No abstract

Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.

Results of an air pollution source apportionment study using the Positive Matrix Factorization method (PMF) based on sampling of air pollution from a site of Nuclear Research Center, National University of Mongolia 2004-2009 and Zuun ail site 2008-2009 presented. From the statistical analysis of the data, it was possible to allocate factors to sources associated with coal combustion, motor vehicles, road dust, and soil.

In the past two decades, inhabitants of Ulaanbaatar city have been increased more than three times. The Air and soil of Ulaanbaatar city contaminated seriously due to the densely populated behavior. We proposed to develop a massive computational model for pollution, which cares about contamination factors. This paper is a part of the purpose. We consider the filtered equation express conservation of mass and momentum in a Newtonian incompressible flow in a computational fluid dynamic model for Ulaanbaatar’s wind field. The terrain around the city has been built for numerical computation. The finite element method was used for numerical analysis, from the results, we can characterize the wind field.