Abstract—Four approaches for managing crop residues and mitigating PM 2.5 pollution in Chiang Mai were studied: Crop Residue Management (CRM), Community-Led Awareness (CLA), Policy Enforcement and Incentives (PEI), and Case-Based Reasoning (CBR). The approaches are assessed based on five key metrics: Cost Efficiency, Adaptability to Local Context, Sustainability, Impact on PM 2.5 Pollution, and Flexibility and Continuous Improvement by demonstrating how Case-Based Reasoning (CBR) outperforms traditional methods. It highlights CBR’s ability to provide dynamic, locally tailored solutions that evolve over time, enhancing long-term sustainability and air quality management. Among the methods, CBR emerges as the most effective, outperforming others in all metrics, with scores of 85% for Cost Efficiency, 90% for Adaptability to Local Context, 85% for Sustainability, 90% for Impact on PM 2.5 Pollution, and 95% for Flexibility and Continuous Improvement. Unlike traditional models, CBR uses historical data and local context to provide adaptive, dynamic solutions that evolve. This adaptability allows for continuous improvement, reducing reliance on trial-and-error methods. While mechanized solutions, community awareness programs, and policy enforcement are valuable, they face challenges such as high costs, limited adoption, and weak enforcement. CBR, however, addresses these limitations by offering cost-effective, tailored strategies that enhance long-term sustainability and reduce PM 2.5 emissions from agricultural burning. The study suggests integrating CBR with real-time monitoring tools and exploring its application in other regions with similar challenges to further enhance its impact. Future research should focus on combining CBR with advanced technologies, like artificial intelligence and machine learning, to predict and mitigate PM 2.5 pollution more effectively, ensuring a more dynamic and responsive approach to air quality management.
Keywords—PM 2.5, sustainability, air pollution, Thailand, Chiang Mai, case-based reasoning
Cite: Thacha Lawanna, Zhai Fan, and Jittima Wongwuttiwat, "Applying Case-Based Reasoning (CBR) for Environmental Sustainability: Mitigating PM 2.5 Pollution in Chiang Mai, Thailand," International Journal of Environmental Science and Development vol. 16, no. 4, pp. 306-313, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).