An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia

Faculty of Health Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6 Canada.
Air Quality Atmosphere & Health (Impact Factor: 1.8). 03/2013; 6(1):137-150. DOI: 10.1007/s11869-011-0154-3
Source: PubMed


Epidemiologic studies have consistently reported associations between outdoor fine particulate matter (PM2.5) air pollution and adverse health effects. Although Asia bears the majority of the public health burden from air pollution, few epidemiologic studies have been conducted outside of North America and Europe due in part to challenges in population exposure assessment. We assessed the feasibility of two current exposure assessment techniques, land use regression (LUR) modeling and mobile monitoring, and estimated the mortality attributable to air pollution in Ulaanbaatar, Mongolia. We developed LUR models for predicting wintertime spatial patterns of NO2 and SO2 based on 2-week passive Ogawa measurements at 37 locations and freely available geographic predictors. The models explained 74% and 78% of the variance in NO2 and SO2, respectively. Land cover characteristics derived from satellite images were useful predictors of both pollutants. Mobile PM2.5 monitoring with an integrating nephelometer also showed promise, capturing substantial spatial variation in PM2.5 concentrations. The spatial patterns in SO2 and PM, seasonal and diurnal patterns in PM2.5, and high wintertime PM2.5/PM10 ratios were consistent with a major impact from coal and wood combustion in the city's low-income traditional housing (ger) areas. The annual average concentration of PM2.5 measured at a centrally located government monitoring site was 75 μg/m3 or more than seven times the World Health Organization's PM2.5 air quality guideline, driven by a wintertime average concentration of 148 μg/m3. PM2.5 concentrations measured in a traditional housing area were higher, with a wintertime mean PM2.5 concentration of 250 μg/m3. We conservatively estimated that 29% (95% CI, 12-43%) of cardiopulmonary deaths and 40% (95% CI, 17-56%) of lung cancer deaths in the city are attributable to outdoor air pollution. These deaths correspond to nearly 10% of the city's total mortality, with estimates ranging to more than 13% of mortality under less conservative model assumptions. LUR models and mobile monitoring can be successfully implemented in developing country cities, thus cost-effectively improving exposure assessment for epidemiology and risk assessment. Air pollution represents a major threat to public health in Ulaanbaatar, Mongolia, and reducing home heating emissions in traditional housing areas should be the primary focus of air pollution control efforts.

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Available from: Tim K Takaro
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    • "However, while previous studies have estimated the human health impacts from ambient air pollution due to fossil fuel combustion (Anenberg et al., 2010), open biomass burning (Johnston et al., 2012;Marlier et al., 2013), and wind-blown dust (Giannadaki et al., 2014), fewer studies have quantified the impact of residential combustion on ambient quality and human health.Lim et al. (2012)estimated that 16 % of the global burden of ambient PM 2.5 was due to RSF sources but did not estimate premature mortality. Another study concluded that ambient PM 2.5 from cooking was responsible for 370 000 deaths in 2010 (Chafe et al., 2014), but it did not include residential heating emissions, which will cause additional adverse impacts on human health (Johnston et al., 2013;Allen et al., 2013;). Here we use a global aerosol microphysics model to make an integrated assessment of the impact of residential emissions on atmospheric aerosol, radiative effect, and human health. "
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    ABSTRACT: Combustion of fuels in the residential sector for cooking and heating results in the emission of aerosol and aerosol precursors impacting air quality, human health, and climate. Residential emissions are dominated by the combustion of solid fuels. We use a global aerosol microphysics model to simulate the impact of residential fuel combustion on atmospheric aerosol for the year 2000. The model underestimates black carbon (BC) and organic carbon (OC) mass concentrations observed over Asia, Eastern Europe, and Africa, with better prediction when carbonaceous emissions from the residential sector are doubled. Observed seasonal variability of BC and OC concentrations are better simulated when residential emissions include a seasonal cycle. The largest contributions of residential emissions to annual surface mean particulate matter (PM2.5) concentrations are simulated for East Asia, South Asia, and Eastern Europe. We use a concentration response function to estimate the human health impact due to long-term exposure to ambient PM2.5 from residential emissions. We estimate global annual excess adult (> 30 years of age) premature mortality (due to both cardiopulmonary disease and lung cancer) to be 308 000 (113 300–497 000, 5th to 95th percentile uncertainty range) for monthly varying residential emissions and 517 000 (192 000–827 000) when residential carbonaceous emissions are doubled. Mortality due to residential emissions is greatest in Asia, with China and India accounting for 50 % of simulated global excess mortality. Using an offline radiative transfer model we estimate that residential emissions exert a global annual mean direct radiative effect between −66 and +21 mW m−2, with sensitivity to the residential emission flux and the assumed ratio of BC, OC, and SO2 emissions. Residential emissions exert a global annual mean first aerosol indirect effect of between −52 and −16 mW m−2, which is sensitive to the assumed size distribution of carbonaceous emissions. Overall, our results demonstrate that reducing residential combustion emissions would have substantial benefits for human health through reductions in ambient PM2.5 concentrations.
    Full-text · Article · Jan 2016 · Atmospheric Chemistry and Physics
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    • "Despite being an important element in understanding the transformation of the Mongolia Plateau and its coupled humannatural system, current literature on urbanization and its drivers, as well as its environmental impact, have been limited. Past research on urban landscape and environmental change on the plateau tends to focus on one aspect, such as analyzing urban land-use change through RS/GIS (Amarbayasgalan, 2008; Amarsaikhan et al., 2009; Fan et al., 2013), its driving forces (Tsogtsaikhan , 2003), rural to urban migration (Miller, 2013; Tsogtsaikhan, 2003; Wang, 1997), the efficiency of land use (Du, 2003; Mei and Hai, 2009; Mei and Han, 2010), evaluation of environment change (Dong et al., 2008; Ji et al., 2009; Luo, 2000; Sun, 2005; Zhang et al., 2008; Amarsaikhan et al., 2008, 2009, 2011; Bagan et al., 2009), or the consequences on the health of urban residents (Allen et al., 2013; Bolormaa, 2011; Dong et al., 2008; Guttikunda, et al., 2013; Sonomjamts et al., 2014; UNDP, 2011; Warburton et al., 2013; World Bank, 2004). To date, there has not been an integrated assessment of urbanization and the consequential impacts on the urban environment. "
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    ABSTRACT: Driven by drastic socioeconomic changes in China and Mongolia, urbanization has become one of the most significant driving forces in the transformation of the Mongolian Plateau in the past 30 years. Using Hohhot and Ulaanbaatar as case studies, we developed a holistic approach to examine the socioeconomic and natural driving forces for urbanization and to investigate the impact on the urban environment. We used a multidisciplinary approach and relied on a variety of data sources to assess the changes of the landscape and environment of the two cities. We detected a rapid urbanization in Hohhot and Ulaanbaatar, both in terms of urban population growth and urban land expansion, from 1990 to 2010, with a much faster speed in 2000-2010. The local geo-physical conditions have constrained the spatial direction of expansion. Ulaanbaatar lagged behind Hohhot for about a decade when measured by indicators of urban population and urban land. Both cities have a degraded urban environment and a growing air pollution epidemic. While Hohhot had worse air pollution than Ulaanbaatar in the early 2000s, the gap between the two cities became smaller after 2010. The research presented here highlights the following as key determinants for urbanization and environmental change: (1) the co-evolution of urbanization, economic development, and environmental change; (2) the urbanization of transitional economies driven by the change of the economic structure, i.e., the development by both manufacturing and tertiary sectors and the change in the primary sector; and (3) the recent institutional changes and increased integration with the global economy.
    Full-text · Article · Oct 2015 · Environmental Research
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    • "). The absence of variables characterizing detailed pollution sources often leads to a smaller proportion of variability explained by LUR models, which was concluded by Allen et al. (2013) and Saraswat et al. (2013). Moreover, recently published spatiotemporal LUR models (Patton et al. 2014; Smargiassi et al. 2012) usually incorporated meteorological conditions, especially wind speed and direction, as these factors play a crucial role in the spatiotemporal pattern of air pollutants (Gupta and Christopher 2009; McKendry 2000). "
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    ABSTRACT: Fine particulate matter (PM2.5) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m3 (SD = 13.7). PM2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m3. This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.
    Full-text · Article · Dec 2014 · Environmental Science and Pollution Research
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