Heavy metals inflow into Lake Muhazi, Rwanda.
Most tropical African lakes are facing pollution problems due to the rapid population growth and industrializa- tion in their riparian communities. Lake Muhazi in Rwanda is one of such lakes which has experienced a dramatic decrease in fish production since the eighties, with also reports of low water transparencies and high turbidities. The lakeshores are now being developed for ecotourism and this requires sound environmental management to make the planned activities viable. The purpose of this study was to assess heavy metal pollution in the tributary rivers of Lake Muhazi. The concentration of heavy metals in major tributaries was monitored for the period July to October 2007. The parameters studied are cadmium, chromium, copper, iron, lead, manganese, zinc, pH and temperature. Fourteen sampling stations on the Inflow Rivers and the spillway were monitored fortnightly. The samples collected for heavy metal analysis were preserved and stored in cooler boxes and analyzed in the laboratory using Standard Methods. Temperature and pH were measured in the field using HACH field testing kits. The concentration (mean ± standard deviation) of Zn was found to be 0.040±0.103 mg/L, Cd 0.031±0.007 mg/L, Pb 0.487±0.452 mg/L, Fe 7.53±13.34 mg/L, Mn 1.01±1.31 mg/L, chromium 0 mg/L and copper 0 mg/L. The concentrations of measured parameters deviate much from the recommended ones thus posing serious problems to aquatic life. It has also been shown that the high metal concentration levels in Lake Muhazi are related to landuse activities in the catchment. It is thus recommended that farming practices and erosion be controlled in the catchment to contain pollutant discharges into the lake. Lead is the main anthropogenic pollutant which has been found in the watershed of Lake Muhazi.
Spatio-seasonal variation of water quality influenced by land use and land cover in Lake Muhazi
Understanding the influence of land use/land cover (LULC) on water quality is pertinent to sustainable water management. This study aimed at assessing the spatio-seasonal variation of water quality in relation to land use types in Lake Muhazi, Rwanda. The National Sanitation Foundation Water Quality Index (NSF-WQI) was used to evaluate the anthropogenically-induced water quality changes. In addition to Principal Components Analysis (PCA), a Cluster Analysis (CA) was applied on 12-clustered sampling sites and the obtained NSF-WQI. Lastly, the Partial Least Squares Path Modelling (PLS-PM) was used to estimate the nexus between LULC, water quality parameters, and the obtained NSF-WQI. The results revealed a poor water quality status at the Mugorore and Butimba sites in the rainy season, then at Mugorore and Bwimiyange sites in the dry season. Furthermore, PCA displayed a sample dispersion based on seasonality while NSF-WQI’s CA hierarchy grouped the samples corresponding to LULC types. Finally, the PLS-PM returned a strong positive correlation (+ 0.831) between LULCs and water quality parameters in the rainy season but a negative correlation coefficient (− 0.542) in the dry season, with great influences of cropland on the water quality parameters. Overall, this study concludes that the lake is seasonally influenced by anthropogenic activities, suggesting sustainable land-use management decisions, such as the establishment and safeguarding protection belts in the lake vicinity.
Texture Feature Extraction of RGB, HSV, YIQ and Dithered Images using GLCM, Wavelet Decomposition Techniques
When changing the format of an image from simple RGB to HSV, YIQ and Dithered image, the characteristics of image also change. In this paper, the similar images in the above formats are retrieved using statistical and structural retrieving techniques i.e. GLCM (Gray Level Co-occurrence Matrix) and Wavelet Decomposition techniques. The best results are coming for dithered, HSV images by using GLCM technique for feature extraction and by using Wavelet decomposition; HSV images are giving the best results. While other formats also give the correct retrieval but from the accuracy point of view, HSV images are retrieved with better accuracy using both techniques.