This article highlights the transformative role of AI in air quality modeling, emphasizing its benefits and challenges. It discusses the potential of AI to enhance traditional deterministic models while identifying limitations like data needs and diagnostic capabilities. The collection invites submissions on various topics related to AI’s application in air quality studies, aiming to broaden the discourse in this crucial area.
Artificial intelligence (AI) is revolutionizing our comprehension of air pollution, particularly concerning its sources and impacts on health. By harnessing AI in air quality modeling, researchers can achieve not only faster computational times but also detailed, fine-scale predictions. Nonetheless, challenges like extensive data and energy demands for training, and limited diagnostic capabilities must be addressed to fully leverage AI’s potential in this field.
Traditionally, deterministic numerical models have been instrumental in deciphering air pollution and understanding its interactions within both natural and human-made systems. These models have empowered policymakers with the accuracy needed to formulate strategies for public health improvement. Meanwhile, AI models stand to elevate the insights garnered from numerical approaches, prompting inquiries into their development and application.
This new collection takes a deep dive into the promising opportunities and inherent limitations surrounding the use of AI in air quality modeling, particularly for studies focusing on atmospheric composition, pollution attribution, and exposure assessments. We encourage the submission of Original Research articles, Reviews, Perspectives, and Comments on various topics, including:
– Development and evaluation of AI air quality models
– Human exposure assessments employing AI models
– Source attribution and emission influences on pollution variability
Join us in this exploration as part of the wider ‘Modeling of Airborne Composition and Concentrations’ initiative, fully embracing the evolving landscape of air quality science.
The integration of AI into air quality modeling promises substantial advancements in our understanding of pollution and its effects. Despite its potential, researchers must navigate challenges regarding data, energy consumption, and model diagnostics. The exploration is open for contributions to deepen this evolving discourse, particularly focusing on human exposure and pollution sources. As we advance, the synergy of traditional and AI-based models will be pivotal in shaping public health strategies.
Original Source: www.nature.com
