Heavy metals, despite their essential roles as minerals in biological systems, pose a significant threat to human health and the environment due to their toxic properties. Even at low concentrations, heavy metals such as lead, mercury, arsenic, and cadmium can cause adverse effects on humans and animals. Consequently, stringent regulations have been established to limit heavy metal concentrations in water resources. However, existing laboratory-based analytical methods for heavy metal detection are time-consuming, expensive, and require skilled personnel. The current detection limit required by several health organizations around the globe is below 10 ppb for Lead, Mercury, Chromium, and Arsenic. The current state of the art which can accomplish low levels of detection is either expensive to operate or incapable of achieving the required trace level sensing. This thesis aims to address the need for a simple, cost-effective, and portable method for detecting heavy metals in water.
The thesis begins by reviewing the current state-of-the-art heavy metal sensing methods, highlighting their limitations and the requirement for sample preconcentration. Various preconcentration techniques are discussed, emphasizing their performance parameters and advancements in trace-level detection. Furthermore, the thesis identifies the gaps in current technology, particularly in the context of developing a reliable and user-friendly method for testing heavy metal concentrations in drinking and surface waters.
The primary objective of this thesis is to develop a preconcentration-based colorimetric method for detecting heavy metals in water. This method aims to overcome the limitations of existing techniques by offering high sensitivity and a limit of detection below regulatory ranges without the need for complex equipment or extensive sample preparation. The thesis contributes to the advancement of the state-of-the-art by providing a simplified, portable, and efficient solution for in-line detection of heavy metal contamination in water resources. This has been achieved through the design and deployment of sensor utilizing a novel architecture, measuring heavy metal ions down to the sub ppb level. we were able to detect ions such as copper and Lead at concentrations below 0.5 ppb with a limit of detection (LOD) of 0.14 ppb.
Overall, this thesis combines knowledge from the fields of analytical chemistry, sensor technology, and environmental science to address the pressing need for a practical and accessible method for monitoring heavy metal concentrations in water. By achieving this goal, the research will contribute to safeguarding public health and promoting sustainable water resource management. / Thesis / Doctor of Philosophy (PhD) / Heavy metals can be found naturally and are needed in small amounts for our bodies to function properly. However, many heavy metals are toxic and can cause serious health problems even at very low concentrations. These metals can contaminate water sources through activities like mining and improper waste disposal. Currently, detecting heavy metals in water requires expensive equipment and skilled experts in a laboratory setting. This process is time-consuming and not easily portable for on-site testing. The existing methods also have limitations such as low sensitivity or the need for complex procedures.
This thesis aims to improve the way we detect harmful heavy metals in water. The goal of this thesis is to develop a simpler and more sensitive method for detecting heavy metals in water. The focus is on using color-changing dyes that react to the presence of heavy metal ions. However, these dyes often have detection limits higher than what is considered safe, so the thesis also explores ways to concentrate the samples to improve sensitivity. By addressing these challenges, the thesis aims to contribute to the development of a reliable and easy-to-use method for testing heavy metal concentrations in drinking and surface waters, helping to protect public health and identify potential sources of contamination.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28848 |
| Date | January 2023 |
| Creators | Fathalla, Mohamed |
| Contributors | Selvaganapathy, Ravi, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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