2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia
| Format: | General Document |
|---|
| _version_ | 1860798293112520704 |
|---|---|
| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2024-11-20 |
| format | General Document |
| id | 16883 |
| institution | UniSZA |
| originalfilename | 16883_59125e534b7be65.pdf |
| person | Nadiana Ariffin |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16883 |
| sourcemedia | Server storage Scanned document |
| spelling | 16883 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16883 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu English UniSZA East Coast Environmental Research Institute application/pdf 1.7 Microsoft® Word 2019 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin Copyright©PWB2025 164 Water quality management—Malaysia Dissertations, Academic Principal Component Analysis (PCA) Nadiana Ariffin Rainwater harvesting, Malaysia -- Case studies Rainwater quality Hydrological modeling 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia This study evaluates the quality of rainwater in Malaysia from 2017 to 2020, emphasizing the importance of clean water and the necessity of chemical analysis for assessing rainwater safety and quality, particularly in the context of rainwater harvesting. The research aims to evaluate rainwater acidity (pH), classify rainwater quality in different study areas based on the presence of pollutants, and develop a comprehensive rainwater quality model. The ultimate goal is to establish a Rainwater Pollution Index (RPI) to provide detailed information on local rainwater quality and its suitability for harvesting. Rainwater samples were collected from 24 gauge stations across Malaysia, including Seberang Perai, Bayan Lepas, Petaling Jaya, Tanah Rata, Batu Embun, Muadzam Shah, Kuantan, Melaka, Kluang, Senai, Mersing, Bukit Keledang, Sitiawan, Alor Star, Chuping, Kuala Terengganu, Bachok, Kota Bharu, Kuching, Bintulu, Tawau, Kota Kinabalu, Lembah Danum, and Labuan. Chemometric techniques were applied for data analysis, focusing on 23 chemical parameters: ammonium (NH4+), calcium (Ca2+), fluoride (F-), magnesium (Mg2+), potassium (K+), sodium (Na+), nitrate (NO3-), sulphate (SO42-), acetate (C2H3O2-), chloride (Cl-), formate (CH2O2-), MSA (CH4O3S), oxalate (C2O42-), copper (Cu), iron (Fe2+), manganese (MnO2), mercury (Hg), nickel (Ni2+), cadmium (Cd2+), lead (Pb2+), zinc (Zn2+), pH, and conductivity. Descriptive statistical analysis, Pearson's correlation analysis, principal component analysis (PCA), hierarchical agglomerative cluster analysis (HACA), and discriminant analysis (DA) were employed to ensure the precision and accuracy of the results. The RPI was constructed using PCA data, which identified seven principal components responsible for 61.479% of the total variance in chemical components. Over the study period, pH levels in rainfall exhibited notable trends across various monitoring stations. In 2017, Melaka recorded the lowest pH value of 4.60, while in subsequent years (2018, 2019, and 2020), Sitiawan recorded the lowest mean pH values of 4.39, 4.41, and 4.56, respectively. PCA identified seven significant pollutants in rainwater samples based on their chemical composition. The RPI results categorized rainwater quality as follows: 60.22% of samples (1,261 samples) were considered Good, 29.89% (626 samples) were Moderate, 7.45% (156 samples) were Medium, 2.01% (42 samples) were Bad, and 0.43% (9 samples) were Very Bad. The study concludes that the application of chemometric techniques in developing the Rainwater Pollution Index (RWPI) is efficient, precise, and cost-effective. These methods provide deep insights into the chemical composition of rainwater, ensuring its quality for harvesting and daily use, which is crucial for public health. The findings highlight rainwater's potential as a viable resource for non-potable applications and underscore the importance of regular monitoring to prevent health risks from contamination. The RWPI serves as a valuable tool for policymakers and environmental agencies, aiding informed decision-making for water resource management and effective rainwater harvesting strategies. This study enhances the scientific understanding of rainwater quality in Malaysia and offers practical solutions to improve water resource sustainability, supporting broader environmental protection and public health goals. 2024-11-20 uuid:8BCBB3C1-01CF-4085-9EAB-FBA08361B44E 16883_59125e534b7be65.pdf Rainwater in Malaysia Analysis of Rainwater Quality Rainwater Harvesting Modelling of Rainwater Quality Rainwater Pollution Index (RPI) Hierarchical Cluster Analysis (HACA) Discriminant Analysis (DA) Environmental Sustainability Water Resource Management Rainwater — Analysis Water quality — Malaysia Thesis |
| spellingShingle | 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia |
| state | Terengganu |
| subject | Water quality management—Malaysia Dissertations, Academic Rainwater harvesting, Malaysia -- Case studies Rainwater quality Hydrological modeling Rainwater — Analysis Water quality — Malaysia |
| summary | This study evaluates the quality of rainwater in Malaysia from 2017 to 2020, emphasizing the importance of clean water and the necessity of chemical analysis for assessing rainwater safety and quality, particularly in the context of rainwater harvesting. The research aims to evaluate rainwater acidity (pH), classify rainwater quality in different study areas based on the presence of pollutants, and develop a comprehensive rainwater quality model. The ultimate goal is to establish a Rainwater Pollution Index (RPI) to provide detailed information on local rainwater quality and its suitability for harvesting. Rainwater samples were collected from 24 gauge stations across Malaysia, including Seberang Perai, Bayan Lepas, Petaling Jaya, Tanah Rata, Batu Embun, Muadzam Shah, Kuantan, Melaka, Kluang, Senai, Mersing, Bukit Keledang, Sitiawan, Alor Star, Chuping, Kuala Terengganu, Bachok, Kota Bharu, Kuching, Bintulu, Tawau, Kota Kinabalu, Lembah Danum, and Labuan. Chemometric techniques were applied for data analysis, focusing on 23 chemical parameters: ammonium (NH4+), calcium (Ca2+), fluoride (F-), magnesium (Mg2+), potassium (K+), sodium (Na+), nitrate (NO3-), sulphate (SO42-), acetate (C2H3O2-), chloride (Cl-), formate (CH2O2-), MSA (CH4O3S), oxalate (C2O42-), copper (Cu), iron (Fe2+), manganese (MnO2), mercury (Hg), nickel (Ni2+), cadmium (Cd2+), lead (Pb2+), zinc (Zn2+), pH, and conductivity. Descriptive statistical analysis, Pearson's correlation analysis, principal component analysis (PCA), hierarchical agglomerative cluster analysis (HACA), and discriminant analysis (DA) were employed to ensure the precision and accuracy of the results. The RPI was constructed using PCA data, which identified seven principal components responsible for 61.479% of the total variance in chemical components. Over the study period, pH levels in rainfall exhibited notable trends across various monitoring stations. In 2017, Melaka recorded the lowest pH value of 4.60, while in subsequent years (2018, 2019, and 2020), Sitiawan recorded the lowest mean pH values of 4.39, 4.41, and 4.56, respectively. PCA identified seven significant pollutants in rainwater samples based on their chemical composition. The RPI results categorized rainwater quality as follows: 60.22% of samples (1,261 samples) were considered Good, 29.89% (626 samples) were Moderate, 7.45% (156 samples) were Medium, 2.01% (42 samples) were Bad, and 0.43% (9 samples) were Very Bad. The study concludes that the application of chemometric techniques in developing the Rainwater Pollution Index (RWPI) is efficient, precise, and cost-effective. These methods provide deep insights into the chemical composition of rainwater, ensuring its quality for harvesting and daily use, which is crucial for public health. The findings highlight rainwater's potential as a viable resource for non-potable applications and underscore the importance of regular monitoring to prevent health risks from contamination. The RWPI serves as a valuable tool for policymakers and environmental agencies, aiding informed decision-making for water resource management and effective rainwater harvesting strategies. This study enhances the scientific understanding of rainwater quality in Malaysia and offers practical solutions to improve water resource sustainability, supporting broader environmental protection and public health goals. |
| title | 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia |
| title_full | 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia |
| title_fullStr | 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia |
| title_full_unstemmed | 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia |
| title_short | 2024_Analysis and modelling of rainwater quality: A case study of rainwater harvesting in Malaysia |
| title_sort | 2024_analysis and modelling of rainwater quality: a case study of rainwater harvesting in malaysia |