Understanding, analysis, monitoring and modelling of urban expansions are most important for urban planners in the process of sustainable development. This article investigated spatio-temporal process of urban expansion using cellular automata (CA) model integrated with artificial neural network (ANN) method. This integrated model was calibrated with data from 1974 to 2018, and used to predict expansions for 2030 and 2040 with two datasets of explanatory variables including slope, forest, river, specially protected areas, road, railroad and urban centres in an urban and non-urban area. The urban suitable area was estimated by criteria evaluation weights and analytic hierarchy process (AHP). The results show that the urban growth of Ulaanbaatar was equal to +36,201 ha (84.8%) for the period of 1974–2018 and this city will be projected to reach approximately +43,111 ha by 2030 (+50.3% from 2018) and +111,441 ha by 2040 (+44.3% from 2030 and totally +72.3% from 2018).

This research summarizes land use and city expansion, as well as the dynamics of urbanization, over recent years in Ulaanbaatar city, Mongolia. The study applies fractal geometry to describe land uses in Ulaanbaatar city using a mathematical procedure and geographic information system (GIS) urban analysis, and measures urban sprawl using an index relation of area and perimeter. Land-use parcels’ shape, area perimeter relations, sprawl statement and geometry of city structure are considered. The research presents the growth of Ulaanbaatar city in two time series, 2000 and 2010. The main findings stated here are that Ulaanbaatar is growing in an unsystematic and fractal way, even though the geometrical growth has not been strictly planned and has a most irregular shape of urban sprawl.

New technology has provided new tools for effective spatial planning. Through the example of locating suitable sites for urban development in Ulaanbaatar, this paper illustrates how multicriteria decision analysis and geographical information systems can be used for more effective urban planning. Several constraint and factor criteria were identified, transformed into map layers, and weighted together using the analytic hierarchy process. Besides localization results, this study shows the effect of using poor elevation data and how a sensitivity analysis can be applied to yield further information, spot weighting weaknesses, and assess the quality of the criteria.

Many studies have been conducted on air pollution in Ulaanbaatar city. However, most have focused on the sources of pollutants and their characteristics and distribution. Although the location of the city subjects it to unavoidable natural conditions where air pollution accumulates during the cold season, nature-based solutions have not yet been considered in the projects implemented to mitigate air pollution levels. Therefore, this study aims to determine the combined influence of geography and atmospheric factors on cold season air pollution. The spatiotemporal variations in the variables were investigated using meteorological observation data from 1991 to 2020 in the different land-use areas. Then, atmospheric stagnation conditions and air pollution potential parameters were estimated from daily radiosonde data. Subsequently, the temporal variations in air pollutants were studied and correlated with estimates of the above parameters. In the Ulaanbaatar depression, the stable cold air lake (colder than −13.5 °C), windless (34–66% of all observations), and poor turbulent mixing conditions were formed under the near-surface temperature inversion layer in the cold season. Moreover, due to the mountain topography, the winds toward the city center from all sides cause polluted air to accumulate in the city center for long periods. Air pollution potential was categorized as very high and high (<4000 m²·s⁻¹), in the city in winter, indicating the worst air quality. Thus, further urban planning policy should consider these nature factors.

This study investigated the effects of different grazing systems on plant communities, and examined the causes of Mongolian grassland desertification. The typical steppes near the Chinese-Mongolian border were studied using quadrat sampling and remote sensing methods. Aboveground biomass in the steppe areas differed significantly among the three grazing systems (p < 0.05): Biomass in the grazing-prohibited areas (455.9 g) was greater than that in the rotational-grazing areas (268.4 g) and the continuous grazing areas (122.2 g). Aboveground biomass was well correlated with the Soil Adjusted Vegetation Index (SAVI; y = 5600x² + 260x + 110; R² = 0.67; p < 0.05). The relative mean deviation between the aboveground biomass was calculated using this regression and the measured biomass was 29.1%. The Soil Adjusted Vegetation Index (SAVI) values for nomadic-grazing areas were greater than those for continuous-grazing areas in 1989, 2005, 2011, and 2016, and were significantly greater in 2011 and 2016. The SAVI values for the continuous-grazing areas were slightly, but not significantly greater, than those for the nomadic-grazing areas in 1993. Plant species that dominated in moderately degraded areas were most dominant in nomadic-grazing areas, followed by continuous-grazing areas and grazing-prohibited areas. Plant species that dominated in lightly and heavily degraded areas were most dominant in continuous-grazing areas, followed by nomadic-grazing areas and grazing-prohibited areas. Generally, continuous grazing caused more serious grassland degradation than did nomadic grazing, and nomadic-grazing areas tolerated more intense grazing than did continuous-grazing areas.