Determination of physiochemical properties and metal levels in soil, water, and plant from Alice landfill site
- Authors: Maphuhla, N G
- Date: 2017
- Subjects: Heavy metals -- Environmental aspects Soil pollution Water -- Pollution
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/6224 , vital:29521
- Description: The state of soil is of great significance because it is a common medium for plant growth, which provides important nutrients to plants. Water pollution is the build-up of harmful substances in water bodies to the level that results in health problems for people and animals. Heavy metal pollution (of soil, water, and plants) and their health effects on people is a persistent social issue, and several types of research have recognized health risks of residents living close to open dumpsites. Dump sites are sources of heavy metal impurity and toxicity to the surrounding environment. Analyses were done on water and soil samples for temperature, pH, Electrical conductivity (EC), Total dissolved solids (TDS), alkalinity, organic matter, organic carbon and total hardness. The pH results range from slightly acidic (6.79) to neutral soil pH (7.09), and have been recorded within the normal range from WHO. All the determined physicochemical properties in soil and water have been recorded within the normal range, except for EC in water which was found to be above the permissible limits by WHO. The heavy metals concentration was determined using the AAS technique. The results obtained shows that the dumpsite‘s soil consists of high metal concentration when compared to control site. The concentration in dumpsites ranges between 1.2783 ± 0.83 mg/kg to 26.3213 ± 6.37 mg/kg. The descending order for selected metal concentrations were in this following order Mn> Cu>Hg>Pb. The Pb and Hg mean concentration was recorded above permissible limits, while the Mn and Cu were within the normal range suggested by WHO. In both water and Acacia karroo samples the Cu was not detected. The trend of metal concentration in water sample was found to be in this order Hg> Mn > Pb> Cu, while in Acacia karroo metal concentration is Hg> Mn> Pb> Cu. The one-way ANOVA test was used to compare the mean concentration of selected metals in each sampling site. The results show that there is a statistically significant difference between the mean concentrations of selected metals; this is supported by the value of F-static and p-value (p <0.05)
- Full Text:
- Date Issued: 2017
- Authors: Maphuhla, N G
- Date: 2017
- Subjects: Heavy metals -- Environmental aspects Soil pollution Water -- Pollution
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/6224 , vital:29521
- Description: The state of soil is of great significance because it is a common medium for plant growth, which provides important nutrients to plants. Water pollution is the build-up of harmful substances in water bodies to the level that results in health problems for people and animals. Heavy metal pollution (of soil, water, and plants) and their health effects on people is a persistent social issue, and several types of research have recognized health risks of residents living close to open dumpsites. Dump sites are sources of heavy metal impurity and toxicity to the surrounding environment. Analyses were done on water and soil samples for temperature, pH, Electrical conductivity (EC), Total dissolved solids (TDS), alkalinity, organic matter, organic carbon and total hardness. The pH results range from slightly acidic (6.79) to neutral soil pH (7.09), and have been recorded within the normal range from WHO. All the determined physicochemical properties in soil and water have been recorded within the normal range, except for EC in water which was found to be above the permissible limits by WHO. The heavy metals concentration was determined using the AAS technique. The results obtained shows that the dumpsite‘s soil consists of high metal concentration when compared to control site. The concentration in dumpsites ranges between 1.2783 ± 0.83 mg/kg to 26.3213 ± 6.37 mg/kg. The descending order for selected metal concentrations were in this following order Mn> Cu>Hg>Pb. The Pb and Hg mean concentration was recorded above permissible limits, while the Mn and Cu were within the normal range suggested by WHO. In both water and Acacia karroo samples the Cu was not detected. The trend of metal concentration in water sample was found to be in this order Hg> Mn > Pb> Cu, while in Acacia karroo metal concentration is Hg> Mn> Pb> Cu. The one-way ANOVA test was used to compare the mean concentration of selected metals in each sampling site. The results show that there is a statistically significant difference between the mean concentrations of selected metals; this is supported by the value of F-static and p-value (p <0.05)
- Full Text:
- Date Issued: 2017
Developing an ionospheric map for South Africa
- Authors: Okoh, Daniel Izuikeninachi
- Date: 2009
- Subjects: Ionosphere -- South Africa , Shortwave radio , Ionospheric electron density -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5459 , http://hdl.handle.net/10962/d1005244 , Ionosphere -- South Africa , Shortwave radio , Ionospheric electron density -- South Africa
- Description: This thesis describes the development of an ionospheric map for the South African region using the current available resources. The International Reference Ionosphere (IRI) model, the South African Bottomside Ionospheric Model (SABIM), and measurements from ionosondes in the South African Ionosonde Network, were incorporated into the map. An accurate ionospheric map depicting the foF2 and hmF2 parameters as well as electron density profiles at any location within South Africa is a useful tool for, amongst others, High Frequency (HF) communicators and space weather centers. A major product of the work is software, written in MATLAB, which produces spatial and temporal representations of the South African ionosphere. The map was validated and demonstrated for practical application, since a significant aim of the project was to make the map as applicable as possible. It is hoped that the map will find immense application in HF radio communication industries, research industries, aviation industries, and other industries that make use of Earth-Space systems. A potential user of the map is GrinTek Ewation (GEW) who is currently evaluating it for their purposes
- Full Text:
- Date Issued: 2009
- Authors: Okoh, Daniel Izuikeninachi
- Date: 2009
- Subjects: Ionosphere -- South Africa , Shortwave radio , Ionospheric electron density -- South Africa
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5459 , http://hdl.handle.net/10962/d1005244 , Ionosphere -- South Africa , Shortwave radio , Ionospheric electron density -- South Africa
- Description: This thesis describes the development of an ionospheric map for the South African region using the current available resources. The International Reference Ionosphere (IRI) model, the South African Bottomside Ionospheric Model (SABIM), and measurements from ionosondes in the South African Ionosonde Network, were incorporated into the map. An accurate ionospheric map depicting the foF2 and hmF2 parameters as well as electron density profiles at any location within South Africa is a useful tool for, amongst others, High Frequency (HF) communicators and space weather centers. A major product of the work is software, written in MATLAB, which produces spatial and temporal representations of the South African ionosphere. The map was validated and demonstrated for practical application, since a significant aim of the project was to make the map as applicable as possible. It is hoped that the map will find immense application in HF radio communication industries, research industries, aviation industries, and other industries that make use of Earth-Space systems. A potential user of the map is GrinTek Ewation (GEW) who is currently evaluating it for their purposes
- Full Text:
- Date Issued: 2009
A new empirical model for the peak ionospheric electron density using neural networks
- Authors: McKinnell, L A
- Date: 1997
- Subjects: Ionospheric electron density Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5478 , http://hdl.handle.net/10962/d1005264
- Description: This thesis describes the search for a temporal model for predicting the peak ionospheric electron density-(foF2). Existing models, such as the International Reference Ionosphere (IRI) and 8KYCOM, were used to predict the 12 noon foF2 value over Grahamstown (26°E, 33°8). An attempt was then made to find a model that would improve upon these results. The traditional method of linear regression was used as a first step towards a new model. It was found that this would involve a multi variable regression that is reliant on guessing the optimum variables to be used in the final equation. An extremely complicated modelling equation involving many terms would result. Neural networks (NNs) are introduced as a new technique for predicting foF2. They are also applied, for the first time, to the problem of determining the best predictors of foF2. This quantity depends upon day number, level of solar activity and level of magnetic activity. The optimum averaging lengths of the solar activity index and the magnetic activity index were determined by appling NNs, using the criterion that the best indices are those that give the lowest rms error between the measured and predicted foF2. The optimum index for solar activity was found to be a 2-month running mean value of the daily sunspot number and for magnetic activity a 2-day averaged A index was found to be optimum. In addition, it was found that the response of foF2 to magnetic activity changes is highly non-linear and seasonally dependent. Using these indices as inputs, the NN trained successfully to predict foF2 with an rms error of 0.946 MHz on the daily testing values. Comparison with the IRI showed an improvement of 40% on the rms error. It is also shown that the NN will predict the noon value of foF2 to the same level of accuracy for unseen data of the same type.
- Full Text:
- Date Issued: 1997
- Authors: McKinnell, L A
- Date: 1997
- Subjects: Ionospheric electron density Neural networks (Computer science)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5478 , http://hdl.handle.net/10962/d1005264
- Description: This thesis describes the search for a temporal model for predicting the peak ionospheric electron density-(foF2). Existing models, such as the International Reference Ionosphere (IRI) and 8KYCOM, were used to predict the 12 noon foF2 value over Grahamstown (26°E, 33°8). An attempt was then made to find a model that would improve upon these results. The traditional method of linear regression was used as a first step towards a new model. It was found that this would involve a multi variable regression that is reliant on guessing the optimum variables to be used in the final equation. An extremely complicated modelling equation involving many terms would result. Neural networks (NNs) are introduced as a new technique for predicting foF2. They are also applied, for the first time, to the problem of determining the best predictors of foF2. This quantity depends upon day number, level of solar activity and level of magnetic activity. The optimum averaging lengths of the solar activity index and the magnetic activity index were determined by appling NNs, using the criterion that the best indices are those that give the lowest rms error between the measured and predicted foF2. The optimum index for solar activity was found to be a 2-month running mean value of the daily sunspot number and for magnetic activity a 2-day averaged A index was found to be optimum. In addition, it was found that the response of foF2 to magnetic activity changes is highly non-linear and seasonally dependent. Using these indices as inputs, the NN trained successfully to predict foF2 with an rms error of 0.946 MHz on the daily testing values. Comparison with the IRI showed an improvement of 40% on the rms error. It is also shown that the NN will predict the noon value of foF2 to the same level of accuracy for unseen data of the same type.
- Full Text:
- Date Issued: 1997
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