Wuhan Area Research Articles

The Demographic Implication for Promoting Sponge City Initiatives in the Chinese Megacities: A Case of Wuhan

Urbanisation and ever-intensified rainstorms exacerbated urban waterlogging in some Chinese cities. In 2013, the Chinese government proposed a nationwide initiative, Sponge City, for managing the flood risk using the nature-based solution (NBS) approach. Pilot projects have been implemented among thirty selected cities, including Wuhan. Because the effectiveness of implementing NBS relies on the participation of the well-informed public, this study aims at identifying the factors affecting the awareness of the public about the Sponge City program. The viewpoint of people in Wuhan on urban floods and the Sponge City initiatives was surveyed among 1600 participants using a face-to-face questionnaire in mostly Wuchang area of Wuhan; more than 900 of them were further interviewed. The majority of participants, though recognising the threats from flooding, were lacking awareness and understanding of the Sponge City initiatives. The Chi-square analyses of association revealed that the level of awareness is affected by education, age and residential time; these demographic factors also affected their interpretation of the direct experiences of the water environment and governmental water management. To optimise communicating the relevant policy to the public, the content and the advertising tools for promoting Sponge City may need to be mindfully customised for targeted demographic groups.

Generating continuous fine-scale land cover mapping by edge-guided maximum a posteriori based spatiotemporal sub-pixel mapping

Global land cover mapping activities are of great important for retrieving the environment we are living in. However, the trade-off between spatial and temporal resolution makes it difficult to obtain the continuous fine-scale land cover product for detail and frequent land surface analysis. To overcome the difficulty, this study proposed a modified maximum a posteriori (MAP) based spatiotemporal sub-pixel mapping (SSM), for a long-term and fine-scale land cover mapping. The proposed approach consists of three parts, i.e., data fidelity term, spatial regularization term, and enhanced temporal regularization term, which can make use of historical fine-scale image for a better super-resolution of the current coarse image. One regional experiment was implemented for the algorithm validation, and one real experiment was investigated, by generating continuous land cover mapping products (2010–2015) at Landsat-like spatial resolution from time series of MODIS images. Compared with traditional methods, the proposed approach reconstructed more pleasant spatial details and had greater quantitative accuracy, outperforming the state-of-the-art spatiotemporal sub-pixel mapping method by averagely 2.58% for all the inspected dates in OA metric. This study not only provides a generalized research framework for generating continuous fine-scale land cover product from daily available MODIS images, but also validates the usability of the time series products for detail land cover monitoring, and reveals a finding that the study area of Wuhan is a well practitioner of the sustainable development policy during its urbanization process.

Construction and optimization of ecological security patterns based on social equity perspective: A case study in Wuhan, China

Ecological Security Patterns (ESPs) are important nature-based solutions for ecological problems caused by urbanization and industrialization. Previous studies on ESPs focus on natural conditions only, yet do not incorporate the need for social equity. This study aims to address this quandary for the study area of Wuhan by identifying and extracting ecological sources and ecological corridors, followed by an ecological sensitivity assessment, minimum cumulative resistance model and buffer analysis, in order to construct and optimize ecological security patterns. The optimization for the construction of the most suitable ecological security patterns relies on social equity aspects, including the quality of urban ecological services and the improvement of the well-being of urban residents. The results demonstrate: (1) There are 19 ecological sources in Wuhan and 51 ecological corridors with a total length of 840.10 km. (2) To cater for the optimization of social equity needs, it was necessary to add one additional amendment source, with an area of 14.89 km2, and 25 new amendment corridors with a total length of 287.8 km. (3) Optimizing the ecological security patterns in Wuhan is possible through the derivation of an ecological restoration area, ecological improvement area, ecological control area and ecological shield area. The results indicate the feasibility of simultaneously protecting urban ecological security and realizing social equity.

Is Cold Apparent Temperature Associated With the Hospitalizations for Osteoporotic Fractures in the Central Areas of Wuhan? A Time-Series Study.

Osteoporosis is alarming problem due to aggravation of global aging, especially in China. Osteoporotic fracture (OF) is one of the most severe consequents of osteoporosis. Many previous studies found that environmental factors had adverse effects on human health. Cold temperature was associated with OF and bone metabolism in prior observational and experimental researches. However, few studies had been conducted on the acute effect of low temperature and OF. Data on daily meteorological factors and hospitalizations for OF were collected from Wuhan, China, between January 1, 2017 to December 24, 2019. Apparent temperature (AT), comprehensively considered a variety of environmental factors, was calculated by ambient temperature, relative humidity and wind speed. A generalized linear regression model combined with distributed lag non-linear regression model (DLNM) with quasi-Poisson link was used to explore the association between AT and the number of hospitalizations for OF. Subgroup analyses stratified by gender, age and the history of fracture were applied for detecting susceptible people. The exposure-response curve of AT and OF were generally U-shaped with lowest point at 25.8°C. The significant relationship of AT-OF existed only in cold effect (−2.0 vs. 25.8°C) while not in warm effect (37.0 vs. 25.8°C). Statistically significant risks of OF for cold effects were only found in females [RR = 1.12 (95%CI: 1.02, 1.24) at lag 2 day], aged <75 years old [RR = 1.18 (95%CI: 1.04, 1.33) and 1.17 (95%CI: 1.04, 1.33) at lag 2 and 3 days, respectively] and people with history of fracture [RR = 1.39 (95%CI: 1.02, 1.90) and 1.27 (95%CI: 1.05, 1.53) at lag 1 and 2 days, respectively]. The significant associations of AT on OF were only found in cold effect. The females, people aged <75 years and people with history of fracture possibly appeared to be more vulnerable. Public health departments should pay attention to the negative effect of cold AT and take measures in time.

Geographical Detection of Urban Thermal Environment Based on the Local Climate Zones: A Case Study in Wuhan, China

The urban morphology has impacts on the urban thermal environment, which has drawn extensive attention, especially in metropolitan regions with intensive populations and high building densities. This study explored the relationship between the urban morphology and spatial variation of land surface temperature (LST) in Wuhan by using the local climate zone (LCZ) and seven natural and social factors. A deep learning model (light LCZ model) was used to generate LCZ map in Wuhan, and a geographic detector model was utilized to explore the driving mechanism of LST spatial differentiation. The results show that the LST difference between LCZ classes in summer is greater than that in winter, and the LST of the built-up classes (LCZ 1–10) are significantly higher than that of the vegetation classes in summer. Among the six residential building classes (i.e., LCZ 1–6), LCZ 1 is characterized by compact and high buildings and has the largest average LST. Building density and height have a warming effect, and the building density has a stronger effect than the height. Compared with other natural and social factors, LCZ has the largest explanatory power for LST spatial differentiation in the main urban area and surrounding areas of Wuhan, with explanatory power (q) values reaching 0.660 (summer) and 0.316 (winter). The types of interaction for all pairwise cases are mutual and nonlinear. The strongest interaction is MNDWI-NDBI combination (0.780) in summer and LCZ-NDBI combination (0.460) in winter.

Characteristics and Source Analysis of Water-soluble Inorganic Pollution in PM2.5 During Summer in Central China

In order to investigate the pollution characteristics and sources of water-soluble ions in atmospheric PM2.5 in different regions of central China during summer, Wuhan, Suizhou, and Pingdingshan were selected as urban, suburban, and rural monitoring stations, respectively, to collect PM2.5 samples, and the mass concentration of PM2.5 in the atmosphere and the contents of eight water-soluble ions were analyzed. The results showed that ρ(water-soluble ions) at the three sites showed obvious spatial distribution characteristics, with Pingdingshan[(36.29±9.82) μg·m-3] > Wuhan[(32.55±10.05) μg·m-3] > Suizhou[(26.10±6.23) μg·m-3], accounting for 52.47%, 51.32%, and 48.61% of the PM2.5 mass concentration, respectively. In the Pingdingshan station, the proportion of water-soluble ions was the largest due to biomass combustion in the rural area. Additionally, SNA (SO42-, NO3-, and NH4+) were the main ionic components, accounting for 95.65%, 96.12%, and 97.33% of the total water-soluble ions, respectively. The mean values of SOR of the Wuhan (0.64) and Suizhou (0.63) stations were higher than that of the Pingdingshan station (0.50), whereas the NOR values of the Wuhan (0.18) and Pingdingshan (0.19) stations were higher than that of the Suizhou station (0.15). The difference in SOR and NOR among stations was affected by the secondary conversion mechanism, the ammonia-rich environment, and the surrounding traffic sources, respectively. The PM2.5 at the Wuhan and Pingdingshan stations was in general alkaline, whereas at the Suizhou station it was neutral or weakly acidic, which was mainly caused by differences in NH4+. NH4+ mainly existed in the form of (NH4)2SO4 and NH4NO3 at the Wuhan and Pingdingshan stations, whereas at the Suizhou station it mainly existed in the form of (NH4)2SO4 or (NH4)HSO4. PCA-MLR analysis revealed that the Wuhan (89.27%) and Suizhou (67.38%) stations were the most affected by secondary conversion sources, whereas the Wuhan station was also affected by industrial sources (8.54%) and coal sources (2.27%). The pollution sources of the Suizhou station also included biomass combustion (24.42%) and dust sources (8.25%). The Pingdingshan station was most affected by biomass combustion (58.37%), followed by dust and combustion sources (38.05%) and traffic sources (3.58%). The analysis of potential sources of SNA (PSCF) showed that the main potential source areas of Wuhan were the boundary of Hubei, Henan, and Anhui and the southwest area of Anhui. Suizhou and Pingdingshan were affected by long-distance transport, and the main potential source regions were distributed in Shanghai, Jiangsu, and Anhui provinces from the east coast to the west.

Combining MSPA-MCR Model to Evaluate the Ecological Network in Wuhan, China

Rapid urbanization development and construction has seriously threatened the connectivity of habitat patches in cities and hindered the construction of ecological networks in highly urbanized areas. Among them, China is affected by early compressed urbanization, and the broken ecological space in cities and towns has attracted the extensive interest of researchers. To avoid the subjective randomness and single analysis of ecological space in urbanization areas, this paper takes the central urban area of Wuhan as the main research area. It comprehensively evaluates the ecological network space by combining the MSPA-MCR model. The main conclusions are as follows: (1) Identifying the ecological landscape types with important ecological significance in the study area: Core (88.29%), Islet (0.25%), Perf (0.63%), Edge (9.74%), Loop (0.22%), and Bridge (0.14%). Through the dPC landscape index, seven important ecological sources south of the middle reaches of the Yangtze River were identified. (2) According to the comprehensive factors of natural and human factors constructed by the MCR model, the minimum cumulative ecological resistance surface was established, with an average value of 2.65, a maximum value of 4.70, and a minimum value of 1.00, showing a trend that the ecological resistance values in the central and eastern parts are lower than those in the western part. (3) According to the standard deviation ellipse, the distribution direction of NE–SW in ecological sources was analyzed. The ecological sources distributed in the north were less, and the spatial distribution was scattered on the whole. The strong global positive correlation and local spatial aggregation characteristics of ecological resistance surface were evaluated according to spatial autocorrelation. Based on the gravity model, the interaction intensity of ecological corridors between source areas was evaluated, and the importance of ecological corridor protection and restoration was quantitatively analyzed. The research results provide scientific and reasonable references and a basis for ecological planning of Wuhan central city.

Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis

Wuhan (China) is facing severe consolidation subsidence of soft soil and karst collapse hazards. To quantitatively explore the extent and causes of land subsidence in Wuhan, we performed multitemporal interferometry (MTI) analysis using synthetic aperture radar (SAR) data from the TerraSAR-X satellite from 2013 to 2017 and the Sentinel-1A satellite from 2015 to 2017. MTI results reveal four major subsidence zones in Wuhan, namely, Hankou (exceeding −6 cm/yr), Xudong-Qingshan (−3 cm/yr), Baishazhou-Jiangdi (−3 cm/yr), and Jianshe-Yangluo (−2 cm/yr). Accuracy assessment using 106 levelling benchmarks and cross-validation between the two InSAR-based results indicate an overall root-mean-square error (RMSE) of 2.5 and 3.1 mm/yr, respectively. Geophysical and geological analyses suggest that among the four major subsiding zones, Hankou, Xudong-Qingshan, and Jianshe-Yangluo are located in non-karstic soft soil areas, where shallow groundwater (< 30 m) declines driven by engineering dewatering and industrial water depletion contribute directly to soft soil compaction. Subsidence in the Baishazhou-Jiangdi zone develops in the karst terrain with abundant underground caves and fissures, which are major natural factors for gradual subsidence and karst collapse. Spatial variation analysis of the geological conditions indicates that the stage of karst development plays the most important role in influencing kart subsidence, followed by municipal construction, proximity to major rivers, and overlying soil structure. Moreover, land subsidence in this zone is affected more via coupling effects from multiple factors. Risk zoning analysis integrating subsidence horizontal gradient, InSAR deformation rates, and municipal construction density show that the high-risk areas in Wuhan are mainly distributed in the Tianxingzhou and Baishazhou-Jiangdi zone, and generally spread along the metro lines.

Research on the Matching Relationship between the Supply of Urban Ecological Recreational Space and the Demand of Residents-A Case Study of an Urban Development Area in Wuhan.

An urban ecological recreational space (UERS), which connects the natural environment with urban residents, is an important guarantee for developing a livable city and improving the well-being of residents. However, there is a serious imbalance between the supply of UERSs and the demand of residents in many big, rapidly developing cities. Previous studies usually used indicators such as scale or quantity to measure the supply level of UERS enjoyed by residents, ignoring its own quality differences. Therefore, taking the urban development area of Wuhan as the research object, we measured the quality of UERS from four dimensions using the entropy method and designed a method to measure the supply service level under the hierarchical travel threshold to analyze the supply level of UERSs based on a community unit. Finally, combined with the demand characteristics of different groups, the matching relationship between supply and demand of UERSs in each community is quantitatively analyzed. The results show the following: (1) The quality of UERS in urban development area of Wuhan varies greatly and its distribution is extremely uneven. (2) The level of supply services and the demand level vary greatly, and the overall performance has a trend of decreasing from the city center to the periphery. (3) The overall matching relationship between supply and demand of UERS is not ideal, and more than half of the communities are in supply deficit or without services. Our study provides a novel perspective on quantifying the supply–demand relationship of UERS. It can more accurately guide decision-makers and planners in determining areas with mismatches between the supply and demand of UERSs and in making targeted layouts of UERSs and relevant policies.

Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

A Dynamic Neural Network Optimization Model for Heavy Metal Content Prediction in Farmland Soil

To improve the accuracy of soil heavy metal content prediction, this paper proposes a dynamic neural network optimization model (DNNOM). The model is based on a radial basis function neural network (RBFNN). The weights and bias of the output layer of the RBFNN were generated using the adaptive dynamic genetic optimization algorithm (ADGOA), and the center point of the hidden layer of the RBFNN was determined using an efficient density peak clustering algorithm (EDPC). An adaptive variance measure (AVM) was then used to generate the width vector of RBFNN hidden layer. The model was applied to the prediction soil heavy metal content in six new urban areas in Wuhan. Through comparison with support vector machine(SVM), light gradient boosting machine(LightGBM), RBFNN, and genetic algorithm optimizes the radial basis function neural network(GA-RBFNN), the experimental results demonstrate that the DNNOM is closer to the real value than the other four models, and the four error indicator values are also significantly lower than those of the other comparison models, which have higher prediction accuracy. Especially when compared with RBFNN, the MAPE and SMAPE of DNNOM have dropped by 3.98% and 3.9%, respectively.

How Did Different Hybrid States of Green-Blue-Grey Infrastructures Impact on Land Surface Temperature at Multiple-Scales? A Case Of Highly Urbanized Area in Wuhan

How Did Different Hybrid States of Green-Blue-Grey Infrastructures Impact on Land Surface Temperature at Multiple-Scales? A Case Of Highly Urbanized Area in Wuhan

Experiment and Simulation for the Effect of Window Opening Behavior on Indoor Air Status Points in Wuhan Area in Summer

Experiment and Simulation for the Effect of Window Opening Behavior on Indoor Air Status Points in Wuhan Area in Summer

WH-MAVS: A Novel Dataset and Deep Learning Benchmark for Multiple Land Use and Land Cover Applications

Over the past decade, many excellent data sharing efforts have enriched the remote sensing scene classification (SC) methods. These datasets have achieved great success in complex high-level semantic information interpretation. However, most existing datasets are collected from standard and ungeoreferenced image patches for algorithm training and evaluation. These datasets do not fit for practical applications and cannot be directly applied in further geographical study. Accordingly, we provide a large range high-resolution SC dataset with multiple time phases, called “ <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> u <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</b> an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</b> pplication <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> HR <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> cene classification dataset (WH-MAVS).” It facilitates the study of SC and scene change detection (SCD) algorithms. Moreover, it can also be directly employed to perform a variety of real-life land use application tasks. To the best of our knowledge, this is the first free, publicly available, georeferenced, and annotated dataset to cover almost an entire megacity. The WH-MAVS was collected and annotated from Google Earth imagery with the same spatial resolution and uniform nonoverlapping patch size, covering the central area of Wuhan, China. The total number of scene samples is 47 137, which belong to 14 classes with 23 567 labeled patches for each time phase in 2014 and 2016, respectively. The geographic coordinates of all samples in both time phases exhibit one-to-one correspondence with 23 202 unchanged image patches of scene categories and 365 changed ones. The distribution of the number of samples in each class is highly imbalanced; moreover, there are large intraclass differences and indistinguishable interclass variances. These characteristics are closer to the real land use/land cover application tasks and introduce further challenges to the related algorithm research. In addition, we conducted benchmark experiments on SC and SCD based on the WH-MAVS dataset with widely used deep learning models. DenseNet169 was found to achieve the best performance. The overall accuracies are 91.07% and 92.09%, respectively, in the 2014 and 2016 validation sets of WH-MAVS. Furthermore, SCD obtained by DenseNet169 has a binary change detection accuracy of 89.56% and a multiple (from–to) change detection accuracy of 86.70%. Over and above the research value of the algorithm, it is also proven to have practical applications in fields such as urban planning, landscape pattern analysis, and urban dynamic monitoring and analysis.

Fine-Grained Classification of Urban Functional Zones and Landscape Pattern Analysis Using Hyperspectral Satellite Imagery: A Case Study of Wuhan

Landscape mapping and pattern analysis of fine-grained urban functional zones (UFZ) are important in urban planning and urban development. Multi-spectral images have largely enabled only coarse-grained land-use classification of urban areas. By contrast, hyperspectral images have shown potentials to facilitate fine-grained classification of urban areas. In this paper, we evaluated and analyzed the classification of fine-grained UFZ in the central city of Wuhan, Hubei, China, using GaoFen-5 (GF-5) hyperspectral satellite imagery. We first compared the performance of hyperspectral data (GF-5) and multispectral data (Landsat 8) for the classification of fine-grained functional areas of cities by employing two classical classification algorithms and two deep learning methods. We also propose the deep learning-based SSUN-CRF algorithm, to enable better landscape pattern analysis. We then analyzed the landscape pattern of the seven administrative districts in the main urban areas of Wuhan by combining ten landscape indicators, based on the precise UFZ classification results obtained from the hyperspectral data. Experimental results illustrated the following: (1) Compared to multispectral images, hyperspectral images can allow for a more accurate UFZ classification. (2) Deep learning classification algorithms can better exploit hyperspectral image data, with the SSUN-CRF algorithm, in particular, being able to achieve an overall accuracy of 93.86% and a Kappa coefficient of 92.08%. (3) We were able to analyze the landscape pattern of Wuhans main urban areas based on the results of the UFZ classification. The results indicated that hyperspectral remote sensing imagery shows significant potentials in mapping fine-grained urban functional zones.

Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km2, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature &gt;35 °C will expand to 15.264 and 33.612 km2, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented.

The Genotypes and Clinical Characteristics of Thalassemia on Children in Wuhan Region

To investigate the genotypes and clinical characteristics of thalassemia on children in Wuhan region. A total of 159 patients diagnosed as thalassemia in Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology from December 2017 to December 2019. The patients were retrospectively analyzed for their types of mutations, detection rates and clinical characteristics. Among the 422 samples, 159 samples were finally diagnosed as thalassemia through genetic testing, the total detection rate was 37.68%. The detection rate of α, β and αβ-thalassemia was 17.30%, 20.14% and 0.24% respectively. Among α-thalassemia, αα/-SEA was the most common one, with a composition ratio of 68.49%(50/73), followed by αα/-α3.7 (19.18%), αα/-α4.2 (6.85%) and αα/ QS (1.37%). 9 types of β-thalassemia gene mutations were detected, and the most common three mutations were IVSII-654(C→T), with a composition ratio of 40.00%, CD41-42(-TTCT) (20.00%) and CD17(A→T)(16.47%). Two novel mutations of β-thalassemia, HBB: c.92-2A＞T and HBB:c.-23A＞G were detected. Among all the positive patients, 134 (84.28%) were 0-3 years old, 19 (11.95%) were 4-6 years old, and 6 (3.77%) were 7 years of age or older. There were 147 patients with mild anemia (92.45%), 11 patients with moderate anemia (6.92%), and 1 patients with severe anemia (0.63%). The MCV of 94(59.12%) patients was lower than 65 fL, and that of 51(32.08%) patients was between 65 fL and 80 fL, while 14(8.81%) patients was higher than 80 fL. MCV in β-thalassemia group was lower than that in α-thalassemia group, and the difference showed statistically significant (P<0.05). The genotypes of thalassemia in children in Wuhan area are diverse, and most of them are mild thalassemia, and diagnosed under 3 years old. Children with β-thalassemia have smaller red blood cell volumes than those with α-thalassemia.

Revisiting the valuable locales in our cities? Visualizing social interaction potential around metro station areas in Wuhan, China

A complete tour of metro users consists of their journeys inside the carriage and various activities outside the carriage, in particular, those in or around metro station areas (MSAs). To fathom out the spatiality and magnitude of those activities, which involve substantial interactions among people or with urban spaces, we assume that (a) metro users who spent 30 min or more together in or around the same MSA would physically interact with at least another person there; (b) the more an MSA sees metro riders co-presenting there the higher social interaction potential (SIP) there is; (c) SIP of an MSA is positively correlated with the number of distinct riders co-presenting in that MSA. By exploiting two-day metro smartcard data of Wuhan, China, we use the number of distinct riders co-presenting in that MSA to measure and visualize the MSA-level SIP in that city. Our visuals show the SIP varies across MSA and time of the day. Some MSAs have higher SIP in the daytime whereas other MSAs have higher SIP at the nighttime. Few MSAs continuously have high SIP. These results inform us where and when SIP would be the highest and the lowest across MSAs, which can facilitate metro operators’ monitoring and management of MSAs on the one hand and help businessmen and officials decide where and when to provide services and/or sell products across MSAs.

Extracting the location of flooding events in urban systems and analyzing the semantic risk using social sensing data

The aggregation of the same type of socio-economic activities in urban space generates urban functional zones, each of which has one function as the main (e.g., residential, educational or commercial), and is an important part of the city. With the development of deep learning technology in the field of remote sensing, the accuracy of land use decoding has been greatly improved. However, no finer remote sensing image could directly obtain economic and social information and it has a high revisit cycle (low temporal resolution), while urban flooding often lasts only a few hours. Cities contain a large amount of “social sensing” data that records human socio-economic activities, and GIS is a natural discipline with strong socio-economic ties. We propose a new GeoSemantic2vec algorithm for urban function recognition based on the latest advances in natural language processing technology (BERT model), which utilizes the rich semantic information in urban POI data to portray urban functions. Taking the Wuhan flooding event in summer 2020 as an example, we identified 84.55% of the flooding locations in social media. We also use the new algorithm proposed in this paper to divide the main urban area of Wuhan into 8 types of urban functional zones (kappa coefficient is 0.615) and construct a “City Portrait” of flooding locations. This paper summarizes the progress of existing research on urban function identification using natural language processing techniques and proposes a better algorithm, which is of great value for urban flood location detection and risk assessment.

Influencing Factors Associated With Mental Health Outcomes Among Dental Medical Staff in Emergency Exposed to Coronavirus Disease 2019: A Multicenter Cross-Sectional Study in China

Background: The epidemic infection of coronavirus disease 2019 (COVID-19) may have a profound impact on dentistry, mainly due to the mode of transmission of the pathogen, which poses a risk to almost all dental operations. Therefore, this study aimed to investigate the prevalence and influencing factors of anxiety, depression, perceived stress, and acute stress disorder among dental medical staff in emergency situations during the COVID-19 epidemic.Methods: From April 3, 2020, to April 10, 20204, a multicenter cross-sectional study was conducted among 808 first-line dental professionals at an emergency department in mainland China. A self-designed questionnaire was used to collect general demographic information. The 7-item Generalized Anxiety Disorder Scale (GAD-7), 9-item Patient Health Questionnaire (PHQ-9), 10-item Perceived Stress Scale (PSS-10), and Acute Stress Disorder Scale (ASDS) were used to assess the severity of symptoms of anxiety, depression, perceived stress, and acute stress disorder (ASD), respectively.Results: The prevalence rates of depression, anxiety, perceived pressure, and ASD among the frontline dental medical staff were 46.4, 36.3, 65.2, and 1.1%, respectively. The frontline dental medical staff who were working in the Wuhan area reported experiencing more anxiety (p = 0.038) and perceived stress (p < 0.001) compared with those who were not working in the Wuhan area. The frontline dental medical staff who were working in a general hospital reported experiencing more dissociation symptoms (p = 0.001) compared with those working in a specialized or private hospital. Individuals with a past medical history reported experiencing more anxiety (p = 0.009), depression (p < 0.001), and perceived stress (p = 0.003) than those without, and individuals with lower levels of education showed higher levels of anxiety (p = 0.038). Binary logistic regression analysis results (after controlling for other confounders) suggested that having a past medical history was a risk factor for both anxiety (p = 0.002; OR = 2.441; 95% CI, 1.384–4.306) and perceived stress (p = 0.001; OR = 1.417; 95% CI, 1.145–1.754).Conclusions: The prevalence of mental symptoms was high among the first-line emergency dental staff. Male sex, working in the Wuhan area, working in a general hospital, a past medical history, and lower levels of education were risk factors. Therefore, we need to pay close attention to the mental health problems of frontline dentists during the COVID-19 outbreak and adopt active preventive strategies to maintain their physical and mental health.