Prediction Model of Health Index for Petrochemical Industrial Area, Paka, Terengganu and Gebeng, Pahang, Malaysia
DOI:
https://doi.org/10.11113/mjfas.v21n3.4165Keywords:
Health index, health quotient, atmospheric dust, petrochemical industrial area, heavy metals.Abstract
Atmospheric dust can contain various heavy metals which are known to have detrimental effects on human health. Understanding the spatial distribution of these heavy metals is essential for evaluating the potential risks connected with exposure to atmospheric dust. The purpose of this study is to develop a prediction model that uses the concentrations of heavy metals in the study area to evaluate health hazards in both adults and children. High volume air samplers were loaded with filter paper at a constant flow rate for dust sampling in Paka and Gebeng. The aqua regia method was used to treat the dust samples. 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 study area. 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.
References
Salthammer, T., Schieweck, A., Gu, J., Ameri, S., & Uhde, E. (2018). Future trends in ambient air pollution and climate in Germany – Implications for the indoor environment. Building and Environment, 143, 661–670. https://doi.org/10.1016/j.buildenv.2018.07.038
Shao, L., Liu, P., Jones, T., Yang, S., Wang, W., Zhang, D., Yang, C.-X., Xing, J., Hou, C., Zhang, M., Feng, X., Li, W., & BeruBe, K. (2022). A review of atmospheric individual particle analyses: Methodologies and applications in environmental research. Gondwana Research, 110, 347–369. https://doi.org/10.1016/j.gr.2021.06.013
Adani, M., Mircea, M., D'Isidoro, M., Costa, M. P., & Silibello, C. (2015). Heavy metal modelling study over Italy: Effects of grid resolution, lateral boundary conditions and foreign emissions on air concentrations. Water, Air, & Soil Pollution, 226(1), 46. https://doi.org/10.1007/s11270-015-2324-7
Ripin, S. N. M., Hasan, S., Kamal, M. L., & Hashim, N. M. (2014). Analysis and pollution assessment of heavy metal in soil, Perlis. Malaysian Journal of Analytical Sciences, 18(1), 155–161.
Mitra, S., Chakraborty, A. J., Tareq, A. M., Emran, T. B., Nainu, F., Khusro, A., Idris, A. M., Khandaker, M. U., Osman, H., Alhumaydhi, F. A., & Simal-Gandara, J. (2022). Impact of heavy metals on the environment and human health: Novel therapeutic insights to counter the toxicity. Journal of King Saud University - Science, 34 (3), 101865. https://doi.org/10.1016/j.jksus.2022.101865
Aksu, A. (2015). Source of metal pollution in the urban atmosphere (A case study: Tuzla, Istanbul). Journal of Environmental Health Science and Engineering, 13(1), 79. https://doi.org/10.1186/s40201-015-0220-9
U.S. Environmental Protection Agency. (2024). National Ambient Air Quality Standards (NAAQS) for PM. https://www.epa.gov/pm-pollution/national-ambient-air-quality-standards-naaqs-pm
Nurhaini, F. F., Lestiani, D. D., Syahfitri, W. Y. N., Kusmartini, I., Sari, D. K., Kurniawati, S., & Santoso, M. (2023). Determination of nutrient and toxic elements in food reference materials by suspension preparation and TXRF analysis. International Food Research Journal, 30(2), 463–471.
Xing, G., Sardar, M. R., Lin, B., & Lin, J. M. (2019). Analysis of trace metals in water samples using NOBIAS cheate resins by HPLC and ICP-MS. Talanta, 204, 50–56. https://doi.org/10.1016/j.talanta.2019.05.049
Canizo, B. V., Escudero, L. B., Pérez, M. B., Pellerano, R. G., & Wuilloud, R. G. (2018). Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools. Food Chemistry, 242, 272–278. https://doi.org/10.1016/j.foodchem.2017.09.047
Ismail, A. S., Abdullah, A. M., & Samah, M. A. A. (2017). Environmetric study on air quality pattern for assessment in northern region of Peninsular Malaysia. Journal of Environmental Science and Technology, 10(3), 186–196.
Azid, A., Rani, N. A. A., Samsudin, M. S., Khalit, S. I., Gasim, M. B., Kamarudin, M. K. A., Yunus, K., Saudi, A. S. M., & Yusof, K. M. K. K. (2017). Air quality modelling using chemometric techniques. Journal of Fundamental and Applied Sciences, 9(2S), 443–466. https://doi.org/10.4314/jfas.v9i2s.32
Azid, A., Juahir, H., Toriman, M. E., Endut, A., Rahman, M. N. A., Kamarudin, M. K. A., Latif, M. T., Saudi, A. S. M., Hasnam, C. N. C., & Yunus, K. (2016). Testing and evaluation selection of the most significant variables of air pollutants using sensitivity analysis. Journal of Testing and Evaluation, 44(1), 376–384. https://doi.org/10.1520/JTE20140294
Xiao, S., Oladyshkin, S., & Nowak, W. (2020). Forward-reverse switch between density-based and regional sensitivity analysis. Applied Mathematical Modelling, 84, 377–392. https://doi.org/10.1016/j.apm.2020.02.028
Feenstra, D., Molotnikov, A., & Birbilis, N. (2021). Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications. Materials & Design, 198, 109342. https://doi.org/10.1016/j.matdes.2020.109342
Gotwalt, C. M., Jones, B. A., & Steinberg, D. M. (2009). Fast computation of designs robust to parameter uncertainty for nonlinear settings. Technometrics, 51(1), 88–95. https://doi.org/10.1198/tech.2009.0011
Ma, L., Hurtado, A., Eguilior, S., & Borrajo, J. F. L. (2016). A model for predicting organic compounds concentration change in water associated with horizontal hydraulic fracturing. Science of the Total Environment, 625, 1164–1174. https://doi.org/10.1016/j.scitotenv.2017.12.319
Hutchins, M. G., Abesser, C., Prudhomme, C., Elliott, J. A., Bloomfield, J. P., Mansour, M. M., & Hitt, O. E. (2018). Combined impacts of future land-use and climate stressors on water resources and quality in groundwater and surface water bodies of the upper Thames river basin, UK. Science of the Total Environment, 631, 962–986. https://doi.org/10.1016/j.scitotenv.2018.03.028
Barzegar, R., Moghaddam, A. A., Adamowski, J., & Ozga-Zielinski, B. (2018). Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stochastic Environmental Research and Risk Assessment, 32(3), 799–813. https://doi.org/10.1007/s00477-017-1404-y
Kim, N. (2016). A robustified Jarque–Bera test for multivariate normality. Economics Letters, 140, 48–52.
Lee, D. (2020). Data transformation: A focus on the interpretation. Korean Journal of Anesthesiology, 73(6), 503–508.
van Ginkel, J. R., van der Ark, L. A., Emons, W. H. M., & Meijer, R. R. (2023). Handling missing data in principal component analysis using multiple imputation. In Essays on contemporary psychometrics (pp. 141–161). Springer. https://doi.org/10.1007/978-3-031-10370-4_8
Franceschi, F., Cobo, M., & Figueredo, M. (2018). Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering. Atmospheric Pollution Research, 9(5), 912–922.
Shrestha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software, 22(4), 464–475.
Ul-Saufie, A. Z., Yahaya, A. S., Ramli, N. A., Rosaida, N., & Hamid, H. A. (2013). Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principal component analysis (PCA). Atmospheric Environment, 77, 621–630.
Department of Environment Malaysia. (2020). New Malaysian ambient air quality standard.
Cabaneros, S. M. S., Calautit, J. K. S., & Hughes, B. R. (2017). Hybrid artificial neural network models for effective prediction and mitigation of urban roadside NO2 pollution. Energy Procedia, 142, 3524–3530. https://doi.org/10.1016/j.egypro.2017.12.242
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Siti Noor Syuhada Muhammad Amin, Azman Azid, Muhammad Yusran Abdul Aziz, Sharifah Wajihah Wafa Syed Saadun Tarek Wafa, Nurul Alia Azizan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
