Yishak-Ali/Air-Pollution-in-Ethiopia-Report

Examining air pollution exposure and its associated burden of disease in Ethiopia using Python's pandas, geopandas, matplotlib, and linearmodels. # Analyzing Air Pollution and Associated Burden of Disease in Ethiopia ## Introduction This Python data project and report aims to examine air pollution exposure and its associated burden of disease in Ethiopia, especially in the context of urbanization, by addressing the four main questions below: 1. How has Ethiopia's population trended over time and geographically, especially in the context of urbanization? 2. What is the air pollution exposure in the country, both outside and in households? How has this changed historically? 3. What is the associated burden of disease caused by air pollution in Ethiopia? Have health outcomes improved or worsened? 4. Beyond Ethiopia, what are the general effects of urbanization and air pollution, individually and at their interaction, on under-5 mortality rates? **Public Health and Policy Use Case:** The project ultimately seeks to provide data-informed policy and investment directions for the developing nation. In understanding where pollution exposure is highest, why that might be the case, and what impact it has on public health, policy directives and fund allocations can be more effectively geared to tackle air pollution in the country. The main body of work is structured as a report with research pulling data from multiple sources, key figures highlighting trends, and detailed write-ups explaining figures and walking through findings and policy recommendations. For a condensed PPT of the important findings, see Air Pollution in Ethiopia - Short Form. ## Key Findings The project analyses highlighted important trends with regard to urbanization and demographic settlement, outdoor and indoor air pollution exposure, and associated health outcomes. Additionally, a fixed effects regression model was used in tackling the 4th objective outlined above. **Identified areas of focus for policy and investment:** - Low urbanization rate and high urban slum rate - Unsafe ambient air quality exasperated by emissions from an age …