Urban heat island (UHI) is a phenomenon that occurs in cities worldwide. Therefore, there is an increasing need for studies on the changes in UHI intensity and long-term trends based on macroscopic characteristics related to urbanization. In this study, changes in seasonal UHI intensity based on urban area were analyzed for eight Asian mega cities from 1992–2012. The results indicate that the change in pattern of UHI intensity varies for different cities and seasons. UHI intensity increased as the urban area size increased. Furthermore, the dependency of UHI intensity on the economic situation was also demonstrated. With respect to the seasons, significantly increasing trends appeared during the summer. Moreover, depending on urban characteristics such as geography and climate, increasing trends appeared during other seasons. Population was also found to affect UHI intensity by generating anthropogenic heat; however, its effect as an individual factor appeared to be insignificant. This is a macroscale study that analyzes the effect of urban area size on UHI intensity. Future studies on urbanization factors and levels influencing the UHI intensity using higher resolution materials are required

With the growing interest in healthy living worldwide, there has been an increasing demand for more accurate measurements of the concentrations of air pollutants such as NO₂. In particular, analyzing the characteristics and sources of air pollutants by region could improve the effectiveness of environmental policies applied in accordance with the environmental characteristics of individual regions. In this study, a detailed nationwide NO₂ concentration map was generated using the cokriging interpolation technique, which integrates ground observations and satellite image data. The root-mean-square standardized (RMSS) error for this technique was close to 1, which indicates high accuracy. Using spatially interpolated NO₂ concentration data, an administrative unit map was generated. When comparing the data for four NO₂ data sources (observation data, satellite image data, detailed national data interpolated using cokriging, and NO₂ concentrations averaged by an administrative unit based on the interpolated NO₂ concentration data), the average concentrations were highest for remote sensing data. Land use regression (LUR) models of urban and non-urban regions were then developed to analyze the characteristics of the NO₂ concentration by region using NO₂ concentrations for the administrative units.

Flood risks in the industrial sector and economic damages are increasing because of climate change. In addition to changes in precipitation patterns due to climate change; factors that increase flood damage include infrastructure deterioration and lack of storage facilities. Therefore; it is necessary for companies and the government to actively establish flood management policies. However; no evaluation method is currently available to determine which items should be invested in first by small and medium-sized enterprises that have limited finances. Because the government should make comprehensive and fair decisions; the purpose of this study is to propose priority investment risk items and an assessment method to decide which companies should be invested in first in flood risk management due to climate change. The multispatial scale of the method takes both the location and characteristics of the company into account. Future climate change scenarios were used to evaluate the changing patterns of flood risks. We developed the relative Flood Risk Assessment for Company (FRAC model) methodology to support the government’s policymaking. This method was applied to four companies belonging to four different industries and three risk items were derived that are likely to harm the company owing to flooding.

Biodiversity is rapidly declining globally and efforts are needed to mitigate this continually increasing loss of species. Clustering of areas with similar habitats can be used to prioritize protected areas and distribute resources for the conservation of species, selection of representative sample areas for research, and evaluation of impacts due to environmental changes. In this study, Northeast Asia (NEA) was classified into 27 bioclimatic zones using statistical techniques, and it was re-clustered into 14 groups to identify the environmental characteristics of these zones. In particular, we added land cover variables into the clustering to reflect not only simple climate but also social factors influencing biological habitats. In total, 53 bioclimatic variables were constructed, and principal components were generated using correlation analysis and principal component analysis (PCA). The iterative self-organizing data analysis technique algorithm (ISODATA) was used to cluster the principal components and land cover variable. The constructed bioclimatic zones were assigned codes and descriptive names based on aridity, seasonality, and naturality, and their environmental characteristics were identified. This study is significant in that it improved the understanding of biological habitats of NEA and established a basis for monitoring the distribution of species and for efficient and systematic management of biodiversity.

Biodiversity is rapidly declining globally and targeted efforts are needed to mitigate the loss of species. Conventional conservation efforts have focused on establishing protected areas and restoring degraded lands in order to maintain current conditions or restore ecosystems to a pre-damaged state. However, as the climate changes, the current bioclimatic zones will be re-distributed globally. Historical distribution patterns may no longer serve as an effective guide for supporting biodiversity under climate change. In response to these challenges, this study proposes a spatially explicit strategy for biodiversity conservation that takes climate change into account using bioclimatic classification. The bioclimatic classification maps of Northeast Asia (NEA) were constructed for three historical time periods (the 1980s, 1990s, and 2000s) and two future time periods (the 2050s and 2070s) using five general circulation models (GCMs) in representative concentration pathway (RCP 8.5) scenarios. It was predicted that, in general, zones are shifting north, and some zones are expanding or shrinking rapidly. Based on an analysis of latitudinal and areal change for each zone, the bioclimate vulnerability index (BVI) and naturality index (NI) were developed to quantify the impact of environmental change. As a result of the BVI analysis, the distribution of vulnerable zones is expected to shift northward and expand. As is evident with the increased vulnerability of the subarctic region caused by the expansion of the temperate climate, the extent of vulnerable zones will increase. Also, the southern regions of NEA are becoming vulnerable due to the transformation of the temperate zone to a more subtropical zone. Quadrant graphs based on the BVI and NI were created to present appropriate strategies for each zone. Our proposed framework shows that conservation strategies should be modified based on the changes in the relative position of each zone over time.

Keywords: Urbanization, Climate change, Global Warming, Surface temperature, GLDAS-2, MODIS Due to urbanization, urban areas are shrinking green spaces and increasing concrete, asphalt pavement. So urban climates are different from non-urban areas. In addition, long-term macroscopic studies of urban climate change are becoming more important as global urbanization affects global warming. To do this, it is necessary to analyze the effect of urbanization on the temporal change in urban temperature with the same temperature data and standards for urban areas around the world. In this study, time series analysis was performed with the maximum, minimum, mean and standard values of surface temperature during the from 1980 to 2010 and analyzed the effect of urbanization through linear regression analysis with variables (population, night light, NDVI, urban area). As a result, the minimum value of the surface temperature of the urban area reflects an increase by a rate of 0.28K decade^-1 over the past 31 years, the maximum value reflects an increase by a rate of 0.372K decade^-1, the mean value reflects an increase by a rate of 0.208 decade^-1, and the standard deviation reflects a decrease by rate of 0.023K decade^-1. And the change of surface temperature in urban areas is affected by urbanization related to land cover such as decrease of greenery and increase of pavement area, but socioeconomic variables are less influential than NDVI in this study. This study are expected to provide an approach to future research and policy-planning for urban temperature change and urbanization impacts.