Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).
Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).
The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.
The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.
The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.
Kind of Data
Observation data/ratings [obs]
The dataset includes the followings:
- Urban population
- PM10 concentration level
The database covers the following countries:
Antigua and Barbuda
Bosnia and Herzegovina
Central African Republic
Congo, Dem. Rep.
Egypt, Arab Rep.
Hong Kong, China
Iran, Islamic Rep.
Korea, Dem. Rep.
Papua New Guinea
Sao Tome and Principe
St. Kitts and Nevis
St. Vincent and the Grenadines
Syrian Arab Republic
Trinidad and Tobago
United Arab Emirates
Virgin Islands (U.S.)
Yugoslavia, FR (Serbia/Montenegro)
Producers and sponsors
Kiran D. Pandey, David R. Wheeler, Uwe Deichmann, Kirk E. Hamilton, Bart Ostro and Katie Bolt
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including acronym and year of implementation)
- the survey reference number
- the source and date of download
Kiran Dev Pandey et al. Air Pollution in World Cities (APWC) 2000. Ref. WLD_2000_APWC_v01_M. Dataset downloaded from http://microdata.worldbank.org on [date].
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
DDI Document ID
Date of Metadata Production
DDI Document version
DDI Document - Version 02 - (04/21/21)
This version is identical to DDI_WLD_2000_APWC_v01_M but country field has been updated to capture all the countries covered by survey.