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<p>Air pollution in Delhi is a severe environmental challenge, driven by high levels of particulate matter (PM10) and gaseous pollutants such as NO₂ and CO. Conventional pollution control methods often struggle to manage resources efficiently and respond dynamically to changing environmental conditions. This study presents a fuzzy logic-based framework designed to enhance resource allocation (RA), optimize inventory management, and strengthen pollution control strategies. The proposed system integrates real-time pollution data to adaptively distribute resources, minimize waste, and improve operational efficiency in urban pollution management. The methodology employs fuzzy logic models, including CLEAR (Contaminant-Level Environmental Allocation Refinement), FLOP-RA (Fuzzy Logic Optimization for Pollution Resource Allocation), and PULSE (Pollution Utilization and Logistics System Enhancement), which process inputs such as pollutant concentrations, temperature, and traffic density. Optimization algorithms use these inputs to determine precise resource deployment, ensuring timely and effective interventions. Data collected from multiple regions in Delhi informs the system, enabling adaptive responses that reduce emissions and optimize logistics. The framework achieved a 15% reduction in resource wastage and a 20% improvement in pollution control efficiency, demonstrating its effectiveness in managing dynamic environmental conditions. Future work will focus on extending the system to other cities, enhancing predictive models, integrating renewable energy solutions, and promoting stakeholder collaboration and policy alignment. By combining real-time data, fuzzy logic modeling, and efficient resource management, the framework provides a sustainable and adaptive approach to improving air quality and reducing urban carbon emissions.</p>

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Enhancing Pollution Reduction with Fuzzy Logic Models for Efficient Resource Allocation and Inventory Management

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Lata K Kamthekar,
  • Pragya P. Samadhiya

摘要

Air pollution in Delhi is a severe environmental challenge, driven by high levels of particulate matter (PM10) and gaseous pollutants such as NO₂ and CO. Conventional pollution control methods often struggle to manage resources efficiently and respond dynamically to changing environmental conditions. This study presents a fuzzy logic-based framework designed to enhance resource allocation (RA), optimize inventory management, and strengthen pollution control strategies. The proposed system integrates real-time pollution data to adaptively distribute resources, minimize waste, and improve operational efficiency in urban pollution management. The methodology employs fuzzy logic models, including CLEAR (Contaminant-Level Environmental Allocation Refinement), FLOP-RA (Fuzzy Logic Optimization for Pollution Resource Allocation), and PULSE (Pollution Utilization and Logistics System Enhancement), which process inputs such as pollutant concentrations, temperature, and traffic density. Optimization algorithms use these inputs to determine precise resource deployment, ensuring timely and effective interventions. Data collected from multiple regions in Delhi informs the system, enabling adaptive responses that reduce emissions and optimize logistics. The framework achieved a 15% reduction in resource wastage and a 20% improvement in pollution control efficiency, demonstrating its effectiveness in managing dynamic environmental conditions. Future work will focus on extending the system to other cities, enhancing predictive models, integrating renewable energy solutions, and promoting stakeholder collaboration and policy alignment. By combining real-time data, fuzzy logic modeling, and efficient resource management, the framework provides a sustainable and adaptive approach to improving air quality and reducing urban carbon emissions.