Search

Urbanization can be a challenge and an opportunity for earthquake risk mitigation. However, little is known about the changes in exposure (for example, population and urban land) to earthquakes in the context of global urbanization, and their impacts on fatalities in earthquake-prone areas. We present a global analysis of the changes in population size and urban land area in earthquake-prone areas from 1990 to 2015, and their impacts on earthquake-related fatalities. We found that more than two thirds of population growth (or 70% of total population in 2015) and nearly three quarters of earthquake-related deaths (or 307,918 deaths) in global earthquake-prone areas occurred in developing countries with an urbanization ratio (percentage of urban population to total population) between 20 and 60%. Holding other factors constant, population size was significantly and positively associated with earthquake fatalities, while the area of urban land was negatively related. The results suggest that fatalities increase for areas where the urbanization ratio is low, but after a ratio between 40 and 50% occurs, earthquake fatalities decline. This finding suggests that the resistance of building and infrastructure is greater in countries with higher urbanization ratios and highlights the need for further investigation. Our quantitative analysis is extended into the future using Shared Socioeconomic Pathways to reveal that by 2050, more than 50% of the population increase in global earthquake-prone areas will take place in a few developing countries (Pakistan, India, Afghanistan, and Bangladesh) that are particularly vulnerable to earthquakes. To reduce earthquake-induced fatalities, enhanced resilience of buildings and urban infrastructure generally in these few countries should be a priority. [ABSTRACT FROM AUTHOR]

[Display omitted] • We develop a framework to estimate future Flood Regulation Service (FRS) dynamics. • The framework considers the joint impacts of both climate change and urbanization. • Future FRS supply–demand imbalance will continue to intensify. • Future impacts of climate change in Baiyangdian Lake Basin cannot be ignored. • Cities should actively adapt to climate change to reduce flood risks in the future. Estimating the supply–demand relationship's future dynamics and trend of Flood Regulation Service (FRS) is important for preventing flood risks and choose an appropriate future development path. Less attention has been given to assessing the future trends in this relationship under the joint influence of climate change and urbanization. This paper aims to quantify future trends in the FRS supply–demand relationship under the joint impacts of climate change and urbanization. To this end, we took the Baiyangdian Lake Basin as an example and developed a "Climate Change-Urbanization-FRS supply and demand" framework by combining future climate scenarios from the ScenarioMIP (Scenario Model Intercomparison Project) dataset and socioeconomic data from the SSPs (Shared Socioeconomic Pathways) to estimate the FRS supply and demand dynamics during the period from 2020 to 2050. The results show that the framework has a reasonable ability to estimate the FRS supply–demand relationship. The correlation coefficients between estimated supply–demand ratios and the historical values exceeded 0.9. The average FRS supply–demand ratio among the four scenarios shows a significantly downward trend between 2020 and 2050. The FRS is expected to decrease by 0.159 (36.6 %), resulting in the imbalanced area increasing from 38.3 km2 (4.1 % of the whole basin) to 11049.7 km2 (36.2 %). We believe that although urbanization is the dominant factor contributing to the intensified imbalance trend (with a contribution of 71.2 %), influences from climate change also cannot be ignored (with a contribution of 28.8 %). Therefore, adaptation actions to climate change should be actively taken to reduce urban flood risks in the future. [ABSTRACT FROM AUTHOR]

Effective and timely quantification of the spatiotemporal pattern of urban expansion in China is important for the assessment of its environmental effects. However, the dynamics of the most recent urban expansions in China since 2012 have not yet been adequately explained due to a lack of current information. In this paper, our objective was to quantify spatiotemporal patterns of urban expansion in China between 1992 and 2015. First, we extracted information on urban expansion in China between 1992 and 2015 by integrating nighttime light data, vegetation index data, and land surface temperature data. Then we analyzed the spatiotemporal patterns of urban expansion at the national and regional scales, as well as at that of urban agglomerations. We found that China experienced a rapid and large-scale process of urban expansion between 1992 and 2015, with urban land increasing from 1.22 × 104 km2 to 7.29 × 104 km2, increasing in size nearly fivefold and with an average annual growth rate of 8.10%, almost 2.5 times as rapid as the global average. We also found that urban land in China expanded mainly by occupying 3.31 × 104 km2 of cropland, which comprised 54.67% of the total area of expanded urban land. Among the three modes of growth—infilling, edge expansion, and leapfrog—edge expansion was the main cause of cropland loss. Cropland loss resulting from edge expansion of urban land totalled 2.51 × 104 km2, accounting for over 75% of total cropland loss. We suggest that effective future management with respect to edge expansion of urban land is needed to protect cropland in China. [ABSTRACT FROM AUTHOR]

Urban spatial expansion is known as an important indicator of urbanization. In order to provide a reference for urban spatial expansion in the future high-quality development strategy of the Yellow River Basin (YB) cities in China, it is necessary to identify and calculate urban spatial expansion patterns. For this reason, we provide a "Spatiotemporal pattern-Center of gravity migrationt-Expansion pattern" solution to identify and calculate urban spatial expansion patterns in the YB. More specifically, 78 prefecture-level cities in the YB were selected as the subjects of the study, using the Defense Meteorological Satellite Program/Operational Line Scan System (DMSP/OLS) and the National Polarimetric Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data (NTL), together with the center of gravity shift and common edge detection models, to identify the YB urban expansion patterns from 2000–2018. The results suggest that: (1) on the spatial pattern, there is a obvious difference in the expansion intensity and growth rate of the urban built-up (UB) areas of cities in the upper and middle reaches of YB. In addition, there are also certain differences between the expansion patterns of provincial capital cities and non-capital cities; (2) The UB areas of YB has steadily expand from 3,500 km2 in 2000 to 10,600 km2 in 2018, amongst which the expansion of provincial capital cities is the most obvious 1919 km2; (3) Interestingly it is also discovered that urban expansion in Qinghai Province, the sourceland of the YB, takes place in a diffuse way, with the shifting of the centre of gravity for four types of total area, net increase in area, rate of growth and intensity of expansion followed a "northwest to southeast" tendency of development. [ABSTRACT FROM AUTHOR]

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes. Independent variables including altitude, slope, and distance from barren, crop, greenery, roads, and urban areas for 1998, 2008, and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran. The proposed method was compared with the random forest (RF) algorithm as the baseline model. Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net (0.87) over the RF (0.82) algorithm. Furthermore, evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately, independent from applying a cellular automata (CA) model. Therefore, the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely. [ABSTRACT FROM AUTHOR]

• Localized SSPs were used to assess the effects of urban expansion on NHQ. • Localized SSPs can better describe the actual situation of socioeconomic development. • The effects of future urban expansion on NHQ can be simulated more effectively. • The error of simulated results was reduced by about 50%. • This approach will provide more reliable guidance for improving sustainability. Effectively evaluating the effects of future urban expansion on natural habitat quality (NHQ) is critical for improving the sustainability of regional and even global cities. However, because of the difficulties in simulating spatiotemporal distribution of future urban expansion and the uncertainties in future socioeconomic development, effectively evaluating the effects of future urban expansion on NHQ is still challenging. Using the Hohhot-Baotou-Ordos-Yulin (HBOY) urban agglomeration in China as an example, this study simulated future urban expansion and evaluated its effects on NHQ. We first quantified the NHQ spatial patterns in HBOY using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Second, we simulated urban expansion in HBOY from 2017 to 2050 by coupling localized Shared Socioeconomic Pathways (SSPs) and the Land Use Scenario Dynamics-urban (LUSD-urban) model. Finally, we evaluated the effects of future urban expansion on NHQ by calculating the degradation rate of NHQ in the entire region and in different types of cities. We found that coupling localized SSPs and the LUSD-urban model can effectively evaluate the effects of future urban expansion on NHQ. Compared with coupling original SSPs and the LUSD-urban model, in HBOY case study, our method reduced the absolute of the evaluation error from 5.17 to 11.43% to 2.86–5.17%. The results showed that under all localized SSPs, the urban expansion from 2017 to 2050 in large cities will lead to the most obvious decrease in regional NHQ of 4.85–7.32%, while that in medium cities and small cities will be 1.23–2.37% and 0.19–0.35%, respectively. Therefore, we suggest that SSPs should be localized when used to simulate the effects of future urban expansion on NHQ. In addition, in order to achieve the sustainable development goals, the HBOY urban agglomeration should control the urban size and optimize the urban spatial pattern, especially for large cities, in the future to reduce the impacts of urban expansion on NHQ. [ABSTRACT FROM AUTHOR]

Providing accurate assessments of the relationships between urban landscape patterns and PM 2.5 pollution is essential for improving urban sustainability in China. Accordingly, this paper uses a geographically weighted regression model to reveal the relationships between urban landscape patterns and PM 2.5 pollution at different scales in China. First, we identified the level of PM 2.5 pollution in China and quantified the urban landscape patterns based on the landscape metrics in 2015. Then, we analyzed the relationships between urban landscape patterns and PM 2.5 pollution using geographically weighted regression with the county as the basic analytical unit. Finally, the spatial characteristics of the relationships between urban landscape patterns and PM 2.5 pollution were analyzed at the national, regional and provincial scales. We found that the PM 2.5 pollution in China was closely related to urban landscape patterns, with obvious spatial heterogeneity. The total area with a significant correlation between the urban landscape patterns (which were measured using the percentage of urban landscape, edge density, and patch density) and PM 2.5 pollution ranged from 2.07 × 106 km2 to 2.26 × 106 km2, accounting for 42.55%–46.59% of the total PM 2.5 -polluted area in China. The high correlations were concentrated mainly in five provinces, namely, Xinjiang, Shaanxi, Fujian, Chongqing and Guangdong. We also found that the relationships between urban landscape patterns and PM 2.5 pollution were stronger in urban agglomerations. The total area with a significant correlation between the urban landscape patterns and PM 2.5 pollution was 9.20 × 105 km2, occupying 65.55% of the entire urban agglomeration area; this percentage was nearly 14% higher than the national average level. The strongest relationship was observed in the Northern Tianshan Mountains urban agglomeration. Contrasting with previous studies, our study fully considered the spatial autocorrelation and spatial differences between variables by using geographically weighted regression and clarified the spatial heterogeneity of the relationships between urban landscape patterns and PM 2.5 pollution in China. Our findings imply that special attention must be paid to the urban landscape patterns in urban agglomerations during future urban development in China. Furthermore, effective regulations must be implemented to reduce the impacts of urban landscape patterns on PM 2.5 pollution by controlling urban expansion and optimizing the spatial patterns of urban landscapes. Image 1 • We revealed the relationships between ULP and PM 2.5 pollution in China. • The GWR model was used to quantify relationships at multiple scales. • Near half of PM 2.5 polluted regions showed significant relationships. • The relationships were stronger in urban agglomerations. [ABSTRACT FROM AUTHOR]

• We developed a method to assess the dynamics of flood regulation service (FRS). • The new method can ensure comparability across regions and over time. • Urban area proportion is strongly related to the supply–demand relationship of FRS. • The basin should control urban expansion and dredge lake for flood risk prevention. Recognizing changes in the supply–demand relationship of Flood Regulation Service (FRS) is significant for the rational planning of urban patterns and the prevention of flooding risks in river basins. Most existing studies have explored its spatial pattern but pay less attention to its spatiotemporal changes. This study explored the dynamic changes in the supply–demand relationship of FRS in rapidly urbanizing basins. We proposed the Supply-Demand Ratio (SDR) method, which used the Soil Conservation Service Curve Number (SCS-CN) model and the flood vulnerability index to quantify the supply and demand of FRS, and then, we calculated the SDR. Additionally, we calculated the rate of change in SDR from 1990 to 2018 in the basin and its subbasins. The case study in the Baiyangdian Lake Basin showed that the SDR method can effectively measure the changes in the relationship between the supply and demand of FRS in the river basin. The SDR was consistent with the supply–demand bivariate mapping method, with Kappa coefficients of 0.610 and 0.835 in 1990 and in 2018, respectively. Furthermore, we found that from 1990 to 2018, the imbalance of FRS in supply and demand intensified. The SDR for the whole basin decreased by 0.088 from 1990 to 2018, with a change rate of −27.8%. Rapid urbanization is an important reason for intensification of the FRS supply–demand imbalance. The proportion of urban area is the most relevant factor for the supply–demand relationship of FRS, with a Pearson correlation coefficient of −0.845 (p < 0.01). Therefore, we suggest that proactive policies be adopted to defend against flood disaster risks in the future construction of the Xiong'an New Area, which is located downstream from the Baiyangdian Lake Basin. [ABSTRACT FROM AUTHOR]

• A new method was developed to identify urban haze islands. • The new method can accurately and effectively delineate urban haze islands. • Two-thirds of Chinese cities had urban haze islands. • The new method can be used to depict other urban-rural gradients. Air pollution has great impacts on both human health and economic development. The difference in air pollution concentrations between urban and rural areas (urban haze island) is a widespread phenomenon. However, a method that can effectively depict the features of this phenomenon remains lacking. This study developed a method based on the concentric ring approach and modified sigmoid function to identify urban haze islands and extract three features: the background value of urban haze islands (BUHI), the intensity of urban haze islands (IUHI) and the extent of urban haze islands (EUHI). Using this method, we analyzed the PM 2.5 concentration data from 346 Chinese cities in 2016 and examined the spatial characteristics and driving factors of the three features of urban haze islands. The results showed that about two-thirds (218/346) of Chinese cities had a significant urban haze island phenomenon, with an average BUHI of 36.8 μg/m3, an IUHI of 11.3 μg/m3, and an EUHI of 7.7 km. The developed method can successfully capture the variations of PM 2.5 concentration, and the RMSE of the fitting curve was 0.46 μg/m3. On the national scale, natural factors had a greater impact (with the standardized regression coefficients varying from −0.74 ~ 0.80) on the BUHI and IUHI than socioeconomic factors (-0.32 ~ 0.32), while socioeconomic factors had a greater impact (0.29 ~ 0.38) on the EUHI than natural factors (−0.23 ~ 0.17). This study proposes a new and simple method to identify urban haze islands with high efficiency and wide applicability. Our results can be helpful for understanding urban-rural environmental differences and providing guidance for policymakers. [ABSTRACT FROM AUTHOR]