The National Institute of Environmental Research (NIER) under the Ministry of Environment announced that it will hold the ‘2025 International Conference’ from July 2 to 4 at Konkuk University’s Seoul Campus (located in Gwangjin-gu, Seoul) to discuss ways to advance air quality forecasting technology in the Asia-Pacific region.

This international conference will be held in collaboration with the Community Modeling and Analysis System Center (CMAS)* and the International Union of Air Pollution Prevention and Environmental Protection Associations (IUAPPA)**.

* CMAS: An organization that operates an international cooperation network between researchers and practitioners through model development, distribution, and training

** IUAPPA: An international organization that supports the effective introduction and implementation of air quality policies at national and regional levels.

Now in its third year, this event is an Asia-focused conference centered on air quality modeling and analysis. The first was held in China (2018), the second in Japan (2023), and it is being held in Korea for the first time in two years.

This year’s international conference will be attended by around 100 air quality experts from 11 countries, including Korea, the United States, China, Japan, Germany, and Singapore, to promote knowledge exchange and cooperation for the advancement of air quality forecasting technology in the Asia-Pacific region.

The international conference will focus on the integration of “process-based approaches” and “data-driven approaches” to air quality forecasting models. It will feature presentations on: methodologies for integrating traditional physics-based models (community multi-scale air quality, CMAQ)* with artificial intelligence (AI) models, case studies on the development and validation of data-driven air quality forecasting models using machine learning and deep learning, and strategies for improving the accuracy and efficiency of environmental forecasting through hybrid models.

* CMAQ: An air quality model developed by the U.S. Environmental Protection Agency that predicts air pollutants such as ozone and fine dust in three dimensions

The process-based approach enables structured forecasting based on established theories using traditional physics- and chemistry-based air quality models. The data-driven approach, on the other hand, leverages AI technologies such as machine learning and big data processing to quickly extract and incorporate refined features from field-collected data, thereby enhancing prediction accuracy.

Sung Ji-won, Director General of the Atmospheric Environmental Research Department at NIER, stated, “This event will lay the foundation for international joint responses to air environmental issues in the Asia-Pacific region.” She added, “In particular, hybrid air quality modeling that combines physics- and chemistry-based models with artificial intelligence will significantly enhance forecasting performance.”

For further information, please contact the Public Relations Division. 

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