic value of the natural environment. Beyond the ecological consequences, such spoilage of natural water ways has potential social impacts such as hampering livelihoods of local fishermen and farmers, curtailing any recreational uses, and casting a shadow on the "social corporate responsibility" of the source of pollutants. RECOMMENDATIONS AND CONCLUSIONS (a) Recommendations

Based on the findings there is need for the company to embrace cleaner production technologies to ensure an improvement of in-plant pollution prevention measures to minimize the volume and pollution loading in the wastewater before discharge into River Nzoia. Secondly the company should consider carrying out periodic "waste audits", for water use, to identify the level of waste discharge and also they ought to improve its treatment process and observe consistency of the process. Thirdly there is need for the company to incorporated wastewater by-pass channel in the treatment system. Lastly the government should ensure concerned companies comply with environmental standards, through enforcement of regulations and legislation governing the environment. In terms of recommendation for further research, there is need for continuous monitoring of River Nzoia water quality and quantity throughout the year to cover both wet and dry seasons. This will help generate data to explain relationships and trends between pollution loads and their environmental impacts in different seasons. A wide ranging and periodic analysis of pollutants such as; heavy metals, biological, agrochemicals, petroleum hydrocarbons is required within the context of effective and wide sample coverage of the Nzoia River Basin. There is need for a health assessment of impact on the riparian community that makes use of water from River Nzoia, through experiments examining agricultural yields and chemical make-up of produce irrigated. (b) Conclusions

Based on the analyzed parameters wastewater treatment is an imperative environmental challenge facing the Sugar Company. The company releases wastewater with inconsistent loading levels into the adjacent River Nzoia. There was a general decrease in the loading levels based on Temperature, pH, BOD5, COD, and TDS downstream after the effluent discharge point. Findings from this study indicated that mean values for five parameters; pH(5.4) , BOD5 (3,304mg/L), COD (11,158mg/L) ,TDS (1,538mg/L), and TSS (805mg/L) all were above both NEMA and WHO recommended standards for effluent discharge into the environment. Wastewater through the meander fields is discharged at relatively high velocity into the river and no pollution parameter loading is monitored. The study also revealed that four parameters; temperature (36 0C), BOD5


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(468mg/L), COD (640mg/L), and TSS (1,398mg/L) mean pollution levels through the line were above the guidelines given by NEMA and WHO for discharge of wastewater into public water courses. Comparatively, BOD5 and COD loading in wastewater arising from meander fields were lower than that in effluent through the treatment line. Therefore, wastewater treatment system by Mumias Sugar Company was unable to reduce the quantity of chemical species responsible in increasing pollution loading due to the study parameters in the company effluent. Decrease in pollution levels in most of the parameters downstream was indicative of the River Nzoia's self purification power. Cumulatively, loading in the wastewater at the discharge points is significant and hence can be detrimental to the receiving environment. As a result, pollution loads recorded in the wastewater suggests that extremely high removal efficiencies will have to be achieved for standards compliance. There is also need to contain waste water flows that by-pass the treatment plant to lower impacts of pollution on the river water. REFERENCES Achoka, D.J. (1998). Levels of the physico-chemical parameters in the liquid effluents from Pan paper mills at Webuye and in river Nzoia. Doctoral thesis at SES Moi University Eldoret, Kenya. American Public Health Association (APHA) (1998). Standard methods for examination of water and wastewater, 18th ed.American Public Health Association, Washington DC. Chapman, D. (1992). Water quality assessment. A guide to the use of biota, Sediments and water in environmental monitoring. London, Chapman and Hall, publisher. Davies, T.C. (1996). Chemistry of Pollution of Natural Waters in Western Kenya. J. Africa Earth Sciences, 23: 547 ­ 563. Elsevier Science Ltd. Environmental Cost Management Centre (ECMC), (2004). Environmental Audit Report for Mumias Sugar Company. The ECM center. Environmental Management and Co-ordination Act (EMCA), (1999). Environmental Management and Coordination Act, Government Printers, Nairobi. Environmental Technology Program for Industry (ETPI) (2001). Environmental Report of the Sugar Sector of Pakistan by Environmental Technology Program for industry. ( GoK, (2002). District Development Plan for Butere/Mumias (2002-2008). Government printers, Nairobi. Hatcher, B. G. (1989), 'Review of research relevant to the conservation of shallow Tropical marine ecosystems', Oceanography and Marine Biology Annual Review, 27: 337-414. Karuri, W.A. (1995). Studies on some heavy metals and water quality parameters in Tannery and municipal sewer effluents with reference to lake Nakuru. M.Phil thesis. SES Moi University, Eldoret, Kenya. Kebeney, J.K. (1996). The impacts of wastewater from Kamiti Tanners on river Gatharaini Nairobi, Kenya. M.Phil thesis. SES Moi University, Eldoret, Kenya. Keya, N., Ang'edu, E. C., Omuterema, O.S., and Obondo, O.B. (2006). Evaluation of environmental pollution caused by the sugar industry in western Kenya and mitigation options. Western University College of Science and Technology. Unpublished.


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Kulkarni, P.D. (1993). Laboratory manual in environmental engineering. Ashwin J Shah Jaico Publishing House 121 MG Road Bombay. Khazenzi, A.J. (1996). Sewage and solid medical waste pollution loads in Eldoret Municipality. M. Phil thesis. SES Moi University, Eldoret, Kenya. Kenya Water for Health Organization (KWAHO) (2005).Butere- Mumias District.( Accessed September 20, 2006. Mackenzie, C. H. (1996). The Ecosystem. Island Press, CA USA pp 240. Momanyi, T. (1999). Characterization of Webuye Pan Paper waters. M. Phil. thesis, Moi University. Eldoret, Kenya Mugenda, O.M. and Mugenda A.G. (1999). Research Methods. Quantitative and Qualitative Approaches. African Centre for Technology Studies. Nairobi, Kenya. National Environment Management Authority (NEMA) (2003). The environmental (Impact assessment and audit) regulations. Kenya gazette supplement No. 56, Legal notice No.101. ( June 13, 2008. Ndagijimana, A. (1999). The impacts of Eldoret municipal and industrial effluents on River Sosiani: Public health aspects. M Phil thesis SES Moi University, Eldoret, Kenya. Nile Basin Capacity Building Network (NBCBN) (2005). Flood and Catchment Management. Accessed September 2007. Nzoia River Basin Management Initiative (NRBMI) (2006). A public private partnership. ( Accessed August 5, 2007. Odongo, S.P. (1991).Levels of mercury in Kesses Dam and Endaragwa, Endaragweti, Sambul and Sosiani Rivers in Uasin Gishu District. M Phil thesis SES Moi University, Eldoret, Kenya. Omuterema, O. S. (2005). Approaches for Integrated Management of Wastewater in Sugar industry in Kenya. Proceedings of the 12 th KSST Biannual Conference 12th ­ 28th Oct 05, Nzoia Sugar Co. Bungoma, Kenya. Onsdorff, K. A. (1996). Pollution; Water Environment. Law pract, J. 4: 14-18. Otieno, T.A.A. (1995). Pollution of Lake Victoria by inorganic fertilizers in the west Kano irrigation scheme. M.Phil thesis. SES Moi University, Eldoret, Kenya. Thomann V. R. and Mueller A. J. (1987). Principles of surface water modeling and control. Harper & Row, Publishers, New York. Tyagi, O.D. and Mehra, M. (1990). A textbook of environmental chemistry. New Delhi, Anmol Publications. Wasike, S.K.W. (1996). Contingent valuation of river pollution control and domestic water supply in Kenya. PhD thesis, SES Moi University, Eldoret, Kenya. World Health Organization (WHO/UNEP) (1996).Water Quality Monitoring. A practical guide to design and instrument 1st edition. Chapman and Hall publishers, London. World Health Organization (WHO) (2006). A compendium of standards for wastewater reuse in the Eastern Mediterranean Region. Report of the WHO/AFESD Regional consultation to review national priorities and action plans for wastewater reuse and management (WHO). ( June 14, 2008. World Health Organization (WHO) (1984). Guidelines for drinking water-Water quality, volume 1. Recommendations, Geneva.



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