ArticleScientific reports2026
Environmental education as a means of combating growing environmental pollution: an optimized- explainable artificial intelligence (XAI) approach.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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 R
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