Optimizing Urban Air Quality: Integrating Data for Effective Countermeasure Evaluation

Overview

Urban air pollution, particularly fine particulate matter (PM2.5), poses a significant public health risk globally. Current methods of assessing air quality lack comprehensive integration of meteorological, pollutant, and traffic data, limiting their effectiveness in predicting and evaluating air quality. This proposal aims to address this gap by leveraging deep learning techniques to develop a model that integrates these diverse datasets to predict air quality and evaluate the effectiveness of countermeasures. The proposed model will be trained using historical data and validated against real-world air quality measurements. Additionally, specific countermeasures will be evaluated using this model to gain a proper understanding of what factors make certain countermeasures more effective. Through this research, insights into the most effective countermeasures and the key factors influencing air quality changes will be gained, providing valuable guidance for urban planning and pollution control efforts.

Competition history

• AJAS 2025 Category not listed

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