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Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures. [ABSTRACT FROM AUTHOR]

Urban land subsidence is one of the important limiting factors for urban development, and strengthening the monitoring of urban surface subsidence contributes to long-term urban planning and construction needs. As a subcentral city in the Yangtze River Delta region of China, it is particularly important for Heifei to enhance the monitoring of surface subsidence. In this study, the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) and small baseline subset (SBAS) techniques were used to process 139 scenes of Sentinel-1A SAR satellite ascending orbit data covering the central urban area of Hefei from July 2015 to July 2020, obtaining subsidence information for the region. The results show that the overall urban area of Heifei is relatively stable, but there are certain deformation trends in local areas. Four regions with obvious deformation were selected for the in-depth analysis of their deformation time series. The experimental study in this paper demonstrates that the PS-InSAR and SBAS techniques can achieve the accurate monitoring of large-scale ground subsidence in urban areas, ensuring the safety of buildings and their surrounding environments, and providing information support for urban security maintenance monitoring. [ABSTRACT FROM AUTHOR]

Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes. However, the long-range, S-band operational radar network has severe limitations in providing high-resolution observations in the lower atmosphere due to the earth curvature. This paper presents an innovative technique of using shorter-range, low-power, fast scanning X-band radar for monitoring wildfires. Sample observations from wildfire events in southern China show that the polarimetric radar correlation coefficient of wildfire echoes varies from 0.55 to 0.75, with an average value of 0.61; the differential reflectivity varies from 2.28 to 5.08 dB with an average value of 3.99 dB; the differential propagation phase varies from 40.27° to 69.27°, with an average value of 56.66°; and all observables exhibit subtle changes at different scanning elevation angles. In addition, changes in echo intensity observed by higher elevation scans lag behind those observed by lower elevation scans, and the change of wildfire area lags behind the variation of echo intensity. Given the emerging interest in deploying such compact, cheaper X-band systems for urban hydrometeorological applications, it is expected that these radars, customized with flexible scan strategy, will provide new opportunities and advantages in wildfire detection and mitigation. • Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes • The operational long-range, S-band radar has severe limitations in providing high-resolution low-level measurements • The compact and low-power X-band dual-polarization radar is more efficient in monitoring wildfire and its evolution • The polarimetric observables, especially the correlation coefficient, can effectively identify the wildfire smoke plumes [ABSTRACT FROM AUTHOR]

In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China. [ABSTRACT FROM AUTHOR]

In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area. [ABSTRACT FROM AUTHOR]

A smart city is regarded as a new form of urban development proposed by humans to cope with urban problems. Smart cities are of great interest to theoretical circles and are favored by city governments. However, in China, research on common problems and solution strategies in the process of smart city construction is still insufficient. To address this insufficiency, we utilized Python language and the Selenium library to realize a web crawler and construct a smart city corpus background library. Then, the word2vec model was used for word vectorization to divide policy texts into phrases, and 132 policies from 85 cities above the prefecture level in China were analyzed using the Gaussian mixture model (GMM). The results showed many problems in the smart city construction process in China, including the homogenization of content, the wide topic coverage, the influence of higher-level governments, the mismatch with the local situation, and the neglect of relationship governance. Moreover, an efficiency-governance model was designed, and it held certain theoretical and practical guiding significance for further promoting the healthy and sustainable development of smart city construction in China. [ABSTRACT FROM AUTHOR]

Historical news media reports serve as a vital data source for understanding the risk of urban ground collapse (UGC) events. At present, the application of large language models (LLMs) offers unprecedented opportunities to effectively extract UGC events and their spatiotemporal information from a vast amount of news reports and media data. Therefore, this study proposes an LLM-based inventory construction framework consisting of three steps: news reports crawling, UGC event recognition, and event attribute extraction. Focusing on Zhejiang province, China, as the test region, a total of 27 cases of collapse events from 637 news reports were collected for 11 prefecture-level cities. The method achieved a recall rate of over 60% and a precision below 35%, indicating its potential for effectively and automatically screening collapse events; however, the accuracy needs to be improved to account for confusion with other urban collapse events, such as bridge collapses. The obtained UGC event inventory is the first open access inventory based on internet news reports, event dates and locations, and collapse co-ordinates derived from unstructured contents. Furthermore, this study provides insights into the spatial pattern of UGC frequency in Zhejiang province, effectively supplementing the statistical data provided by the local government. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals, adopted by all United Nations Member States, emphasize sustainable cities and communities, aiming to strengthen development planning and foster positive links between urban and rural areas by 2030. As the newly leading platform for urbanization development, characteristic towns ('Tese Xiaozhen' in Chinese) contribute to the target of sustainable urbanisation, wherein town policies are especially important for leading the sound development of characteristic towns. However, the effect of these characteristic towns policies remains uncertain. Additionally, most related studies have utilized qualitative methodologies in policy evaluation, which may be inadequate to guide actual practice. Thus, in order to accurately acknowledge the effect of characteristic towns policies, a policy evaluation framework is established in this paper, utilising the multiple streams theory, text mining and the Policy Modelling Consistency Index model (PMC-Index model). Then, taking 225 policy documents promulgated in China from 2015 to 2022 as the research objects, policy evaluation indicators were selected by combining the multiple streams theory to improve the PMC-Index model and a text mining method. Thereafter, an empirical analysis was conducted to evaluate the consistency of 10 characteristic towns policies chosen. The results indicate that the mean value of the PMC indexes of the 10 examined policies is 7.13 in the total of 9 points, which means that the general performance of the characteristic town policies is satisfactory, yet the low scores in terms of effectiveness, content, and guarantee. In the end, some recommendations and suggestions are proposed for clarifying administrative attributes, optimizing the integration of urban and rural resources, and adjusting the policy hierarchy to a bottom-up approach. This study not only provides a policy evaluation framework to comprehensively understand the consistency of global urbanization policies but also offers a beneficial reference for promote urban–rural sustainable development. [ABSTRACT FROM AUTHOR]

Intensification of short‐duration rainfall extremes contributes to increased urban flood risk. Yet, it remains unclear how upper‐tail rainfall statistics could change with regional warming. Here, we characterize the non‐stationarity of rainfall extremes over durations of 1–24 hr for the rapidly developing coastal megalopolis of the Greater Bay Area, China. Using high‐resolution, multi‐source, merged and gridded data we observe greater increases in rainfall intensities over the north‐central part of the region compared with the southern coastal region. Our results show, for the first time, that urbanization nonlinearly increases rainfall intensities at different durations and return periods. Over short durations (≤3‐hr) and short return periods (2‐yr), urban areas have the greatest scaling rates (≥19.9%/°C). However, over longer durations (≥9‐hr) rural areas have greater scaling rates, with a lower degree of dependency on both durations and return periods. Plain Language Summary: Short‐duration (sub‐daily) rainfall extremes are major drivers of flash floods and hence significant disruptions to society. Previous modeling and statistical studies show that urbanization intensifies short‐duration rainfall extremes. However, there has been less attention to regional variations in rates of rainfall intensification under a warming climate, particularly for extreme events with return periods that are comparable to or longer than the years of record. In this study, we investigate changes in rainfall extremes over the Greater Bay Area, China using long records of high‐resolution data merged from gauge networks, satellite observations, and reanalysis products. This enables us to evaluate changes in low‐frequency rainfall extremes (2‐ to 100‐yr return periods) over different land surfaces, under a warming climate. We find that increases in rainfall extremes significantly depend on the duration and return period of events, with the largest scaling occurring for short‐duration "nuisance" rainfall intensities over urban areas. Key Points: Non‐stationarities of sub‐daily rainfall extremes over a coastal megalopolis exhibit marked land cover and duration dependenciesUrban areas show more prominent intensification of events over short durations and short return periods compared with rural areasRural areas show smaller nonstationary variabilities across durations and return periods and a lower peak scaling rate than urban areas [ABSTRACT FROM AUTHOR]

Incorporating three-dimensional (3D) spatial measurement into the urban expansion measurement system plays an important role in understanding the degree of urban expansion and regulating and governing it. This article proposes an urban concrete forest and constructs an Urban Volume Sprawl Index to quantitatively study the spatiotemporal characteristics of the three-dimensional expansion of Chinese prefecture-level cities. Furthermore, geographic detectors are used to study its influencing factors. The following conclusions are drawn: (1) Between 1990 and 2018, the volume of Chinese cities has increased from 414.67 to 1,406.29 km3, with a growth rate of 239.13%. (2) The overall growth rate of urban volume shows a gradual slowing trend during the research period, from 55.99% to 40.83%. (3) Between 1990 and 2018, the Urban Volume Sprawl Index nationwide gradually slowed down (from 5.18% to 3.90%), with the largest rate reported in the western region (4.1%), the middle in the eastern region (3.46%), and the smallest in the central region (3.28%). (4) Government and economic factors have the greatest impact on the three-dimensional expansion of Chinese cities (over 69%), with social factors in the middle, and natural factors having the weakest explanatory power (less than 15%). (5) There are differences in the degree of urban expansion between two-dimensional and three-dimensional perspectives. A total of 38.1% of cities have a greater degree of urban expansion from a two-dimensional (2D) analysis perspective, which are mainly small cities located in the western and eastern regions. A total of 5.4% of cities (mainly located in the Yangtze River Delta and Pearl River Delta regions) have a greater degree of urban expansion from a 3D perspective. The remaining cities (56.5%) go through the same level of urban expansion in both analysis dimensions. A 3D urban expansion analysis can better explain and reveal the actual production and living space provided by urban expansion than 2D analysis. It can provide temporal data for urban land efficiency evaluation, ghost city identification, etc., and it has good application and promotion prospects. [ABSTRACT FROM AUTHOR]

Owing to the increasingly complex economic environment and difficult employment situation, a large number of new occupations have emerged in China, leading to job diversification. Currently, the overall development status of new occupations in China and the structural characteristics of new occupation practitioners in different cities are still unclear. This study first constructed a development index system for new occupation practitioners from five dimensions (group size, cultural appreciation, salary level, occupation perception, and environmental perception). Relevant data to compare and analyze the development status of new occupation practitioners were derived from the big data mining of China's mainstream recruitment platforms and the questionnaire survey of new professional practitioners which from four first-tier cities and 15 new first-tier cities in China. The results show that the development level of new occupation practitioners in the four first-tier cities is the highest, and the two new first-tier cities, Chengdu and Hangzhou, have outstanding performance. The cities with the best development level of new occupation practitioners in Eastern, Central, and Western China are Shanghai, Wuhan, and Chengdu, respectively. Most new occupation practitioners in China are confident about the future of their careers. However, more than half of the 19 cities are uncoordinated in the five dimensions of the development of new occupation practitioners, especially those cities with middle development levels. A good policy environment and social environment have not yet been formulated to ensure the sustainable development of new occupation practitioners. Finally, we proposed the following countermeasures and suggestions: (1) Establish a classified database of new occupation talents. (2) Implement a talent industry agglomeration strategy. (3) Pay attention to the coordinated development of new occupation practitioners in cities. [ABSTRACT FROM AUTHOR]

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries' GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning. [ABSTRACT FROM AUTHOR]

Monitoring and assessing coastal subsidence is crucial to mitigating potential disaster risks associated with rising sea levels. Nansha District in Guangzhou City, representing global coastal soft-soil urban areas, faces significant challenges related to ground subsidence. However, the current understanding of the status, causative factors, and risk (includes subsidence susceptibility and vulnerability) assessment of ground subsidence in Nansha District is unclear. To address this gap, we utilized the SBAS-InSAR technique, analyzing 49 Sentinel-1A images from December 2015 to June 2019, for systematic ground subsidence monitoring. Subsequently, we assessed subsidence risk using a comprehensive index method and a risk matrix. Our findings indicate that subsidence velocity primarily ranged from −40 to −5 mm/a, with a spatial pattern of increasing subsidence from inland to coastal areas. The cumulative subsidence process unfolded in four distinct stages. The genesis of land subsidence was linked to an endogenous geological context dominated by soft-soil deposition, influenced by external factors such as surface loading and groundwater extraction. High-risk zones were concentrated in key engineering development areas, transportation pipeline trunk lines, and densely populated regions, demanding special attention. This study provides a foundational resource for disaster prevention and control strategies in Nansha District and similar coastal cities. [ABSTRACT FROM AUTHOR]

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China's population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf's law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies. [ABSTRACT FROM AUTHOR]

Facing severe air pollution in its North Plain, the central government of China initiated the Joint Clean Air Action (JCAA) in 2017 to facilitate pollution mitigation efforts across the region. While quite a few studies investigated the effectiveness of this regulation, much less attention is paid to the pollution spillover effects. We empirically examine the effects, and show that 1) air quality in the east of the target cities has been improved due to positive spillover of improved air quality under the JCAA; 2) the beneficiary spillover lasts for two seasons and disappeared in autumn and winter; 3) air quality in the north, south and west directions are almost not changed; 4) wind direction and topography, two determinants of atmospheric transport, have a considerable influence over the spillover effects. Our study provides a fresh perspective to understand the impacts of the JCAA policy and underlines the necessity of taking both pollution and air quality spillover effects into the cost-benefit analysis. Implications: Pollution regulations in one place may increase pollution in other places, as production and emissions are re-allocated under the incentives induced by regional-specific regulations. This phenomenon has long been recognized in the literature as pollution spillover. However, if the relevant production and emissions are not re-allocated, at least not re-allocated in large quantities, local air quality improvement induced by regulations may also benefit the neighboring areas. We call this effect air quality spillover. Both spillover effects should be rigorously evaluated, which is of scientific interest by itself and also contributes to a comprehensive cost-benefit analysis of environmental regulations. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in private car ownership in China, most cities face the problem of insufficient parking spaces, leading to frequent occurrences of parking space conflicts. There is a wide variety of parking locks available on the market. However, most of them lack advanced intelligence and cannot cater to the growing diverse needs of people. The present study attempts to devise a smart parking lock to tackle this issue. Specifically, the smart parking lock uses a Raspberry Pi as the core controller, senses the vehicle with an ultrasonic ranging module, and collects the license plate image with a camera. In addition, algorithms for license plate recognition based on traditional image-processing methods typically require a high pixel resolution, but their recognition accuracy is often low. Therefore, we propose a new algorithm called UNET-GWO-SVM to achieve higher accuracy in embedded systems. Moreover, we developed a WeChat mini program to control the smart parking lock. Field tests were conducted on campus to evaluate the performance of the parking locks. The test results show that the corresponding effective unlocking rate is 99.0% when the recognition error is less than two license plate characters. The average time consumption is controlled at about 2 s. It can meet real-time requirements. [ABSTRACT FROM AUTHOR]

The development and utilization of land in the main urban area have significantly impacted the stability of the regional geological environment through various means, such as increased load and subway construction, primarily manifested as rock and soil mass deformation leading to geological hazards. Therefore, it is worth exploring how to reduce the occurrence of karst ground subsidence (KGS) through reasonable land development and control measures in the main urban areas with large-scale developments of buried karst formations. This study focuses on the main urban area of Wuhan City. An evaluation model for KGS was constructed using the analytic hierarchy process (AHP) and comprehensive index evaluation method by analyzing the geological conditions that affect KGS. The susceptibility zoning of KGS was obtained with GIS spatial analysis technology. The results show that the susceptible areas can be divided into extreme, high, medium, and weak susceptibility, accounting for 4.93%, 15.30%, 33.21%, and 46.56%, respectively, which are consistent with the distribution density of past KSGs. Furthermore, by selecting the subway construction as a human activity type, it indirectly discusses the influence of land development intensity on KGS. The results show that past KSGs are mainly concentrated in areas with high engineering construction density and significant land development intensity. Based on the above, strategies for regional land development and prevention and control of KGSs are proposed. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon "3060" plan for the future of China. [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research. Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward. Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality. In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world. [ABSTRACT FROM AUTHOR]

Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 108 m3). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China. • A first country-wide 10 m building height map for China. • Shading index is the most important variable in estimating building height. • High degree of building height accuracy with RMSE of 6.1 m. [ABSTRACT FROM AUTHOR]

The presence of an urban active fault carries a geological hazard risk that threatens urban construction. Therefore, detecting such faults is crucial for mitigating potential risks and ensuring safe urban development. The grounded-wire source short-offset transient electromagnetic method (SOTEM) offers an opportunity to identify faults with high-conductivity. The results of a SOTEM survey conducted in Shunping, Hebei Province, were presented to detect buried faults and locate the industrial park. Data were analysed using the one-dimensional inversion method with SOTEMsoft software, which was denoised using the short-time sliding window adaptive singular value decomposition algorithm based on the wavelet threshold. Despite the challenges posed by urban features, particularly traffic, the survey results located the fault and hold significant potential to reveal its extent and depth. The study highlights the importance of avoiding fault sites during urban construction, even though the area is currently tectonically stable. This paper reveals an unusual opportunity to study the effectiveness of electromagnetic methods for detecting buried faults in cities and reveal geological structures covered by Quaternary formations. [ABSTRACT FROM AUTHOR]

We studied China's Common Prosperity process by assessing and comparing the level of Common Prosperity in different regions of China and made some beneficial recommendations to government departments. The research data comes from the China Statistical Yearbook, which includes data from 31 provinces and cities from 2015 to 2020. According to the relevant research, eleven evaluation indicators were selected. We combined GRA with the TOPSIS method for scoring and the K-means clustering algorithm for dividing the GRA-TOPSIS scoring results into three evaluation levels. Then, the convolutional neural network model was used to predict and simulate the level of common prosperity. Taking 2020 as an example, the results show: (1) From 2015 to 2020, China's Common Prosperity level reached its highest point in 2020. Due to the impact of COVID-19 in 2019, the scores of 31 regions are generally lower than in the previous four years. The situation changed in 2020; (2) In terms of regional distribution, the economic development of Beijing, Shanghai, and other eastern regions is relatively good, with a higher degree of Common Prosperity than that of other regions; (3) The average prediction accuracy is high in our model. It can be close to 100%, indicating that the model has a good prediction effect. In addition, we made recommendations based on the research results, which have good references for actively promoting common prosperity. [ABSTRACT FROM AUTHOR]

After rapid development in recent decades, China has laid a strong economic foundation and established material conditions. Despite this, the country still confronts a range of challenges that impede higher-quality economic and social development. To measure high-quality regional development, eco-wellbeing performance (EWP) has become an important tool that seeks to strike a balance between economic development, human wellbeing, and environmental protection. This paper proposes a three-stage network efficiency evaluation model to decompose EWP into three stages, namely ecological economic efficiency (EEE), economic innovation efficiency (EIE), and innovation wellbeing efficiency (IWE). A directional distance function (DDF) model was utilized to assess the overall EWP efficiency and phase-in efficiency for 248 cities over the period from 2016 to 2020. The study results indicate that EWP in China is generally low. In terms of the three sub-stages of EWP, the first stage performs optimally, the third stage performs the second best, and the second stage is the worst. The research suggests that the principal reason for the generally low EWP may be linked to the efficiency of the transformation of economic investments into innovative outcomes in the second stage. From a regional viewpoint, EWP generally shows a gradually decreasing trend from the east, central, and west to the northeast, but the stage performance varies among regions. The eastern region has lower EEE, but both EIE and IWE perform better than the national average; the central region is well-balanced between the three stages; the western region leads the country in EEE, but lags in both EIE and IWE; and the northeast region has inferior performance in all stages. This study's findings provide vital reference values for policy-makers to determine key points for enhancing EWP in different regions of China. [ABSTRACT FROM AUTHOR]

Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or submetric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatiotemporal scale. From the viewpoint of learning strategies, there is a nontrivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, and hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named the Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m resolution from 2016–2021 Sentinel-2 images. CBRA is the first full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning strategies, CBRA achieves good performance with a F1 score of 62.55 % (+10.61 % compared with the previous BRA data in China) based on 250 000 testing samples in urban areas and a recall of 78.94 % based on 30 000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and good agreement with other multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping (https://github.com/zpl99/STSR-Seg , last access: 12 July 2023). CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; 10.5281/zenodo.7500612). [ABSTRACT FROM AUTHOR]

The integration of PV and energy storage systems (ESS) into buildings is a recent trend. By optimizing the component sizes and operation modes of PV-ESS systems, the system can better mitigate the intermittent nature of PV output. Although various methods have been proposed to optimize component size and achieve online energy management in PV-ESS systems, the optimal interconnection between them has received less attention. In order to maximize the effectiveness of systems with limited component sizes and address the impact of uncertainty on the system, an optimization framework is proposed for determining the optimal size of the PV-ESS system. The proposed framework consists of five parts: determination of optimal size, analysis of component output characteristics, system state prediction, parameter calibration of energy management strategies, and update of system components output features, and it considers uncertain factors, including climate, different components, and battery degradation caused by irregular charging and discharging, to establish the model for energy saving. To validate the results, four different climates in a year were considered. The obtained results indicate that the proposed framework can effectively achieve the optimal working state of the system, realizing a matching degree of 94.55% between the offline size optimization and online management strategy. The proposed framework's universality and effectiveness were demonstrated through simulation analysis across four cities with different climates in China. [ABSTRACT FROM AUTHOR]

Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%–28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks. [ABSTRACT FROM AUTHOR]

Dust storm disasters have emerged as a significant environmental challenge in East Asia. However, relying on a single monitoring method to track dust storms presents limitations and can be variable. Therefore, it is necessary to use a combination of ground and remote sensing monitoring methods to explore the source and impact range of dust storms in order to fully characterize them. To achieve this, we examined the sources and impact ranges of dust storms in East Asia from 1980 to 2020 using both ground station data and remote sensing data. In addition, we focused on three specific dust storm events in the region. Our results indicate that the central source areas of dust storms are located in southern Mongolia and the Taklamakan Desert in China. Dust storms are mainly transported and spread in the northwestern region, while they are relatively rare in the southeastern region. The HYSPLIT model simulations reveal that the primary source directions of dust storms in East Asia are northwest, west, and north, the region involved includes Kazakhstan, southern Mongolia, and the Taklimakan Desert in China. The vertical structure of the dust storm layer depends on the source of the dust storm and the intensity of the dust storm event. Dust grain stratification probably occurs due to differences in dust storm sources, grain size, and regularity. These findings demonstrate that a combination of ground-based and remote sensing monitoring methods is an effective approach to fully characterize dust storms and can provide more comprehensive information for dust storm studies. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

Under the threat of global climate change, China has proposed a dual carbon goal of peak carbon and carbon neutrality. As the vital carrier for territorial spatial planning, production–living–ecological (PLE) spaces drive carbon emissions and are important to the dual carbon goals. In this study, carbon emissions and sinks of PLE spaces in cities in Heilongjiang Province from 2005 to 2020 were calculated and spatial–temporal changes were analyzed. The carbon emission structure was analyzed in segmentation sectors. The land use changes and socioeconomic factors on carbon emissions were analyzed, and emission reduction strategies were implemented. The results show the following: (1) Carbon emissions from production and living spaces increased yearly. Carbon sinks were smaller than emissions, but capacity was stable. (2) Higher-emission cities were concentrated in southwest Heilongjiang, and carbon emission differences between regions gradually increased. (3) Among carbon emission sectors, agricultural and household made up smaller proportions, while animal husbandry, industrial, transportation, and traffic travel contributed most. Carbon emission structures were transformed by adjusting urban development and industrial structure. (4) For most cities, industrial space was the main emission space, but agricultural production and urban–rural living spaces dominated in some cities. (5) GDP, urbanization rate, and area of city paved roads suppressed emissions in cities with decreased carbon emission grades. The industrial structure and coal consumption inhibited emissions in cities with maintaining and increasing carbon emissions grades. [ABSTRACT FROM AUTHOR]

While rapid urbanization promotes social and economic development, it exacerbates human outdoor thermal comfort, which increases the risks to human health. This paper uses four thermal comfort indices and multiple satellite observations to explore the urbanization effect on summer heat stress in Guangdong from 1979–2018, a coastal province of China. Two types of thermal comfort index are used here, namely the direct thermal comfort index (Heat Index, HI; Temperature–Humidity Index, THI; Discomfort Index, DI) and the physiological thermal comfort index (Universal Thermal Climate Index, UTCI). We compare the differences in the urbanization effects on the changes in the three direct thermal comfort indices (HI, THI, and DI) and a physiological thermal comfort index (UTCI). The results show that all four thermal comfort indices indicate an overall warming trend. Of them, urban sites show a higher warming trend than rural sites, indicating that heat stress changes are significantly influenced by urbanization from 1979–2018, which is consistent with the effect of urbanization on surface air temperature. However, except for the UTCI, this warming of direct thermal comfort indices affected by urbanization has become insignificant under the regional vegetation greening from 2004–2018 (also consistent with surface air temperature). This is primarily attributed to the different effects of wind speed on the physiological thermal comfort index in urban and rural areas: Decreasing wind speeds in urban areas lead to an increase in UTCI, while wind speeds in rural areas increase instead and decrease UTCI, thus widening the UTCI differences between urban and rural areas. Our results indicate that urbanization has a different effect on thermal comfort indices. When using the thermal comfort index, it is necessary to consider that different thermal comfort indices may bring different results. UTCI considers more factors that affect human heat perception, so it can better describe human outdoor thermal comfort. It also highlights the importance of urban ventilation and urban greenness in mitigating urban outdoor thermal comfort in the sustainable construction of future urbanization in coastal cities. [ABSTRACT FROM AUTHOR]

The superimposed effects of sea level rise caused by global warming and land subsidence seriously threaten the sustainable development of coastal cities. In recent years, an important coastal city in China, Zhuhai, has been suffering from severe and widespread land subsidence; however, the characteristics, triggers, and vulnerability assessment of ground subsidence in Zhuhai are still unclear. Therefore, we used the SBAS-InSAR technique to process 51 Sentinel-1A images to monitor the land subsidence in Zhuhai during the period from August 2016 to June 2019. The results showed that there was extensive land subsidence in the study area, with a maximum rate of −109.75 mm/yr. The surface had sequentially undergone a process of minor uplift and decline fluctuation, sharp settlement, and stable subsidence. The distribution and evolution of land subsidence were controlled by tectonic fractures and triggered by the thickness of soft soil, the intensity of groundwater development, and the seasonal changes of atmospheric precipitation. The comprehensive index method and the analytic hierarchy process were applied to derive extremely high subsidence vulnerability in several village communities and some traffic arteries in Zhuhai. Our research provides a theoretical basis for urban disaster prevention in Zhuhai and the construction planning of coastal cities around the world. [ABSTRACT FROM AUTHOR]

The accurate estimation of urban extreme precipitation is essential for urban design and risk management, which is hard for developing countries, due to the fast urbanization and sparse rain gauges. Satellite precipitation products (SPPs) have emerged as a promising solution. Not only near real-time SPPs can provide critical information for decision making, but post-processed SPPs can also offer essential information for climate change adaption, risk management strategy development, and related fields. However, their ability in urban extreme precipitation estimation has not been examined in detail. This study presents a comprehensive evaluation of four recent SPPs that are post-processed, including IMERG, GSMaP_Gauge, MSWEP, and CMFD, for their ability to capture urban extreme precipitation in mainland China at the national, city, and inner-city scales. The performance of the four SPPs was assessed using daily observations from the 821 urban gauges from 2001 to 2018. The assessment includes: (1) the extreme precipitation estimates from the four SPPs in the total urbanized areas of mainland China were evaluated using correlation coefficients (CC), absolute deviation (AD), relative deviation (RB), and five extreme precipitation indices; (2) The extreme precipitation estimates over 21 Chinese major cities were assessed with the two most important extreme indices, namely the 99th percentile of daily precipitation on wet days (R99) and total precipitation when daily precipitation exceeding R99 (R99TOT); and (3) Bivariate Moran's I (BMI) was adopted to assess the inner-city spatial correlation of R99 and R99TOT between SPPs and gauge observations in four major cities with most gauges. The results indicate that MSWEP has the highest CC of 0.79 and the lowest AD of 1.61 mm at the national scale. However, it tends to underestimate urban precipitation, with an RB of −8.5%. GSMaP_Gauge and IMERG performed better in estimating extreme values, with close extreme indices with gauge observations. According to the 21 major cities, GSMaP_Gauge also shows high accuracy in estimating R99 and R99TOT values, with the best RB and AD in these cities, while CMFD and MSWEP exhibit the highest CC values for R99 and R99TOT, respectively, indicating a strong correlation between their estimates and those obtained from gauge observations. At the inner-city scale, MSWEP shows advantages in monitoring the spatial distribution of urban extreme precipitation in most of cities. The study firstly provided the multiscale assessment of urban extreme precipitation by SPPs over mainland China, which is useful for their applications. [ABSTRACT FROM AUTHOR]

Hefei's gross domestic product (GDP) growth rate ranks first among all cities in China, and it was the fastest-growing city in China from 2000 to 2020. The high-speed economic development inevitably led to rapid changes in land cover types, e.g., an increment in artificial features (built-up) and decrement in natural features (vegetation). However, (1) many previous studies focus on the land cover change in metropolis or at the global scale, yet few focus on underdeveloped but fast-growing cities; (2) land cover studies mainly focus on global variations, yet seldom on local characteristics. Thus, it is of great significance to monitor the land cover change for the city with the fastest economic growth in China based on the long time-series satellite images from both global and local perspectives. In this study, with support from huge amounts of data (including 719 Landsat TM/ETM+/OLI satellite images, land surface temperature, nighttime satellite images, DEM, multiple land cover products, and various auxiliary data), processing and parallel computing abilities of the GEE platform, classification maps of land cover in Hefei from 2000 to 2020 are produced based on a random forest machine learning method, and the spatio-temporal variations and driving factors are analyzed from both global and local viewpoints. The results show that: (1) the classification accuracy is excellent; the average overall accuracy is 93% and the Kappa coefficient is 0.88; (2) the general spatio-temporal variations in land cover in Hefei from 2000 to 2020 are obvious; the built-up area expanded from 419.72 km2 to 1530.20 km2, with a total growth rate of 264.58%. With the expansion of the built-up area, the vegetation coverage decreased by 16.61% (1652.56 km2); (3) the land surface temperature shows an increment trend in the new town yet a decrement trend in the old town due to the change in vegetation coverage and the decentration of administration centers; further analysis shows that the population and the social economy are two driving factors for land cover changes. It is worth noting that both the area and coverage of vegetation in the old town and water body area in Hefei increased significantly, although the fast urbanization inevitably caused a decrement in vegetation and water area in the whole city, indicating both the high-speed economic development and improvement in green surfaces simultaneously experienced in Hefei from 2000 to 2020. [ABSTRACT FROM AUTHOR]

Exploring the driving factors behind urban polycentricity is of paramount significance for advancing the implementation of urban polycentric strategies. In this, paper takes 41 cities in the China's Yangtze River Delta region are taken as the research objects, and the degree of urban polycentricity is measured using non-parametric and social network methods. Then, the geographic probe method is applied to reveal the driving factors of the spatial differentiation of urban polycentricity. The research results showed that (1) the distribution of hot and cold spots of the urban polycentricity index was roughly the same between 2006 and 2015, while and the distribution of hot and cold spots of the urban polycentricity index varied greatly between 2015 and 2020. (2) In 2010, the proportions of the secondary and industry tertiary industries and the topographic relief were the main driving factors of urban polycentricity. By 2020, the population size and topographic relief were the key drivers of urban polycentricity. [ABSTRACT FROM AUTHOR]

With rapid urban population growth and industrial agglomeration, the urban land supply is becoming gradually tight. Improving land use quality (LUQ) is becoming increasingly critical. This study was carried out in the Luohe built-up zones between 2013 and 2021. The aim is to explore the growth characteristics of LUQ and determine the association between the inner urban location and the growth rate from the perspective of spatial heterogeneity. Therefore, based on a socio-economic-environmental framework, we selected an integration/GDP/population/artificial-surface Rate, and a remote-sensing-based ecological index to construct a LUQ assessment framework that is more stable and applicable for developing urban areas. Additionally, then, multiscale geographical weighted regression is adopted, which can better help us explore the scale of the location factors. The results show that: (1) The LUQ overall growth is gradually slowing. High-quality areas clustered in the urban center and subsystem elements spread outward along the national and provincial highways to drive boundary expansion; (2) In the W/E/SE direction, land use tends more towards physical sprawl than usual development and expansion; (3) Location factors were distinguished as global, semi-global, and local. The global factors constitute the homogenized locational space. Semi-global and local factors constitute a heterogeneous locational space. The latter is critical to guide LUQ growth. LUQ assessment can promote intensive land use. Exploring location factors can further guide the LUQ spatial growth and provide data in support of urban planning. [ABSTRACT FROM AUTHOR]

Precipitation intensity–duration–frequency (IDF) curves play a crucial role in the design and planning of urban infrastructure to reduce the risk of urban flooding and rainfall‐triggered landslides. However, changing rainfall characteristics in a warming climate render conventional IDF curves inappropriate due to the statistical assumption of stationarity. In this study, we develop vine copula‐based projections of future IDF curves at sub‐daily to multi‐day time scales with a multi‐model ensemble of five regional climate simulations over China. Stochastic spatiotemporal downscaling of precipitation is achieved to generate extreme precipitation simulations at a high spatial (0.1°) and temporal (3 hourly) resolution. These downscaled simulations are combined by the vine copula to improve the reliability and accuracy of climate‐model‐based IDF curves relative to historical observations. Our findings reveal that climate‐model‐based stochastic downscaling of precipitation reproduces the IDF curves well based on historical observations in China. The vine copula multi‐model ensemble approach outperforms Bayesian model averaging by generating more accurate and reliable IDF curves. The urban areas of 196 Chinese cities are projected to experience an increase in extreme precipitation of up to 30% in intensity and nearly two times the frequency of historical events under a high emission scenario (RCP8.5). The current urban infrastructure of more than half of the 196 cities would thus be inadequate to prevent losses caused by rainfall‐triggered hazards if designed solely based on historical precipitation observations. Compared to the climate‐model‐based IDF curves, we find that statistical IDF curves based on a nonstationary time covariate (i.e., extrapolating historical trends) are likely to underestimate the risk of urban infrastructure failures under a warming climate. This work highlights that urban infrastructure design guidelines in China should be upgraded to adapt existing IDF curves to the changing climate. Key Points: Stochastic spatiotemporal downscaling of precipitation is achieved to derive intensity–duration–frequency (IDF) curves at sub‐daily to multi‐day time scalesA vine copula multi‐model ensemble approach is proposed to improve the accuracy and reliability of climate‐model‐based IDF curvesExtreme precipitation is projected to increase by up to 30% in intensity and nearly two times in frequency over 196 Chinese cities [ABSTRACT FROM AUTHOR]

China's rapid urbanization has been accompanied by serious urban sprawl. Instead of measuring the physical urban boundaries (PUBs), most of existing studies in China rely on yearbook statistics to describe the growth of urbanized area; therefore, the understanding of the actual form and quantity of urban sprawl are restrained. As the statistical unit is generally at or above the county level, these studies tend to omit the lower-level "larger towns". This paper discusses the measurement of urban sprawl and compactness using multi-source data on the GIS platform through the case study of the Wen-Tai region in China. GlobeLand30 remote sensing image data, vector road network data, NPP/VIIRS nighttime light data, and points of interest (POIs) data are adopted. The new method enhances the identification of built-up areas in larger towns. Besides, the 2020s' PUBs of this region, data for 2010 and 2000 are retraced to assess the urban expansion rate, and two approaches are used to discuss the urban growth pattern. Additionally, a compactness model is constructed from four dimensions, i.e., the compactness of external contour, accessibility of road network, land-use intensity, and functional diversity, by which a high-resolution visual analysis tool is created for the provincial government to monitor urban sprawl. [ABSTRACT FROM AUTHOR]

The intensification of extreme precipitation has aggravated urban flood disasters, which makes timely and reliable precipitation information urgently needed. As the high-quality and widely used satellite precipitation products, Integrated Multi-satellitE Retrievals for GPM (IMERG), have not been well investigated in coastal urban agglomerations where damages from precipitation-related disasters are more severe. With precipitation measurements from local high-density gauge stations, this study evaluates three IMERG runs (IMERG ER, IMERG LR, and IMERG FR) in the southeast coastal urban region of China. The evaluation shows that the three IMERG products severely overestimate weak precipitation and underestimate heavy precipitation. Among the three runs, the post-corrected IMERG FR does not show a substantial improvement compared to the near-real-time IMERG ER and IMERG LR. The performance of IMERG varies depending on the precipitation pattern and intensity, with the best estimation ability occurring in the coastal urban region in summer and in the northern forests in winter. Due to the year-round urban effect on precipitation variability, IMERG cannot detect precipitation events well in the central high-density urban areas, and has its best detection ability on cultivated lands in summer and forests in winter. Within the urban agglomeration, IMERG shows a poorer performance in areas with higher urbanization levels. Thus, the IMERG products for coastal urban areas need considerable improvements, such as regionalized segmental corrections based on precipitation intensity and the adjustment of short-duration estimates by daily or sub-daily precipitation measurements. [ABSTRACT FROM AUTHOR]

Effectively monitoring the size of a city in real time enables the scientific planning of urban development. Models that utilize the distribution and variations in city size generally use population data as inputs, which cannot be obtained in a timely and rapid manner. However, night-time light (NTL) remote sensing may be an alternative method. A case study was carried out on the Yangtze River Delta (YRD) in China, and the rank–size rule, the law of primate cities, and the Gini coefficient were employed to monitor the variation in city size in the study area. The urban areas extracted based on NTL remote sensing were utilized instead of the traditionally used population data to evaluate the variations in city size from 2012 to 2017. Considering the empiricism and subjectivity of the thresholding method, urban areas were extracted from NTL data combined with the normalized differential vegetation index and land-surface temperature data based on the artificial neural network algorithm. Based on the results, the YRD did not fit the distribution of the primate cities from 2012 to 2017. However, this region satisfied the rank–size rule well, which indicated that the development of medium–small cities was more prominent than that of larger cities, and the dispersed force was larger than the concentrated force. Notably, the city size reached a relatively balanced level in the study area. Further, sensitivity analysis revealed that the relatively low extraction accuracy of urban areas of few small cities had little effect on the results of city size variations. Moreover, the validation of city size computed from statistical population data and its comparison with results calculated based on the statistical data of urban areas aligned with the results of this study, which indicates the rationality and applicability of monitoring the variations in city size using the urban areas extracted from NTL remote sensing instead of population data. [ABSTRACT FROM AUTHOR]

Rapid urbanization has widely induced fragmented landscapes and further negatively affected ecological functions. The edge effect is an approach commonly used to investigate these negative impacts. However, edge effect research tends to focus on the impacts that a certain landscape receives from its adjacent lands rather than to assess all the influences of the landscape edges in a region, even though the latter is critical for regional ecological planning. To fill in this gap, the concept of neighboring effect is raised and analyzed in this paper with a case study of Xintai City in Northern China. Results show that the neighboring effects are generally negative for ecological functions, especially in regions that experience rapid urbanization or heavy human activities. The U-shaped relationship between the neighboring effect of a patch and its distance to the nearest township center indicates that the border region of urban and built-up areas suffers the most negative influences due to the intense interactions between different land uses. The heterogeneous effects of influencing factors in urban and rural areas were revealed by the regression results. Socioeconomic development has more important influence on neighboring effects on ecological functions in rural areas than in urban areas, and local cadres' support of environmental protection matters only in rural areas for a less ecological functional loss. This study quantitatively examined the negative ecological effects of landscape fragmentation during rapid urbanization and calls for more attention to ecological planning at the local scale. [ABSTRACT FROM AUTHOR]

For studies of urban development, it is an important method for obtaining the distribution of impervious surface (IS) areas and their dynamic change from remote sensing data. The dilemma of the same spectrum for different features and different spectrums for the same features, posed by the complexity of the IS objects, is the fundamental obstacle encountered in the extraction of urban IS areas. In this study, an automatic extraction method for urban IS areas is proposed and analyzed, based on classification and regression tree (CART) and ensemble learning strategies. The Sentinel-2 MSI data of 30 cities in China from 2018 to 2020 were selected for IS extraction experiments. We perform temporal and spatial modeling of the splitting threshold offset in the classification model to explore the effect of time and space on IS extraction. The obtained offset models show that the temporal variation is not significant, while the spatial offsets have more obvious linear relationships. [ABSTRACT FROM AUTHOR]

Various flood resilience enhancement measures have been proposed to deal with the growing problem of urban flooding. However, there is a lack of evaluation about the applicability of these measures at a community scale. This paper investigates the effects of two types of flood resilience enhancement measures: engineering measures and adaptive measures, in order to explore their effectiveness in different flood-prone communities. A community-scale oriented flood resilience assessment method is used to assess the impact of different types of measures. A case study is applied in three communities that suffer from waterlogging problems in Jingdezhen city, China. Results show that there are spatial differences of flood resilience in three flood-prone communities. Future scenarios present a poorer performance in flood resilience compared to current scenarios due to the effects of urbanization and human activities. Engineering measures are suitable for the old communities with high-density residential areas when sitting alongside the river, for example the communities of Fuliang and Zhushan. On the other hand, adaptive measures exhibit more efficiency in improving flood resilience in all communities, especially effective for the new city town Changjiang where engineering measures are nearly saturated. The findings can help local governments develop appropriate flood resilience enhancement strategies for different types of communities. [ABSTRACT FROM AUTHOR]

Interrelationships between urban and rural areas are fundamental for the development and safeguarding of viable future living conditions and quality of life. These areas are not well-delineated or self-sufficient, and existing interrelations may privilege one over the other. Major urban challenges facing China and Europe are related to changes in climate, environment, and to decision-making that makes urban and rural landscapes more susceptible to environmental pressures. Focusing on the six European and Chinese cities and surrounding rural areas, under study in the joint EC and MOST-funded REGREEN project, we examine how nature-based solutions (NBS) may assist in counteracting these pressures. We explore urban-rural dependencies and partnerships regarding NBS that can enhance resilience in Europe and China. We analyse differences between European and Chinese systems of governance, reflecting on the significance of the scale of research needed to understand how NBS provide benefits. We highlight interactions between differently delineated sheds (watershed, airshed, natureshed, and peopleshed), which influence the interrelationships between urban and rural areas. There may be one-way or two-way interdependence, and the impact may be uni or multi-directional. The European and Chinese solutions, exemplified in this article, tackle the nexus of environmental and peoplesheds. We discuss complex human interactions (and how to model them) that may, or may not, lead to viable and equitable partnerships for implementing NBS in cities within Europe and in China. [ABSTRACT FROM AUTHOR]

Land use mix (LUM) is a key part of urban form, but the definitions and measures of LUM are debated. This study aims to reveal the spatial characteristics of LUM patterns by using a sample of 216 streets in Shanghai, a megacity of China. Firstly, a systematic quantitative framework for sustainable LUM is proposed. Then, an LUM degree index (LMDI) that integrates diversity, proximity and compatibility is developed to address the possible bias of a single dimension. Results suggest that areas with high diversity are not necessarily mixed, due to inaccessibility or negative externalities of land uses. Subsequently, spatial characteristics of LUM patterns that combine LMDI with dominant mix‐typologies are analyzed. Results show that residential & service‐dominated streets tend to exhibit higher mix, and are mainly distributed in the city center or the administrative core area. However, 44.4% of the streets with residential & industrial‐dominated or equally distributed mix have lower mix degree and are generally located in the outskirts. More optimized policies for urban periphery areas should be considered. LUM patterns present a detailed perspective to measure and depict the spatial features of LUM, and have the potential to assist urban planners in making decisions. [ABSTRACT FROM AUTHOR]

Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management. [ABSTRACT FROM AUTHOR]

In this paper, precipitation data from the Tropical Rainfall Measuring Mission (TRMM), together with atmospheric reanalysis data, are employed to identify warm-season precipitation (1998–2014) changes and their association with rapid urbanization in south China. Three urban clusters (Chenyu, Yangtze Delta, and Fujian Guangdong coast) are focused. The results reveal that, for the inland Chengyu urban cluster, a lack of precipitation trend is likely due to insignificant trends in convective available potential energy (CAPE) and total column water vapor (TCWV). They are likely resulted from a reduced local moisture recycling in urban areas, balanced by an increased evapotranspiration of rural areas, together with a stable advection of water vapor input. For the Yangtze River Delta urban cluster, a negative trend in precipitation is associated with a slightly decreased CAPE and an increased TCWV, but is very likely related to urbanization induced an increased planetary boundary layer (PBL) and reduced land surface evaporation. For the Fujian Guangdong coast urban cluster, a marked positive precipitation trend is well explained by positive trends in CAPE and TCWV. The increased precipitation likely benefits from enhanced moisture recycling due to improved vegetation cover in rural areas, and enhanced advection moisture inputs due to urbanization along the coast. These results suggest urbanization effects on precipitation vary with regional conditions. In the coastal area, urbanization enhances sea breezes, which may benefit precipitation if sea breezes go along with the prevailing moisture. In inland area, urbanization likely leads to a warmer-dryer climate if large-scale land cover keeps stationary. [ABSTRACT FROM AUTHOR]

Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008-15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran's I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%-95%, and < 75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%),mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran's I = 0.47) and the cold frontal system in the Pearl River delta (Moran's I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 258C over Beijing and T > 288C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events. [ABSTRACT FROM AUTHOR]

Using a 30-year data (1983–2012) from 428 stations, we study summer precipitation differences between urban and rural areas in 5 Chinese climate regions: the Pearl River Delta (PRD), the Middle and Upper reaches of the Yangtze River (MUYR), the Yangtze River Delta (YRD), the North China Plain (NCP), and Northeast China (NEC). By analyzing heavy rain (HR) (24-h precipitation ≥ 100 mm) and light rain (LR) (0.1 mm ≤ 24 h precipitation ≤ 25 mm), we find that urbanization has had inconsistent effects on precipitation. Higher HR occurs over urban areas of the PRD and YRD than rural areas, and lower HR occurs over urban areas of the MUYR, NCP and NEC than rural areas. The urban LR is less than the rural LR in all climate regions. The correlation between precipitation and the convective available potential energy (CAPE) or humidity explains the regional differences in urbanization effects on HR. HR is greatly affected by the CAPE in the PRD and YRD, where the CAPE is high (> 600 J/kg at 14:00 LT), water vapor is abundant (> 45 kg/m2 at 14:00 LT), and the urban heat island increases the urban HR. However, HR is greatly affected by humidity in the MUYR, NCP, and NEC, where the CAPE and water vapor are less (≤ 500 J/kg and ≤ 40 kg/m2 at 14:00 LT) and the urban HR is mainly suppressed by the urban dry island. Our results indicate that urbanization promotes HR in wet climates but suppresses HR in dry climates during summer in central and eastern China. [ABSTRACT FROM AUTHOR]

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The urban heat island intensity (UHII) is the temperature difference between urban areas and their rural surroundings. It is commonly attributed to changes in the underlying surface structure caused by urbanization. Air pollution caused by aerosol particles can affect the UHII through changing (1) the surface energy balance by the aerosol radiative effect (ARE) and (2) planetary-boundary-layer (PBL) stability and airflow intensity by modifying thermodynamic structure, which is referred to as the aerosol dynamic effect (ADE). By analyzing satellite data and ground-based observations collected from 2001 to 2010 at 35 cities in China and using the WRF-Chem model, we find that the impact of aerosols on UHII differs considerably: reducing the UHII in summer but increasing the UHII in winter. This seasonal contrast is proposed to be caused by the different strengths of the ARE and ADE between summer and winter. In summer, the ARE on UHII is dominant over the ADE, cooling down surface temperature more strongly in urban areas than in rural areas because of much higher aerosol loading, and offsets the urban heating, therefore weakening UHII. In winter, however, the ADE is more dominant, because aerosols stabilize the PBL more in the polluted condition, weakening the near-surface heat transport over urban areas in both vertical and horizontal directions. This means that the heat accumulated in urban areas is dispersed less effectively, and thus the UHII is enhanced. These findings shed new light on the impact of the interaction between urbanization-induced surface changes and air pollution on urban climate. [ABSTRACT FROM AUTHOR]