Assessment of pollution impacts on the ecological integrity of the Kisian and Kisat rivers in Lake Victoria drainage basin, Kenya

Please download to get full document.

View again

of 11
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Information Report



Views: 2 | Pages: 11

Extension: PDF | Download: 0

Related documents
Macro-invertebrate assemblages were used as bioindicators to assess the ecological integrity of Rivers Kisat (influenced by urban development) and Kisian (influenced by agriculture) using community attributes and the Index of Biotic Integrity. Six
  African Journal of Environmental Science and Technology Vol. 3 (4), pp. 097-107, April, 2009 Available online at ISSN 1991-637X © 2009 Academic Journals Full Length Research Paper Assessment of pollution impacts on the ecological integrity of the Kisian and Kisat rivers in Lake Victoria drainage basin, Kenya Kobingi, Nyakeya 1 *, Raburu, Philip Okoth 2 , Masese, Frank Onderi 2  and Gichuki, John 1 1 Kenya Marine and Fisheries Research Institute (KMFRI), P. O. Box 1881 Kisumu, Kenya. 2 Department of Fisheries and Aquatic Sciences, Moi University, Kenya, P. O. Box 1125, Eldoret, Kenya. Accepted 14 February, 2009 Macro-invertebrate assemblages were used as bioindicators to assess the ecological integrity of Rivers Kisat (influenced by urban development) and Kisian (influenced by agriculture) using community attributes and the Index of Biotic Integrity. Six stations, three per river, were selected to correspond to different impact types and intensities along the rivers. Physico-chemical parameters and nutrients were determined for each station on a monthly basis from November 2007 to April 2008. Two-way analysis of variance was used to compare water quality and nutrient parameters, and macro invertebrate community attributes between the two rivers, with the river and station as the main factors. Significant differences were accepted at 95% confidence level. There were inconsistencies in the variation of physico-chemical parameters along the two rivers. However, River Kisat recorded higher values for all physico-chemical parameters considered, except pH and DO. Different indices and metrics representing the structural and functional organization of macro invertebrates were computed and evaluated for responsiveness to physico-chemical parameters and nutrient levels. Macro invertebrate diversity, richness and evenness values failed to delineate stations according to the different levels of degradation they were experiencing. However, the differences were captured by the index of biotic integrity, which separated stations into different classes of quality. River Kisat stations in urban areas scored lowest index values, less than 15 out of 25, while two river Kisian stations scored the highest value, more than 19. The index provided evidence of response to changes in ecosystem integrity exhibited by resident macro invertebrate assemblages to pollution arising from both point and non-point sources. Key words:  Urban rivers, water quality, physico-chemical parameters, macro invertebrates. INTRODUCTION In most developing countries, point and non-point source pollution are major environmental problems affecting water quality. The situation is exacerbated by lack of or scarcity of treatment for domestic wastes (Dudgeon, 1992) and poor agricultural practices (Iwata et al., 2003). In East Africa, land use changes caused by rapid urba-nization and clearance of forests to create room for agriculture have emerged as major stressors of streams and rivers (Kibichii et al., 2007; Kasangaki et al., 2008). In Kenya, degraded water quality, losses of biodiversity and altered hydrography have been recorded among streams and rivers draining urban areas (Ndaruga et al., *Corresponding author. E-mail: 2004). On the other hand, deforestation and cultivation have been found to cause an increase in water tem-perature, conductivity, total suspended and dissolved solids and turbidity (Kibichii et al., 2007). Animal overuse on the riparian areas has been found to increase ammo-nia and nitrite as a consequence of increased run-off of animal wastes into streams (Kibichii et al., 2007). Near-stream human activities like sand mining, bathing, laun-dry and row crop agriculture have been reported to cause the greatest influence on stream habitat and biotic cha-racteristics (Mathooko, 2001; Raburu et al., 2009). In lower catchments of Lake Victoria Basin- Kenya, streams, rivers and the lake itself serve as the major source of freshwater to the riparian communities and their livestock. For town and city residents, portable water sup-  098 Afr. J. Environ. Sci. Technol. ply by relevant authorities is less than 60% and most people use the water directly without prior treatment. Against this backdrop, increased intensity of agriculture and deforestation coupled with the rapid growth of urban centers and industrial activities pose a potential threat in degrading small streams and rivers that drain directly into the lake. Because of their urban set up these small eco-systems are often not protected by buffer zones that allow for the absorption of immense run-off from Jua Kali sheds and settlements on the riparian areas. The pro-blem is worsened by the fact that most industries and malfunctional sewerage facilities discharge directly into the small streams and rivers which act as conduits for delivery of the wastes into the lake (Lung’ayia, 2002). This has consequently led to sedimentation and euthro-phication that have affected domestic and industrial water supply (Ntiba, et al., 2001). Macro invertebrate assemblages have been used as bioindicators of stream biological integrity (Collins et al., 2008; Miltner et al., 2004; Stepenuck et al., 2002). Within this framework, the use of a multimetric approach that utilizes the index of biotic integrity (IBI) (Karr, 1981) has gained interest in biological assessment of rivers and streams in urban and suburban catchments (e.g. Collins et al., 2008; Miltner et al., 2004). In the upper reaches of Lake Victoria basin results have indicated the usefulness of the index in assessing the biological integrity of studied rivers and streams (Masese et al., 2009a, b; Raburu et al., 2009). In the lower catchments of Lake Victoria basin, urban and suburban developments are replacing agricul-tural land use in most of the catchments. Studies that have compared the two land use types indicate that a low level of watershed urbanization triggers a biological res-ponse of greater strength and magnitude than watershed agriculture (Wang and Lyons, 2003). However, in the tropics inferences about the relative importance of urban and agricultural land use are limited, especially where the independent influences of the two land use types on water and overall ecosystem integrity are to be distin-guished. Although a number of rivers highly influenced by anthropogenic activities drain their water directly into the lake, information on urbanization and agriculture, which are the prominent land use types, on lowland streams within the lower catchments of Lake Victoria Basin is minimal. This study, therefore, focused on two streams within the same ecoregion, one in an urbanizing catch-ment and the other in an agricultural catchment, with the aim of utilizing macro invertebrate community attributes and a Macro invertebrate Index of Biotic Integrity (M-IBI) in relation to water quality and nutrient parameters to assess the effects of the two land-use practices on water quality and overall ecosystem integrity. MATERIALS AND METHODS Study area  The study was conducted on Rivers Kisian and Kisat in the lower   catchments of Lake Victoria Basin, Kenya (Figure 1). The two rivers occur within the same ecoregion in which they share common relief features, altitude and longitude range and climate. The area occu-pied by Winam Gulf of the lake, into which the two rivers drain, has tertiary and alkali volcanic and sedimentary rocks (Johnson et al., 2000). The Kisian River srcinates from Maragoli Forest and is sur-rounded by the catchments of Riat and Kodiaga hills. These are areas that have rocks that are continuously weathering and re-leasing ions (Mg 2+  and Ca 2+ ) in the river. There is sand harvesting in the river near the bridge (Otonglo-Kiboswa road). Activities on the river are relatively less intense, although farming activities along the river are evident (maize, tomatoes, millet, and kales). The com-munities living along this river extract water for domestic use. Before it drains its waters into the lake at Usoma Bay it passes through a swamp. The source of River Kisat is a small swamp on the eastern suburbs of Kisumu city. Drainage and subsequent cultivation of the land has accelerated the drying up of the once vast swamp, leaving the river to trickle. Grazing fields for livestock mainly cattle, small farms of maize and vegetables and a few of millet and sorghum are in the vicinity. All these activities have modified most of the area that is also being converted for residential use. The middle reach of Kisat passes through an area with small farms of maize, sorghum, vegetables where cultivation is done up to the riverbanks. As it flows into the lake it passes through the densely populated Obunga slums, which lack sanitation facilities and streams of sewage and residues from makeshift distilleries of local brew (chang’aa), an illicit whisky, enters the river at various points. After the slums the river flows through the Kisumu industrial area and the main industries include textile mill, soap and fish processing factories, salt works, motor garages and stores for various items, including toxic chemi-cals. The factories have no facilities for treating effluents and are connected to the sewage drainage system or discharge their waste effluents directly into the river. The Kisumu city municipal sewage treatment plant is located at the lower part of the river. However, the sewage plant has not worked for several years, and untreated sewage was being discharged into the river. A golf course is located just before the mouth of the river at Kisumu Bay. The Kisat River is greatly overwhelmed by multiple wastewater discharges and chemical effluents right from the source to the mouth. Sampling design Selection of sampling stations  The two rivers were selected on the basis of differing hydrology, habitat condition and human activity. Sampling stations were selected to represent different ecological and environmental varia-tions within each river, in order to understand the influence of natural and human induced stress on physical, chemical and biological attributes of the water quality. Three sampling stations were selected along each river. In the Kisian River station KI1 was located in a forested section at the source. Station KI2 was located in an agricultural area where vegetable farming was the main activity while Station KI3 was located after the river passes through a swamp before it enters into the lake. In Kisat River sampling sta-tion K1 was located near in area with minimal human activity, 0.5 km from the source. Station K2, 9 km from the source, was located at Obunga slums where domestic effluents and other wastes were being deposited into the river, and finally K3 was located after the industrial and municipal waste discharges. Data collection Data on physico-chemical parameters and macro invertebrates were collected monthly for a period of six months from November    Nyakeya et al. 099 Figure 1.  A map representing the study area showing the location of sampling points. Stations K1 - K3 are influenced by suburban and urban development while stations KI1-KI3 are influenced by agricultural development. 2007 to April 2008. The following physical-chemical parameters were measured in situ  ; pH and temperature were by microproces-sor pH meter, conductivity by a WTW microprocessor conductivity meter LF6 and turbidity by Hach 2100P Turbidimeter. Triplicate samples for dissolved oxygen were fixed in situ   before they were determined by the Winkler method in the laboratory (APHA, 1998). Triplicate samples for nutrients (nitrogen, silicates and phos-phorous) were collected and analyzed according to Wetzel and Likens (2000). Samples for water quality were collected before sampling for macroinvertebrates to prevent contamination. Sampling for macro invertebrates was done where water sam-ples had been collected using a scoop net (0.5 mm mesh size). Quantitative triplicate samples were collected from runs, riffles and pools from each station. Sampling was done for a standard three minutes by disturbing a 1 m 2  area for each microhabitat. Samples were sorted live in a white plastic tray and then poured into vials and preserved with 70% ethanol. The samples were then pooled to make one composite sample per station. In the laboratory samples were processed and identified to genus level according to Macan (1977), Merritt and Cummins (1996), Nilson, (1996, 1997), Quigley (1977) and Scholtz and Holm (1985). Taxonomic lists of species known to be present in Kenya were also useful (Johanson, 1992; Mathooko, 1998). Functional feeding groups were assigned according to (Merritt and Cummins, 1996) and assignations that have been used on Kenyan fauna (Dobson et al., 2002). The macro invertebrate diversity, richness and abundance were determined for each sampling station and sampling occasion using number of taxa, total number of individuals and relative abundance of each taxon. Relative abundance (R.A) was calculated as the proportion (percentage by numbers) of each taxon in a station. The  100 Afr. J. Environ. Sci. Technol. relative abundance was calculated as: R. A = No. of individuals of one taxon*100 Total no. of individuals in a station The Shannon-Weaver diversity index (Shannon and Weaver, 1949) was used to assess diversity as follows: H’ = -   ((n/N) * In (n/N)), where n = number of individuals of a taxon; N = total number of individuals in the station. An associated evenness H’/H’max (Pielou, 1975) was also calculated. The Simpson Index (D s ) (Simpson, 1949) was used as a measure of taxon richness. The index is given by:  ==  −−= nii  N  N nn 111S )}1({ )}1({ D  Where; n  1  is the number of species in the sample N is the total number of individuals in the station. Macro invertebrate community attributes for index development In order to develop a macro invertebrate based index of biotic inte-grity (M-IBI) for the two streams, 10 macro invertebrate assemblage attributes, termed metrics, were selected a priori and tested to determine their response to the different human impacts types. These attributes included number of taxa, EPT and intolerant taxa, percent Ephemeroptera + Plecoptera + Trichoptera (EPT), intole-rant, tolerant, no-insects, and predator individuals and percent gatherer genera. These macro invertebrate metrics have been used widely in developing M-IBIs where they have proved their utility as discriminators of pollution gradients (Fore and Karr, 1996; Kerans and Karr, 1994; Thorne and Williams, 1997; Weigel et al., 2003; Masese et al., 2009a; Raburu et al., 2009). To assess point-source pollution from industries, domestic wastes and municipal sewage effluents as well as non-point agricultural inputs, the Biological Monitoring and Working Party’s Average Score Per Taxon (BMWP-ASPT) biotic index, which was developed in Britain (ISO-BMWP, 1979), was used. The index has been used in several countries, including India (De Zwart and Trivedi, 1994), Australia (Chessman, 1995) and in Ghana (Thorne and Williams, 1997). Index development Metrics were screened for range, responsiveness to disturbance and redundancy with other metrics. On the basis of these results, we selected a set of non-redundant metrics that responded to a variety of disturbance types and included different metric classes. In order to combine the selected metrics into an index, each metric was transformed into a dimensionless number by scoring. The scored metrics were then summed to obtain the final index score. For the range test, all richness metrics with a range of 5 or less were eliminated while percentage metrics with a range of less than 10% were also eliminated (Klemm et al., 2003). Responsiveness of metrics to disturbance was evaluated using Pearson’s correlation analysis with physico-chemical and nutrient parameters. Metrics not correlated with any of the parameters was eliminated. Redundancy in the remaining metrics was evaluated by Pearson correlation coefficients and visual inspection of scatter plots. Metrics with a correlation coefficient ( r  )   0.85 were considered redundant. Only one metric from a group of redundant metrics was included in the final index. Metrics that passed the screening process were included in the final index. Scaling and scoring criteria We used a 1, 3, 5 scoring system, which has been commonly used in developing fish and macro invertebrate IBIs (Karr, 1981; Kerans and Karr, 1994; Barbour et al., 1999; Raburu et al., 2009) (Table 2). Because all streams in the region are considered degraded in one way or another, reference sites were not used to establish the scoring criteria. Instead, the highest value for each metric across all sites was used as a reference (Karr and Chu, 1999). For positive metrics (that is those that increased with improving conditions), the upper expectation was the 95 th  percentile of the highest value of a metric across all sites. The ranges of values from 0 to the 95th percentile were then trisected. Values above the upper one-third received a score of 5; those in the middle received a score of 3 while those in the lower one-third received a score of 1, corres-ponding to unimpaired, intermediate and impaired biota respectively (Barbour et al., 1999; Raburu et al., 2009). For negative metrics, those that decreased with improving condition, the lower expec-tation was the 5 th  percentile. The range from the 5 th  percentile was trisected but scoring done in reverse, i.e. values above the upper one third received a score of 1, those in the middle a score of 3 while those in the lower one-third a score of 5. To calculate the M-IBI value for each station all the metric scores were added. Condition categories Criteria used for contrasting biological conditions at sites (e.g., good, fair, poor) using IBIs or single metrics may be established with many methods (Stevenson et al., 2004). In this study integrity classes for condition categories were defined using highest M-IBI value for all stations, which was considered to represent reference conditions at the study area. In this case the reference site was arrived at posterior based on the highest M-IBI value   across all sites. Data analysis Data on physical-chemical parameters and nutrients were expres-sed as (means ±SE) for each station. Macro invertebrate count data was log transformed (Log 10  x+1) while percentage data was Arcsine transformed before analysis.   Two-way ANOVA was used to compare physico-chemical parameters and macro invertebrate community attributes between the two rivers with river and station as the main factors (Zar, 2001). Where there were no interactions, one-way ANOVA was re-run with stations as the only main factor and post hoc Duncan’s Multiple Range Test (DMRT) performed to identify the stations that differed from one another. Pearson’s corre-lation coefficients were used to determine the inter-relation-ships between physico-chemical and nutrient parameters and macro-invertebrate community attributes. Analysis was in Minitab for Win-dows (Version 13) and significant differences for all inference tests were accepted at p < 0.05.   RESULTS Physico-chemical parameters Results for physico-chemical parameters and nutrients for the six stations in the two rivers are shown in Table 1. There were significant differences in all physico-chemical and nutrient parameters between the two rivers (p < 0.05), except pH and silicates. While comparing the tem-perature range among the sampling stations in the two rivers, there was inconsistency in the mean values in  Nyakeya et al. 101 Table 1.  Mean (± SE) value for physicochemical parameters and nutrients for the Kisian and Kasat Rivers during the study period, November 2007 to April 2008. KI1 R. Kisian Stations K1 R. Kisat K3 Physico-chemical parameters KI2 KI3 K2 Temperature (ºC) 23.5±0.9 23.4±0.6 23.2±0.5 24.2±0.9 25.2±9.3 24.6±0.9 DO (mg/l) 7±0.2 6.7±0.4 5.0±0.6 5.8±0.3 0.1±0.1 2.4±0.1 pH 7.8±0.2 7.8±0.3 7.7±0.2 7.7±0.2 7.3±0.1 7.2±0.2 Conductivity (µS/cm) 114.9±3.3 105±2.3 146.6±10.8 435.6±48 795.2±31.8 612.9±3.8 Turbidity (NTUs) 80.0±5.2 70.5±1.4 63.1±2.5 98.9±17.4 325.4±40.3 348.1±37.2 Nutrients TP (mg/l) 0.9271±36.3 0.8264±41.9 0.9348±63.2 0.8512±60.7 1.0906±41.5 1.0069±42.5 TN (mgl/l) 4690±38.1 5856±131 5730±438 4948±208 7116.7±79.7 6068.4±91.7 Silicates (mg/l) 13.6±0.6 12.8±0.4 13.0±0.2 14.0±0.4 11.6±0.3 12.3±0.6 0%20%40%60%80%100%KI1 KI2 KI3 K1 K2 K3 Sampling stations    R  e   l  a   t   i  v  e  a   b  u  n   d  a  n  c EPT Coleoptera Hemiptera OdonataDiptera Oligochaeta Mollusca Others   Figure 2.  Relative abundance of macro invertebrate groups in Rivers Kisian and Kisat during the study period. both rivers with K2 in river Kisat recording the highest (25.2 ± 9.3ºC) whereas KI3 in river Kisian recorded the lowest (23.2 ± 0.5ºC). Comparing the DO levels in the two rivers, stations K2 had the lowest than any of the stations in both rivers, with a mean value of 0.1 ± 0.1mg/l. DO in river Kisian decreased downstream while in River Kisat there was a sharp decrease at station K2. There were significant differences in both conductivity and turbidity among all the stations sampled (p < 0.001). Sampling station K2 had the highest mean value of conductivity (795 ± 31.8 µS/cm) while station KI2 had the least (105 ± 2.3 µS/cm). The lowest mean value for turbidity (63.1± 2.5 NTUs) was recorded at station KI3  while the highest was recorded at station K3 (348.1± 37.2 NTUs). Nutrients River Kisat recorded the higher TP and TN concentra-tions as compared to River Kisian. Station K2 recording the highest mean value for TP (1.0906±41.5 mg/l) while K2 recorded the least (0.8264±41.9 mg/l). Station K1 re-corded the lowest TN mean value (4.690 ± 38.1 mg/l) while KI2 recorded the highest (7.1167±79.7 mg/l). Both TN and TP showed a significant difference in the sampl-ing dates (p < 0.001). The month of March recorded the highest concentration of TN whereas the higher concen-tration of TP was recorded during the month of April. Sili-cates for the two rivers showed no significant difference. However, there were significant differences among the
View more...
We Need Your Support
Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks

We need your sign to support Project to invent "SMART AND CONTROLLABLE REFLECTIVE BALLOONS" to cover the Sun and Save Our Earth.

More details...

Sign Now!

We are very appreciated for your Prompt Action!