Search

Red phosphorus/borocarbonitride heterojunction composites were prepared by a vaporization–deposition method for excellent photocatalytic activity towards tetracycline degradation.

Quantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characteristics and how the built environment influences urban aggregation. In this study, five elements are collected in Wuhan, China, namely population density, floor area ratio, business POIs, road network and built-up area as the representative of urban population, economic activities and land use. An inverse S-shape function is employed to fit the elements’ macro distribution. An aggregation degree index is proposed to measure the aggregation level of urban elements. The kernel density estimation is used to identify the aggregation patterns. The spatial regression model is used to identify the built environment factors influencing the spatial distribution of urban elements. Results indicates that all urban elements decay outward from the city center in an inverse S-shape manner. The business Point-of-Interest (POI) density and population density are highly aggregated; floor area ratio and road density are moderately aggregated, whereas the built-up density is poorly aggregated. Three types of spatial aggregation patterns are identified: a point-shaped pattern, an axial pattern and a planar pattern. The spatial regression modeling shows that the built environment is associated with the distribution of the urban population, economic activities and land use. Destination accessibility factors, transit accessibility factors and land use diversity factors shape the distribution of the business POI density, floor area ratio and road density. Design factors are positively associated with population density, floor area ratio and built-up density. Future planning should consider the varying spatial concentration of urban population, economic activities and land use as well as their relationships with built environment attributes. Results of this study will provide a systematic understanding of aggregation of urban land use, population, and economic activities in megacities as well as some suggestions for planning and compact development.

The COVID-19 epidemic widely spread across China from Wuhan, Hubei Province, because of huge migration before 2020 Chinese New Year. Previous studies demonstrated that population outflows from Wuhan determined COVID-19 cases in other cities but neglected spatial heterogeneities of their relationships. Here, we use Geographically Weighted Regression (GWR) model to investigate the spatially varying influences of outflows from Wuhan. Overall, the GWR model increases explanatory ability of outflows from Wuhan by 20%, with the adjusted R2 increasing from ~0.6 of Ordinary Least Squares (OLS) models to ~0.8 of GWR models. The coefficient between logarithmic of outflows from Wuhan and COVID-19 cases in other cities is generally less than 1. The sub-linear scaling relationship indicates the increasing returns of outflows was restrained, proving the epidemic was efficiently controlled outside Hubei at the beginning without obvious local transmissions. Coefficients in GWR models vary in cities. Not only cities around Wuhan but also cities having close connections with Wuhan experienced higher coefficients, showing a higher vulnerability of these cities. The secondary or multi-level transmission networks deserve to be further explored to fully uncover influences of migrations on the COVID-19 pandemic.

Evidence for the multifaceted responses of terrestrial ecosystems has been shown by the weakening of CO

Understanding the scaling characteristics in China is critical for perceiving the development process of rapidly urbanising countries. This paper conducts a comprehensive scaling analysis with quantitative assessment of a large number of diverse urban indicators of 275 Chinese cities. Our findings confirm that urban scaling laws can also be applied to rapidly urbanising China but demonstrate some unique features echoing its distinct urbanisation. Chinese urban population agglomeration results in more effective economic production but the economies of scale for infrastructure are less obvious. Some urban indicators associated with infrastructure and living facilities surprisingly scale super-linearly with urban population size, contrary to expected sublinear scaling behaviours. In developing countries, different-sized cities have diverse agglomeration, industrial and resource allocation advantages, which can be reflected by scaling exponents. We characterise these unique features in detail, exploring the spatial disparities and temporal evolution of scaling exponents ( β). Strong regional variations and differences are particularly pronounced in Northeast China and the Beijing-Tianjin-Hebei Urban Agglomeration. Scaling exponent variations over time reflect the temporal evolution of the urban system and measure the coordination and balance of urbanisation. Economic output was most efficient in 2009 and β of GDP was slightly greater than 1.15 in recent years. Urban land expansion has been accelerating since 2000 with β remaining around 0.85–0.90. The study of urban scaling in China is enlightening in elaborating the uniqueness and coordination of urban development in rapidly urbanising countries and provides support in formulating differentiated urban planning for different-sized cities to promote coordinated development.

Ecological regions of medium fragility account for 55 % of China's land. Large-scale afforestation and land reclamation have been carried out in these areas over the past few decades. However, how future climate change poses risks and challenges to them remains unclear. By establishing a multi-algorithm framework combining machine learning algorithms with multi-source dataset, our work predicts Normalized Difference Vegetation Index (NDVI, a proxy for vegetation greenness) and its variations in the 21st century under different climate scenarios. We find that vegetation greening (i.e., NDVI increase) in northern and southwestern China is unstable over four 20-year periods from 2020 to 2100. However, a strikingly prominent greening is expected to occur on the Qinghai-Tibet Plateau until the end of this century. Future warming can not only exacerbate the difficulties of vegetation conservation and restoration in vulnerable ecological regions, also threaten these new croplands, stymieing ambitions to increase crop production in China. Our results underscore the crucible that a warming climate presents to current restoration projects. We highlight the urgency of adapting to climate change to achieve ambitious goals of carbon sequestration and food security in China.

Natural photic regime has been drastically altered by the artificial night sky luminance. Despite evidence of sufficient light brightness inducing plant physiology and affecting phenology, generalization regarding effects of light pollution on plant phenology across species and locations is less clear. Meanwhile, the relative contributions and joint effects of artificial light pollution and climate change or other anthropic stressors still remain unknown. To fill this knowledge gap, we utilized in situ plant phenological observations of seven tree species during 1991-2015 in Europe, night-time light dataset and gridded temperature dataset to investigate the impacts of the artificial light pollution on spatial-temporal shifts of plant phenological phases under climatic warming. We found 70% of the observation sites were exposed to increased light pollution during 1992-2015. Among them, plant phenological phases substantially delayed at 12-39% observation sites of leaf-out, and 6-53% of flowering. We also found plant species appeared to be more sensitive to artificial light pollution, and phenology advancement was hindered more prominently and even delay phenomenon exhibited when the color level showed stronger sky brightness. Linear mixed models indicate that although temperature plays a dominant role in shifts of plant phenological phases at the spatial scale, the inhibitory effect of artificial light pollution is evident considering the interactions. To our knowledge, this study is the first to quantitatively establish the relationship between artificial light pollution and plant phenology across species and locations. Meanwhile, these findings provide a new insight into the ecological responses of plant phenology to the potential but poorly understood environmental stressors under this warmer world and call for light pollution to be accorded the equal status as other global change phenomena.

Cellular automata (CA) are effective tools for simulating urban dynamics. Coupling top-down and bottom-up CA models are often used to address macro-scale demand and micro-scale allocation i...

Urban form characterizes the spatial structure of fixed elements within a city, which affects daily life and has significant influence on environmental sustainability. Measuring the spatiotemporal characteristics of an urban form and its relationship with sustainable development is the basis of urban planning. Taking 27 large cities in the United States, Europe and China as examples, we developed a ternary graph to quantify urban forms based on the density distribution of the built-up area. The urban forms were divided into the following classes: central-compact, central-sprawl, decentralized-compact, and decentralized-sprawl. Spatially, the cities in the three regions have experienced rapid urban growth, while the urban forms vary greatly from region to region. Urban forms are dominated by decentralized-sprawl in the United States, and central-compact in Europe and China. Temporally, approximately 80% of sample cities kept the urban form class both in 1990–2000 and 2000–2014. It is noted that 40% of sample cities in China tended to grow in a more sprawling pattern in 2000–2014 than in 1990–2000. From the land-saving aspect of urban sustainable development, compact and central spatial growth can significantly reduce per capita land consumption. Population density decreases in all sample cities, but compact and central spatial growth slowed the rate of population density reduction.

Rapid urbanization accelerates urban expansion, especially in populous areas, such as Southeast Asia. The urban forms and changes at the macro level and the dynamics at the patch level are interrelated. Considering its spatiotemporal interdependences and global-local interactions, we propose a framework to quantify urban expansion by combining macro patterns and micro dynamics. Taking three Southeast Asian megacities, Bangkok, Ho Chi Minh City (HCMC), and Manila, as examples, we calculate the urban land densities in concentric rings (macro pattern) and the proximity expansion index (PEI) of new urban patches (micro dynamic) to compare the urban form changes and expansion patterns based on Landsat imagery in 1990, 2000, and 2014. The results show that the urban form changes have close relationships with the local urban patch dynamics. The macro- and micro-level results in Bangkok and Ho Chi Minh City are interrelated and consistent and the explainable inconsistent results in Manila further reveal the necessity of combination of two scopes. The three megacities developed in different manners, thereby resulting in diverse urban forms and changes. Other methods and technologies combining macro and micro perspectives are encouraged to better understand urban expansion.

It is widely observed urban land expands faster than population grows, which results in declining urban population densities over time. However, it is not known how densities decline. By taking 35 major Chinese cities as examples, we propose an exponential model to quantify the temporal decline in urban population densities from 2001 to 2015. The average adjusted R2 of the exponential model for 35 cities is 0.68, indicating a moderate but powerful correlation with temporal variations in densities. The exponential model implies that the temporal decline in densities in cities is occurring at a constant but differentiated rate. Apart from Beijing and Shanghai, the annual decline rates vary from 1% to 9%, with an average of 4.65%, which means urban population densities declined by 4.65% per annum from 2001 to 2015. The bivariate correlation analysis and multiple regression model reveal that the initial urban population density and the growth rates of urban land, population, and GDP in a city are significantly correlated with the decline rates and that these four factors can explain 72.3% of the variance in the decline rates among cities. We further verified the transferability and applicability of the exponential model by applying it to 20 U.S. cities. Using a global dataset, we found the temporal decline rate in densities in Chinese cities was greater than those in India and other countries or regions, which highlights the necessity of efficient urban development strategies to slow down the temporal decline in urban population densities in China.

As cities constitute the main sources of CO2 emissions, accurate simulation and prediction of urban CO2 emissions are becoming increasingly necessary for understanding environmental impacts and supporting the policy-making toward a low-carbon development. However, most previous studies on estimating CO2 emissions have only considered the effects of socioeconomic driving factors while disregarding the contributions of urban spatial structures (with the exception of those of urban expansion) to carbon abatement. Therefore, this study presented a model that integrates system dynamics, cellular automata and support vector regression to evaluate the impacts of different socioeconomic developments and urban spatial structures on the CO2 emissions of Guangzhou city. In the integrated model, system dynamics was used to model the developments of socioeconomic variables including urban land-use demand. An artificial neural network cellular automata model based on patch simulation strategy was then built to simulate the urban spatial structures, which were further quantified by landscape metrics. Using both socioeconomic variables and landscape metrics, a support vector regression with polynomial kernel function was finally employed to predict CO2 emissions. Through comparisons drawn between the simulated results and actual data, the integrated model coupling socioeconomic factors and urban spatial structures was demonstrated to be an effective tool for accurately simulating CO2 emissions. Furthermore, scenario simulations derived from the integrated model showed that the scenario of executing moderate population and economic growth, more technological investment, and the compact development of urban spatial pattern constitutes the best development mode for Guangzhou to balance economic growth and CO2 emissions reduction. From these findings, it is suggested that the government should not only develop a series of socioeconomic policies on carbon mitigation but also construct an ideal urban structure of compact and multiple-nuclei development through urban planning and spatial optimization for building a low-carbon city.

As an infrastructure-related urban indicator, urban land theoretically has a sub-linear scaling relationship with urban population, which has been evidenced around various urban systems. However, scaling relationships between different types of urban land such as residential, industrial land and population size are still unclear, which helps to understand how urban land expansion response to population growth with an internal structure view. Here, we take more than 500 Chinese cities as examples to investigate scaling relationships of population and land with eight types and their changes over time (2012–2018). Results show that eight types of urban land all have a robust scaling law with population, but with different scaling exponents, or even opposite scaling regimes. The whole urban land and other seven sub-types are all in line with the expected sub-linear behaviors, presenting the economies of scale. And scaling exponents of transportation land and green land decreased over time while exponents of other sub-types are stable. Industrial land surprisingly has a super-linear scaling relationship with population, presenting a faster speed than urban population. Smaller residuals of cities in the scaling regression model (Scale-Adjusted Metropolitan Indicators, SAMIs) of residential and industrial land are negatively correlated with housing prices, indicating the shortage of land supply compared to other cities of the same size spurred the rise of housing prices. While SAMIs of industrial land are positively correlated with industrial productions, demonstrating that increasing land supply is a powerful means of industrial outputs and economic growth. Our structure-based scaling analysis enriches the understanding of population-land relationship and supports differentiated land supply in land use and urban planning according to urban population size.

The spatial relationship of newly grown urban patches to existing urban areas lies at the core of understanding the properties of urban expansion dynamics. Some existing landscape metrics have been used to identify patch expansion types, i.e., infilling, edge-expansion and outlying, capturing the evolution process of urban expansion patterns based on quantifying spatial relationship. However, these existing metrics cannot comprehensively describe the spatial distributions of all new patches relative to old built-up areas, especially for outlying patches, which are the significant elements affecting the urban expansion pattern. We propose a new landscape metric, the Proximity Expansion Index (PEI), to address this problem by incorporating two factors of proximity - distance and boundary sharing rate to old patches. The value of PEI is continuous and has the clear physical meaning for depicting the gradient of the spatial relationship. The landscape expansion types are then redefined by PEI, while the sprawl level of outlying patches is clearly reflected. The variants of PEI are designed as global indices to capture information of the dynamic process of urban expansion from a bottom-up view. We selected Wuhan, a metropolis in central China, as a case area to evaluate PEI based on four periods of remote sensing images (1995, 2000, 2005 and 2010). The results show that the spatial pattern of urban expansion becomes increasingly dispersed, demonstrating that PEI is capable of capturing information of urban expansion evolution. PEI can depict the spatial relationship between new and old patches in a more detailed way by comparing PEI and previous metrics. Using PEI, we can also discover regions of great significance, called outlying seed regions, which have a profound impact on the coalescence of urban morphology.

In response to global warming, a spatiotemporal inventory of city-level CO2 emissions is of great importance for the developing countries, especially for China of which CO2 emissions increase rapidly but lack in energy statistics at urban scale. Currently the nightlight imagery from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) has been widely used as a promising data source for estimating the spatiotemporal distributions of CO2 emissions. However, most previous studies ignored the saturation problem of DMSP-OLS nightlight imagery which could result in more uncertainties in CO2 emission estimations. Moreover, little work was carried out to evaluate the important emission sources from a large population living in the unlit areas with the consideration of different emission levels. To address these issues, this study proposed an improved allocating model to map the city-level energy-related CO2 emissions of mainland China in 2000, 2005, 2010, and 2013 based on an enhanced vegetation index (EVI) adjusted nighttime light index (EANTLI) and LandScan population data. Through the comparison with the original nightlight images, the EANTLI is proved to increase inter-urban variability and alleviate DMSP-OLS saturation. Meanwhile, the accuracy assessment with the statistical data of CO2 emissions at the level of city units has also demonstrated that the proposed model is appropriate and reliable in estimating CO2 emissions not only in the lit areas but also in the unlit areas. The model outputs presented that cities with huge amounts of CO2 emissions mainly agglomerated in the southern and eastern coastal regions, while spatial distributions of cities with small amounts of CO2 emissions mainly appeared in western and central regions. These results could improve the understanding of regional discrepancies of spatiotemporal CO2 emission dynamics at urban scale, and provide a scientific basis for policymaking on viable CO2 emission mitigation policies.

Urban boundary is the spatial basis for urban statistics and urban planning. However, the city definition by which the urban boundary is determined is not comparable among different countries. A globally consistent delimitation of hierarchical urban boundaries (HUBs) is rare but urgently needed. In this research, we proposed a model to consistently identify HUBs according to physical urban entity. The main quantitative basis of our model is the density, size and spatial relationship of the artificial impervious surface. We produced a vector dataset of global HUBs (named GHUB) based on 30-meter resolution remote sensing products. Cross-product comparison shows the effectiveness and advantages of our model. We furtherly, for the first time, investigated and compared the physical form and spatial characteristics of global urban settlements through the same lens supported by GHUB. We have identified 10,242 urban settlements with an area above five square kilometers, 39.18% of them are located within 100 km of the coastline. Urban settlements are not the “reinforced concrete forest” as commonly thought. The global average proportion of open space within urban settlements is 41.51%. The total area of urban open space in the world is 279.47 thousand square kilometers. Notably, we verified the Rank-size rule of urban settlement is universal, meanwhile, the verification results of different urban systems in 47 countries show that the Zipf’s exponent varies from country to country. Our work can help to consistently investigate global urban settlements without being affected by various definitions of cities. The GHUB can not only be applied to various urban studies but also can support global affairs such as the Sustainable Development Goal 11 due to its international comparability.

Urban expansion has complicated impacts on vegetation coverage and growth, including direct impact caused by the conversion of productive land to non-productive impervious surfaces and indirect impact due to urbanization effects on plant growth. Vegetation improves the urban environment and enhances residents’ quality of life, yet our understanding of impacts on vegetation under rapid urbanization remains limited. We proposed an improved framework to explore the dynamic comprehensive impacts of urbanization on vegetation net primary productivity (NPP) and further analyzed these impact characteristics in different expansion patterns in Wuhan during 1990–2020. The results showed 3378.92 g C m−2 yr−1, 3453.56 g C m−2 yr−1 and 4090.82 g C m−2 yr−1 of NPP loss caused by direct impact during 1990–2000, 2000–2010 and 2010–2020, respectively, while NPP increased by 811.33 g C m−2 yr−1, 2435.91 g C m−2 yr−1 and 4640.42 g C m−2 yr−1 due to indirect impact, during the same time periods. The indirect impact offset approximately 24.01%, 70.53% and 113.43% of NPP loss during the three periods. Direct loss and indirect impact strengthened as the urban expansion intensity (UEI) gradient increased, and the compensation of indirect impact to direct impact was more obvious when the UEI reached less than 20%. Compensation increased more steadily in the compact urban pattern. This study provides fundamental support for forecasting the ecological consequences of future urbanization as well as local ecological policy decision-making.

Land-use conversion from natural areas to agriculture and human settlements is causing global biodiversity loss. We proposed a human land-use disturbance index (LDI) to assess habitat loss and fragmentation in global biodiversity hotspots from 1992 to 2015. Negative (LDI > 1) and positive (LDI

Improving the radiation resistance of epoxy resin (EP) is pivotal for the reliability of equipment and the safety of facilities in the nuclear industry. Hexagonal boron nitride (h-BN) is attractive in the preparation of radiation-resistant materials due to its good radiation stability and neutron shielding capability. In this work, h-BN/EP composites with enhanced radiation resistance were fabricated by solution blending. The tensile strength and thermal properties of the composites after γ-ray and neutron irradiation were investigated. The results showed that the addition of h-BN improved the mechanical property and the glassy transition temperature of the resin. The presence of low-level h-BN was favorable to enhance the radiation resistance of EP. As for composites with the 0.05% mass percentage of h-BN, the absorbed dose required to decrease relative tensile strength by 50% was about 300 kGy, which was higher than that of neat EP. The intrinsic mechanism of radiation resistance was attributed to the oxygen barrier effect as investigated by XPS and EPR. Then, benefiting from the absorbing neutrons capability of boron atoms, an addition of 0.55% h-BN to the EP resin could reduce the neutron transmittance of the resin by 5.6%. This study demonstrates that the blending with h-BN can increase the radiation-resistant property of EP resin, meanwhile augmenting the neutron shielding ability.

Urban scaling laws assume that the performance of a city largely relies on its urban population size. However, two cities with the same population size may have vastly different economic outputs, which reveals that factors apart from urban size (a measurement of intra-urban interactions) determine their economic outputs. Economic production is essentially the product of social interactions. Urban population size and interurban interactions reflected by population mobility were both considered to evaluate the scaling of urban economic outputs in this paper. We quantified the scaling relationship between urban economic outputs and interurban interactions, and compared it with the paradigm derived from urban size. Results showed that urban economic outputs scale with urban interactions across cities and present the same super-linear scaling regimes but with a greater scaling exponent. A deeper looking showed that interurban interactions definitely bring a more obvious super-linearity than population size but the scaling relationship between urban size and economic outputs is more robust. Urban population size has a greater impact on Gross Domestic Product (GDP), secondary and tertiary industries output products, total retail sales of consumer goods and total wage with 60– 65% relative contribution. For exports, interurban interactions and urban population size are almost equally important. We also proved that interactions between cities are significantly positively correlated with urban extra growth. These findings provide convictive evidence that in addition to population size, interurban interaction is also crucial for exploring the scaling of urban growth. Our results are enlightening to the study of mechanisms and evolutions of urban scaling that interurban interaction besides urban population size should both be a vital consideration to urban economic outputs.

The lockdown of cities against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA’s Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes. We collected daily NTL products for four weeks (one month) before and after the date of lockdown in each city, which were then summarized as weekly and monthly averaged NTL images after pre-processing (cloud removing, outlier detection, etc.). Results show that NTL overall decreased after the lockdown of cities, but with regional disparities and varying spatial patterns. Asian cities experienced the most obvious reduction of NTL. Particularly, the monthly averaged NTL in Mumbai, India, decreased by nearly 20% compared to one month before. However, there was no significant decline in NTL in European cities. African cities also experienced stable changes of NTL. Spatially, city centers darkened more obviously than the urban periphery. Facing emergencies, NTL data has broad applications in monitoring socioeconomic dynamics and assessing public policies in a near real-time manner.