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• This study summarizes all existing and unpublished data on the Late Triassic plant fossils in Guangdong. • Four regional assemblages have been distinguished in the Late Triassic by marine fossils such as ammonites and bivalves. • The Late Triassic climate in the Guangdong region was mainly warm and humid, either tropical or subtropical. • During the Late Triassic, central and northern Guangdong were located at similar latitude, along the southern coastline of the South China Block. Triassic-Jurassic deposits are well developed in Guangdong Province, South China. In particular, the Upper Triassic coal-bearing strata which alternate between marine and terrestrial deposits yield diverse and abundant fossil plants, ammonites and bivalves. These strata provide a significant reference for the study of palaeoenvironmental variations in the lower latitude regions. In this paper, we review the major progress on Late Triassic floral studies in this area. Specifically, we compare the regional plant assemblages from different fossil sites in central and northern Guangdong which have been dated with marine fauna. 155 species belonging to 56 genera of fossil plants have been reported so far in northern and central Guangdong. The Late Triassic floras in Guangdong are mainly composed of Bennettitales and ferns, followed by horsetails, seed ferns, cycads, ginkgos and conifers. In ascending order, four regional plant fossil assemblages can be recognized in the Late Triassic deposits, namely the Pachypteris - Lindleycladus Assemblage (Julian), the Pterophyllum - Baiera Assemblage (late Julian), the Clathropteris - Otozamites Assemblage (Tuvalian), and the Danaeopsis - Anomozamites Assemblage (Rhaetian). In general, the Late Triassic climate in the Guangdong region was mainly humid and warm and either tropical or subtropical. The fossil plants corroborate palaeomagnetic evidence that the central and northern Guangdong region was located at approximately the same latitude as it is today and formed the southern coastline of the South China Block during the Late Triassic. Palaeogeographically, the transgression started at the end of the Julian and the south coastal terrane consisted of a western bay, a peninsula and an eastern bay. During the regression period, post-Rhaetian, the bays evolved into a gulf coastal plain. [ABSTRACT FROM AUTHOR]

In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m. [ABSTRACT FROM AUTHOR]

Hydrogen is a type of clean secondary energy, and the hydrogen energy supply is vital to regional energy development. Guangdong is at the forefront of hydrogen energy deployment in China, and aims to achieve peak carbon by 2030. However, hydrogen production research is challenging due to complex technical routes, technical maturity levels, life-cycle carbon emissions, and costs. Multi-time-scale research is necessary to explore optimal production paths and their contributions to CO 2 emission reduction. This study discussed key technologies and the technical maturity of five hydrogen production routes: production from industrial byproducts and coal, wind, hydro-, and photovoltaic power. Policy scenarios were designed to investigate the potential of these routes and technology deployment intensity under medium- and long-term scenarios. This paper proposes a roadmap for hydrogen development and provides a reference for hydrogen production planning in Guangdong. This methodology can be applied in other regions for hydrogen energy development research. • Key technology maturity of the hydrogen production routes was evaluated. • Roadmap of hydrogen production in Guangdong Province was designed. • A method for research of hydrogen supply scenarios was proposed. [ABSTRACT FROM AUTHOR]

A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy. • A pseudo-local reserve market is established considering EV's reserve potential. • Optimal bidding strategy for DSO in China's electricity markets is constructed. • A framework for EV coordinated V2G operation is developed. • The proposed method can limit load deviation cost for more profits. [ABSTRACT FROM AUTHOR]

• From the perspective of spatial cross-sectional data, this study explores causal inference in the ecological environment. • Two primary causal pathways are identified. • The study unveils the intricate mechanisms among diverse factors within the ecological environment system. The ecological environment is closely related to natural conditions, climate change, and human activities. However, existing research predominantly concentrates on evaluating the direct impacts of various factors on the ecological environment, yet lacks an in-depth exploration of their causal relationships and pathways. This paper introduces a new perspective to research in this field by incorporating spatial cross-sectional data analysis methods. The study utilized the 2020 China's High-resolution Eco-Environmental Quality (CHEQ) dataset to evaluate the spatial differentiation characteristics of Ecological Environmental Quality (EEQ) within the western urban agglomeration of Guangdong Province. A framework of "terrain-urbanization-climate" influencing factors was constructed, and the Geographical convergent cross mapping (GCCM) method was employed to infer the causal relationship between EEQ and influencing factors. Additionally, the spatial distribution characteristics of influencing factors on ecological environment quality were studied using the Multiscale Geographically Weighted Regression Model (MGWR). The results indicate that seven influencing factors including Elevation, Population, Precipitation, TEMP, TMMX, TMMN, and Slope have clear causal relationships with EEQ and exhibit bidirectional coupling relationships. There exist complex causal relationships among influencing factors, revealing the interaction mechanisms and causal pathways among multiple factors in the ecological environment system. These analyses enrich our understanding of the intricate and complex relationships between the ecological environment and influencing factors, providing a valuable reference for ecological protection and sustainable development. [ABSTRACT FROM AUTHOR]

Carbon price prediction serves as a market indicator and economic driver, facilitating the adoption of more environmentally friendly production methods to achieve the emission reduction targets outlined in the Paris Agreement. In recent years, time series analysis and decomposition techniques have been widely applied to carbon price forecasting. However, few researchers have considered the issue of data feature drift caused by real-time decomposition. Specifically, as the sample size increases, data features undergo changes, rendering the trained models unable to fit the new data. This paper explains the underlying reasons for this phenomenon from new perspectives and proposes a novel paradigm that replaces the intrinsic mode function with a single-step fuzzy particle. This new paradigm corrects the issue of data feature drift and concentrates noise into a completely new sequence during the preprocessing stage. In the subsequent processing steps, the loss of correlation between the sequence and time lag, as well as the sequence and carbon prices, is addressed through multi-information association. This paradigm hybrid model can be applied to deterministic and interval multi-step predictions. Experimental results on the datasets from Guangdong and Hubei demonstrate that the proposed model outperforms other comparative models, achieving better predictive results than existing decomposition-based forecasting models. In the case of Guangdong, the normalized root mean square error (NRMSE%) for one-step, three-step, and five-step deterministic predictions are 2.18%, 2.51%, and 2.89%, respectively. The average interval score (AIS) for interval predictions are −0.357 and − 0.2375, respectively. • Provide new perspectives on the data feature drift caused by real-time decomposition. • Propose a novel paradigm to address the practical challenges of real-time decomposition. • Multi-information association solves the problem of correlation loss in traditional decomposition techniques. • This paradigm can be combined with secondary decomposition and applied to deterministic and interval multi-step prediction. [ABSTRACT FROM AUTHOR]

The construction of an ecological security pattern (ESP) is an important undertaking in ensuring the stability of the ecosystem and the long-term growth of humanity. Existing research, However, existing research often overlooks the quantification of policy implications in ESP development. The objective of this paper was to bridge this gap by integrating policy quantification analysis (PQA) with nature, society and into ESP construction and providing targeted ecological management recommendations. The policy texts were quantified into reusable research data, which served as a foundation for the selection of indicators and the optimization of ecological security patterns, by using machine learning techniques. The methodology "Patch-matrix-corridor" was used to create a spatially correlated ecological security pattern. Through factor analysis and Random Forest (RF), the impact of each indicator on ecological security was thoroughly examined, offering substantial support for policy development and ESP construction planning. The Guangdong-Hong Kong-Macao Greater Bay Area(GBA) was selected as the study area, the study found that: (1) The proposed framework PQA based-ESP could provide a useful eco-management tools. (2) Policy adaptability had a substantial impact on environmental security in the GBA, but the ecosystem service index has the highest impact. (3) Applying the framework, we identified a total of 1,993 km2 of ecological sources, 23 key ecological corridors, 58 general ecological corridors, and 31 ecological nodes. An improved ESP, characterized as "one circle, two bays, three rivers, multiple areas, corridors, and nodes," was constructed in the GBA. (4) Given the multi-scale analysis, tailored ecological management policy recommendations were proposed for each city in the GBA. The study's findings offer actionable solutions for the international community facing growing ecological challenges. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma is one of the most prevalent malignant tumors in Guangdong, China. In the field of medicine, predicting the recurrence of early-stage nasopharyngeal carcinoma after radiotherapy holds significant importance. Our objective was to develop a novel classification model aimed at predicting recurrence following radiotherapy for early nasopharyngeal carcinoma. This paper introduces an innovative approach by combining unsupervised diagnostic feature learning (biclustering algorithm) with Adaboost ensemble learning to create a novel classification model. Notably, the nasopharyngeal carcinoma dataset underwent biclustering algorithm application for the first time. Initially, the Borderline-SMOTE oversampling algorithm was employed to address the dataset's imbalance issue. Subsequently, the biclustering algorithm, which is based on an improved multi-objective genetic algorithm (NSGA-II), was utilized to seek bicluster outcomes exhibiting consistent representation patterns. The attributes of these bicluster outcomes were assessed and employed as diagnostic rules for constructing weak classifiers. Ultimately, the Adaboost ensemble learning technique was employed to amalgamate the weak classifiers into a robust classifier. Following the application of 10-fold cross-validation, the model exhibited an accuracy of 80.33%, sensitivity of 79.55%, specificity of 80.43%, and GMean of 79.99%, accompanied by an AUC value of 0.904. The presented classification model outperformed alternative classification models in terms of classification accuracy and generalization when applied to the nasopharyngeal carcinoma dataset. Consequently, it serves as a valuable tool to aid medical professionals in predicting the likelihood of recurrence in the cervical lymph nodes following radiotherapy for early-stage nasopharyngeal carcinoma. • Developed a novel classification model for nasopharyngeal carcinoma MR feature data. • Using the Borderline-SMOTE algorithm to address the imbalance issue. • Innovatively combining a biclustering algorithm with FSDND. • Experimental results demonstrate the effectiveness of this classification model. [ABSTRACT FROM AUTHOR]

Along with habitat destruction and the intensification of climate change, economic and social development is threatening the living of species. In the context of China's domestic economic circulation, the spatial structure of biodiversity loss is complicated by the decentralization of production, with many regions affecting the survival of species in other regions. Based on the environmentally extended MRIO model, this paper calculated the subnational biodiversity loss footprint through structural path analysis and social network analysis, identified the regional linkages and spatial networks of species loss, and traced the conservation responsibility to specific sectors. Our findings indicate that Yunnan, Tibet, Guangdong, and Sichuan possess the most significant biodiversity footprints, comprising over 40% of the total footprint. In terms of threat patterns, Yunnan and Tibet have the largest intra-regional footprints, indicating their important impact on species within the region; Guangdong and Zhejiang have the largest extra-regional footprints, indicating their role in threatening other species through cross-regional trades. The primary industry, tap water and food and tobacco sectors are major biodiversity footprint producers. Overall, some eastern and coastal areas are the final drivers of biodiversity loss in China, driven by increasingly complex trade links. Notably, the directionality of species loss in biodiversity hotspots is evident, with Shandong and Guangdong significantly influencing the biodiversity of Yunnan and Guangxi, respectively. This study systematically analyzed the complete network of biodiversity loss and divided four biodiversity protection communities to promote the realization of trans-regional protection and compensation. Examining the biodiversity loss through the lens of networks, and connecting economic activities to species, serves as a warning to decision-making bodies that a future of increasingly intricate domestic trade could pose a critical threat to biodiversity, necessitating collaborative conservation efforts. [Display omitted] • 1.Inter-provincial trade contributes to the complex structure of biodiversity loss. • The characteristics of biodiversity footprints vary widely among provinces. • Some eastern and coastal provinces are the final drivers of biodiversity loss. • Four protection communities are established through social network analysis. [ABSTRACT FROM AUTHOR]

• A method based on Neural Network is proposed for provincial-level CO 2 emissions estimation. • An autoencoder is proposed to downscale the provincial-level CO 2 emissions to city-level. • A complete city-level CO 2 emissions inventory of GBA and surrounding cities is complied. • Spatial dependence and agglomeration characteristics of CO 2 emissions are analyzed. • Spatio-temporal heterogeneity of factors affecting CO 2 emissions is investigated. Significant attention has been given to the issue of CO 2 emissions worldwide, especially for China as the largest emitter. Cities, as the main carriers of China's economic development, account for up to 85 % of CO 2 emissions from energy use in the country. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and surrounding cities play important leading roles in low-carbon development. However, compiling city-level CO 2 emissions is challenging due to the limited availability of data. In this paper, a new idea is proposed by using SSA-BP for estimating provincial-level CO 2 emissions and downscaling provincial-level CO 2 emissions to city-level CO 2 emissions by reconstructing missing values of city-level CO 2 emissions with an autoencoder. This study compiles a city-level CO 2 emissions inventory and conducts a spatio-temporal analysis of CO 2 emissions in the GBA and surrounding cities. The results show that CO 2 emissions exhibit a spatial distribution pattern with spatial dependence. Furthermore, according to the analysis of spatio-temporal heterogeneity, GDP influenced CO 2 emissions the most within the five influencing factors, and the influences of GDP, population, and energy intensity were mainly positive, while the influence of patents was mainly negative, and that of trade was both positive and negative. [ABSTRACT FROM AUTHOR]

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China's rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers. • This study proposed a social media data-based index for assess rural population hollowing (RPH). • The index is effective in evaluating the RPH at fine spatial resolutions such as townships. • We revealed the spatially non-stationary relationship between RPH and the associated factors. • We further put forwards the policy zoning and planning implications for rural revitalization. [ABSTRACT FROM AUTHOR]

Understanding the patterns of energy usage through simulation and prediction from multiple perspectives is crucial for alleviating energy-environmental conflicts and achieving sustainable energy system development. Despite their importance, multidimensional interactions among the production, demand, and supply sides have not been systematically characterized, thus representing a significant research gap. This paper aims to address this gap by developing a a multidimensional factorial enviro-economic (MFEE) model, which offers an in-depth investigation of Guangdong's energy system. This model, designed for retrospective decomposition (1997–2017) and prospective projections (2035), uniquely links historical and future analysis from systematic and sectoral perspectives through structural decomposition analysis, sectoral evolution analysis, and the Biproportional Scaling RAS method. Our findings reveal that the final demand level and Research & development (R & D) are the principal drivers of increased energy usage. We also found that the wholesale and retail sector have the most significant impact on demand and input sides, accounting for over 70 %. Furthermore, the interaction of key sectors from the production-based perspective notably influences system symbiosis. To effectively manage these dynamics, our study recommends regulating and controlling measures for the circulation sector that consider the benefits of both the energy sector and the accommodation and catering sector. This comprehensive analysis, therefore, offers scientifically-supported guidance for sustainable energy system development. • A multidimensional factorial enviro-economic (MFEE) model from Guangdong perspective is developed. • Approaches of retrospective decomposition and prospective projection for energy systems are conducted. • Interactive effects of various socioeconomic activities on economy and environment are characterized. • The main effects of wholesale and retail sector is the strongest, accounting for more than 70 %. [ABSTRACT FROM AUTHOR]

To address the deficiencies in traditional communication towers in mobile communication engineering, such as heavy weight, easy corrosion and poor durability, this study proposes replacing traditional communication towers with GFRP communication lattice towers with lightweight, high-strength and good durability. Firstly, GFRP material test and GFRP circle tube bending capacity test are conducted. The maximum bending moment that can be subjected to the anchorage end of GFRP circular tube is 18.20 kN·m. Finite element analysis of GFRP circular tubes verifies the accuracy of the bending test results. Secondly, a GFRP communication lattice tower with a height of 25 m is designed adopting GFRP circular tubes as tower columns. Through the bending test of the GFRP tower, it is verified that the connection between the columns and web members of the GFRP tower is reliable and the force pattern of the GFRP tower is reasonable. The large deflection theory can be adopted to the displacement of GFRP towers accurately. Thirdly, according to the actual engineering requirements, the finite element analysis of 25 m-high GFRP tower under the basic wind pressure of 0.45 kN/m2 is carried out by using 3D3S (a spatial structure design software). It can be obtained that the vibration mode of the GFRP communication tower shows two forms of lateral bending and axial torsion. 0° wind load direction has the greatest influence on the displacement of the GFRP tower. The stress ratio of each component in the GFRP tower is less than 1, indicating that no damage occurs in the GFRP tower. Finally, GFRP towers have good economic benefits, good environmental performance and wide range of application after comparing the GFRP tower and traditional steel tower. The demonstration project of the GFRP communication lattice tower is established in Qingyuan City, Guangdong Province, China. When subjected to a typhoon with a wind speed of 17.26 m/s, The horizontal displacement of the GFRP tower top is 10 m, which is about 2/5 of the height of GFRP tower. Although the displacement is large, the GFRP tower is still not destroyed. It can be concluded from the research in this paper that GFRP towers have low cost, good mechanical properties, good durability, and wide application range, which can promote the rapid construction of 5 G communication base stations. [Display omitted] • The bending moment of the GFRP tube can reach 18.20 kN·m. The finite element analysis match well with bending test. • A 25-m-high GFRP lattice tower is designed. The deformation of GFRP tower can be calculated by large deflection theory. • The vibration mode of the GFRP tower shows two deformation forms: lateral bending and axial torsion around the z-axis. • The GFRP tower has a large displacement but no damage, when subjected wind with the speed of 17.26 m/s. • Applying GFRP tower can effectively improve the construction efficiency of 5 G communication base station. [ABSTRACT FROM AUTHOR]

[Display omitted] • Groundwater vulnerability zonation investigation is conducted for GHM Greater Bay Area. • Three different perspectives are comprehensively considered in this study. • Groundwater risk, function value and contamination status are superposed using the AHP method. • Quantification and visualization of groundwater contamination prevention regionalization. • The AHP-DRASTICH model is an effective method to evaluate the groundwater risk in shallow aquifers. The groundwater contamination assessment and prevention regionalization are an important part of groundwater investigation, which is of great significance to groundwater environmental management and contamination control. Although the Guangdong-Hong Kong-Macao Greater Bay Area (GHM Greater Bay Area) has faced significant groundwater contamination since 2020, there's been no investigation into its vulnerability zonation. In this paper, groundwater in a typical area of south China was taken as the research object. We analyzed groundwater contamination risk by considering both the groundwater vulnerability and the load from contamination sources. Using the analytic hierarchy process (AHP) method, we superposed the groundwater risk, function value and contamination status, and realized the quantification and visualization of groundwater contamination prevention regionalization. Based on the DRASTIC model, the zones of low, moderate, high, and very high vulnerability are respectively 14.2 %, 24.28 %, 37.67 %, and 20.45 % of the study area. Results show that the groundwater vulnerability grade in the area is III-IV, which means it is easy to be polluted. The functional value of groundwater in the south-central part of the region is relatively high. Most of the areas with high prevention and control levels are located in plain areas and the Piedmont plain area with an area of 460 km2. The secondary control levels (key protection area) are mainly located in the middle, southwest, and northeast of the plain, with an area of 6944 square kilometers. Consequently, the ideas and contamination prevention zone maps might provide a scientific foundation for long-term planning and management and groundwater conservation in the Guangdong-Hong Kong-Macao Greater Bay Area and peripheral zones. [ABSTRACT FROM AUTHOR]

As the population and industries continue to gather in cities, such cities face acute shocks caused by various natural disasters and high pressure caused by human interference. The means of accurately assessing and improving the level of urban resilience (UR) to cope with disturbances and shocks, enhance regional resilience, and improve sustainable urban development has become a hot topic. In this study, we constructed an UR assessment framework using "social-economic-institutional-ecological-engineering" to explore the dynamic evolution of spatial and temporal patterns in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, to reveal the main factors influencing the evolution of this spatial pattern and the strength and interaction mechanisms among such influencing factors and to dynamically simulate the spatial and temporal evolution characteristics in the next 16 years. The results show that from 2000 to 2019, the difference in UR level in the GBA is obvious. Hong Kong and Macao are obviously ahead of other cities, and the comprehensive resilience level of Guangzhou and Shenzhen cities in the mainland is developing rapidly. The difference in per capita consumption capacity, industrial structure optimization, foreign trade vitality, infrastructure support and other factors are the main causes of the spatial differentiation of UR in the GBA. In the future, the UR level of the GBA urban agglomeration will show an overall upwards trend. The three development poles of Guangzhou-Foshan, Shenzhen-Hong Kong, and Zhuhai-Macao have obvious advantages in spatial distribution. The results of the study can enrich regional UR research, and also provide theoretical references to facilitate the high-quality development of the area. [Display omitted] • We constructed a "social-economic-institutional-ecological-engineering" urban resilience assessment framework. • The paper developed an evaluation system for the urban resilience to reveal its spatial and temporal evolution patterns. • The main factors influencing the evolution of the spatial patterns of urban resilience is investigated. • Our results provide a basis for the development of comprehensive management framework for urban resilient. [ABSTRACT FROM AUTHOR]

In this paper, the typical 10-lane highway in the reconstruction and expansion project is taken as an example to clarify the noise propagation and distribution characteristics of ultra-wide cross section. With the aid of the highway noise prediction model based on the acoustic environment guidelines and field measurements, the noise propagation and distribution characteristics under ultra-wide cross section were summarized through visualized noise maps and quantitative data analysis. The results show that for the highway with ultra-wide cross section, reducing the speed of small vehicles can improve noise reduction in the horizontal direction by about 1 dB(A) than reducing the speed of large and medium-sized vehicles. In addition, the noise reduction effect of all floors can be improved by 0.2–0.9 dB(A) in the vertical direction, which proves that the noise reduction effect is more obvious by reducing the speed of small vehicles. Besides, the noise sound pressure level variation range on low-rise floors increases more significantly with increased traffic volume than high-rise floors. The variation between different building floors is also similar to the increase in traffic volume in the vertical direction. The result can provide theoretical guidance for the prevention of highway noise under ultra-wide cross section. • The noise propagation of highway under ultra-wide cross section was investigated. • 2D and 3D spatiotemporal noise maps were presented to analyze traffic noise. • Noise reduction effect of reducing small vehicles' speed was more obvious. • Provide reference for the prevention of highway noise of extension engineering. [ABSTRACT FROM AUTHOR]