Geographical Issues

Spatiotemporal Pattern Dynamics of Air Pollution in Ulaanbaatar Using Sentinel 5P Satellite Data

2026-03-20T17:24:55+08:00

Ulaanbaatar is characterized by severe air pollution during the winter season due to meteorological and geographical conditions such as frequent temperature inversions, low wind speeds, and its basin-shaped topography, which favors the accumulation of pollutants. In addition, emissions from household coal combustion, thermal power plants, and increasing vehicle traffic significantly affect the city’s air quality. Therefore, a comprehensive assessment of the spatial distribution and temporal variability of major air pollutants is essential for effective air quality management. In this study, Sentinel-5P (TROPOMI) satellite observations from the winter seasons (November–February) of 2018–2024 were used to evaluate the spatial distribution and temporal dynamics of nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), and the Absorbing Aerosol Index in Ulaanbaatar. Daily satellite products were processed using temporal averaging to generate monthly mean raster datasets. The spatial patterns of air pollutants were analyzed through spatial mapping and statistical methods, including boxplot analysis. The results indicate that NO₂, CO, and the Aerosol Index exhibit high concentrations in central urban districts, densely populated ger areas, around thermal power plants, and along major transportation corridors. In contrast, ozone (O₃) concentrations remain relatively low and stable during winter due to reduced photochemical activity. Furthermore, a temporary decrease in pollutant concentrations was observed during the COVID-19 lockdown period, followed by an increasing trend after economic activities resumed. This study demonstrates that satellite-based observations provide valuable information for developing spatial air pollution monitoring systems, identifying pollution hotspots, and supporting evidence-based urban air quality management and policy development in Ulaanbaatar.

Analyzing the impact of social media on Gen Z travel decisions using TAM and UTAUT models

2026-04-15T10:16:24+08:00

The purpose of this study was to assess the influence of social media platforms on the travel decisions of Generation Z, individuals born between 1997 and 2012. Additionally, the study aims to provide insights for travel companies on how to create effective digital marketing strategies that align with the behaviors of this generation. By 2025, Generation Z will emerge as a significant consumer group in the travel market, becoming a proactive segment of adults capable of making independent financial decisions. Their travel spending continues to grow each year, but their decision-making process differs from that of Generation X or Millennials. They rely on social media rather than traditional marketing methods like TV ads and brochures. Generation Z's travel decision-making was analyzed using the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the impact of social media platforms on variables such as perceived usefulness, ease of use, social influence, and performance expectancy. In this research, a questionnaire consisting of 35 questions was administered to 157 participants using the Google Forms platform, and the results were analyzed with the SPSS software. The study's results indicate that the impact of social media platforms on Generation Z's travel decisions can be better understood through the UTAUT model's Social Influence and Performance Expectations factors. It shows that Generation Z places a high value on the reliability of information found on social media. These platforms act as significant sources of information and emotional triggers in their travel planning, helping them make decisions influenced by friends, influencers, and online content.

Comparative Analysis of Pedestrian Sidewalk Standards in Ulaanbaatar, Mongolia

2026-04-15T11:11:11+08:00

Walkability and pedestrian accessibility are central to sustainable urban development but remain particularly challenging in cold-climate cities, where thermal comfort and seasonal conditions strongly influence mobility patterns. In Ulaanbaatar, a rapidly urbanizing city with an extreme continental climate (mean annual temperature of −1.3°C), pedestrian infrastructure is constrained by vehicle-oriented street design and limited right-of-way allocation. Although Mongolia has adopted the national pedestrian planning standard UCS 0901B:2022, its spatial adequacy across different urban contexts has not been systematically evaluated. This study assesses pedestrian infrastructure across four representative street typologies—peri-urban (ger-area) redevelopment, commercial, modern residential, and institutional corridors—using comparative cross-sectional analysis combined with international benchmarking against nine global cities. The results identify pedestrian width as a key spatial determinant of functional pedestrian environments. Two critical thresholds, approximately 2.0 m and 2.5 m, are shown to govern the feasibility of buffer space and canopy-forming vegetation in cold-climate conditions. International comparisons indicate that pedestrian widths in Ulaanbaatar are approximately 40–60% narrower than typology-matched global references across all street categories. These findings highlight a systematic mismatch between current standards and functional spatial requirements. The study provides the first empirical evidence in Ulaanbaatar supporting the adoption of typology-specific pedestrian width targets and integrated green buffer requirements. It therefore recommends revising UCS 0901B:2022 to move beyond a single uniform minimum standard toward a more context-sensitive, performance-based framework for pedestrian planning in cold-climate cities.

Spatial Changes in Land Use and Building Density of the Baga Toiruu Area, Ulaanbaatar city

2026-04-19T11:26:21+08:00

By examining the interrelationships among urban spatial structure, building density, height distribution, street networks, and green space organization, it is possible to evaluate balanced urban development. The Baga Toiruu area, the historic core of Ulaanbaatar, represents a zone of high spatial and cultural significance, concentrating administrative, educational, cultural, and commercial functions. Recent redevelopment has intensified building density and height, leading to the degradation of traditional urban morphology and a decline in green and open spaces. This study presents the first GIS-based application in Ulaanbaatar using a fishnet grid approach to quantify morphological change through three indices: the Building Coverage Index (BCI), Building Height Index (BHI), and Green Space Index (GI). The research analyzes changes across 243.3 ha of redevelopment land using orthophoto imagery, land-use data, and spatial datasets for the years 2010, 2015, 2020, and 2025. A total of 1,067 grids (50 × 50 m) were generated to assess spatial patterns and temporal trends. Results indicate that between 2010 and 2025, the proportion of areas classified as “very low” GI increased from 42% to 56%, while high and very high BCI categories rose from 5% to 9%. The average BHI increased from 3.34 to 3.56, demonstrating a clear inverse relationship among the indices. Increased building density and height correspond directly with reduced green space coverage, providing quantitative evidence of growing morphological imbalance. These findings highlight the need for planning strategies that preserve historical urban structure, regulate building density and height, and ensure the long-term sustainability of green spaces in central Ulaanbaatar.

Geospatial Analysis-Based Fire Risk Zonation in Ulaanbaatar City

2026-04-28T17:04:06+08:00

The objective of this study is to identify the key factors influencing fire risk in Ulaanbaatar and to develop an integrated spatial fire risk zonation map using the Analytic Hierarchy Process (AHP) and Multi-Criteria Decision Analysis (MCDA). This study integrates multiple datasets, including population distribution, fire incident records, the locations and storage capacities of fuel stations and depots, electrical substations, and a soil moisture index. All datasets were standardized, aggregated into hexagonal tessellation units, and converted into raster format. The weights of each criterion were determined using the AHP method, and a composite fire risk index was calculated. The results indicate that 37 hexagonal cells are classified as high-risk, 186 as medium-risk, and 604 as low-risk zones across the city. Furthermore, the distribution of children within these zones shows that 34% of the total child population resides in high-risk areas, while 46.3% lives in medium-risk areas. This spatial clustering pattern indicates that fire risk is closely linked to the urban structure and planning characteristics of Ulaanbaatar. These findings provide a scientific basis for urban safety assessment, disaster risk management, and effective resource allocation.

Spatial Distribution of Land Ownership in Mongolia: An Empirical Study Based on the 2025 National Land Registry Unified Report

2026-05-01T10:27:49+08:00

This study aims to assess spatial disparities and inequality in land ownership in Mongolia using quantitative and spatial analysis methods based on the 2025 National Land Registry Unified Report. Although the privatization of land since the enactment of the law in 2003 has been a crucial part of the market transition, analyses of regional-level distribution and inequality remain scarce. We measured differences at the provincial and capital city levels using a descriptive-comparative approach, Geographic Information Systems (GIS) spatial mapping, the Gini coefficient, and the Lorenz curve. The results indicate that by the end of 2025, 738,135 citizens, or 20.8% of the national population, had acquired land ownership; however, spatial and structural disparities are stark. Land ownership coverage accounts for 11.3% of the population in Khuvsgul province, whereas it reaches 92.4% in Tuv province. The inter-provincial land ownership Gini coefficient is 0.49, indicating a moderate level of spatial inequality, which is further corroborated by the Lorenz curve analysis showing that the bottom 45.5% of units hold only 18.3% of owned land. Furthermore, enterprises hold 57.6% of the total land, indicating a high concentration in the land-use structure. Therefore, policy reforms are required to introduce market-based mechanisms tailored to the specific dynamics of urban and rural land supply and demand.

Study of near-surface and elevated temperature inversion layers over Ulaanbaatar city, Mongolia

2026-05-08T10:18:50+08:00

Intensifications of global warming, climate change, aridity, and human activities are causing significant changes in the regime of atmospheric phenomena and processes. The accumulation of air pollution in settlements during the cold season is a natural phenomenon driven by surface, elevated, and double temperature inversions, as well as their fluctuations. By creating a stable atmospheric state, temperature inversion significantly weakens horizontal wind speed, vertical air movement (convection), and turbulent exchange. Consequently, it is an essential meteorological phenomenon for urban planning, air quality assessment, and the accurate analysis of local microclimates. This research investigated characteristics, specifically frequency, thickness, and intensity of near-surface and elevated temperature inversions over Ulaanbaatar, utilizing radiosonde data from the 'Ulaanbaatar' station and automatic meteorological station records from 2012 to 2024. The results indicate that in Ulaanbaatar, the highest frequency of surface inversions occurs in January (reaching 68%), while elevated inversions are observed year-round with an average frequency of 49%. Notably, the simultaneous occurrence of both surface and elevated inversions in winter reaches 35%, forming a stable atmospheric layer with a total thickness of approximately 700 meters. In recent years (specifically since 2022), an increase in average air temperatures and wind speeds during the cold season has been observed. This trend has had a favorable impact on the dissipation of inversion layers and the dispersion of air pollutants. Consequently, the findings of this study possess significant theoretical and practical value for forecasting urban air quality and identifying high-risk atmospheric conditions associated with temperature inversions.

A Review of Soil Moisture Mapping based on Remote Sensing Technology

2026-05-13T09:40:38+08:00

Soil moisture is one of the key components of the land surface that is highly sensitive to climate change. It plays a crucial role in the water exchange between the land surface and the atmosphere and serves as an important hydrological variable that directly influences droughts, floods, ecosystem stability, and agricultural productivity. In recent years, methods for estimating and mapping soil moisture based on remote sensing techniques have developed rapidly. However, studies that comprehensively evaluate the diversity of satellite data, modeling approaches, and algorithms used in this field remain relatively limited. This study aims to analyze soil moisture estimation studies based on remote sensing methods published in high-impact scientific journals and to identify key research trends and fundamental concepts. A total of 4,933 articles have been published in this field over the past nine years, showing a steady increase in publication output. Based on a content analysis of 66 selected articles, a keyword co-occurrence network analysis revealed three major clusters, focusing on satellite types, data processing methods, and application domains. From a methodological perspective, the studies can be classified into three main groups: index-based approaches, machine learning and deep learning approaches, and physical and physically-based modeling approaches. Index-based methods such as OPTRAM, STIM, and TVDI account for 15.1% of the studies, while machine learning approaches account for 59.1%, and physical or physically-based models represent 25.8%. Machine learning and deep learning approaches have demonstrated the highest performance in producing high-resolution soil moisture maps (R = 0.86–0.94, RMSE = 0.03–0.05 m³/m³). Therefore, recent studies increasingly focus on developing approaches that integrate multi-source data and machine learning techniques.