While populations in low- and middle-income countries are exposed to some of the highest levels of air pollution and its consequences, the majority of economics research on the topic is focused on high-income settings where there is greater data availability. We compare and evaluate the three principal sources of air pollution data (regulatory-grade monitors, satellite, and low-cost monitors) in a sub-Saharan Africa context in terms of accuracy of PM2.5 measurement across different spatial and temporal frequencies and their performance when studying policy externalities. Satellite data is closely aligned with data from the regulatory-grade monitors at lower temporal frequencies. The low-cost monitors underestimate PM2.5 relative to the other data sources. Calibration, especially context-specific calibration, of the low-cost monitors’ data improves its alignment with other data sources. We use each data source to evaluate the air pollution externality of mobility-reduction policies using a difference-in-differences design and find similar results, especially in terms of percent reduction. We consider policymakers’ constraints to air pollution monitoring in low-income settings and demonstrate that co-locating one regulatory-grade monitor in a network of low-cost monitors can capture the spatial variation of pollution across an urban area and achieve better accuracy than either of these data sources alone. This provides a framework for policymakers to generate the data needed to evaluate environmental policies and externalities.
Repository name | URI |
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Reproducible Research Repository (World Bank) | https://reproducibility.worldbank.org |
Paper exhibits were reproduced on a computer with the following specifications:
– OS: Windows 11 Enterprise, version 23H2
– Processor: 11th Gen Intel(R) Core(TM) i5-1145G7 @ 2.60GHz 1.50 GHz
– Memory available: 15.7 GB
– Software version: Stata 18.0 MP, Python 3.12.4, QGIS 3.38.1
Runtime: 1 hour.
Users need to gain access to the datasets MERRA-2, Senegal Air Quality, Purple Air Monitors Dakar, and Weather Data for Dakar; reproduce the Python environment; run the notebook "master.ipynb"'; and change the global path in line 26 of the main do-file and run it to reproduce the results.
Some data is restricted and has not been included in the reproducibility package.
Author | Affiliation | |
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Bridget Hoffmann | Inter-American Development Bank | bridgeth@iadb.org |
Sveta Milusheva | World Bank | smilusheva@worldbank.org |
2024-09
Location | Code |
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Senegal | SEN |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
Name | URI |
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Modified BSD3 | https://opensource.org/license/bsd-3-clause/ |
Name | Affiliation | |
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Niall Oliver Maher | World Bank | nmaher@worldbank.org |
Reproducibility WBG | World Bank | reproducibility@worldbank.org |
Name | Abbreviation | Affiliation | Role |
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Reproducibility WBG | DIME | World Bank - Development Impact Department | Verification and preparation of metadata |
2024-09-25
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