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Air pollution poses a significant challenge in urban environments, affecting both the health of the population and the ecological balance. This chapter examines how cooperation between citizens, the scientific community, and digital tools can help predict air pollution levels and support sustainable urban initiatives. The study focuses on computational simulation methods, including neural networks and prediction mechanisms, as well as collaborative programs where citizens collect data through mobile devices and local stations. The findings indicate that the incorporation of civil society enhances the reliability of the models, streamlines institutional measures, and strengthens collective awareness and social participation in the problem. This proposal highlights the transformative value of the synergy between technological innovation and active participation of the population.

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Collaborative Data Science for Citizen-Driven Air Pollution Monitoring and Prediction

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  • 发表日期：

    2026年

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  • Fanny E. Berigüete Alcántara,
  • José S. Santos Castillo

摘要

Air pollution poses a significant challenge in urban environments, affecting both the health of the population and the ecological balance. This chapter examines how cooperation between citizens, the scientific community, and digital tools can help predict air pollution levels and support sustainable urban initiatives. The study focuses on computational simulation methods, including neural networks and prediction mechanisms, as well as collaborative programs where citizens collect data through mobile devices and local stations. The findings indicate that the incorporation of civil society enhances the reliability of the models, streamlines institutional measures, and strengthens collective awareness and social participation in the problem. This proposal highlights the transformative value of the synergy between technological innovation and active participation of the population.