Publications·November 30, 2011
Ulaanbaatar's population has likely been facing air pollution challenges for several decades, through the development of Ulaanbaatar (UB) as a growing industrial city with increased construction, traffic, and power generation facilities located in a valley with a relatively dry climate. But it was the rapid expansion of the surrounding ger areas that started to indicate that UB was facing severe air pollution challenges. The study contains (a) the results of air quality monitoring from June 1, 2008, to May 31, 2009 in eight locations, including for the first time in the city's ger areas; (b) the outcome of air quality modeling to predict the effects of various pollution abatement measures; (c) a comparison of over 50,000 hospital records in six of nine administrative districts in UB to correlate the incidence of cardiovascular and respiratory disease, and premature death, caused<BR>by exposure to excessive particulate matter (PM) pollution; and (d) a willingness-to-pay survey of 629 respondents in Ulaanbaatar to estimate the value placed by UB residents on avoiding premature diseases and mortality, thus providing a basis to quantify the health costs of air pollution and the benefits of reducing air pollution. The report shares the findings of these efforts and recommends an air pollution reduction strategy. This study marks the beginning of a continuous assessment of issues and options as new information and data is learned on solutions to this severe public health crisis.
This World Bank report is part of the broader AMHIB (Air Monitoring and Health Impact Baseline) project focused on Ulaanbaatar, Mongolia, specifically from June 2008 to May 2009.
It provides comprehensive air quality measurements, data correction procedures, source analysis, and recommendations for long-term monitoring.
Key Points:
1. Air Quality Monitoring (2008–2009)
PM10 and PM2.5 were continuously monitored at 8 stations under AMHIB and 4 additional stations under GTZ (German Technical Cooperation).
Monitors included a variety of instruments: Dusttrak, Gent samplers, Partisol FRM-Model 2000, KOSA, and GRIMM 107.
Instrument inconsistencies necessitated inter-comparison campaigns and correction factors.
2. Air Pollution Levels
PM concentrations were extremely high:
PM10 annual averages ranged from 250–700 µg/m³ in ger areas.
PM2.5 annual averages up to 600+ µg/m³.
Peak concentrations in winter due to coal burning for heating.
PM concentrations often 20-30 times above WHO guidelines.
3. Source Contributions
Residential heating in ger areas (coal and wood burning) is the primary source of PM pollution.
Suspended soil and road dust is also a major contributor, especially to PM10.
Vehicles, power plants, and heat-only boilers (HOBs) contribute less to ground-level concentrations.
4. Seasonal and Meteorological Factors
Pollution spikes correlate with:
Colder temperatures → more heating.
Low wind speeds → poor dispersion.
Inversions trapping pollutants at ground level.
Spring dust storms also contribute, especially to coarse particles (PM10-2.5).
5. Data Quality and Correction
Major efforts were made to adjust raw data based on:
Instrument-specific biases.
Humidity corrections (especially for Dusttrak monitors).
Corrected results were used to model exposures and health impacts more accurately.
6. Health and Exposure Risks
Residents of ger areas face the highest exposure, severely exceeding national and WHO standards.
Exposure to very high PM2.5 levels can lead to serious health impacts, including respiratory and cardiovascular diseases.
7. Recommendations
Establishment of a long-term, standardized air quality monitoring network.
Focus on ger areas for interventions.
Enhance capacity building for local agencies in monitoring and data management.
Importance of modeling and health risk assessment in future studies.
Key Words / Topics:
PM10
PM2.5
Coal Burning
Ger Areas
Monitoring Stations
Instrument Inter-comparison
Dust Suspension
Health Risk Assessment
Meteorological Influence
Seasonal Variation
Air Quality Management
Corrected Data
Source Apportionment
Residential Heating
Urban Pollution Hotspots
Public Health