2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area
| Format: | General Document |
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| _version_ | 1860797996335104000 |
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| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2023-10-29 |
| format | General Document |
| id | 15348 |
| institution | UniSZA |
| internalnotes | Sila masukkan subject wajib Dissertations, Academic. Terima kasih... |
| originalfilename | 15348_c71dee944d1a3f6.pdf |
| person | Siti Noor Syuhada Binti Mohd @ Muhammad Amin |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15348 |
| sourcemedia | Server storage Scanned document |
| spelling | 15348 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15348 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 Faculty of Bio-resources & Food Industry English application/pdf 1.5 206 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin SAMBox 2.3.4; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) 15348_c71dee944d1a3f6.pdf 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area Siti Noor Syuhada Binti Mohd @ Muhammad Amin Health – Environmental Aspects Copyright©PWB2025 2023-10-29 Atmospheric dust can contain various heavy metals, such as lead, mercury, cadmium and arsenic, which are known to have detrimental effects on human health. Understanding the spatial distribution of these heavy metals is crucial for assessing the potential risks associated with exposure to atmospheric dust. The objectives of this study were to determine the spatial variability of the heavy metal concentration of selected atmospheric dust around the study area, to identify the sources of heavy metals in the atmospheric dust around the study area and to develop prediction model of health risk in adults and children using the heavy metals concentrations related to the study area. High volume air samplers were loaded with filter paper at a constant flow rate for dust sampling in Paka and Gebeng. The dust samples were processed by using aqua regia method. The data were analysed by using chemometrics and Artificial Neural Network (ANN) to show the spatial and prediction model of health index from heavy metals concentrations in the petrochemical industrial area of East Coast, Malaysia. The health risks were assessed based on United States Environmental Protection Agency (USEPA) health risk model. The results showed the total heavy metal concentrations were found to be in the following decreasing order of iron (Fe) (0.218 mg/L ± 0.192), zinc (Zn) (0.083 mg/L ± 0.059), lead (Pb) (0.079 mg/L ± 0.119), cadmium (Cd) (0.004 mg/L ± 0.003), copper (Cu) (0.004 mg/L ± 0.004) and arsenic (As) (0.001 mg/L ± 0.001) for northeast monsoon whereas Fe (0.407 mg/L ± 0.270), Zn (0.110 mg/L ± 0.092), Pb (0.088 mg/L ± 0.118), Cd (0.008 mg/L ± 0.008), Cu (0.004 mg/L ± 0.005) and As (0.004 mg/L ± 0.001) for southwest monsoon. Three principal components factor for northeast and southwest monsoon respectively were extracted from Principal Components Analysis (PCA), which are based on eigenvalue (>1.0). During northeast monsoon, Factor 1 revealed Fe and As. Factor 2 revealed Cu, Cd and Zn and Factor 3 revealed Pb. During southwest monsoon, Factor 1 revealed Pb, Cd and Zn. Factor 2 revealed Fe and Cu and Factor 3 revealed As. The estimations of HQ for pathways in this study decreased in the order of ingestion>dermal contact>inhalation. Health index (HI) prediction model value for adults decreased in the order of Fe>Pb>Cd>As>Zn>Cu whereas Fe>Pb>Cd>As>Cu>Zn HI prediction model value for children. HI values of these metals for children were higher than adults. However, the values of health risk obtained in this study are in the receivable range (HI<1.0). Fe concentrations were recorded highest in both areas and seasons, while PCA revealed three factor analysis of heavy metals for both areas and seasons with As being the most identifying sources of heavy metals through sensitivity analysis. ANN was an efficient technique to compute prediction models of health index in the study area. This study suggested that the prediction models approached, will provide a better insight into air quality information to understand potential environmental health hazard towards people and mitigation strategies plan in the future. Dissertations, Academic Sila masukkan subject wajib Dissertations, Academic. Terima kasih... Health Index Prediction Petrochemical Industry Environmental Health Modeling Thesis |
| spellingShingle | 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area |
| state | Terengganu |
| subject | Health – Environmental Aspects Dissertations, Academic |
| summary | Atmospheric dust can contain various heavy metals, such as lead, mercury, cadmium and arsenic, which are known to have detrimental effects on human health. Understanding the spatial distribution of these heavy metals is crucial for assessing the potential risks associated with exposure to atmospheric dust. The objectives of this study were to determine the spatial variability of the heavy metal concentration of selected atmospheric dust around the study area, to identify the sources of heavy metals in the atmospheric dust around the study area and to develop prediction model of health risk in adults and children using the heavy metals concentrations related to the study area. High volume air samplers were loaded with filter paper at a constant flow rate for dust sampling in Paka and Gebeng. The dust samples were processed by using aqua regia method. The data were analysed by using chemometrics and Artificial Neural Network (ANN) to show the spatial and prediction model of health index from heavy metals concentrations in the petrochemical industrial area of East Coast, Malaysia. The health risks were assessed based on United States Environmental Protection Agency (USEPA) health risk model. The results showed the total heavy metal concentrations were found to be in the following decreasing order of iron (Fe) (0.218 mg/L ± 0.192), zinc (Zn) (0.083 mg/L ± 0.059), lead (Pb) (0.079 mg/L ± 0.119), cadmium (Cd) (0.004 mg/L ± 0.003), copper (Cu) (0.004 mg/L ± 0.004) and arsenic (As) (0.001 mg/L ± 0.001) for northeast monsoon whereas Fe (0.407 mg/L ± 0.270), Zn (0.110 mg/L ± 0.092), Pb (0.088 mg/L ± 0.118), Cd (0.008 mg/L ± 0.008), Cu (0.004 mg/L ± 0.005) and As (0.004 mg/L ± 0.001) for southwest monsoon. Three principal components factor for northeast and southwest monsoon respectively were extracted from Principal Components Analysis (PCA), which are based on eigenvalue (>1.0). During northeast monsoon, Factor 1 revealed Fe and As. Factor 2 revealed Cu, Cd and Zn and Factor 3 revealed Pb. During southwest monsoon, Factor 1 revealed Pb, Cd and Zn. Factor 2 revealed Fe and Cu and Factor 3 revealed As. The estimations of HQ for pathways in this study decreased in the order of ingestion>dermal contact>inhalation. Health index (HI) prediction model value for adults decreased in the order of Fe>Pb>Cd>As>Zn>Cu whereas Fe>Pb>Cd>As>Cu>Zn HI prediction model value for children. HI values of these metals for children were higher than adults. However, the values of health risk obtained in this study are in the receivable range (HI<1.0). Fe concentrations were recorded highest in both areas and seasons, while PCA revealed three factor analysis of heavy metals for both areas and seasons with As being the most identifying sources of heavy metals through sensitivity analysis. ANN was an efficient technique to compute prediction models of health index in the study area. This study suggested that the prediction models approached, will provide a better insight into air quality information to understand potential environmental health hazard towards people and mitigation strategies plan in the future. |
| title | 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area |
| title_full | 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area |
| title_fullStr | 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area |
| title_full_unstemmed | 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area |
| title_short | 2023_Prediction Model of Health Index for Selected Petrochemical Industrial Area |
| title_sort | 2023_prediction model of health index for selected petrochemical industrial area |