Abstract
This work aimed at the use and understanding the impact of education in solving the growing environmental pollution and radiation exposure, which are both attributed to natural phenomena and human activities. It’s a case study of two different universities in Libya namely; Omar Al-Mukhtar University, of Natural Resources and Environmental Sciences and Qubba Branch, University of Derna, Libya that are willing to utilize their knowledge in mitigating and combating environmental pollution. The total population of students studying environmental science and environmental education in these universities is 425, whereby, 402 students responded to the questionnaire used in the current study. This questionnaire comprises of four sections; socio-demographic section, knowledge, concern, willingness and behavior. Whereby; knowledge/environmental education was considered as the dependent variable while the other variables are considered as the independent variables. Descriptive statistics of the data using graphical representation of the obtained results demonstrates that 82.2% of the students respond with 5 and above (on a scale of 1 to 10), indicating that they know the major environmental pollution. Also, 45% of the students respond with 9 and 10 in demonstrating that they have knowledge on the major causes of environmental pollution. Furthermore, 72.2% of the responders responds with 6 and above to indicate that they know the major solutions for environmental pollution and based on this answers, interpretable artificial intelligence was used to determine the impacts of the independent variables on the targets. Overall, the performance results demonstrated that GPR-BO-M2 showed the highest performance among all the combinations used in modelling stage with R2-values = 0.951/0.937, RMSE = 0.684/0.651, MSE = 0.467/0.424 and MAE = 0.263/0.232. Hence, the results obtained in this work can be utilized by students, educationist, policy makers and experts in understanding and mitigating environmental pollution.
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Data availability
The data will be provided on a reasonable request from the corresponding author (Abdullahi Garba Usman).
References
Aghapour, S. & Bina, B. Distribution and health risk assessment of natural fluoride of drinking groundwater resources of Isfahan, Iran, using GIS (2018).
Kazi, T. G. et al. Toxic metals distribution in different components of Pakistani and imported cigarettes by electrothermal atomic absorption spectrometer. J. Hazard. Mater. 163 (1), 302–307. https://doi.org/10.1016/j.jhazmat.2008.06.088 (2009).
Pocock, L. Ethics Editor and Publisher, vol. 11, no. 4 (2013).
Mahgoub Idris, S. A., Altohame Jalgaf, G. G. & Mohammed, M. F. A. Assessment of heavy metal pollution in Tobruk Bay, Libya: A focus on anthropogenic impacts and pollution indices. Mar. Pollut. Bull. 214, 117709. https://doi.org/10.1016/j.marpolbul.2025.117709 (2025).
Abuazza, A. The Arab Spring movement: a catalyst for reform at the psychiatric hospital in Tripoli, Libya. Int. Psychiatry. 10 (3), 56–58. https://doi.org/10.1192/s1749367600003842 (2013).
Khatri, N. & Tyagi, S. Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Front. Life Sci. 8 (1), 23–39. https://doi.org/10.1080/21553769.2014.933716 (2015).
IS, B., AM, K. M. E. & RH, E. R. Assessment and measurement of atmospheric pollution (particulate matter) around Zawiya City-Libya. J. Environ. Anal. Toxicol. 07 (04). https://doi.org/10.4172/2161-0525.1000473 (2017).
Faridi, S. et al. Ambient air quality standards and policies in Eastern Mediterranean Countries: A review. Int. J. Public. Health. 68, 1–10. https://doi.org/10.3389/ijph.2023.1605352 (2023).
Kollmuss, A. & Agyeman, J. Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environ. Educ. Res. 8(3), 239–260. https://doi.org/10.1080/13504620220145401 (2002).
Usman, A. G., Isik, S., Abba, S. I. & Mericli, F. Artificial intelligence based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method. Turk. J. Chem. 44 (5). https://doi.org/10.3906/kim-2003-6 (2021).
Ayturan, Y. A., Ayturan, Z. C. & Altun, H. O. Air pollution modelling with deep learning: A review. Int. J. Environ. Pollut. Environ. Model. 1(3), 58–62. https://ijepem.com/doc/ijepem-18-03-01.pdf (2018).
Hameed, M. et al. Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. Neural Comput. Appl. 28, 893–905. https://doi.org/10.1007/s00521-016-2404-7 (2017).
Malviya, A. & Jaspal, D. Artificial intelligence as an upcoming technology in wastewater treatment: a comprehensive review. Environ. Technol. Rev. 10 (1), 177–187. https://doi.org/10.1080/21622515.2021.1913242 (2021).
Tung, T. M. & Yaseen, Z. M. Review papers A survey on river water quality modelling using artificial intelligence models: 2000–2020. J. Hydrol. 585(October 2019), 124670. https://doi.org/10.1016/j.jhydrol.2020.124670 (2020).
Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M. & Khorasani, N. Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air. Sci. Rep. 11 (1), 1805. https://doi.org/10.1038/s41598-021-81455-6 (2021).
Abba, S. I. et al. Optimization of extreme learning machine with metaheuristic algorithms for modelling water quality parameters of Tamburawa water treatment plant in Nigeria. Water Resour. Manag. 1377–1401. https://doi.org/10.1007/s11269-024-04027-z (2024).
Agrawal, P., Abutarboush, H. F., Ganesh, T. & Mohamed, A. W. Metaheuristic algorithms on feature selection: A survey of one decade of research (2009–2019). IEEE Access. 9, 26766–26791. https://doi.org/10.1109/ACCESS.2021.3056407 (2021).
Mohamed, A. W., Hadi, A. A. & Mohamed, A. K. Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. Int. J. Mach. Learn. Cybern. 11 (7), 1501–1529. https://doi.org/10.1007/s13042-019-01053-x (2020).
Uniyal, N., Pant, S., Kumar, A. & Pant, P. Nature-inspired metaheuristic algorithms for optimization. Meta-heuristic Optim. Tech. Appl. Eng. 10, 1 (2022).
Ancker, J. S., Kern, L. M., Abramson, E. & Kaushal, R. The triangle model for evaluating the effect of health information technology on healthcare quality and safety. J. Am. Med. Inf. Assoc. 19 (1), 61–65. https://doi.org/10.1136/amiajnl-2011-000385 (2012).
Abba, S. I., Benaafi, M., Usman, A. G. & Aljundi, I. H. Inverse groundwater salinization modeling in a sandstone’s aquifer using stand-alone models with an improved non-linear ensemble machine learning technique. J King Saud Univ. - Comput. Inf. Sci.. https://doi.org/10.1016/j.jksuci.2022.08.002 (2022).
Abdullahi, H. U., Usman, A. G. & Abba, S. I. Modelling the absorbance of a bioactive compound in HPLC method using artificial neural network and multilinear regression methods. 6(2), 362–371 (2020).
Uzun Ozsahin, D. U. et al. COVID-19 prediction using black-box based pearson correlation approach. Diagnostics. 13 (7), 1264. https://doi.org/10.3390/diagnostics13071264 (2023).
Sulaiman, S. O., Shiri, J., Shiralizadeh, H., Kisi, O. & Yaseen, Z. M. Precipitation pattern modeling using cross-station perception: regional investigation. Environ. Earth Sci. 77, 1–11. https://doi.org/10.1007/s12665-018-7898-0 (2018).
Giuseppe, Z., Manuela, G., Marta, B. & Vincenzo, G. Application of artificial neural network on mono- and sesquiterpenes compounds determined by headspace solid-phase microextraction-gas chromatography-mass spectrometry for the Piedmont ricotta cheese traceability. J. Chromatogr. A. 1071, 1–2. https://doi.org/10.1016/j.chroma.2004.11.083 (2005).
Van Dao, D. et al. A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a monte carlo simulation. Sustain. 12 (3). https://doi.org/10.3390/su12030830 (2020).
Gbadamosi, A. et al. New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system. Int. J. Hydrogen Energy. 50, 1326–1337. https://doi.org/10.1016/j.ijhydene.2023.09.170 (2023).
Usman, A. G. et al. Evidential neural network model for groundwater salinization simulation: A first application in hydro-environmental engineering. Water (Switzerland). 16 (20). https://doi.org/10.3390/w16202873 (2024).
Zhang, J., Zheng, Q., Chen, D., Wu, L. & Zeng, L. Surrogate-based Bayesian inverse modeling of the hydrological system: An adaptive approach considering surrogate approximation error. Water Resour. Res. 56 (1). https://doi.org/10.1029/2019WR025721 (2020).
Larrañaga, P. et al. Machine learning in bioinformatics. Brief. Bioinform. 7 (1), 86–112. https://doi.org/10.1093/bib/bbk007 (2006).
Usman, A. G., Işik, S., Abba, S. I. & Meriçli, F. Chemometrics-based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high-performance liquid chromatography, J. Sep. Sci. (December 2020), 1–7. https://doi.org/10.1002/jssc.202000890 (2021).
Abba, S. I., Usman, J. & Abdulazeez, I. Enhancing Li + recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials, 15129–15142. https://doi.org/10.1039/d4ra02385d (2024).
Abba, S. I. et al. Mapping of groundwater salinization and modelling using meta-heuristic algorithms for the coastal aquifer of eastern Saudi Arabia. Sci. Total Environ. 858(July 2022), 159697. https://doi.org/10.1016/j.scitotenv.2022.159697 (2023).
Dogara, M., Usman, A. A. G., Ozsahin, D. U., Umar, A. & Abba, I. S. I. Novel ensemble machine learning paradigms for the prediction of antioxidant activity of Bryophyllum pinnatum (Lam.) Oken, Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 0123456789. https://doi.org/10.1007/s40011-024-01619-y (2024).
Usman, A. G., Jibrin, A. M., Mati, S. & Abba, S. I. Evidential-bio-inspired algorithms for modeling groundwater total hardness: A pioneering implementation of evidential neural network for feature selection in water resources management. Environ. Chem. Ecotoxicol. 7, 494–505. https://doi.org/10.1016/j.enceco.2025.02.012 (2025).
Usman, A. G., Ahmad, M. H., Danraka, N. & Abba, S. I. The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach. Bull. Natl. Res. Cent. https://doi.org/10.1186/s42269-021-00586-y (2021).
Benaafi, M., Yassin, M. A., Usman, A. G. & Abba, S. I. Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering (GIS, and Statistical Techniques, 2022).
Abba, S. I. & Usman, A. G. Modeling of Water Treatment Plant Performance using Artificial Neural Network: Case Study Tamburawa Kano-Nigeria (2020).
Usman, A. G. et al. Optimization algorithms for modeling conversion and naphtha yield in the catalytic co-cracking of plastic in HVGO. Process Saf. Environ. Prot. 196, 106958. https://doi.org/10.1016/j.psep.2025.106958 (2025).
Khan, I. A. et al. Optimization of CO2 absorption rate for environmental applications and effective carbon capture. J. Clean. Prod. 490, 144707. https://doi.org/10.1016/j.jclepro.2025.144707 (2025).
Ramzi, M., Kashaninejad, M., Salehi, F., Sadeghi Mahoonak, A. R. & Ali Razavi, S. M. Modeling of rheological behavior of honey using genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system. Food Biosci. 9 (1), 60–67. https://doi.org/10.1016/j.fbio.2014.12.001 (2015).
Arabameri, M. et al. Oxidative stability of virgin olive oil: evaluation and prediction with an adaptive neuro-fuzzy inference system (ANFIS). J Sci. Food Agric. https://doi.org/10.1002/jsfa.9777 (2019).
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O.A.M.H., E.B. and A.G.U. participated in the conceptualization, writing the original draft, validation, simulation. E.B. reviewed the manuscript.
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Hamad, O.A.M., Baysen, E. & Usman, A.G. Environmental education as a means of combating growing environmental pollution: an optimized- explainable artificial intelligence (XAI) approach. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42335-z
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DOI: https://doi.org/10.1038/s41598-026-42335-z
