To our knowledge, our study is the first to demonstrate increased acute systemic inflammation following exposure to airport-related UFPs. Health effects associated with roadway traffic exposure were distinct. This study emphasizes the importance of multi-pollutant measurements and modeling techniques to disentangle sources of UFPs contributing to the complex urban air pollution mixture and to evaluate population health risks.

Invasive cervical cancer may be approximately one third less frequent in women who have used an IUD. This possible noncontraceptive benefit could be most beneficial in populations with severely limited access to screening and concomitantly high cervical cancer incidence.

Background Accurately assessing individual ambient air pollution exposure is a crucial part of epidemiological studies looking at the adverse health effect of poor air quality. This is particularly challenging in developing countries with high levels of air pollution but having sparse monitoring networks with a lack of consistent data. Methods We evaluated the performance of 6 different machine learning algorithms in predicting fine particulate matter (PM 2.5 ) concentrations in Ulaanbaatar, Mongolia from 2010 to 2018. We found that the algorithms produce robust results based on performance metrics. Results Random forest (RF) and gradient boosting models performed the best with leave-one-location-out cross-validated R 2 of 0.82 for when using data from the entire study period. After applying tuned models on the hold-out test set, R 2 increased to 0.96 for the RF and 0.90 for the gradient boosting model. We also predicted PM 2.5 concentrations for each administrative area (khoroo) of the city using RF and maps of predictions show spatiotemporal variations that are in line with the location of the ger district, city center, and population density. Conclusion Our results provide evidence of the advantage and feasibility of machine learning approaches in predicting ambient PM 2.5 levels in a setting with limited resources and extreme air pollution levels.

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Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.