AI-Driven Air Quality: A Path to Sustainable Development

Abstract

Air pollution remains a critical global environmental and public health crisis, severely impacting ecological systems and hindering progress toward the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). Accurate, real-time forecasting of air quality is paramount for effective mitigation and policy formulation. This paper investigates the pivotal role of Artificial Intelligence (AI) and its associated models in enhancing the precision, efficiency, and scalability of air pollution predictions. We analyze various AI methodologies—including Machine Learning (ML), Deep Learning (DL), and hybrid models—and their comparative performance against traditional statistical techniques. Furthermore, the study critically evaluates the mechanism through which AI-driven predictive insights translate into actionable policy interventions, thereby accelerating the attainment of sustainable development objectives in urban environments. We identify current challenges, such as data quality and model interpretability, and propose future research directions, particularly the integration of geo-spatial AI and low-cost sensor networks, to foster truly smart and sustainable cities.