- This data was extracted from the WHO data base and downloaded on January 26th, 2017 from: "http://www.who.int/entity/phe/health_topics/outdoorair/databases/airquality_dimaq_pm25.csv"

- Country files were created using the WHO data in support of the ARSET SDGs webinar conducted on Mar 15-29, 2017.
- No modification to data was performed by ARSET 

- Modelled Global Ambient Air Pollution estimates

Estimation of global health risks from exposure to ambient air pollution requires a comprehensive set of air pollution exposure data covering all inhabited areas. The recently developed Data Integration Model for Air Quality (DIMAQ) has produced estimates based on data from ground measurements together with information from other sources including data from satellite retrievals of aerosol optical depth and chemical transport models. It provides estimates of annual exposures of PM2.5 levels at high spatial resolution (0.1° × 0.1°, which equates to approximately 11x11km at the equator) globally.

The sources of data include: Ground measurements from 6 003 monitoring locations around the world, satellite remote sensing; population estimates; topography; and information on local monitoring networks and measures of specific contributors of air pollution from chemical transport models. The DIMAQ model calibrates data from these sources with ground measurements. This model has provided produced estimates of air quality, expressed in terms of median concentrations of PM2.5, for all regions of the world, including areas in which PM2.5 monitoring is not available.

- This model has been developed by an international group of experts, and led by the University of Bath and WHO.

- More Details can be found on:
http://www.who.int/phe/health_topics/outdoorair/databases/modelled-estimates/en/

- The following journal article describes the method in detail:

"Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution" by Gavin Shaddick, Matthew L. Thomas, Amelia Jobling, Michael Brauer, Aaron van Donkelaar, Rick Burnett, Howard Chang, Aaron Cohen, Rita Van Dingenen, Carlos Dora, Sophie Gumy, Yang Liu, Randall Martin, Lance A. Waller, Jason West, James V. Zidek, Annette Prüss-Ustün

https://arxiv.org/abs/1609.00141