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.
Pollution and society
“The earth does not belong to man; man belongs to the earth. This we know. All things are connected like the blood which unites one family. All things are connected. Whatever befalls the earth befalls the sons of the earth. Man did not weave the web of life: he is merely a strand in it. Whatever he does to the web, he does to himself.”
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.