Urbanization and climate change are together exacerbating water scarcity-where water demand exceeds availability-for the world's cities. We quantify global urban water scarcity in 2016 and 2050 under ...four socioeconomic and climate change scenarios, and explored potential solutions. Here we show the global urban population facing water scarcity is projected to increase from 933 million (one third of global urban population) in 2016 to 1.693-2.373 billion people (one third to nearly half of global urban population) in 2050, with India projected to be most severely affected in terms of growth in water-scarce urban population (increase of 153-422 million people). The number of large cities exposed to water scarcity is projected to increase from 193 to 193-284, including 10-20 megacities. More than two thirds of water-scarce cities can relieve water scarcity by infrastructure investment, but the potentially significant environmental trade-offs associated with large-scale water scarcity solutions must be guarded against.
•U-Net and LSTM were integrated to develop an urban expansion model.•The integrated model considers the multiscale neighborhood information by U-Net and the time series information of historical UE ...by LSTM.•The proposed model was applied in the Beijing-Tianjin-Hebei urban agglomeration.•This model can greatly improve the accuracy of urban expansion simulation.
Simulating urban expansion (UE) accurately is fundamental for projecting ecological and environmental impacts of future UE, for optimizing the urban landscape patterns, and for improving urban sustainability. We proposed a new UE model by integrating a convolutional neural network (i.e., U-Net) and a recurrent neural network (i.e., long short-term memory, LSTM), and applied it in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA). The results yielded a high overall accuracy (99.18 %), a Kappa coefficient of 0.88 and a figure of merit of 0.13, which are greater than those of existing models. Such improvements are attributed to the multiscale neighborhood information powered by U-Net and the time series information of historical urban expansion uncovered by LSTM. The urban land in the BTHUA is projected to peak at 8736–9155 km2 during the period 2039–2043, which is an increase in the range of 10.99–16.31 % compared with that in 2020. The results are useful for supporting urban planning in the BTHUA, while the proposed UE model has the potential to be employed worldwide.
Light pollution (LP), induced by human activities, has become a crucial threat to biodiversity on the Tibetan plateau (TP), but few studies have explored its coverage and dynamics. In this study, we ...intended to measure the spatiotemporal patterns of LP on the TP from 1992 to 2018. First, we extracted the annual extent of LP from time-series nighttime light data. After that, we analyzed its spatiotemporal patterns at multiple scales and identified the natural habitats and the species habitats affected by LP. Finally, we discussed the main influencing factors of LP expansion on the TP. We found that the LP area increased exponentially from 1.2 thousand km2 to 82.8 thousand km2, an increase of nearly 70 times. In 2018, LP accounted for 3.2% of the total area of the TP, mainly concentrated in the eastern and southern areas. Several national key ecological function zones (e.g., the Gannan Yellow river key water supply ecological function zone) and national nature reserves (e.g., the Lalu Wetland National Nature Reserve) had a large extent of LP. The proportion of LP area on natural habitats increased from 79.6% to 91.4%. The number of endangered species with habitats affected by LP increased from 89 to 228, and more than a quarter of the habitats of 18 endangered species were affected by LP. We also discovered that roadways as well as settlements in both urban and rural areas were the main sources of LP. Thus, to lessen LP’s negative effects on biodiversity, effective measures should be taken during road construction and urbanization on the TP.
•The ESs of the YRDUA will decrease significantly from 2022 to 2050.•Conversion of cropland to urban land will result in more than 90% of the ESs losses.•Quantifying the impact of urban expansion on ...ESs for sustainable urban development.
Assessing the effects of future urban expansion on ecosystem services (ESs) is essential for the sustainability of cities worldwide. Nonetheless, evaluating these effects of future urban expansion on ESs remains challenging due to the uncertainties associated with socioeconomic development and the intricate nature of urban expansion. In this research, we initially integrated the localized Shared Socioeconomic Pathways (SSPs) and the Land Use Scenario Dynamics-urban (LUSD-urban) model to project the urban expansion of the Yangtze River Delta urban agglomeration (YRDUA). Subsequently, we quantified the impacts of urban expansion on ESs utilizing the Integrated Valuation of ESs and Tradeoffs (InVEST) model. The outcomes indicate that the urban land in the YRDUA is projected to expand by 1,020.19–12,282.04 km2 at a growth rate of 3.71–44.67% from 2022 to 2050. Simultaneously, habitat quality (HQ), carbon storage (CS), water retention (WR), and air purification (AP) are expected to decline by 0.34–4.24%, 0.48–5.82%, 0.39–4.75% and 0.20–2.45%, correspondingly. Most importantly, the primary cause of ES losses is the conversion of cropland to urban land, accounting for more than 90% of the total ES losses. The results offer crucial contextual insights to support future synergistic development policies for urbanization and ecological conservation in the YRDUA under increased climate change.
The Tibetan Plateau (TP) is an important area that affects global sustainable development. Quantifying spatiotemporal patterns of urbanization is crucial for maintaining the sustainability on the TP. ...This study took Xining City, the largest city on the TP, as an example to understand the urban expansion in this region in the past 50 years. We combined the high-resolution spy satellite data and China’s long-term urban land dataset (CULD) to quantify the urban expansion of Xining City. The object-oriented random forest classification was performed to extract urban land from spy satellite data in 1969, and the inter-annual correction was used to combine urban land information from 1969 to 2017. We found that the proposed approach can accurately quantify the urban expansion of Xining City over the past half century with an overall accuracy of 91% and a kappa coefficient of 0.86. Such high accuracy benefits from the fine resolution of spy satellite data and the consistency of CULD. We also found that Xining City experienced accelerated and fragmented urban sprawl to higher altitude areas, as a result of socioeconomic development and topographical limitations. The acceleration of urban expansion was more obvious, and the urban landscape fragmentation was more serious at high altitude areas. Such urban expansion encroached on cropland and grassland, and caused increased risks of landslides and other geological disasters. Therefore, Xining City urgently needs to promote the development of compact cities to control urban sprawl at higher altitude areas and provide a reference for improving urban sustainability across the TP. In this study, we analyzed the urban expansion of Xining city from 1969 to 2017, and provided a reliable way to understand the long-term spatiotemporal urbanization based on remote sensing, which has the potential for wide applications. In addition, the extracted urban information can help to improve the urban sustainability of Xining City and the entire TP.
Accurately simulating urban expansion is of great significance for promoting sustainable urban development. The calculation of neighborhood effects is an important factor that affects the accuracy of ...urban expansion models. The purpose of this study is to improve the calculation of neighborhood effects in an urban expansion model, i.e., the land-use scenario dynamics-urban (LUSD-urban) model, by integrating the trend-adjusted neighborhood algorithm and the automatic rule detection procedure. Taking eight sample cities in China as examples, we evaluated the accuracies of the original model and the improved model. We found that the improved model can increase the accuracy of simulated urban expansion in terms of both the degree of spatial matching and the similarity of urban form. The increase of accuracy can be attributed to such integration comprehensively considers the effects of historical urban expansion trends and the influences of neighborhoods at different scales. Therefore, the improved model in this study can be widely used to simulate the process of urban expansion in different regions.
The renovation and expansion of highway is a technical, comprehensive, and complex work, and the determination of its optimal scheme requires the consideration of multiple factors. In the process of ...highway construction, road engineers attempt to select an optimal scheme with the aim of saving cost, reducing construction difficulties, and protecting environment. It is difficult to assess because of the large number of attribute indices and the diversity of data distribution. In order to scientifically evaluate the comprehensive benefits of highway reconstruction and extension (HRE) schemes, an evaluation model of HRE scheme based on multiple attribute decision making (MADM) was established. This paper constructed the evaluation index system of HRE based on the four aspects of technology, economy, environmental impact, and landscape. This paper has made two major improvements. First, a method was proposed to determine the weight of evaluation index by coupling fuzzy analytic hierarchy process (FAHP) with difference driven principle (DDP). Then, technique for order preference by similarity to an ideal solution (TOPSIS) method was optimized by grey relational analysis (GRA) method. Finally, the MADA evaluation model is utilized to analyze the ranking of optimal scheme selection problem by relative closeness as a decision index. The results showed that the subjective intention of decision makers was considered and also fully the objective information of data itself was fully mined. The optimal scheme was scheme 3 through the analysis of five HRE schemes. This model highlighted the differences among the schemes and also avoided the problem of excessive differences. The results were consistent with the actual situation, verifying the feasibility and practicability of the model.
•We quantified park visitors’ positive emotions using social media data and sentiment analysis.•Both park type and landscape attributes matter to the positive emotions of visitors.•We identified key ...characteristics of urban parks affecting visitors’ positive emotions.•Multi-sorce social media data and machine learning can facilitate urban sentiment studies.
Improving the positive emotions of urban populations is essential for meeting the United Nations Sustainable Development Goals (SDGs) of “good health and well-being” and “sustainable cities and communities”. Urban parks generally may enhance people's positive sentiments, but little is known about explicitly linking the landscape composition and configuration of urban parks directly with visitors' sentiments based on social media data. The main objective of this study, therefore, was to identify key landscape attributes that influence this relationship in the Beijing metropolitan region. We first crawled 55,441 valid text data items from Sina Weibo for 99 urban parks within the fifth ring road of Beijing. Then, we quantified the positive emotions of visitors to urban parks using social media data and sentiment analysis. Finally, we evaluated the differences in visitors' positive emotions among different types of parks and used Random Forest to identify urban park attributes that were correlated with positive emotions. We found that visitors to different types of urban parks had different levels of positive emotions. Specifically, visitors to comprehensive parks and cultural relics parks were significantly happier than visitors to community parks. Visitors to parks between the third and fourth ring roads in Beijing had the lowest levels of positive emotions. Positive emotions were found to be positively correlated with park size and the mean size of water bodies but negatively correlated with large areas of impervious surfaces. This study sheds new light on the relationship between park landscape patterns and visitors' positive emotions through a new approach based on social media data. The research methods and findings may inspire similar studies in other cities and countries, which are needed to improve park planning and management and thus enhance urban sustainability.
Roadside sensor data fusion is an essential component of the vehicle-road cooperation system, effectively enhancing the interactive perception level among road targets. However, due to the complex ...road environment, occlusion, and other problems, the single sensor has low accuracy in the process of target tracking. How to realize the fusion of multi-sensor trajectory tracking data is the main problem to be solved at present. Therefore, a new multi-sensor data fusion method for roadside camera, LiDAR, and millimeter wave (MMW) radar is proposed in this study. According to the change of reflection intensity caused by the shift of LiDAR point cloud with the change of distance, and the detection accuracy of MMW radar used in this paper, the weight parameters of LiDAR and MMW radar in the fusion process are determined. Finally, the target missed detection rate and trajectory disconnected repair rate were customized, and experimental tests were conducted in five natural environments to verify the robustness of the proposed method.
•The coupled acoustic-structural method is employed to model the underwater explosion loading and the dam-reservoir-foundation interaction.•The Concrete Damage Plasticity (CDP) model including the ...strain rate effect is used to model the concrete material behavior to blast loading.•The influence of the initial stress from hydrostatic pressure and dead weight on the nonlinear dynamic response characteristics of a 300 m high arch dam subjected to far-filed underwater explosion are discussed.
Blast performance assessment of 300 m high arch dams is an important topic having been extensively studied in recent years. Because the arch dam is convex on the upstream face, the dam body is mainly in a compressive stress state under the action of upstream water pressure. However, due to the complexity of the problem, the initial stress is ignored in the blast response analysis of arch dams. For this purpose, the influence of initial stress on nonlinear dynamic response and failure modes of a 300 m high arch dam to the far-field underwater explosion has been studied in this paper. The acoustic-structural approach is employed to model the underwater explosion loading and the dam-reservoir-foundation interaction. The Concrete Damage Plasticity (CDP) model including the strain rate effect is used to model the concrete material behavior to blast loading. Damage development processes of high arch dams to the far-field underwater explosion are investigated. Nonlinear dynamic response characteristics of high arch dams with different initial stress states subjected to the far-field underwater explosion are discussed in terms of damage modes, residual displacement, and damage dissipated energy. The results show that the initial stress has a significant influence on the nonlinear dynamic and failure modes of high arch dams to the far-field underwater explosion. The initial stress state of high arch dams under hydrostatic pressure and deadweight should be the basic condition of blast response analysis.
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