Case Study Of Wuhan Research Articles

Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan

In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.

Effects of Land Use on Land Surface Temperature: A Case Study of Wuhan, China

In this study, we aim to understand the impact of land use on the urban heat island (UHI) effect across an urban area. Considering the case study of Wuhan, China, land use factors and land surface temperatures (LSTs) of 589 planning management units were quantified in order to identify the spatial autocorrelation of LST, which indicated that a traditional regression would be invalid. By investigating the relationships between land use factors and the LST in summer, based on spatial regression models including the spatial lag model and the spatial error model, four conclusions were derived. First, the spatial error model effectively explains the relationships between LST and land use factors. Second, the impact on LST of the percentage of industrial areas is significant even though the impacts of land cover and building-group morphology indicators are combined, indicating that anthropogenic heat emission of industrial production contributes to high LSTs. Third, the relationship between the percentage of commercial area and LST is significant in the Pearson correlation analysis and traditional regression models, while not significant in spatial error model, suggesting that the urban heat environment of a commercial area is determined by the land use factors of the surrounding area. Fourth, the UHI effect in industrial and commercial areas could be precisely mitigated by not locating industrial areas beside residential areas, and setting up buffer zones between commercial areas and surrounding traditional residential areas. Overall, the results of this study innovatively deepen the understanding of the impact of the percentage of different urban land use types on the urban heat environment at the scale of planning management units, which is conducive to formulating precise regulation measures for mitigating UHI effects and improving public health.

Village classification in metropolitan suburbs from the perspective of urban-rural integration and improvement strategies: A case study of Wuhan, central China

Urban-rural integration is an ideal urban-rural relationship that balances and coordinates urban and rural development. Suburbs, as transitional zones between urban and rural areas, are usually treated as part of urban areas and are insufficiently considered in this integration. However, during rapid urbanization, the suburbs of metropolitan cities attract huge populations and industrial agglomeration. Mixed land use types, population structure, industrial construction, and underdeveloped infrastructure cause the suburbs of big cities to face significant spatial differentiation of socioeconomic development and public services, together with an ecological environmental pressure brought about by drastic land use change. The complexity, gradual change, and dynamics of suburbs render it difficult to map them effectively. In addition, due to different conditions such as the development basis, traffic conditions, spatial location, and resource endowments, the type and degree of integration of villages around metropolises are also differentiated, which determines their future development direction. Against this background, this study first maps the suburban boundaries at the village scale using a logistic-geographically weighted regression (GWR) model, which is a localized parameter estimation method. This study classifies the types of suburban villages in metropolitan areas using three dimensions: socioeconomic, spatial, and ecological integration. Finally, improvement suggestions have been proposed for villages with different types of urban-rural integration. Following findings are summarized: 1) The logistic-GWR model identifies 2626 suburban villages in Wuhan; 2) the integration differentiation identifies 370, 564, 216, and 365 suburban villages that exhibit comprehensive, ecological, socioeconomic, and spatial disintegration, respectively; and 3) solutions for promoting urban-rural integration of suburban villages are provided accordingly.

Exploring the Impact of Urban Built Environment on Public Emotions Based on Social Media Data: A Case Study of Wuhan

In the era of public participation in government, public emotions and expectations are important considerations influencing urban construction, planning, and management. A desirable urban environment can make people feel at ease and comfortable and contribute to promoting positive public emotions. However, in the process of rapid urban development, the high-density and overloaded urban built environment has triggered people’s mental tension and anxiety and has contributed to negative emotions. Thus, this study aimed to explore the spatial distribution of public emotions and urban built environments in cities and to thoroughly investigate the correlation between urban built environments and public emotions. Considering the lack of dynamic elements analysis and emotions spatial analysis in previous studies, this study takes Wuhan City as an example, uses social media big data as the basis for text emotion analysis, introduces dynamic traffic elements, and establishes a multidimensional urban built environment measurement index system from five aspects: land use, spatial form, road and traffic, green space and open space, and daily life service facilities. Subsequently, the spatial distribution characteristics of public sentiment and urban built environment elements in Wuhan were analyzed. Finally, a geographically weighted regression method was used to analyze the degree of influence of different urban built environment elements on public emotions. The results showed that public emotions in Wuhan are not homogeneously distributed in terms of score and space and that there are significant differences. The urban built environment has a significant influence on public emotions. Higher land use mix, higher road network density, higher number of public transportation facilities, higher number of public open spaces, lower traffic congestion, and impact of freight transportation play important roles in promoting positive emotions. Therefore, in the process of urban construction, planners and decision makers should purposefully improve the quality of the built environment. Measures can include improving the mix of land functions, alleviating traffic congestion, avoiding the negative effects of freight traffic, rationally constructing green and open spaces, and improving various living facilities. This can help contribute toward improving urban functions and urban environments, and promote the construction of a people-oriented healthy city.

Evaluation of Subjective Feelings of Outdoor Thermal Comfort in Residential Areas: A Case Study of Wuhan

Outdoor thermal comfort in urban residential areas is closely related to the daily lives of their residents. Nine residential communities in Wuhan, a typical city in central China, were selected as the research object. A questionnaire survey, meteorological parameter measurements, and data analyses were used to investigate the influences of factors such as the characteristics of the spatial environments and the types of outdoor activities undertaken by the residents on the levels of comfort. This investigation was expected to provide a reference for improving these levels. The results show that the level of satisfaction of those in areas with building heights of more than 15 stories reached 51%. Wind speeds were higher in medium-density areas while comfort was the highest in residential areas with densities between 22% and 30%. High-quality areas, which benefitted mainly from higher sensations of wind speed, had the highest levels of comfort but the strongest sensations of heat. A positive correlation exists between green areas and comfort. Where the green coverage rate exceeded 70.0%, temperature satisfaction increased to 33.3–66.7%. Areas with impervious paving are negatively correlated to temperature sensations. Areas of 400–625 m2 had levels of temperature satisfaction lower than 21.5%.

Relationship between Built Environment and COVID-19 Dispersal Based on Age Stratification: A Case Study of Wuhan.

The outbreak of COVID-19 (coronavirus disease 2019) has become the focus of attention in the field of urban geography. Built environment, such as the layout of public spaces like transportation hubs and urban open spaces, is an important factor affecting the spread of the epidemic. However, due to the different behavior patterns of different age groups, the intensity and frequency of their use of various built environment spaces may vary. Based on this, we selected patients that were infected, with a non-manipulated time period, and the classification of human behavior patterns; we then conducted a regression analysis study on the spatial distribution and building environment of these COVID-19 patients. The results showed that the spatial distribution of young and middle-aged patients (18–59 years old) was more homogeneous, while the spatial distribution of elderly patients (60 years old and above) had a strong clustering characteristic. Moreover, the significant built environment factors exhibited in the two populations were extremely different. More diverse urban facilities and public spaces exhibited influential properties for older patients, while middle-aged and young adults were more influenced by commuting facilities. It can be said that the built environment shows different influences and mechanisms on the transmission of respiratory infectious diseases in different populations. Therefore, the results of this paper can inform decision makers who expect to reduce the occurrence of urban respiratory infectious diseases by improving the urban built environment.

A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan

Rapid urbanization not only helps the urban economy achieve rapid growth but also imposes great pressure on resources and the environment. The green and blue infrastructure (GBI) in urban areas appears to be an important measure to enhance the economic benefits of the urban environment, but related research is still limited. In this article, we combine big data and machine learning methods to investigate the economic benefits of GBI in the city of Wuhan in Hubei Province. Specifically, the daily travel routes of citizens are analyzed at two scale levels (the housing and neighborhood scales); in our analysis, we start with the structural attributes of citizens’ own houses, move on to the city street views that citizens walk through, pass by the locational amenities where citizens spend their leisure time, and arrive at open spaces located throughout the city. The above trajectory dataset includes 30 variables, which were used as the input into ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale GWR (MGWR). The results show that MGWR (adj. R2 = 0.524 at the housing scale, while adj. R2 = 0.869 at the neighborhood scale) with all control variables and the GBI attribute variables have the highest goodness of fit: the closer GBI is to urban residents, the higher the economic benefits are regardless of the area; and the higher the street visible green rate is, the greater the economic benefits are. Therefore, urban areas can appropriately increase the number of well-designed small GBIs located near urban residents. Our research provides insights into how big data and machine learning can be employed in frameworks to characterize the economic benefits of GBI and can be applied in other countries and regions.

Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China.

The surface urban heat island (SUHI) effect poses a significant threat to the urban environment and public health. This paper utilized the Local Climate Zone (LCZ) classification and land surface temperature (LST) data to analyze the seasonal dynamics of SUHI in Wuhan based on the Google Earth Engine platform. In addition, the SUHI intensity derived from the traditional urban–rural dichotomy was also calculated for comparison. Seasonal SUHI analysis showed that (1) both LCZ classification and the urban–rural dichotomy confirmed that Wuhan’s SHUI effect was the strongest in summer, followed by spring, autumn and winter; (2) the maximum SUHI intensity derived from LCZ classification reached 6.53 °C, which indicated that the SUHI effect was very significant in Wuhan; (3) LCZ 8 (i.e., large low-rise) had the maximum LST value and LCZ G (i.e., water) had the minimum LST value in all seasons; (4) the LST values of compact high-rise/midrise/low-rise (i.e., LCZ 1–3) were higher than those of open high-rise/midrise/low-rise (i.e., LCZ 4–6) in all seasons, which indicated that building density had a positive correlation with LST; (5) the LST values of dense trees (i.e., LCZ A) were less than those of scattered trees (i.e., LCZ B) in all seasons, which indicated that vegetation density had a negative correlation with LST. This paper provides some useful information for urban planning and contributes to the healthy and sustainable development of Wuhan.

Effects of Institutions on Spatial Patterns of Manufacturing Industries and Policy Implications in Metropolitan Areas: A Case Study of Wuhan, China

Manufacturing space is a spatial system that combines the interaction between capital and institutions at the enterprise, industry, and spatial levels. It is also an important functional type that promotes the spatial evolution of big cities. Most studies focus on the effects of a single institutional type on the manufacturing space of big cities and lack systematic and complete exploration of the institutional mechanism. Current empirical research on typical industrial cities in China is insufficient. This study uses a GIS spatial analysis technique and a Poisson regression model to analyze the mechanism by which institutions have influenced the spatial patterns of manufacturing industries in the Wuhan metropolitan area since the 1990s. The results show that land policy, development zone policy, urban planning, transportation strategy, and eco-environmental policy all have a significant impact on the restructuring process and distribution pattern of the manufacturing industries through incentives and constraints. This study expands our understanding of the influence mechanism of manufacturing spatial patterns and proposes spatial guiding strategies and policy implications for the spatial transformation of urban manufacturing.

Impact of Urban Water Networks on Microclimate and PM2.5 Distribution in Downtown Areas: A Case Study of Wuhan

Rapid urbanization in China has resulted in highly dense urban forms and marked reductions in ecological resources, which in turn have caused severe urban heat islands and PM2.5 pollution. In typical cities with a water network, such as Wuhan, the use of water resources to improve the urban climate environment has become an urgent issue. In this study, we explored the impact of water networks on the microclimate and PM2.5 distribution in Wuhan by using multi-source remote sensing data and the weather research and forecasting model. Our results indicated that the water network has a blocking effect on PM2.5. Taking pollutant sources into consideration, the coverage of the water surface is positively correlated with a PM2.5 concentration of 0.606–0.703; the concentration of PM2.5 over suburban lakes with an area larger than 60 km2 is approximately 6%–13% higher than that of the surrounding land surface. We also found that the water network reduced the average temperature of the downtown and the urban hinterland at noon by 1 K and 0.5 K, respectively, and increased the height of boundary layer at night by more than 60 m—an approximately one-third increase—which helped decrease the level of PM2.5. In addition, the impact of water bodies located in different “ring loops” on the downtown microclimate varied significantly. Water bodies located downtown significantly increased the night wind speed in downtown areas to as much as 2 m/s, while those in nearby and outer suburbs increased the wind speed downtown by more than 1 m/s at noon.

Spatial Characterization of Urban Vitality and the Association With Various Street Network Metrics From the Multi-Scalar Perspective.

In the context of rapid urbanization in developing countries, the spatial organization of cities has been progressively restructured over the past decades. However, little has been done to understand how the physical expansion affected the reorganization of socioeconomic spaces in cities. This study explores the association between various street network metrics and urban vitality and how it changes across different scales using geographic big data through a case study of Wuhan, China. Urban vitality is characterized by four components: concentration, accessibility, livability, and diversity. The new technique of spatial design network analysis (sDNA) is employed to characterize street network metrics, including connectivity, closeness, betweenness, severance, and efficiency, with 16 localized network variables. Furthermore, the stratified spatial heterogeneity between street network metrics at multiple scales and the four components of urban vitality is investigated using the Geodetector tool. First, concentration, accessibility, and diversity decline with distance from the urban center, whereas livability has a fluctuating upward trend with distance from the urban core. Second, the correlation between street network characteristics and urban vitality is sensitive to different spatial scales. Third, connectivity explains the largest amount of the variance in urban vitality (over 40%), while both betweenness and closeness explain roughly 28% of urban vitality. Efficiency and severance contribute 22 and 10% to the spatial heterogeneity of urban vitality, respectively. The study sheds light on the mechanisms between street configurations and urban vitality from the multi-scalar perspective. Some implications are provided for the improvement of the streets' urban vitality.

Investigating the spatiotemporal non-stationary relationships between urban spatial form and land surface temperature: A case study of Wuhan, China

This article aims to examine the pathways of urban spatial forms in mitigating urban heat island (UHI) intensity across urban areas in different seasons. By investigating the case of Wuhan, we quantified urban spatial form (USF) factors concerning land use, land cover, and building-group morphology and the land surface temperatures (LST) in summer, autumn and winter of 2,357 block units. The seasonal relationships between USF and LST are explored at the large scale of the whole study area and different zones of block units categorized by floor area ratio. The results suggest that relationships between USF and LST vary spatially and temporally. First, although most urban spatial form factors show significant correlations on LST at the global scale, the correlations vary significantly among low, medium, and high floor-area-ratio zones. Second, USFs largely contribute to the variance of LST in summer and autumn compared with winter. Third, building morphology exerts more impact on LST in relatively highly intensive built-up zones, while land use and land cover factors have more impact in low-intensity areas. Lastly, UHI can be precisely mitigated by optimizing USFs considering the varied relationships between USFs and LST. This study theoretically deepens the understanding of the variation of the relationships between built-up urban areas and UHI at the scale of the basic planning regulation units, which is conducive to formulating down-to-earth regulation measures to cool cities and communities.

Modernization imposed: SOM’s practice in China – A case study of Wuhan

AbstractSkidmore, Owings & Merrill Limited Liability Partnership as one of the best ‘localized’ overseas design companies in China, has participated in more than ten large-scale urban design projects in Wuhan, which has greatly changed the urban image and architectural style of Wuhan. Its space model of ‘grid road system + high-rise building’ replaces the traditional urban pattern of ‘street + residence’, causing huge cultural conflicts and historical rupture. Based on the planning of Wuhan Hanzheng street as an example, this paper attempts to uncover the cause of formation, the motivation mechanism of ‘grid road system + high-rise buildings’ space model, and to explore how Chinese cities realize the urban modernization with Chinese characteristics.

Study on the improvement of Elderly activity demand in Outdoor Public Space in Urban Communities -A case study of Wuhan in China

This paper studies the activities characteristics of the elderly aged 60-75 years old who can take care of themselves in the community public space, and puts forward the design principles of the community public space in the aging society. Using the methodology of experience design, through naturalistic observation, questionnaires, interviews and shadowing, the spatial and temporal characteristics of daily activities of the elderly are analyzed. In the community public space, the behavior of the elderly is essentially a social activity to get more attention and communication. This determines the needs, time, frequency and duration of activities of the elderly in community public space. In the community public space, the behavior of the elderly has the characteristics of long-term and regularity, and its behavior changes slightly with the space，the objective factors that affect the elderly are gender, age and climate. The subjective factors are lifestyle, family members and income level.

Optimization of land use structure to balance economic benefits and ecosystem services under uncertainties: A case study in Wuhan, China

Rapid economic growth and urbanization in China have led to significant changes in land use structure and seriously threatened the sustainability of urban ecosystems. A Fuzzy Multi-Objective Linear Programming (FMLP) model is developed in this paper to determine an optimal land use structure with social, economic and ecological benefits of lands considered comprehensively. Ecosystem service assessment is integrated into the FMLP model for emphasizing the ecological benefits of land use, and the uncertainty in the optimization process have been addressed. Wuhan is selected as a case study. The results indicate that (1) the relative equilibrium between economic and ecological benefits can be achieved by establishing the FMLP model. (2) The optimized land use structure can increase the ecosystem service value (ESV) at a lower cost of economic benefits. (3) The optimal land use allocation can contribute to a growth of ecological benefit by 1.51 billion RMB and economic benefit by 348.6 billion RMB compared to the government's land use planning. (4) The ESVs of 11 types of ecosystem service functions are all increased in varying degrees through land use structure optimization. (5) There are huge differences in economic and ecological benefits between the central and remote urban areas of Wuhan. The economic benefit is 603 folds of the ecological benefit in central urban area while the ESV in remote urban area is 25 folds of that of the central urban area in Wuhan. These findings could provide valuable decision-making support for the sustainable land use planning in Wuhan. The FMLP model is proven to be feasible and applicable for land use planning under uncertain conditions and thus could be applied to other cities with unbalanced land use structure.

Evaluation for block-scale solar energy potential of industrial block and optimization of application strategies: A case study of Wuhan, China

Industrial blocks are an important part of the city which is very suitable for PV application because of its large-scale, unified property rights and simple form. In this study, a multi-dimensional evaluation method of solar energy potential suitable for the block-scale is constructed. Then, 48 cases of industrial blocks in a typical city of china were selected as examples. Basing on morphological parameters, all cases were classified into 7 types by clustering algorithm. Afterwards, the total amount and distribution of the solar energy potential were calculated. Finally, the strategies of the photovoltaic system application in blocks were proposed according to types of blocks. It is found that there is significant difference in solar energy potential between different types of industrial blocks. Among them, solar energy potential of single-storey types is the highest up to 90.85 kW h/m2•year, while solar energy potential of high-rise type blocks is relatively lower which is only 55.64 kW h/m2•year. In addition, block shape has obvious impact on solar energy potential, among which building density having the greatest influence, as building density increases from 20 % to 55 %, the solar energy potential also increased by 138.74 % from 42.54 kW h/m2•year to 101.56 kW h/m2•year.

Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China

Abstract Urban vitality is a spatial phenomenon and a public service. Previous studies often did not measure the fairness of the accessibility of urban vitality. This research analyzed the spatial distribution of urban vitality in Wuhan as a case study area using big data from multiple sources. The study used the two-step floating catchment area method (2SFCA) to measure the accessibility level of each residential district to the vibrant zones (VZs). Furthermore, the inequality in the level of accessibility of residential areas with different housing prices was assessed on the basis of the Gini coefficient. The main conclusions are as follows: (1) the Gini coefficient of reaching the accessibility level of high-grade VZs is 0.426, and the corresponding sub-high-grade Gini coefficient is 0.274. This shows that the inequality in accessibility of different residential areas is more obvious as the level of vitality is higher and (2) residential areas with high housing prices have greater accessibility than those areas with low housing prices. It was also noticed that the level of accessibility and fairness in vitality is generally higher in central urban areas than in suburban areas. The Gini coefficient of high-grade vitality is generally higher than that of sub-high-grade. The results of this study provide a reference for researching the accessibility level of urban vitality, which considers the needs of population, and can also provide guidelines in urban planning regarding the allocation of services and resources.

Variance of the impact of urban green space on the urban heat island effect among different urban functional zones: A case study in Wuhan

The urban heat island effect (UHI) is frequently observed in cities, and is associated with environmental effects. Urban green space (UGS) has been shown to play an important role in mitigating the UHI. However, impacts from the spatial pattern of UGS among different urban functional zones are largely ignored. This study established a spatial regression model to reveal the relationship between landscape metrics of UGS and land surface temperature (LST), in order to characterize the different effects of the spatial pattern of UGS on UHI among urban functional zones. The result shows that the proportion of UGS and shape complexity metric show a general negative impact on LST both overall and in individual urban functional zones; while none was found with aggregation, the fragmentation metric and connectivity. An area-related metric can be used to explain LST in public service zones and has a negative impact on LST. The research proves that UGS in different urban functional zones play different roles in mitigating the UHI effect. The cooling effects are related to the spatial pattern of UGS. Accordingly, it is recommended to consider the different roles of the spatial pattern of UGS in different urban functional zones in mitigating UHI for a liveable environment.

Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China

The COVID-19 pandemic undoubtedly has a great impact on the world economy, especially the urban economy. It is urgent to study the environmental pathogenic factors and transmission route of it. We want to discuss the relationship between the urban living environment and the number of confirmed cases at the community scale, and examine the driving forces of community infection (e.g., environment, ecology, convenience, livability, and population density). Besides, we hope that our research will help make our cities more inclusive, safe, resilient, and sustainable. 650 communities with confirmed COVID-19 cases in Wuhan were selected as the research objects. We utilize deep learning semantic segmentation technology to calculate the Visible Green Index (VGI) and Sky View Factor (SVF) of street view and use Partial Least Squares Structural Equation Modeling (PLS-SEM) to study the driving forces of pandemic situation. Temperature and humidity information recorded by sensors was also used for urban sensing. We find that the more SVF has a certain inhibitory effect on the virus transmission, but contrary to our intuitive perception, higher VGI has a certain promotion effect. Also, the structural equation model constructed in this paper can explain the variance of 28.9% of the number of confirmed cases, and results (path coef.) demonstrate that residential density of community (0.517) is a major influencing factor for pandemic cases, whereas convenience of community living (0.234) strongly influence it. Communities with good suitability of community human settlement (e.g., construction time, price) are safer in the face of pandemic events. Does the influence of SVF and VGI on the results of the pandemic situation mean that sunlight can effectively block the spread of the virus? This spatial heterogeneity in different communities is helpful for us to explore the environmental transmission route of COVID-19.

Anthropogenic impacts on isotopic and geochemical characteristics of urban streams: a case study in Wuhan, China.

Urbanization and human activities have significantly modified the geochemical signatures of urban streams worldwide. However, the geochemical characteristics of urban streams in Wuhan, one of the core cities in the Yangtze River Economic Belt in China, remain largely unstudied. Here, we examined the stable isotopes and geochemistry of urban streams at 73 locations in the central districts in Wuhan during May 2019. Maps of isotopic signatures reflected a non-free-flowing state in part of the urban stream system in Wuhan. A lower DO and a higher EC level were found in urban streams relative to the adjacent Yangtze River. The Na+, K+, and Cl- concentrations in urban streams were > 3.0 times as high as those in the Yangtze River, and there was a slight increasing trend between 1.1 and 1.4 times for other major ions. The mildly elevated Fe concentration (1.3 times) and markedly elevated Mn concentration (> 5.0 times) were observed in urban streams. Spearman's correlation analysis indicated strong positive bivariate correlations among Na+, K+, and Cl- in urban streams, and an urban geochemical principal component was identified by principal component analysis. Plotting Na/(Na + Ca) versus total dissolved solids (TDS) indicated a potential risk of "urban stream syndrome." These findings can enhance the knowledge of anthropogenic impacts on current urban stream water quality and provide reference for the restoration and improvement of water ecology functions of the urban stream system in Wuhan.