Enhancing Environmental Resilience
Ankit Mahule, Kaushik Roy, Ankush D. Sawarkar, Sagar Lachure
Abstract
This chapter delves into the innovative realm of real-time air quality monitoring systems, harnessing the potential of artificial intelligence (AI) to provide both conceptual frameworks and practical implementations. It explores the integration of weather model data, enhancing real-time air quality assessments. Region-specific case studies illustrate the diverse scenarios where AI-powered monitoring offers significant advantages, serving as templates for establishing comprehensive air quality assessment networks while considering the impact of contextual factors on research outcomes. In light of escalating environmental challenges, the demand for precise and timely air quality information has become imperative. Traditional methods often fall short in delivering real-time data for effective decision-making. AI emerges as a transformative force in reshaping air quality monitoring, emphasizing ML and data analytics algorithms for processing extensive data from sources like satellites, sensor networks, and weather models. These algorithms swiftly analyze data, forecast pollution patterns, and provide critical insights to policymakers. The integration of weather model data further enhances forecasting precision and comprehension of pollution dynamics. Region-specific case studies highlight the practicality and adaptability of AI-based monitoring systems across diverse geographical locations and pollution profiles, offering guidance for stakeholders interested in adopting AI-powered air quality monitoring. This chapter comprehensively reviews how AI, real-time data, and weather models enhance the air quality monitoring system. It emphasized the potential impact on community health, policy development, and comprehension of environmental factors, aligning with global initiatives for a sustainable future.