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Integrated Meteorological–Machine Learning Frameworks for Urban Air Pollution Characterization and Forecasting: Theoretical Foundations, Empirical Interpretations, and Policy-Relevant Implications
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
Urban air pollution has emerged as one of the most persistent and structurally complex environmental challenges of the twenty-first century, intertwining atmospheric science, public health, urban planning, and computational intelligence. The growing availability of high-resolution environmental monitoring data and the parallel evolution of advanced machine learning methodologies have together reshaped the analytical landscape of air quality research. Yet, despite significant progress, the integration of meteorological dynamics with data-driven forecasting models remains theoretically fragmented and empirically inconsistent across geographical contexts. This study develops a comprehensive, publication-ready synthesis that reconceptualizes urban air pollution characterization and forecasting through an integrated meteorological–machine learning framework grounded in established atmospheric theory, time-series analysis, and deep learning paradigms. Anchored empirically and conceptually by large-scale urban observations from major Chinese cities during 2014–2015, this article situates meteorological variability as a central explanatory structure rather than a peripheral control variable in predictive modeling (He et al., 2017).
The research undertakes an extensive theoretical elaboration of pollutant formation, dispersion, and accumulation processes, emphasizing the nonlinearity introduced by meteorological factors such as temperature inversions, wind field heterogeneity, humidity-driven aerosol chemistry, and seasonal synoptic patterns. These physical dynamics are then critically juxtaposed with classical statistical forecasting approaches, including autoregressive integrated moving average models, and contemporary machine learning systems such as gradient boosting, artificial neural networks, and long short-term memory architectures. Rather than privileging algorithmic novelty alone, the analysis foregrounds epistemological questions regarding interpretability, temporal dependency modeling, and the translation of predictive accuracy into actionable environmental governance.
Methodologically, the article articulates a text-based, model-agnostic framework that synthesizes meteorological conditioning, spatiotemporal dependency learning, and explainability-oriented evaluation strategies. The results are interpreted descriptively through a comparative lens, demonstrating how meteorology-aware deep learning systems consistently outperform static or purely historical models in capturing episodic pollution events, particularly under rapidly changing atmospheric conditions. The discussion advances a theoretically grounded critique of current practices, highlighting issues of data bias, urban heterogeneity, and the ethical dimensions of algorithm-driven environmental decision-making. Ultimately, this work contributes a unified conceptual scaffold that bridges atmospheric science and machine learning, offering robust implications for future research, policy formulation, and sustainable urban air quality management (Manisalidis et al., 2020; World Health Organization, 2018).
Keywords
References
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