Artificial Intelligence in Environmental Monitoring and Remediation

Artificial Intelligence (AI) has become a game-changer in environmental monitoring and remediation by providing an effective method to analyze and manage complex datasets through the use of advanced technologies, such as Machine Learning and Deep Learning. With the ability to predict changes in the environment as well as provide support for developing effective remediation strategies more quickly and accurately than ever before, AI has revolutionized the way we approach environmental monitoring and remediation. AI has been instrumental in revolutionizing the way air, water, and soil quality is monitored in real-time by providing faster data analysis, detection of pollution, and the ability to provide recommendations on how best to prevent further damage. In addition, AI has improved the processes of bioremediation and phytoremediation by allowing researchers to optimize operational parameters and select the most appropriate treatment options for environmental remediation projects. The use of AI in conjunction with emerging technologies such as IoT, remote sensing, and robotics will continue to be an important factor in solving the pressing issues posed to the environment. There are still some limitations associated with the use of AI, including the need for high-quality datasets, challenges associated with understanding and interpreting model outputs, and challenges with integrating AI systems into existing infrastructure and regulatory frameworks that govern current environmental policies. Addressing these limitations is paramount to the widespread use of AI-based solutions for environmental monitoring and remediation. In closing, AI represents a unique opportunity for the world to move towards a more sustainable way of managing the environment by providing increased efficiency, accuracy, and overall decision-making abilities. Increased research and development, as well as technological advancements will continue to assist in this effort.

Abbas, S. H., Soomro, W. A., & Ali, S. (2026). MACHINE LEARNING-BASED DOWNSCALING OF CLIMATE MODELS USING REMOTE SENSING AND GIS DATA FOR HIGH-RESOLUTION ATMOSPHERIC FORECASTING. Spectrum of Engineering Sciences, 4(2), 407–425. https://doi.org/10.5281/zenodo.18639201 Afri, A., Grara, N., Mellouk, F. Z., Bouchaala, L., Alzahrani, K. J., Aslam, A. A., Irfan, M., & Ahmed, M. (2026). Decoding Human Exposure to Toxic Metals: Linking Environmental Distribution to Health Outcomes Through Multivariate Analysis. Journal of Chemistry, 2026(1), 4977768. https://doi.org/10.1155/joch/4977768 Ahmed, S., Pasha, A., Kumer, T., Akhtaruzzaman, M., & Hossain, M. (2026). Advancements in Air Quality Monitoring Systems: A Comprehensive Review of Emerging Technologies for Enhancing Environmental Health. Indoor Air, 2026(1), 3080684. https://doi.org/10.1155/ina/3080684 Ahmed, W., Kitajima, M., Tandukar, S., & Haramoto, E. (2026). A full-scale Co-located comparison of virus removal by membrane bioreactor and conventional activated sludge systems. Water Research, 292, 125352. https://doi.org/10.1016/j.coesh.2020.02.009 Akpan, E. E., Akinlade, O., Alabi, O. O., David, O. A., Afe, O. F., & Ajagbe, S. A. (2026a). Optimizing Traffic Signal Control Using Machine Learning and Environmental Data. Artificial Intelligence and Applications, 4(1), 48–56. https://doi.org/10.47852/bonviewaia52024199 Akpan, E. E., Akinlade, O., Alabi, O. O., David, O. A., Afe, O. F., & Ajagbe, S. A. (2026b). Optimizing Traffic Signal Control Using Machine Learning and Environmental Data. Artificial Intelligence and Applications, 4(1), 48–56. https://doi.org/10.47852/bonviewaia52024199 Alagoz, B. B., Keles, C., & Baran, B. (2026). A note on effects of fossil fuel reduction policies on atmospheric carbon dioxide buildup and global warming. Journal of Atmospheric and Solar-Terrestrial Physics, 279, 106724. https://doi.org/10.1016/j.jastp.2026.106724 Ali, I., Selim, A., & Skender, F. (2025). Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age. Turkish Journal of Engineering, 10(1), 129–142. https://doi.org/10.31127/tuje.1796566 Alkharashi, A., Al Mazroa, A., M. Alashjaee, A., V, S., M, D. B., S, S., & S, V. (2026). Machine learning models for biogas potential in sustainable aviation: XGBoost, random forest and ridge regression. Aircraft Engineering and Aerospace Technology, 98(4), 605–613. https://doi.org/10.1108/AEAT-12-2024-0369 AL-Quhfa, H., Mothana, A., & Song, J. (n.d.). 6992  A Systematic Review on Data-Driven Traffic Management for Sustainable Urban Transport. https://doi.org/10.35444/IJANA.2025.17402 Anees, M., Raveesha, L. N., Rakesh, G. K., Muruli, R., & Yashwanth, P. (2026). IoT solutions for environmental monitoring: Current approaches and future directions. Information and Communication Systems, 844–851. https://doi.org/10.1201/9781003650201-134 Anokye, A. (2026). Machine Learning–Enhanced PFAS Contaminant Fate and Transport Modeling for Optimized Groundwater Remediation Strategies at Industrial Sites. Journal of Sustainable Development and Policy, 5(01), 34–72. https://doi.org/10.63125/e3x2tt57 Arey, J. S., Socolofsky, S. A., Kessler, J. D., Zhao, L., Healy, T. M., Canalizo-Hernandez, M. A., & Nedwed, T. J. (2026). Deep-Ocean Sequestration of Excess Produced Gas to Mitigate Greenhouse Gas Emissions from Offshore Flaring. Environmental Science & Technology. https://doi.org/10.1021/acs.est.5c06770