{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2024-09-25","version":"1"},"project_desc":{"authoring_entity":[{"name":"Bridget Hoffmann","email":"bridgeth@iadb.org","affiliation":"Inter-American Development Bank"},{"name":"Sveta Milusheva","email":"smilusheva@worldbank.org","affiliation":"World Bank"}],"output":[{"type":"Working paper","title":"Designing Air Quality Measurement Systems in Data-Scarce Settings","description":"Policy Research Working Paper (PRWP)"}],"software":[{"name":"Python","version":"3.12.4"},{"name":"Stata","version":"18.0"},{"name":"QGIS","version":"3.38.1"}],"scripts":[{"file_name":"RR_SEN_2024_193","zip_package":"RR_SEN_2024_193.zip","title":"Reproducibility package (code and partial data) for Designing Air Quality Measurement Systems in Data-Scarce Settings","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank.","date":"2024-10","dependencies":"All Python dependencies are listed in the file \"Code\/Python\/environment.yml\". All Stata dependency files are stored in the folder \"Code\/Stata\/ado\". "}],"title_statement":{"idno":"RR_SEN_2024_193","title":"Reproducibility package for Designing Air Quality Measurement Systems in Data-Scarce Settings"},"production_date":"2024-09","abstract":"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\u2019 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\u2019 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.","geographic_units":[{"code":"SEN","name":"Senegal"}],"data_statement":"Some data is restricted and has not been included in the reproducibility package.","datasets":[{"name":"Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)","note":"Source: Google Earth Engine. Located at: Data\/Raw\/Satellite. The files were generated by running Code\/JavaScript\/load_merra2_data.js on Google Earth Engine. See README for details. Data was accessed in May 2024.","license_uri":"https:\/\/www.google.com\/intl\/en_US\/help\/terms_maps\/","uri":"https:\/\/code.earthengine.google.com\/","license":"Custom license","access_type":"Restricted and not included in the reproducibility package. "},{"name":"Senegal Air Quality Data","note":"Source: Center for the Control of Air Quality of Senegal (CGQA). Located at: Data\/Raw\/cgqa_hourly.csv, Data\/Raw\/cgqa_daily.csv. The data covers a period from January 2012 to March 2024. Data was provided privately by the data source and it is not publicly available. Data was accessed in March 2024. The data source can be contacted at this email: contact@denv.gouv.sn or with the contact information from this website: https:\/\/www.denv.gouv.sn\/contact-us\/","access_type":"Restricted and not included in the reproducibility package. ","license":"Restricted and not included in the reproducibility package. "},{"name":"Senegal - Subnational Administrative Boundaries","uri":"https:\/\/data.humdata.org\/dataset\/cod-ab-sen","note":"Source: The Humanitarian Data Exchange. Located at: Data\/Raw\/Senegal_ADM3_SHP\/ and Data\/Final\/Dakar_SHP\/. Includes administrative levels 0-3 boundaries of Senegal. Data was accessed in August 2024.","license":"Creative Commons Attribution 4.0 International license","license_uri":"https:\/\/data.humdata.org\/dataset\/cod-ab-sen","access_type":"Included in the reproducibility package"},{"name":"Purple Air Monitors Dakar","access_type":"The data is not included in the reproducibility package. It is forthcoming in the World Bank Development Data Hub.","note":"Source: authors' data collection. See README for files location. Data was accessed in June 2024."},{"name":"Weather Data for Dakar","note":"Source: Visual Crossing Weather Data. Located at: Data\/Raw\/weather_visualcrossing.csv. Data was collected from the data URL for Dakar for the years 2000-2024, hourly. Data was accessed in August 2024.","access_type":"Restricted and not included in the reproducibility package. ","license":"Custom license","license_uri":"https:\/\/www.visualcrossing.com\/weather-services-terms","uri":"https:\/\/www.visualcrossing.com\/weather\/weather-data-services\/dakar,%20senegal?v=api"}],"repository_uri":[{"uri":"https:\/\/reproducibility.worldbank.org","name":"Reproducible Research Repository (World Bank) "}],"technology_requirements":"Runtime: 1 hour.","technology_environment":"Paper exhibits were reproduced on a computer with the following specifications:\n\u2013 OS: Windows 11 Enterprise, version 23H2\n\u2013 Processor: 11th Gen Intel(R) Core(TM) i5-1145G7 @ 2.60GHz 1.50 GHz\n\u2013 Memory available: 15.7 GB\n\u2013 Software version: Stata 18.0 MP, Python 3.12.4, QGIS 3.38.1","reproduction_instructions":"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.","disclaimer":"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.","license":[{"name":"Modified BSD3","uri":" https:\/\/opensource.org\/license\/bsd-3-clause\/"}],"contacts":[{"name":"Niall Oliver Maher","email":"nmaher@worldbank.org","affiliation":"World Bank"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}]},"tags":[{"tag":"DOI"}],"schematype":"script"}