Improving Environmental Awareness Through Algorithm-Guided Experiments on Biodegradable Materials and Soil Health

Year 2025, Volume: 10 Issue: 2, 326 - 341, 01.09.2025

Campos Ugaz Walter Antonio , Cueva Campos Hamilton Vladimir Sánchez Cusma Segundo Avelino Cachay Silva Roberto Carlos Huangal Castañeda Nelson Enrique María Aurora Gonzales Vigo Chávez Gallegos Jessica Paola

https://doi.org/10.28978/nesciences.1744921

Abstract

Increasing environmental awareness and promoting sustainable soil management are crucial for addressing ecological challenges posed by plastic pollution and ensuring the long-term health of our soils. This study introduces a broad, algorithm-guided experimental approach that integrates environmental monitoring, data science and microbiological analysis to assess the biodegrading dynamics of various biodegradable materials like polylactic acid (PLA), polyhydroxyalkanoates (PHAs)and the starch-based composites with advanced machine learning models in a data-handled platform that evaluates the degradation behavior of biodegradable polymers and their impact on the soil's physicochemical and biological properties. Utilizing the Random Forest Regression (RFR) and Support Vector Machines (SVM) examines nutritional cycling efficiency, microbial social structure, carbon dynamics, and parameters such as soil enzyme activities under both laboratory control and field-relevant conditions. Real-time soil monitoring is activated through IoT-based sensors that measure moisture, temperature, pH, and CO₂ flux, which combined with laboratory analyses, feeds in models of predictions that guide repetitive adjustments in material soil interaction. Among the tested materials, starch-based composites performed the fastest biological degradation (72%), followed by PHA (55%) and PLA (28%), correlated with increased microbial activity and enzyme function. The machine learning model performed high predictions (R2> 0.89) able to make real-time decisions. This adaptive model supports deep insight into soil-biopolymer interactions and promotes environmental skills through a visual dashboard that allows users to explain the dynamic soil condition. The interdisciplinary structure not only provides technological progress for soil monitoring but also as an educational tool, which encourages the practices that are informed in permanent agriculture and public environmental stewardship. Overall, the integration of biodegradable materials with intelligent monitoring systems provides a double advantage: An available platform for tangible improvement in soil health and participation science.

Keywords

Biodegradable polymers , Environmental awareness , Machine learning , Random Forest Regression , Soil health , Support vector machines

References

• Akash, K., & Nithish, S. Balamurugan. (2022). Traffic Flow Prediction Using RF Algorithm in Machine Learning. International Academic Journal of Innovative Research, 9(1), 37-41.
• Altai, S. H. M., Youssef, H. M., Muhee, H. A., & Hummadi, A. H. (2025). Study of the relationship between Bacillus subtilis and Azospirillum sp. & Bradyrhizobium sp. isolated from gypsiferous soil. International Journal of Aquatic Research and Environmental Studies, 5(1), 343–353. https://doi.org/10.70102/IJARES/V5I1/5-1-34
• Bandopadhyay, S., Martin-Closas, L., Pelacho, A. M., & DeBruyn, J. M. (2018). Biodegradable plastic mulch films: impacts on soil microbial communities and ecosystem functions. Frontiers in microbiology, 9, 819. https://doi.org/10.3389/fmicb.2018.00819.
• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.
• Brtnicky, M., Mustafa, A., Holatko, J., Gunina, A., Ondrasek, G., Naveed, M., ... & Kucerik, J. (2025). Soil texture-driven modulation of poly-3-hydroxybutyrate (P3HB) biodegradation: microbial shifts, and trade-offs between nutrient availability and lettuce growth. Environmental Research, 278, 121618. https://doi.org/10.1016/j.envres.2025.121618.
• Capolupo, M., Sørensen, L., Jayasena, K. D. R., Booth, A. M., & Fabbri, E. (2020). Chemical composition and ecotoxicity of plastic and car tire rubber leachates to aquatic organisms. Water research, 169, 115270. https://doi.org/10.1016/j.watres.2019.115270.
• Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., ... & Knight, R. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME journal, 6(8), 1621-1624. https://doi.org/10.1038/ismej.2012.8.
• Casida, L. E. JR.; Klein, D. A.; S Antoro, Thomas (1964). Soil dehydrogenase activity. Soil Science 98(6): p 371-376.
• Castillo, M. F., & Al-Mansouri, A. (2025). Big Data Integration with Machine Learning Towards Public Health Records and Precision Medicine. Global Journal of Medical Terminology Research and Informatics, 3(1), 22-29.
• Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and electronics in agriculture, 151, 61-69. https://doi.org/10.1016/j.compag.2018.05.012.
• Emadian, S. M., Onay, T. T., & Demirel, B. (2017). Biodegradation of bioplastics in natural environments. Waste management, 59, 526-536. https://doi.org/10.1016/j.wasman.2016.10.006.
• Fan, Y., Wang, X., Funk, T., Rashid, I., Herman, B., Bompoti, N., ... & Li, B. (2022). A critical review for real-time continuous soil monitoring: Advantages, challenges, and perspectives. Environmental Science & Technology, 56(19), 13546-13564. https://doi.org/10.1021/acs.est.2c03562.
• Far, L. M. (2017). Regression Techniques Using Data Mining in Flowering Plant. International Academic Journal of Science and Engineering, 4(2), 190-197.
• Ferraro, G., & Failler, P. (2020). Governing plastic pollution in the oceans: Institutional challenges and areas for action. Environmental Science & Policy, 112, 453-460. https://doi.org/10.1016/j.envsci.2020.06.015.
• Hayes, D. G., Anunciado, M. B., DeBruyn, J. M., Bandopadhyay, S., Schaeffer, S., English, M., ... & Sintim, H. Y. (2019). Biodegradable plastic mulch films for sustainable specialty crop production. In Polymers for agri-food applications (pp. 183-213). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-19416-1_11.
• Jackson, M. L. (1973). Soil chemical analysis, pentice hall of India Pvt. Ltd., New Delhi, India, 498, 151-154.
• Kale, G., Kijchavengkul, T., Auras, R., Rubino, M., Selke, S. E., & Singh, S. P. (2007). Compostability of bioplastic packaging materials: an overview. Macromolecular bioscience, 7(3), 255-277.https://doi.org/10.1002/mabi.200600168.
• Kandeler, E., & Gerber, H. (1988). Short-term assay of soil urease activity using colorimetric determination of ammonium. Biology and fertility of Soils, 6(1), 68-72. https://doi.org/10.1007/BF00257924.
• Klopfer, E., & Squire, K. (2008). Environmental Detectives—the development of an augmented reality platform for environmental simulations. Educational technology research and development, 56(2), 203-228. https://doi.org/10.1007/s11423-007-9037-6.
• Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674.
• Maganathan, T., Senthilkumar, S., & Balakrishnan, V. (2020, November). Machine learning and data analytics for environmental science: a review, prospects and challenges. In IOP conference series: materials science and engineering (Vol. 955, No. 1, p. 012107). IOP Publishing. https://doi.org/10.1088/1757-899x/955/1/012107.
• McBeth, W., & Volk, T. L. (2009). The national environmental literacy project: A baseline study of middle grade students in the United States. The journal of environmental education, 41(1), 55-67. https://doi.org/10.1080/00958960903210031.
• Mousa, T. U. (2022). The Role of the Accounting Profession in Controlling Environmental Pollution According to Requirements of Social Responsibility in Industrial Companies. International Academic Journal of Social Sciences, 9(1), 29-42. https://doi.org/10.9756/IAJSS/V9I1/IAJSS0904
• Narancic, T., & O'Connor, K. E. (2019). Plastic waste as a global challenge: are biodegradable plastics the answer to the plastic waste problem?. Microbiology, 165(2), 129-137. https://doi.org/10.1099/mic.0.000749.
• Paltseva, A. A. (2025). Participatory science in urban soil research: A framework for overcoming challenges and expanding public engagement. iScience, 28(5). https://doi.org/10.1016/j.isci.2025.112361.
• Patil, S., & Das, A. (2024). Encouraging Future Generations with Environmental Education. International Journal of SDG’s Prospects and Breakthroughs, 2(4), 24-29.
• Shah, A. A., Hasan, F., Hameed, A., & Ahmed, S. (2008). Biological degradation of plastics: a comprehensive review. Biotechnology advances, 26(3), 246-265. https://doi.org/10.1016/j.biotechadv.2007.12.005.
• Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222. https://doi.org/10.1023/b:stco.0000035301.49549.88
• Tabatabai, M. A., & Bremner, J. M. (1969). Use of p-nitrophenyl phosphate for assay of soil phosphatase activity. Soil biology and biochemistry, 1(4), 301-307. https://doi.org/10.1016/0038-0717(69)90012-1.
• Thompson, R. C., Swan, S. H., Moore, C. J., & Vom Saal, F. S. (2009). Our plastic age. Philosophical transactions of the royal society B: Biological Sciences, 364(1526), 1973-1976. https://doi.org/10.1098/rstb.2009.0054.
• Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems engineering, 164, 31-48. https://doi.org/10.1016/j.biosystemseng.2017.09.007.
• Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science, 37(1), 29-38. https://doi.org/10.1097/00010694-193401000-00003.
• Wang, H., Zhang, X., Wu, W., & Liu, H. (2021). Prediction of soil organic carbon under different land use types using Sentinel-1/-2 data in a small watershed. Remote Sensing, 13(7), 1229. https://doi.org/10.3390/rs13071229.