My presentation, titled "Data Foundations for Machine Learning-Based PM₂.₅ Exposure Models in Kampala, Fort Portal, and Jinja City," will delve into our transdisciplinary study examining the intersection of air pollution and respiratory health across three major urban centers in Uganda.

Spanning January 2018 to December 2023, the study leverages a multi-source data ecosystem that integrates:

High-resolution PM₂.₅ measurements from both fixed and mobile air quality sensors,
Meteorological data (temperature, relative humidity, wind speed, wind direction),
Satellite-derived indicators (such as Aerosol Optical Depth), and
Socioeconomic datasets (including population density and income levels) sourced from national and global repositories.

This comprehensive, spatially and temporally harmonized dataset supports the development of robust machine learning-based exposure models, which provide critical insights into the distribution and health impacts of PM₂.₅ in resource-constrained urban environments.

The findings aim to inform targeted air quality management and public health interventions, with a particular focus on equity and environmental justice in Kampala, Fort Portal, and Jinja. I look forward to engaging with fellow researchers, policymakers, and practitioners on how data science can drive better health outcomes across Africa.

Data for Health in Africa Meeting

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Data for Health in Africa Meeting

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