Search Results (37)

Dissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant environmental challenges due to increasing intensive stocking densities and outdated management practices, while also grappling with the systematic monitoring limitations of large-scale operations. To address these challenges, in this study, a random forest-based model was developed for DO concentration retrieval (R² = 0.82) using Landsat 8/9 OLI imagery. The Lindeman, Merenda, and Gold (LMG) algorithm was applied to field data collected from four cities—Foshan, Hong Kong, Huizhou, and Zhongshan—to identify key environmental drivers to the changes in DO concentration in these cities. This study also employed satellite imagery from multiple periods to analyze the spatiotemporal distribution and trends of DO concentrations over the past decade, aiming to enhance understanding of DO variability. The results indicate that the average DO concentration in fishponds across the GBA was 7.44 mg/L with a statistically insignificant upward trend. Spatially, the DO levels remained slightly lower than those in other waters. The primary environmental factor influencing DO variations was the pH levels, while the relationship between natural factors such as the temperature and DO concentration was significantly hidden by aquaculture management practices. The further analysis of fishpond water quality parameters across land uses revealed that fishponds with lower DO concentrations (7.293 mg/L) are often located in areas with intensive human intervention, particularly in highly urbanized regions. The approach proposed in this study provides an operational method for large-scale DO monitoring in aquaculture systems, enabling the qualification of anthropogenic influences on water quality dynamics. It also offers scalable solutions for the development of adaptive management strategies, thereby supporting the sustainable management of aquaculture environments. Full article

With the extensive construction of high-voltage power grid projects in complex mountainous terrains, rainfall-induced slope instability poses a significant threat to the safety of transmission tower foundations. This study focuses on a power transmission and transformation project in Huizhou City, Guangdong Province. Using MIDAS GTS NX 2019 (v1.2), an unsaturated seepage-mechanics coupling model was established to systematically investigate the influence of slope ratios (1:0.75, 1:1, and 1:1.25) on slope stability under rainfall conditions and the reinforcement effects of anti-slide piles. The results demonstrate that slope ratios significantly govern slope responses. For steep slopes (1:0.75), post-rainfall matrix suction loss reached 43.2%, peak displacement attained 74.49 mm, and the safety factor decreased by 12.5%. In contrast, gentle slopes (1:1.25) exhibited superior stability. Anti-slide piles effectively controlled displacement growth (≤9.15%), but pile bending moments increased markedly with steeper slope ratios, accompanied by a notable expansion of the plastic zone at the slope toe. The study reveals a destabilization mechanism characterized by “seepage–strength degradation–displacement synergy” and recommends engineering practices adopting slope ratios of 1:1–1:1.25, combined with anti-slide piles (spacing ≤ 1.5 m) and dynamic drainage measures. These findings provide critical guidance for the design of transmission tower slopes in mountainous regions. Full article

Water environment evaluation is the basis of water resource planning and sustainable utilization. As a successful case of the coordinated progress of ecological protection and economic development, the Xin’an River Basin is a model for exploring the green development model. However, there are still some problems in the synergistic cooperation between the two provinces. Exploring the differences within the basin is a key entry point for solving the dilemma of synergistic governance in the Xin’an River Basin, optimizing the allocation of resources, and improving the overall effectiveness of governance. Based on the DPSIR model, 21 water environment–related indicators were selected, and the entropy weight–TOPSIS method and gray correlation model were used to evaluate the temporal and spatial status of water resources in each county of the Xin’an River Basin. The results show that (1) The relative proximity of the water environment in Xin’an River Basin fluctuated in “M” shape during the ten years of the study period, and the relative proximity reached the optimal solution of 0.576 in 2020. (2) From the five subsystems, the state layer and the corresponding layer are the most important factors influencing the overall water environment of the Xin’an River Basin. In the future, it is intended to improve the departmental collaboration mechanism. (3) The mean values of relative proximity in Qimen County, Jiande City, and Chun’an County during the study period were 0.448, 0.445, and 0.439, respectively, and the three areas reached a moderate level. The water environment in Huizhou District and Jixi County, on the other hand, is relatively poor, and the mean values of proximity are 0.337 and 0.371, respectively, at the alert level. The poor effect of synergistic development requires a multi–factor exploration of reasonable ecological compensation standards. We give relevant suggestions for this situation. Full article

China has a vast land area, with mountains accounting for 1/3 of the country’s land area. Flooding in these areas can cause significant damage to human life and property. Therefore, rainstorms and flood hazards in Huangshan City should be accurately assessed and effectively managed to improve urban resilience, promote green and low-carbon development, and ensure socio-economic stability. Through the Random Forest (RF) algorithm and the Soil Conservation Service (SCS) model, this study aimed to assess and demarcate rainstorm and flood hazard risks in Huangshan City. Specifically, Driving forces-Pressure-State-Impact-Response (DPSIR)’s framework was applied to examine the main influencing factors. Subsequently, the RF algorithm was employed to select 11 major indicators and establish a comprehensive risk assessment model integrating four factors: hazard, exposure, vulnerability, and adaptive capacity. Additionally, a flood hazard risk zoning map of Huangshan City was generated by combining the SCS model with a Geographic Information System (GIS)-based spatial analysis. The assessment results reveal significant spatial heterogeneity in rainstorm and flood risks, with higher risks concentrated in low-lying areas and urban fringes. In addition, precipitation during the flood season and economic losses were identified as key contributors to flood risk. Furthermore, flood risks in certain areas have intensified with ongoing urbanization. The evaluation model was validated by the 7 July 2020 flood event, suggesting that Huangshan District, Huizhou District, and northern Shexian County suffered the most severe economic losses. This confirms the reliability of the model. Finally, targeted flood disaster prevention and mitigation strategies were proposed for Huangshan City, particularly in the context of carbon neutrality and green urbanization, providing decision-making support for disaster prevention and emergency management. These recommendations will contribute to enhancing the city’s disaster resilience and promoting sustainable urban development. Full article

Among the frequent natural disasters, there is a growing concern that storm surges may cause enhanced damage to coastal regions due to the increase in climate extremes. It is widely believed that storm surge risk assessment is of great significance for effective disaster prevention; however, traditional risk assessment often relies on the land use data from the government or manual interpretation, which requires a great amount of material resources, labor and time. To improve efficiency, this study proposes a framework for conducting fast risk assessment in a chosen area based on social sensing data and a deep learning method. The coupled Finite Volume Coastal Ocean Model (FVCOM) and Simulating Waves Nearshore (SWAN) model are applied for simulating inundation of five storm surge scenarios. Social sensing data are generated by fusing POI kernel density and night light data through wavelet transform. Subsequently, the Swin Transformer model receives two sets of inputs: one includes social sensing data, Normalized Difference Water Index (MNDWI) and Normalized Difference Chlorophyll Index (NDCI), and the other is Red, Green, Blue bands. The ensembled model can be used for fast land use identification for vulnerability assessment, and the accuracy is improved by 3.3% compared to the traditional RGB input. In contrast to traditional risk assessment approaches, the proposed method can conduct emergency risk assessments within a few hours. In the coast area of Huizhou city, the area considered to be at risk is 135 km², 89 km², 82 km², 72 km² and 64 km², respectively, when the central pressure of the typhoon is 880, 910, 920, 930 and 940 hpa. The Daya Bay Petrochemical Zone and central Huangpu waterfront are two areas at high risk. The conducted risk maps can help decision-makers better manage storm surge risks to identify areas at potential risk, prepare for disaster prevention and mitigation. Full article

Effectively and rationally allocating land resources, while coordinating urban expansion with internal renewal strategies, is crucial for achieving high-quality regional development in coastal urban agglomerations. Land-use suitability assessment (LSA) is a key method for coastal land-use planning, but it is primarily used to delineate ecological redlines or areas for urban expansion, often overlooking the spatial analysis needed for urban renewal. This is particularly critical in coastal urban agglomerations facing land scarcity and ecological fragility. Here, we combined land use and the Analytical Hierarchical Process (to consider stakeholder priorities) in a Minimum cumulative resistance model (MCRM) to determine suitable coastal urban growth and renewal based on a suite of 12 indicators relevant to development intensity and stock space. Application to the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) indicates a dominance of the Ecological Buffer Zone (70.5%), and the available stock space in the GBA comprises only 9.2% of the total area. Our modeling framework tailored different development strategies for different cities: Huizhou and Zhaoqing had space for urban expansion to varying degrees, while other cities were found to be suitable for urban renewal due to low stock space and high development intensity. Our modeling approach, incorporating stakeholder input and objective evaluation of geographic land-use information, can assist planners in improving ecological security while promoting high-quality developments in coastal areas. Full article

A new cyprinid fish, Opsariichthys rubriventris sp. nov., is described from the Xizhijiang River, a tributary of the Pearl River basin in Huizhou City, Guangdong Province, southern China. The species is distinguished from all other congeners by the following combination of characters: predorsal scales 13–14; lower jaw projecting slightly beyond upper jaw; cheek with two mainly longitudinal rows of tubercles; and lower jaw, belly, pectoral fin, and anterior margin of anal fin in adult males being reddish-orange. The principal component analysis result of the morphological data indicated that O. rubriventris sp. nov. could be clearly distinguished fromfour other congeners. The phylogenetic analysis conducted in this study, utilizing both Maximum Likelihood (ML) and Bayesian Inference (BI) methods, supported the monophyly of the novel species O. rubriventris sp. nov. at the species level. Additionally, the genetic distance analysis revealed that O. rubriventris sp. nov. exhibits a genetic distance ranging from 0.14 to 0.16 with its congeneric species, further affirming its taxonomic status. Full article

Urban agglomerations are becoming increasingly important in driving economic development in China. Accurate representation of the economic development status and spillover effects of cities within an urban agglomeration is the foundation of and an effective approach for promoting the coordinated development of that agglomeration. However, current studies of economic spillovers tend to focus on urban agglomerations as a whole, and there is a lack of scrutiny and validation of research data. Therefore, this study proposes a framework for detecting economic spillover effects within an urban agglomeration based on a prolonged night-time light dataset. Firstly, we explored the most suitable night-time light index to characterize the economic status. Then, we used this index to construct the economic network and hierarchical structure of the urban agglomeration. Finally, we explored the heterogeneity of spillover effects under the hierarchical structure. The results of a case study in the Pearl River Delta (PRD) urban agglomeration show that (1) the total night-time light in built-up areas (BNTL) has the highest Pearson correlation coefficient with GDP, which is 0.82; (2) there is an obvious hierarchical structure within the PRD; (3) there are significant and sustained economic spillover effects among the core cities, with Guangzhou–Foshan and Shenzhen–Dongguan having more obvious spillover effects; and (4) the economic spillover effects within the three metropolitan areas have different characteristics. The Guangzhou–Foshan–Zhaoqing metropolitan area is closely linked, to apparent differences in the pace of spillover effects. The Shenzhen–Dongguan–Huizhou metropolitan area has strong close linkages, with strong synchronization of spillover effects. The Zhuhai–Zhongshan–Jiangmen metropolitan area has not yet formed a stable synergistic development relationship. Overall, the framework can effectively reveal the hierarchical structure and different characteristics of economic spillovers within urban agglomerations, which can provide a scientific reference for policy making related to the coordinated development of such agglomerations. Full article

As one of the world’s four Greater Bay Areas, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) faces many environmental challenges along with rapid economic development, causing significant degradation of aquatic ecosystems. However, there is limited knowledge on long-term environmental changes (i.e., >50 years), and restoration of the degraded aquatic ecosystems in the GBA has become increasingly difficult. This study selects a typical inland water body, the Miaotan Reservoir, from Huizhou City in the GBA, to explore long-term changes in water and the eco-environment over the past 70 years and to provide some restoration and management strategies for degrading aquatic ecosystems in the region. We collected a sediment core from the reservoir center and established an age–depth profile by integrating ²¹⁰Pb and ¹³⁷Cs dating. We then set up high-resolution diatom community succession stratigraphy and multiple indicators (grain size, element, geochemical and social indicators) as responses to environmental changes in the reservoir. Our results show that significant changes have occurred in the ecosystem and environment of the Miaotan Reservoir and its catchment over the past 70 years. The diatom community underwent a gradual transition from absolute dominance of the mesotrophic species Aulacoseira granulata to dominance of the eutrophic species Nicizschia gracilis, Nicizschia palea and Achanathes sp., indicating the onset of water quality degradation and ecosystem changes in the 1990s due to eutrophication. The RDA (Redundancy analysis) results demonstrate that exogenous pollutant inputs into the Miaotan Reservoir resulting from agricultural activities over the period led to serious environmental changes, e.g., toxic algal bloom and heavy metal pollution. This study enriches our understanding of long-term environmental changes in inland lakes and reservoirs in South China and provides insights into the restoration and management of aquatic ecosystems in the GBA. Full article

Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, the integration of high-resolution nighttime light and spatial big data for assessing relative poverty is still limited. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, this study takes the Pearl River Delta, where there is a large disparity between the rich and the poor, as an example. We integrated Luojia 1-01, points of interest, and housing prices to construct a big data poverty index (BDPI). To evaluate the performance of the BDPI, we compared this new index with the traditional multidimensional poverty index (MPI), which builds upon socioeconomic indicators. The results show that the impoverished counties identified by the BDPI are highly similar to those identified by the MPI. In addition, both the BDPI and MPI gradually decrease from the center to the fringe of the study area. These two methods indicate that impoverished counties were mainly distributed in ZhaoQing, JiangMen and HuiZhou Cities, while there were also several impoverished parts in rapidly developing cities, such as CongHua and HuaDu Counties in GuangZhou City. The difference between the two poverty assessment results suggests that the MPI can effectively reveal the poverty status in old urban areas with convenient but obsolete infrastructures, whereas the BDPI is suitable for emerging-development areas that are rapidly developing but still lagging behind. Although BDPI and MPI share similar calculation procedures, there are substantial differences in the meaning and suitability of the methodology. Therefore, in areas lacking accurate socioeconomic statistics, the BDPI can effectively replace the MPI to achieve timely and fine-scale poverty assessment. Our proposed method could provide a reliable reference for formulating targeted poverty-alleviation policies. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is rich in natural and marine resources, and it is scientifically valuable to study the evolution patterns and driving mechanisms of the ecosystem service values (ESVs) of the GBA for the governance and conservation of its ecosystems. Based on the land use changes in the GBA from 2000 to 2020, the ESVs in the GBA were measured at the grid scale, and the Markov model was used to predict the ESVs in 2030; the calculated results were used to analyze the spatial and temporal variation characteristics of the ESVs during the 30-year period, while the driving role of the topographic factors on the ESVs is revealed through the construction of the geographically weighted regression model (GWR). The results show the following: (1) During the 20-year period, the area of arable land and water in the GBA fluctuated greatly, with the area decreasing year by year and shifting mainly into construction land; in terms of shifting the center of gravity of the land, and the center of gravity of the grassland and unused land shifted the greatest distance due to the expansion of construction land, with the center of gravity shifting westward as a whole. (2) The ecosystem services (ESs) in the GBA show obvious aggregation in the spatial distribution, with the total ESVs decreasing year by year. Among them, the areas with an increasing total value are mainly located in the cities of Zhaoqing and Huizhou in the GBA, accounting for 27%, and the areas with a decreasing total value year by year are mainly located in the dense urban areas in the central part of the GBA, accounting for 35%, and the area is increasing, indicating that the habitat quality is deteriorating, and the model prediction shows that the value of ecosystem services in 2030 have a decreasing trend under the development of the natural state. (3) Topographic factors have a significant influence on the ESVs, and in terms of spatial distribution, the areas with the strongest effect are distributed in the northwestern and northeastern parts of the GBA, and the main uses for the land are wood land, arable land, water and the area of the water–land intersection near the sea. Full article

Background: ship emissions have an adverse effect on air quality in coastal regions, and this effect can be exacerbated by onshore winds. Objectives and methods: to investigate the impact of ship emissions on air pollutant concentrations during the onshore wind period in a low-latitude region in China, this study applied the WRF/Chem model to simulate the contribution of ship emissions to PM_2.5 and O₃ by “zero-out” in 2018, in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Results/findings: results show that the onshore winds facilitated the transport of ship-emitted pollutants to inland areas, causing the contribution of ship emissions to PM_2.5 exceeding 4 μg/m³ to areas north of Guangzhou in April and west of the GBA in October. The impact of onshore winds on the ship contribution to the O₃ concentration shows a bidirectional trend both spatially and monthly. The onshore winds raised the ship contribution to O₃ concentrations in April by 1.54 μg/m³, while exacerbated the decreasing contribution in other months. In VOC-sensitive cities such as Foshan, onshore winds exacerbated the negative contribution of ship emissions to O₃ concentrations; while in NOx-sensitive cities such as Huizhou, they enhanced the contribution of ship-induced O₃. Novelty/Improvement: this paper fills a gap in the study of pollutants transportation characteristics from ship emissions under the influence of onshore winds in the GBA. Our results demonstrate the importance of considering meteorological conditions and atmospheric chemical mechanisms regarding the coastal air pollution prevention caused by ship emissions. Full article

It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data (NTL) of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, Emerging Spatio-Temporal Hotspot Analysis (ES-THA), and Theil index (TL), this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta and used a geographical detector to determine the influencing factors of the differentiation. The results of the study showed that NTL could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 18.7385% in the Delta. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference in carbon emissions at the municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou (Gz), Shenzhen (Sz), Zhongshan (Zs), and Huizhou (Hz), were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing (Zq) in the northwestern part of the agglomeration has always been a low-value clustering and a spatio-temporal cold spot because of its population, economy, and geographical location. The carbon emission differences in the Delta cities were mainly caused by carbon emission differences within the cities at the municipal level, and the cities faced the challenge of regional differences in the reduction in per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Therefore, the results of this study provide a scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in the Delta. Full article

As a crucial paradigm for addressing urbanization-related problems, smart community construction is in full swing, and its goal is to enhance residents’ sense of gain. Prior studies have not been able to account for all aspects of smart community construction, especially the evaluation tools from the perspective of residents’ sense of gain. Therefore, this paper seeks to establish a comprehensive evaluation framework for residents’ sense of gain in the smart community through the integrated method, which includes the entropy weight method (EWM), the fuzzy comprehensive evaluation (FCE), and the obstacle degree model (ODM). For the purpose of verifying the feasibility of the evaluation framework, 31 smart communities in 6 Chinese cities (Shenzhen City, Putian City, Huizhou City, Dongguan City, Zhengzhou City, and Luoyang City) were selected. The results indicated that the weight of “Cultural activities for the elderly” indicator is the highest while the “Overall design” indicator is the lowest. In addition, Putian City had the best performance, but Shenzhen City ranked last among the six cities. Moreover, among the 31 communities, the Fengshan community in Putian City performed the best while the Xinglong community in Luoyang City performed the worst. Several suggestions are proposed to improve residents’ sense of gain in smart communities, such as enhancing the quality of healthcare services, meeting the needs of the elderly through multiple channels, and enriching business services. This study not only innovates the evaluation method of smart community construction from the perspective of residents’ sense of gain but also provides suggestions for promoting the sustainable development of the smart community and enabling residents to feel more satisfied. Full article

Huizhou villages are representatives of traditional villages and have a high historical, cultural, and tourism value. In view of the problems of low commercial efficiency due to the small scale of commercial space and the imperfect layout in Longchuan Village, Jixi County, Xuancheng City, Anhui Province, this research explores the spatial advantages of Longchuan Village’s commercial layout through an analysis of street and lane space syntax and a commercial space resistance model. The research on the spatial syntax of streets mainly focuses on the analysis of the spatial accessibility, line-of-sight integration, and spatial comprehensibility of Longchuan Village’s streets. The commercial space resistance model mainly studies the attraction of tourism resources to tourists in order to select the most suitable area for the layout of commercial space. The results of the analysis show the following: (1) The integration degree of traffic and the sight line is relatively high at County Road and Water Street in Longchuan Village, so these two places have better accessibility and more sight lines. (2) Longchuan Village has a good spatial understanding in a small area, so it is not suitable to distribute commercial space but rather to centralize it. (3) In the commercial space layout resistance model, the area around Water Street and Qixing Pond has the smallest spatial resistance and the greatest opportunity for population gathering, making it the most suitable for a commercial layout. On the basis of the analysis results, this paper puts forward an optimization strategy of Longchuan Village’s commercial space layout in a targeted manner to help Longchuan Village achieve a better commercial layout. The research contribution of this paper will help planners and architects to take advantage of space to plan the commercial space of traditional tourist villages so that they can exert a better commercial value and tourism effect and to promote the tourism development of traditional villages across the country. Full article