A ternary plasmonic photocatalyst consisting of Au-decorated V2O5@ZnO heteronanorods was successfully fabricated by an innovative four-step process: thermal evaporation of ZnO powders, CVD of ...intermediate on ZnO, solution deposition of Au NPs, and final thermal oxidization. SEM, TEM, EDX, XPS, and XRD analyses revealed that the interior cores and exterior shells of the as-prepared heteronanorods were single-crystal wurtzite-type ZnO and polycrystalline orthorhombic V2O5, respectively, with a large quantity of Au NPs inlaid in the V2O5 shell. The optical properties of the ternary photocatalyst were investigated in detail and compared with those of bare ZnO and V2O5@ZnO. UV–vis absorption spectra of ZnO, V2O5@ZnO, and Au-decorated V2O5@ZnO showed gradually enhanced absorption in the visible region. In addition, gradually decreased emission intensity was also observed in the photoluminescence (PL) spectra, revealing enhanced charge separation efficiency. Because of these excellent qualities, the photocatalytic behavior of the ternary photocatalyst was studied in the photodegradation of methylene blue under UV–vis irradiation, which showed an enhanced photodegradation rate nearly 7 times higher than that of bare ZnO and nearly 3 times higher than that of V2O5@ZnO, mainly owing to the enlarged light absorption region, the effective electron–hole separation at the V2O5–ZnO and V2O5–Au interfaces, and strong localization of plasmonic near-field effects.
Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the ...spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.
Remote sensing can provide near real-time and dynamic monitoring of drought. The drought severity index (DSI), based on the normalized difference vegetation index (NDVI) and ...evapotranspiration/potential evapotranspiration (ET/PET), has been used for drought monitoring. This study examined the relationship between the DSI and winter wheat yield for prefecture-level cities in five provinces of eastern China during 2001–2016. We first analyzed the spatial and temporal distribution of droughts in the study area. Then the correlation coefficient between drought-affected area and detrended yield of winter wheat was quantified and the impact of droughts of different intensities on winter wheat yield during different growth stages was investigated. The results show that incipient drought during the wintering period has no significant impact on the yield of winter wheat, while moderate drought in the same period can reduce yield. Drought affects winter wheat yield significantly during the flowering and filling stages. Droughts of higher intensity have more significant negative effects on the yield of winter wheat. Monitoring of droughts and irrigation is critical during these periods to ensure normal yield of winter wheat. This study has important practical implications for the planning of irrigation and food security.
Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and ...globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand-supply relationships. In this study, we first investigated the source code of CLUMondo, providing a complete, detailed mechanism of this model. We found that the featured function of CLUMondo-balancing demands and supplies in a many-to-many mode-relies on a parameter called conversion order. The setting of this parameter is a manual process and requires expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. Comparative experiments demonstrated the validity and effectiveness of the proposed automated method. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting in CLUMondo-BNU v1.0. This study facilitates the application of CLUMondo and helps to exploit its full potential.
Henan, China, is likely the most populous agricultural province worldwide. It is China’s major grain-producing area, with a continuously increasing population (96 million), which is greater than 93% ...of countries worldwide. However, this province has been experiencing unprecedented urbanization recently due to national policies and measures, such as a plan to build the capital city of Henan into a national center, resulting in severe conflicts in land use that endanger food security regionally and globally. To facilitate decision-making on this problem, we explored the detailed urban-rural development of Henan by modeling these land-use conflicts. Conventional modeling of a region’s urban-rural development is to navigate trade-offs (a) solely between different land-use types (b) by assuming that each type provides a single service (e.g., croplands produce all the food), and (c) under a polynomial regression-based projection of population. In contrast, we considered both land-use type and intensity, resulting in a detailed land system for Henan. By introducing the concept of land system services (e.g., food production), we established a many-to-many relationship between land system classes and services. These allowed us to carry out the most comprehensive modeling of Henan’s urban-rural development under eighteen combined scenarios of population growth and land-use policies on food production. The modeling results of these scenarios provide a solid basis for making decisions regarding Henan’s urban-rural development. We also revealed the influence mechanism of population growth, land-use policies, and their combinations, highlighting the benefits of securing food production by agricultural intensification rather than merely expanding the area of cropland.
Human-induced impacts, such as urbanization, on regional climate changes and precipitation changes in particular have been attracting increasing international interests. However, there are different ...evaluations of urban effects on precipitation changes in both space and time. In this study, taking Beijing Municipality (BJM) as a case study, the hourly precipitation data from 20 automatic weather stations for a period of 2011–2015 were analyzed using the circular statistical analysis and grange causality test technique. Changes in precipitation intensity, amount, duration, and timing were investigated, and extreme precipitation indices were defined by percentiles and consecutive precipitation processes. Results indicated that impacts on precipitation varied with the type of urbanization. Urban areas with the highest population density were dominated by the slightly longer precipitation duration, higher precipitation intensity and larger precipitation amount with lengthening consecutive dry days. Therefore, urbanization has the potential to intensify precipitation processes. In addition, due to varying topographical features in the vicinity of BJM, complicated precipitation changes can be identified along two sides of the urban area. Larger precipitation amount and higher precipitation intensity can be found along the western side than along the eastern flank of the BJM. Further, higher precipitation amount can be observed in the downwind areas. High-level urban heat island can trigger more pronounced urban precipitation islands which lags behind the UHI. What's more, urban high buildings can benefit slowing down air mass, hence lengthening precipitation events. These results provide useful information for management of urban activities, and offer a new viewpoint for further understanding of the urban precipitation island (UPI) effect.
•New findings about impacts of different urbanization degrees on precipitation changes•Clarification of urbanization-induced intensification of precipitation processes•New light on topographic impacts on precipitation changes in the vicinity of urban areas
Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite ...images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.
•A methodological effort toward weightless index constructions.•A novel weightless algorithm for constructing composite sustainability indexes.•Applicable framework of comparative experiments to ...optimize algorithm parameters.•Assessing global sustainability with novel weightless composite index algorithm.
A composite index based on selected indicators is a widely used tool for guiding, monitoring, and evaluating a society’s level of sustainability. However, determining the weight of each indicator is typically a methodologically problematic and highly controversial process. This paper proposes a weightless strategy for constructing composite sustainability indices based on the mathematical optimization concept of Pareto fronts. The core idea is to model each indicator as an individual objective and explore Pareto fronts within the resulting multi-objective solution space. In practice, a total of 24 typical implementations of the strategy were realized to represent four categories with varying parameter settings, i.e., straightforward/hierarchical implementations with/without avoiding basic indicator accuracy issues. Comparative experiments demonstrated that a hierarchical approach utilizing the goodness of variance fit-based (GVF = 0.80) natural breaks to nullify accuracy problems is the most effective implementation. To demonstrate its usefulness, the strategy implemented using this approach was applied to analyze the world’s sustainability by revising the well-known sustainable society index. This study provides a novel paradigm of composite sustainability indices and represents the first assessment of world sustainability using multiple criteria (indicators) without weights.
The Regional Comprehensive Economic Partnership (RCEP) was formally signed by the Association of Southeast Asian Nations (ASEAN) countries, along with China, Japan, South Korea, Australia, and New ...Zealand. This was a significant step towards regional integration in the Asia-Pacific region. Analysing the trade structure among member states is crucial in understanding the path to regional integration and policy implications of regional cooperation within the RCEP framework. Based on subdivided commodity data, this study reviews the evolution of merchandise trade in the RCEP region in the past two decades. It investigates the current trade structure of the RCEP, emphasising the relative importance of intra-regional versus extra-regional interdependence and the trade asymmetry of the regional members. The results of the study are as follows: First, the overall extent of regional trade integration in the RCEP region increased modestly from 2001 to 2018, indicating that the RCEP region was export-oriented and there was significant room for further expansion of regional trade. Second, most of the commodities traded in the RCEP region demonstrated much higher extra-regional interdependence than intra-regional in 2018, particularly labor-, capital-, and technology-intensive products such as television and radio apparatus. Third, the trade networks of the top five traded commodities were distinguished by large economic asymmetries, with China, Japan, and South Korea being the dominant regional powers. These findings have significant implications for understanding how to promote regional integration and cooperation. Besides expanding intra-regional trade, outward-oriented factors influenced by the regional powers—including consolidating the global advantages of manufacturing, stabilizing supply chains by including large resource countries, and attracting extra-regional investments—were also the main rationales for the conclusion of the RCEP.
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