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<p>Air quality assessment and forecasting are crucial for mitigating public health risks and environmental degradation, necessitating robust analytical tools. Markov models, renowned for their probabilistic state‒transition frameworks, have emerged as valuable in capturing the temporal dynamics of air pollutants. This review comprehensively examines methodologies, applications, and future directions of Markov models in air quality research. We systematically analyze implementations such as discrete-time, continuous-time, and hidden Markov models, alongside their integration with spatial analysis and machine learning. Key applications include short-term pollutant forecasting, source apportionment, and trend analysis under varying environmental conditions. Our analysis reveals that Markov models achieve up to 85% accuracy in predicting next-day PM2.5 levels and can identify pollution sources with 90% precision in urban environments. While Markov models offer computational efficiency, reducing processing time by up to 40% compared to complex neural networks, and flexibility with incomplete data, handling up to 20% missing values without significant performance degradation, limitations such as stationary assumptions and challenges in modelling complex pollutant interactions are critically evaluated. The findings reveal their efficacy in scenarios with well-defined state transitions but reduced accuracy by up to 30% in highly nonlinear systems. Emerging opportunities involve hybrid frameworks that combine Markov models with deep learning, improving forecasting accuracy by 15–25% over traditional methods. Enhanced data assimilation techniques have shown promise in reducing model uncertainty by up to 40%. Future research should focus on refining spatial‒temporal resolution to sub-kilometer and hourly scales, addressing multi-pollutant dependencies involving up to 10 concurrent air quality parameters, and validating models across at least 20 diverse climatic regions globally. This synthesis underscores Markov models' potential in advancing air quality management, offering policymakers and researchers insights to develop adaptive strategies amidst climate change. By bridging methodological advancements with practical applications, Markov models remain instrumental in shaping data-driven environmental policies and fostering sustainable urban planning, potentially reducing air pollution-related health risks by up to 30% in smart cities implementing these models.</p>

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A Comprehensive Review of Markov Models in Air Quality Assessment and Forecasting: Methodologies, Applications, and Future Directions

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Arumugam Raju

摘要

Air quality assessment and forecasting are crucial for mitigating public health risks and environmental degradation, necessitating robust analytical tools. Markov models, renowned for their probabilistic state‒transition frameworks, have emerged as valuable in capturing the temporal dynamics of air pollutants. This review comprehensively examines methodologies, applications, and future directions of Markov models in air quality research. We systematically analyze implementations such as discrete-time, continuous-time, and hidden Markov models, alongside their integration with spatial analysis and machine learning. Key applications include short-term pollutant forecasting, source apportionment, and trend analysis under varying environmental conditions. Our analysis reveals that Markov models achieve up to 85% accuracy in predicting next-day PM2.5 levels and can identify pollution sources with 90% precision in urban environments. While Markov models offer computational efficiency, reducing processing time by up to 40% compared to complex neural networks, and flexibility with incomplete data, handling up to 20% missing values without significant performance degradation, limitations such as stationary assumptions and challenges in modelling complex pollutant interactions are critically evaluated. The findings reveal their efficacy in scenarios with well-defined state transitions but reduced accuracy by up to 30% in highly nonlinear systems. Emerging opportunities involve hybrid frameworks that combine Markov models with deep learning, improving forecasting accuracy by 15–25% over traditional methods. Enhanced data assimilation techniques have shown promise in reducing model uncertainty by up to 40%. Future research should focus on refining spatial‒temporal resolution to sub-kilometer and hourly scales, addressing multi-pollutant dependencies involving up to 10 concurrent air quality parameters, and validating models across at least 20 diverse climatic regions globally. This synthesis underscores Markov models' potential in advancing air quality management, offering policymakers and researchers insights to develop adaptive strategies amidst climate change. By bridging methodological advancements with practical applications, Markov models remain instrumental in shaping data-driven environmental policies and fostering sustainable urban planning, potentially reducing air pollution-related health risks by up to 30% in smart cities implementing these models.