Assessment of seasonal variations in surface water quality characteristics is an essential aspect for evaluating water pollution due to both natural and anthropogenic influences on water resources. In this study, temporal variations of water quality in river Rwizi section within Mbarara municipality, Uganda, were assessed using multivariate statistical methods. This river section is a major source of water for the inhabitants of Mbarara municipality. Water samples from five sites were analyzed for physicochemical parameters such as pH, EC, turbidity, temperature, TSS, TDS, alkalinity, salinity, colour, NH 3-N, , total hardness, BOD, COD, DO, Ca, Mg, Fe, and Mn. About 50% of sites recorded colour above 800 Pt Co, 60% of sites recorded turbidity above maximum permissible limit of 100 mg/l, attributable to erosion and mineral matter. pH for dry season ranged between 6.5 and 8.5 whereas for rainy season was below 6.0. All study sites recorded total Fe above 0.3 mg/l and Mn below 0.5 mg/l, attributable to chemical weathering of host rock materials as well as from industrial effluent. About 60% of sites recorded COD above 100 mg/l, 40% and 80% of study sites showed BOD above 50 mg/l in dry and rainy seasons respectively. Hardness ranged between 50 and 100 mg/l indicating that the water is moderately soft. Colour, turbidity, alkalinity, TSS, TDS, salinity, pH, hardness, Fe, Mn, NH 3-N, BOD, COD, and DO were higher in rainy season, as a result of erosion, discharge of domestic and industrial waste. Mg, Ca, and were higher during dry season due to high evaporation of water from the river. PCA/FA determined that 81.2% of the total variance was explained by the first factor for the dry season and 69.2% for rain season. These results revealed that water pollution resulted primarily from domestic waste water, agricultural runoff and industrial effluents.
Access to clean and safe water and improved sanitation facilities and practices are pre-requisites to a healthy population and therefore have a direct impact on the quality of life and productivity of the population. Thus, information on water quality and pollution sources in such water bodies is important for the implementation of sustainable water use management strategies [
Hence, it is necessary to obtain information on the temporal variation of physico-chemical characteristics of water resources in order to provide baseline information for a monitoring program for water resources in Uganda. For instance, information on temporal variation in physico-chemical characteristics of water resources can help in deciding on the type of water treatment process to be adopted [
Investigations of water quality often require that numerous variables be examined simultaneously [
The study area is located in Mbarara Municipality found in Mbarara District, south western Uganda (
part of the catchment [
Water samples were collected from five sampling sites of river Rwizi for dry and rainy seasons. The collected samples were stored in a 1.5 L polyethylene plastic bottle cleaned with metal free soap, rinsed many times with deionised water and then soaked in acidified potassium dichromate (VI) for 24 hours in order to remove any other contaminants that were not removed by soap and then rinsed again with deionised water. Sampling site selection criteria included natural con- ditions as well as catchments with human activities.
Site 1 (BSU) represents the influence of activities in the Rwizi catchment with- in Mbarara municipality on the quality of the river Rwizi water. Site 2 (Kakoba), site 3 (Katete), site 4 (GBK) are affected by a variety of wastes from residential, agricultural, river sand mining, brick making and industrial activities. Site 5 (Spencon) represents the water entering the Municipality.
The selected water quality parameters were analysed using methods that were adopted from various textbooks and Manuals of analytical chemistry [
The temperature, pH, electrical conductivity (EC), total dissolved solid (TDS) and salinity of the water samples were measured on site by a thermometer, pH meter, EC, CIBA-CORING conductivity meter, TDS and salinity using the multi-parameter water quality monitoring instrument respectively [
Total suspended solids (TSS) was determined by using the photometric method, colour by the platinum-cobalt standard method, and ammonia-nitrogen (NH3-N) by the Nessler method [
Mathematical, statistical calculations and graphical presentations were performed using Statistical Software for Social Sciences (SPSS) 16.0, PAST, Adobe Illustrator 8.0 and Microsoft Excel 2007 softwares. The physico-chemical results were subjected to normality tests by fitting them with normal and lognormal distributions, on the premise that the variables were independent and identically distributed over the study area and sampling period.
Principal component analysis (PCA) is defined as an orthogonal linear transformation that transforms the variables to a new coordinate system such that the greatest variance by any projection of the variables comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on [
Factor analysis (FA) is a multivariate statistical technique that can be utilized to examine the underlying patterns or relationship for a large number of variables and summarize information in a smaller set of factors or components for prediction purposes [
Classification of factor loading is thus strong, moderate, and weak; corresponding to absolute loading values of greater than 0.75, 0.75 - 0.50, 0.5 - 0.3 respectively [
The mean temporal variation in concentration of physico-chemical parameters are presented in
The average colour was higher in the dry season as compared to the rainy season (
The colour, turbidity, TSS, and TDS measurements were higher in the rainy season because of the soil erosion of the prepared agricultural fields with loose top layer of the soil. Also, debris falling off due to the wind and rainfall could have contributed particles to the river water. The increased agricultural land use and built up intensity in the river catchment at the onset of the rainy season also contributed to variation in these parameters. In addition, the increased water volume in the river may have resuspended the bottom sediments due to rapid flow rate of the river water.
Mean salinity and E.C were higher in the rainy season (
Total hardness, NH3-N, and alkalinity (
The mean total iron and manganese concentration were higher in the rain season (
The average COD, BOD, and DO value were higher in the rain season than in the dry season (
The average pH (
The variation in mean temperature (
The highest concentration of calcium and magnesium were recorded in the dry season (
PCA/FA was performed on the normalised data sets containing 19 variables, separately for the two seasons of the year, in order to identify important seasonal water quality parameters [
For each season two factors were obtained through the FA performed on the principal components. In addition, scree plots for both dry and rain seasons revealed that the water quality data sets consist of two component system (
Dry season | Rainy season | |||
---|---|---|---|---|
parameters | factor 1 | factor 2 | factor 1 | factor 2 |
colour | 0.9841 | −0.1775 | 0.9976 | 0.0691 |
turbidity | 0.9865 | −0.1637 | −0.9761 | 0.2176 |
TSS | 0.9997 | −0.1637 | 0.9994 | 0.0345 |
T-Hardness | 0.8648 | −0.0238 | −0.5793 | 0.8151 |
T-Iron | −0.9995 | 0.5021 | 0.263 | 0.9648 |
Alkalinity | 0.9985 | 0.0324 | −0.6876 | −0.7261 |
COD | 0.9889 | −0.0546 | −0.424 | −0.9056 |
BOD | 0.8903 | 0.1484 | −0.4162 | −0.9093 |
DO | 0.9997 | −0.4555 | −0.6324 | −0.7746 |
pH | −0.689 | 0.0226 | 0.9934 | 0.1148 |
TDS | −0.9968 | −0.7248 | −0.8188 | −0.5741 |
E.C | −0.94 | 0.0795 | −0.9988 | −0.0492 |
salinity | −0.9995 | 0.3411 | −0.9787 | −0.2053 |
temperature | −0.8143 | 0.0316 | 0.9308 | −0.3654 |
NH3-N | 0.4175 | 0.5804 | −0.7406 | 0.6719 |
sulphate | 0.7972 | 0.9087 | −0.9233 | 0.3842 |
Mg | 0.518 | 0.6038 | −0.925 | 0.3799 |
Ca | −0.9973 | −0.8554 | −0.9312 | 0.3646 |
Mn | 0.9448 | 0.0737 | −0.9853 | 0.1708 |
Eigen value | 15.4346 | 0.3275 | 13.1508 | 5.8492 |
Variance (%) | 81.235 | 18.765 | 69.215 | 30.785 |
Cummulative (%) | 81.235 | 100 | 69.215 | 100 |
The factor loadings were used to determine the relative importance of a water quality variable compared to other water quality parameters in a factor and do not reflect the importance of the factor itself [
Based on the correlation matrix of variables, the first two factors with Eigen values greater than 1 explained about 100% of the total variance. In the dry season, Factor 1 accounted for about 81.2% of the total variance. It was highly correlated with major physico-chemical variables; colour, turbidity, TSS, total hard- ness, alkalinity, COD, BOD, DO, TDS, EC, salinity, temperature, sulphate, Ca, Fe, and Mn. Factor 2 accounted for about 18.8% of the total variance. This included pH, NH3-N, and Mg. The Mg, Ca, Fe, Mn, called salinity factors, repre- sented the total salt concentration. This was attributed to evaporation and mineralisation of host rock materials. BOD and COD showed strong factor loadings and are important parameters that accounted for water quality variations for the dry season. Dissolved
The colour, turbidity, and TSS with positive strong factor loading values contributed to water quality variations in the two seasons. This has the meaning that the clarity of River Rwizi water was not only dependent on erosion but also on geochemical processes/trophicity factors. This was because there was no soil ero- sion during the dry season.
In the rain season, Factor 1 accounted for about 69.2% of the total variance. It was highly correlated with major physico-chemical variables: colour, turbidity, TSS, TDS, E.C, pH, salinity, temperature, NH3-N, Ca, Mg,
The strong factor loadings on colour, turbidity, TSS, TDS, E.C, salinity,
Factor 2 accounted for about 30.8% of the total variance. The parameters with strong factor loading values in this factor and the most important in water quality variation were Fe, pH, COD, BOD, DO, alkalinity and total hardness. This was a demonstrator that there is entry of organic and inorganic waste from domestic waste water, raw sewage, as well as industrial effluents, with considerable pollution. This caused extra variation in river water quality more times of the study. The result has demonstrated that Fe, Mn, Ca, and Mg may not necessarily be as a result of anthropogenic inputs but may also be due to the soil /sediment characteristics over which the river water flows.
The BOD5 and COD with strong factor loadings were due to entry of waste water effluent and raw sewage, which caused considerable pollution. This was explanatory of the fact that there was entry of domestic waste, industrial waste and raw sewage into the river. Therefore, a close inspection of Eigen values, factor loadings, and significant water quality parameters in each season revealed that besides geochemical phenomena, seasonal regime of the River Rwizi water is controlled by two important hydrological and anthropogenic processes. There are other reports [
In this study, seasonal variation in surface water quality was assessed by using PCA/FA. The results revealed that water pollution resulted primarily from domestic waste water, agricultural runoff, industrial effluents and natural hydrologic processes. All the study sites recorded iron, COD and pH (rain season) above allowable maximum limit in surface water. Also some sites showed higher manganese, BOD and TDS than the standards.
The principal component analysis and factor analysis were used to extract and recognize the factors or origins responsible for water quality variations in the two seasons of the year. PCA/FA determined that 81.2% of the total variance was explained by the first factor for the dry season and 69.2% for rain season. Consequently, it was important that when choosing water quality parameters for implementing environmental monitoring strategies in river basins, the seasonal variations of the parameters in assessment of water quality must be taken into account. This study demonstrated the importance of multivariate statistics for analysis of complex water quality data sets and their interpretation. It also assists in identification of pollution sources. The results provide a better understanding of seasonal variations in water quality of river water systems.
Ojok, W., Wasswa, J. and Ntambi, E. (2017) Assessment of Seasonal Variation in Water Quality in River Rwizi Using Multivariate Statistical Techniques, Mbarara Municipality, Uganda. Journal of Water Resource and Protection, 9, 83-97. http://dx.doi.org/10.4236/jwarp.2017.91007