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Article

Assessment of Ventilation Potential and Construction of Wind Corridors in Chengdu City Based on Multi-Source Data and Multi-Model Analysis

by
Xiaojiang Xia
1,2,
Ling Jian
1,2,*,
Kaiji Ouyang
1,
Xiuying Liu
1,
Xuewen Liang
1,
Yang Zhang
3 and
Bojia Li
1
1
College of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China
2
Research Center for Human Geography of Tibetan Plateau and Its Eastern Slope, Chengdu University of Technology, Chengdu 610059, China
3
School of Architecture, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(10), 1671; https://doi.org/10.3390/land13101671
Submission received: 4 September 2024 / Revised: 10 October 2024 / Accepted: 11 October 2024 / Published: 14 October 2024
(This article belongs to the Special Issue Sustainable Evaluation Methodology of Urban and Regional Planning)
Browse Figures
Figure 1
<p>Location map of the study area. (<b>a</b>): location of Chengdu City in China; (<b>b</b>): location of the central city of Chengdu; (<b>c</b>): location of the Ring Expressway in the central city; (<b>d</b>): extent of the Ring Expressway with building distribution and Ring Roads shown.</p> "> Figure 2
<p>Changes in monthly air temperature, rainfall, and wind speed in Chengdu from 2010 to 2023 (the wind speed data were measured at a height of 10 m above the ground).</p> "> Figure 3
<p>Framework for urban wind environment assessment and multi-level wind corridor system construction.</p> "> Figure 4
<p>Basic evaluation units for urban building ventilation.</p> "> Figure 5
<p>Principles of wind corridor simulation configuration under different dominant wind directions.</p> "> Figure 6
<p>(<b>a</b>) Functional spaces and (<b>b</b>) compensative spaces in the ventilation system.</p> "> Figure 7
<p>Spatial distribution of building morphology indicators. (<b>a</b>): building density; (<b>b</b>): building height; (<b>c</b>): plot ratio; (<b>d</b>): FAI; (<b>e</b>): roughness length; (<b>f</b>): SVF.</p> "> Figure 8
<p>Spatial distribution of terrain, land cover, road traffic indicators, and VRC. (<b>a</b>): elevation; (<b>b</b>): NDVI; (<b>c</b>): water; (<b>d</b>): road openness; (<b>e</b>) VRC.</p> "> Figure 9
<p>Radar distribution of ventilation potential indicators for different urban ring roads. (<b>a</b>): building morphology indicators; (<b>b</b>): terrain, land cover, and road traffic indicators.</p> "> Figure 10
<p>Prevailing wind environment information of Chengdu City. (<b>a</b>): location of Chengdu in Sichuan Province; (<b>b</b>): wind rose diagrams for 14 meteorological stations; (<b>c</b>): annual average prevailing wind frequencies in 16 directions; (<b>d</b>): prevailing wind frequencies in 16 directions for summer and winter seasons.</p> "> Figure 11
<p>Simulation results of the wind corridor network under prevailing summer and winter wind directions.</p> "> Figure 12
<p>Statistics of internal and external LST of UVCs under different prevailing wind directions.</p> "> Figure 13
<p>Selection of experimental and control point locations.</p> "> Figure 14
<p>Field measurements of average maximum wind speed and air temperature inside and outside the UVC.</p> "> Figure 15
<p>Kernel density analysis of the wind corridor network. (<b>a</b>): analysis of linear elements; (<b>b</b>): analysis of point elements.</p> "> Figure 16
<p>Undirected wind corridor network constructed based on complex networks (nodes of the same color belong to the same community, and the average degree for each module is shown in brackets).</p> "> Figure 17
<p>Variations in topological indices of different nodes in the wind corridor network. (<b>a</b>): eigencentrality; (<b>b</b>): closeness centrality; (<b>c</b>): eccentricity; (<b>d</b>): comprehensive importance. The colors of the nodes correspond to the communities identified in <a href="#land-13-01671-f016" class="html-fig">Figure 16</a>.</p> "> Figure 18
<p>Structure of the three-level wind corridor system. (<b>a</b>): summer wind corridors; (<b>b</b>): winter wind corridors.</p> ">
Versions Notes

Abstract

:
The establishment of urban ventilation corridors (UVCs) aims to mitigate the urban heat island effect. While most studies focus on the construction and assessment of the environmental benefit of UVCs, they often overlook the analysis of UVCs’ topological features. This research integrates multi-source data including 3D urban buildings, historical meteorological observations, high-resolution remote sensing, and land use planning, combined with multiple models, including geographic information system spatial analysis, circuit theory, and complex networks. Based on an assessment of urban ventilation potential, the circuit model was applied to extract UVCs aligned with the prevailing wind direction for both summer and winter seasons. Complex network modeling was employed to analyze the topological features of the ventilation network. From the analytical results, a multi-level wind corridor system for Chengdu was quantitatively developed. The results indicate that the city’s overall ventilation resistance is high, with notable spatial clustering, and the southeastern region faces substantial ventilation obstructions. A total of 143 critical ventilation nodes were identified, with the number of air inlets and outlets in summer being significantly fewer than in winter. However, the cooling effect of ventilation corridors in the prevailing summer wind direction is superior to that in winter. The ventilation network comprises 16 communities with distinct ventilation characteristics, exhibiting moderate connectivity, lacking small-world properties, and showing congestion and instability.

1. Introduction

Currently, many cities are characterized by high-density and elevated floor area ratio development patterns. The increase in impervious surfaces [1,2], the reduction in vegetation coverage [3], and proliferation of high-density buildings obstruct airflow [4], intensifying the urban heat island effect (UHIE) [5] and causing severe air pollution [6]. These environmental issues significantly affect the thermal comfort and health of urban residents [7]. Research has shown that providing a comfortable wind source can effectively improve the urban microclimate [8], enhance thermal comfort [9], and facilitate pollutant dispersion [10]. Consequently, many cities have adopted urban ventilation corridor (UVC) designs as a climate planning strategy and management tool. UVCs utilize airflow characteristics to create linear spaces with substantial ventilation potential and low air resistance; they guide fresh, clean air from the urban outskirts into the hot and stuffy areas within the city while diluting and expelling polluted air. In short, scientifically constructing UVCs is crucial for alleviating the UHIE and improving the urban ventilation environment [11].
Kress’s theory of urban local circulation suggests that, based on the climatic functions of the underlying surface, the urban ventilation system can be divided into three components: functional space, compensation space, and ventilation corridors [12]. Functional space refers to urban areas with higher temperatures and more severe air pollution, such as high-density residential and industrial zones. These zones require UVCs to introduce fresh and cool air to improve the environment conditions. Compensation space consists of areas with lower temperatures that provide fresh air, typically located in green spaces and water bodies, offering thermal compensation for the functional space. Ventilation corridors serve as channels connecting the functional and compensation spaces, guiding and facilitating airflow. In actual airflow processes, air paths are often altered when encountering obstacles, with buildings in urban environments being the most common obstructions [8]. Studies have shown that building layout and variations in architectural morphology significantly impact urban ventilation efficiency [13,14,15]. Additionally, green vegetation, water bodies, and roads play crucial roles in enhancing the cooling potential of an area [16]. Therefore, urban ventilation potential can be estimated based on the underlying urban morphology, which is determined by indicators such as building morphology, land cover, and road traffic [17].
The main methods for UVC simulation include wind tunnel experiments, computational fluid dynamics (CFD), and numerical simulations using geographic information system (GIS) technology. Wind tunnel experiments can objectively and accurately simulate the wind environment at the street block level [18], but their application at the urban scale is limited due to model size constraints and high experimental costs. CFD models require significant computational power and resources, making them more suitable for simulating individual buildings and community-scale environments [19]. In contrast, GIS-based numerical simulations analyze urban ventilation environments by calculating urban ventilation potential, making them suitable for city-scale research [20,21]. In this process, the least-cost path (LCP) is used to extract ventilation corridors and is popular among researchers because of its high computational efficiency and intuitive mapping capabilities [22]. However, the LCP method has some limitations: First, it assumes that airflow always moves in the direction of lower wind resistance and treats ventilation corridors as optimal paths for the migration and spread of air, thereby generating only a few primary UVCs [23]. Second, the wind corridor directions identified through the LCP method may vary greatly, making it challenging to form UVCs parallel to the prevailing wind direction with significant cooling effects [24]. As urban internal structures become increasingly complex, the LCP method becomes less capable of comprehensively and accurately assessing urban wind flow. Circuit theory effectively analyzes connectivity issues in complex systems and has been widely applied in social networks and landscape ecology [25]. Xie [26] attempted to simulate air circulation among urban buildings using the concepts of current and resistance. However, representing UVCs and barriers with probability values lacks specific guidance for constructing ventilation corridor systems in urban planning. In sum, new technologies are needed to simulate UVCs and analyze their topological network structure to adapt to the increasingly complex urban environment.
In the late 1990s, the emergence of small-world and scale-free network theories sparked widespread interest in the study of complex networks. The main idea behind this approach is to use nodes and edges in a network to represent relationships between various elements in real-world systems, thus revealing the intrinsic structure and essential characteristics of these systems. Complex networks have found extensive application in research fields such as transportation networks [27], communication grids [28], and management [29], providing new analytical tools and perspectives for optimizing these systems. In the study of UVCs, previous research has primarily focused on their construction strategies and effectiveness assessments, with less emphasis placed on the internal topological characteristics of the ventilation corridor network. This network is a complex network system composed of nodes (critical ventilation nodes) and edges (ventilation paths connecting these nodes). Complex network research reveals the structure and topological relationships among elements within complex systems, aligning with the structure and relationships among elements in the ventilation corridor network. Therefore, applying complex network analysis to uncover the inherent structural characteristics of ventilation corridor networks deepens the understanding of network structures and reveals how these structures contribute to improving urban ventilation efficiency.
In summary, this study utilizes GIS spatial analysis, circuit theory, and complex network models in an interdisciplinary approach, providing a scientific methodology for UVC planning. The key issues addressed include the analysis of the city’s comprehensive ventilation resistance coefficient (VRC), the effectiveness of UVCs identified through the circuit model, and the potential topological characteristics of the ventilation network. This research aims to explore the connectivity and topological features of UVCs based on diverse data and multi-model analysis, with the goal of identifying new approaches and perspectives for constructing urban wind corridor systems.

2. Study Area and Data

2.1. Study Area

The study area is located in the core urban district of Chengdu City, Sichuan Province, China, within the Chengdu Ring Expressway, and covers a total area of 540.90 km2. This area includes three concentric circular roads—the First Ring Road, Second Ring Road, and Third Ring Road—that have lengths of 19.38 km, 28.3 km, and 42.6 km, respectively (Figure 1). The area near and within the Third Ring Road is regarded the city’s core region, with land reserves located primarily to the east and north of the Third Ring Road. Additionally, numerous ecological resources and recreational facilities are planned for the areas beyond the Third Ring Road.
According to research by the National Climate Center, the Sichuan Basin and the Tarim Basin in Xinjiang are the two regions in China with the lowest atmospheric self-cleaning capacity [30]. Chengdu is located in the western part of the Sichuan Basin and is classified as a city with a high frequency of stagnant wind. Monthly air temperature, rainfall, and wind speed data from 2010 to 2023 were collected from various meteorological stations in Chengdu to analyze the city’s background climate (Figure 2). Chengdu experiences ample heat and rainfall, with a long-term average annual temperature of 17.48 °C. The summer months experience higher average temperatures, peaking at 26.66 °C in August. While the winter months experience lower temperatures, recording a low of 6.63 °C in January. The long-term average annual rainfall is notably high at 1046.29 mm. The long-term average annual wind speed is relatively low at 1.43 m/s, which hinders pollutant diffusion and leads to significant heat accumulation within the city. Chengdu has also emerged as a nationally important high-tech industrial base, trade and logistics center, and transportation hub in China, experiencing rapid urbanization in recent years. From 2010 to 2022, the resident population in the central urban area increased by CNY 5.82 million, and the regional gross domestic product (GDP) increased by CNY 1.28 trillion. This economic and population growth has intensified the demand for construction land. In summary, Chengdu is significantly affected by the UHIE (Supplementary Materials Figure S1) and faces serious ventilation challenges, making the scientific planning of the city’s ventilation system an urgent necessity.

2.2. Data and Source

The data utilized in this study include basic meteorological element datasets sourced from the National Ground Meteorological Stations in China [31]. An hourly measurement dataset from 2005 to 2015 was employed to collect climatic information on wind direction and wind speed to compile prevalent wind environment data for the city [32]. Building and road data were sourced from Amap and OpenStreetMap (OSM), respectively. Digital elevation model (DEM) data were obtained from the Geographic Spatial Data Cloud. The 30 m precision land cover fine classification product for 2020 was sourced from Global Land Cover [33]. Land satellite remote sensing data with a 30 m spatial resolution were downloaded from the United States Geological Survey data portal (https://earthexplorer.usgs.gov/, accessed on 18 November 2023). A clear day on 7 May 2022 (with an overpass time was 11:33 a.m. Beijing time) characterized by cloud cover less than 10% and good imaging quality was selected for the inversion of Land Surface Temperature (LST). Urban planning data were derived from the “Chengdu Land and Space Master Plan (2021–2035)” provided by the Chengdu Municipal Bureau of Planning and Natural Resources. Detailed data descriptions are listed in Table 1.

3. Methods

The preliminary data preparation and basic research involve the assessment of the city’s VRC, the determination of prevailing wind directions, and the identification of functional and compensative spaces. By synthesizing prior studies, the urban ventilation potential is constructed using indicators related to building morphology, land cover, road traffic, and terrain. The prevailing winds are illustrated using a wind rose diagram derived from Chengdu-specific meteorological data, while the identification of functional and compensative spaces is achieved by integrating urban temperature fields with land use planning data. The data analysis phase encompasses the application of circuit models to construct UVCs, the identification of key ventilation nodes, and the verification of UVCs using LST and field measurements. Spatial characteristics are examined through kernel density analysis, and topological features are analyzed based on complex network theory. Ultimately, a multi-level urban ventilation corridor system is developed through the spatial overlay analysis of various information sources. The research framework is shown in Figure 3.

3.1. Determining the Scale of Regional Boundaries

The shape and size of the calculation grid can influence the assessment of urban ventilation potential (Figure 4). One approach for selecting research boundaries is to evenly divide the study area into several equally sized regular grids [34]. However, this method ignores the heterogeneity of urban morphology within the grids, leading to fragmented buildings. Another approach is to consider each feature as a unit, where each unit contains multiple buildings with similar characteristics, resulting in irregular grids. Neighborhoods, as a fundamental units for understanding urban morphology and implementing urban management, partially compensate for the lack of precision in large-scale geographic studies and the lack of macroscopic effects in building-scale studies. Therefore, evaluating urban ventilation potential based on neighborhoods as the basic unit is more reasonable. The delineation of neighborhoods is crucial, with the commonly accepted scale typically ranging from 150 to 200 m, sometimes slightly larger in certain areas [35]. The neighborhoods we extracted typically range from 300 to 500 m in size, with densely built areas typically around 200 m. For sparsely populated areas on the outskirts of the city, the boundary length can be extended to around 1 km. In total, the study area is divided into 5582 neighborhoods, with an average area of 0.09 km2.

3.2. Wind Direction and Frequency

The construction of UVCs is closely related to the prevailing wind direction and wind speed in the city. The dominant wind direction, defined as the direction with the highest frequency of observations in the statistical data, serves as the basis for constructing seasonal UVCs [22]. A statistical analysis was conducted on hourly wind direction and speed data from 14 national meteorological stations in Chengdu from 2005 to 2015. We focused on the wind direction frequency data for the entire year, summer, and winter under soft breeze conditions (0.3–3.3 m/s). This range of wind speeds significantly influences urban ventilation efficiency, second only to calm fresh air conditions [32].

3.3. Identification of Functional and Compensative Spaces

By reviewing the existing literature, this study delineates functional and compensatory spaces by integrating of LST data with urban land use planning information. The radiative transfer equation method [36] was used to retrieve the LST values using the thermal infrared bands of Landsat images. The specific formulae refer to the study of Sekertekin et al. [37]. After obtaining the LST data, to eliminate the effects of directly comparing the LST at different times, the mean–standard deviation method was applied to classify the LST data into different intensity levels (Table 2). High-temperature zones and sub-high-temperature zones, in conjunction with land use types such as commercial, industrial, and logistics and warehousing zones, are identified as functional spaces. Low-temperature zones and sub-medium-temperature zones, combined with green spaces, water, and ecological and agricultural protection zones, are designated as compensation spaces.

3.4. Construction of an Urban Ventilation Potential Assessment System

Studies have shown that urban infrastructure designed based on city morphology and building height, while taking wind direction and frequency into account, is effective in optimizing ventilation to address urban issues [39]. Through a synthesis of the existing literature (Table 3), we categorized spatial morphology indicators influencing urban ventilation into four main types: building morphology, land cover, road traffic, and terrain. Building morphology indicators primarily affect internal airflow velocity and direction through parameters such as building density, height, and layout. Land cover indicators influence local wind environments through factors like vegetation transpiration and water evaporation. Road traffic indicators facilitate internal ventilation through their inherent open spaces. Terrain represents the natural geographic features of the city [40]. Considering data availability and indicator effectiveness, we selected ten spatial morphology indicators to comprehensively evaluate the city’s wind environment. These indicators include building density, height, plot ratio, frontal area index (FAI), sky view factor (SVF), roughness length, normalized difference vegetation index (NDVI), water bodies, road openness, and elevation.

3.4.1. Indicators of Building Morphology

(1)
FAI
In the atmosphere, airflow extends downward to the surface of buildings, inevitably influencing the wind field. A program for calculating FAI was developed using C# based on vector building data. FAI was calculated using a 100 m × 100 m grid based on the size of building footprint and block area. The results were then extracted to the block level, serving as an important indicator for assessing ventilation potential. FAI [47] can be expressed as
λ f ( Z , θ ) = A ( θ ) p r o j ( Δ Z ) A T ,
where λ f ( θ ) is the frontal area index, A ( θ ) p r o j ( Δ Z ) is the frontal area of buildings, Δ Z is the height range in the direction of projection area, A T is the area of the grid, and θ is the wind direction. We mainly calculated the average FAI in three dominant wind directions: summer (SSE, 157.5°), winter (NE, 45°), and (E, 90°). A larger FAI indicates stronger obstruction to the wind and lower wind speeds; a smaller FAI indicates weaker obstruction to the wind and higher wind speeds.
(2)
SVF
SVF is an important index that describes the density of urban form in three dimensions and the condition of sky occlusion, reflecting the different geometric shapes of urban streets. The calculation methods for SVF are mainly divided into vector and raster types. Given the raster method’s suitability for rapid calculation of urban surface openness with large-scale data [48], we used a GIS-based raster calculation model to calculate the SVF of the study area and present the results by dividing them into street blocks. SVF [49] can be calculated using Equations (2) and (3):
Ω = 2 π • 1 − ∑ i = 1 n sin γ i n ,
S V F = 1 − ∑ i = 1 n sin γ i n ,
where Ω represents the visible sky angle; γ i accounts for the influence of terrain elevation angle on azimuth angle ( i ) ; n is the number of azimuths angle, which should not be less than 36; and S V F is the normalized visible sky angle, ranging from 0 to 1. A higher S V F indicates less sky occlusion, which is beneficial for urban ventilation.
(3)
Roughness length
Roughness length ( Z 0 ) and zero-plane displacement height ( Z d ) are key physical quantities that reflect the aerodynamic characteristics of the underlying surface. The roughness length refers to the height at which near-surface wind speed decreases to zero, starting from the height of the zero-plane displacement. It is related to the wind resistance of the underlying surface in the urban built environment. Zero-plane displacement height refers to a level above the ground, not at ground level ( Z = 0 ) , but at some height above it ( Z = d ) . This height d is considered a new ground level, effectively shifting the height origin upward. Common calculation methods include meteorological and morphological approaches. The morphological approach can be implemented using GIS and remote sensing data [50]. Research comparing these two methods demonstrates that simulating roughness length using the morphological approach is feasible [51]. Therefore, this method is used to calculate roughness length and zero-plane displacement height. The indices required for this method include (1) building density of a unit area λ ρ = total built-up area/total area of the unit plot; (2) windward area density ( λ f ( Z , θ ) ) = total windward area in the main direction/total area of the unit plot; and (3) volume-based average building height ( h ) = the sum of (each individual building’s height × its volume)/total volume of buildings. The formulas are as follows:
h = ∑ i = 1 n V i × h i ∑ i = 1 n V i ,
Z 0 = ( h − Z d ) × exp − k 0.5 × C D h × λ F ,
where V i represents the volume of individual buildings within the unit, h i represents the height of individual buildings within the unit, and C D h represents the drag coefficient of isolated obstacles, which can be considered a constant (0.8). λ f represents the total windward area density of the unit, calculated by equally weighting the windward area density in the dominant wind direction, applicable to irregular buildings. The Equation (6) can be simplified as
Z 0 = ( h − Z d ) × exp − 0.4 λ f ,
The next formula of the zero displacement height, which is necessary for Equation (6), is a simple power-law approximation of the regular group model:
Z d = h λ ρ 0.6 ,
where λ ρ is the plan area ratio. In the case of irregular arrangements, it gives an approximate value for Z d without taking the volume of the buildings and their recirculation zones into account [52].

3.4.2. Indicators of Land Cover

NDVI is a crucial indicator for assessing the health and coverage of vegetation [53]. NDVI was calculated using Landsat data products following radiometric calibration, atmospheric correction, and outlier removal. It is well established that water bodies exert a significant cooling effect in vegetated areas [16]. Therefore, assessing urban ventilation potential in relation to water bodies is crucial. The land use data are categorized into impervious surfaces, water bodies, forested land, and other types (including cultivated lands), with a primary focus on the impact of water bodies on urban ventilation potential.

3.4.3. Indicators of Road Traffic

The urban road network plays a key role in enhancing regional ventilation by facilitating the influx of fresh air from the suburbs into the city center. In 2017, Chengdu implemented the strategy of “Eastward Advancement, Southward Expansion, Westward Control, Northward Renovation, and Central Optimization”, with “Central Optimization” aimed at improving industrial levels and urban quality within the core urban area. Consequently, industrial land has been removed from Chengdu’s core area, leading to minimal traffic pollution in the study area’s road network. The ventilation capacity of roads was evaluated on their openness. According to the “Urban Residential Area Planning and Design Standards (GB50180-2018)”, streets should be set back according to building heights to maintain an appropriate height-to-width ratio. In cases where the height of street-facing building facades is fixed, the openness of the road is directly proportional to the road width. We assumed that street-facing buildings comply with the relevant regulations for setbacks and used road width as an indicator to measure the openness of urban roads. Based on road classification and attributes, we extracted the road system, including highways, main roads, secondary roads, tertiary roads, and railways. Road classifications were assigned values according to the average width of the red line for each level of road (Table 4).

3.4.4. Urban Comprehensive Ventilation Potential

To calculate the comprehensive VRC, we performed a weighted overlay analysis of urban spatial form indices. Initially, all indicator values are normalized to convert absolute values into relative ones. Due to the differing contributions of each indicator to urban ventilation potential, we used the deviation standardization method, where positive and negative indicators are normalized differently (Equation (8)). Subsequently, using the analytic hierarchy process, we calculated the weights of each factor and assigned respective resistance weights to the normalized factors to obtain the VRC. The direction and weight values of each factor’s contribution are shown in Table 5.
X i j ′ = X i j − min X i j max X j − min X j , p o s i t i v e max X i j − X i j max X j − min X j , n e g a t i v e ,
where X i j represents the i observation value within the factor j , and max X j and min X j are the maximum and minimum values of the factor j , respectively. Considering that the normalized values are between 0 and 1, and to align with circuit theory where lower resistance values are preferred, we calculated the score for each factor as the difference between 1 and the value of each individual factor.

3.5. Identification and Analysis of Urban Wind Corridor Network

3.5.1. Extraction of UVCs and Critical Ventilation Nodes Based on Circuit Theory

Circuit models are introduced to extract UVCs and generate networks. The circuit model uses the stochastic movement of charges within a circuit to simulate the migration and diffusion of ecological flows or species within corridors [54]. This method can simulate the flow of wind within urban environments. In the model, air start and end areas represent the nodes of the circuit. The link between two nodes, representing potential wind paths, is calculated by identifying the path with the least cumulative resistance retrieved from the VRC. Utilizing ArcGIS 10.8, we used the Build Network and Map Linkages tool within Linkage Mapper to load wind start and end vector data and VRC raster data to identify UVCs. Furthermore, we applied the Pinchpoint Mapper tool in an all-to-one mode to perform iterative current calculations on input vector data, thus calculating the corridor’s current density. A higher current density indicates greater importance of the area for urban ventilation. To ensure the model operates effectively, an analytical boundary must be defined within the study area to integrate the VRC and air inlet and outlet areas into a closed circuit. To minimize the impact of the closed boundary on simulation results while maximizing the exploration of potential wind paths, we constructed a virtual resistance surface with a 4000 m radius centered on the study area. The resistance value outside the study area is set higher (set here at 1) to ensure effective current flow on the VRC (Figure 5).

3.5.2. Spatial Measures of the Wind Corridor Network Based on Kernel Density

Kernel density analysis can visualize the spatial density characteristics of wind flow paths and intersections within the wind corridor network [39]. This analysis provides a reference for assessing urban ventilation performance and classifying the wind corridor system. Theoretically, the calculation involves drawing a circular area around the center of each grid cell within a specified search radius. Unlike simple density calculation methods, kernel density analysis uses a non-uniform computation method with Gaussian interpolation, allowing for a more accurate reflection of spatial distribution characteristics. We used line element kernel density analysis to reveal the spatial distribution characteristics of UVC and point element kernel density analysis to show the spatial distribution of corridor intersection nodes. The analysis results are categorized using the natural breaks method. Darker colors indicate higher kernel density values, signifying that the area covers more ventilation paths and critical ventilation nodes. The formula for kernel density analysis is as follows:
f n ( x ) = 1 n h ∑ i = 1 n k x − X i h ,
where x is the sample drawn from the distribution density function f for the point collection, f ( x ) is the value of kernel density estimation f at point x , k x − X i h is the kernel function, h is the bandwidth and h > 0 , and x − X i is the distance from the estimated point x to the real point X i .

3.5.3. Topological Feature Analysis of Wind Corridor Network Based on Complex Network

This study applies complex network analysis grounded in graph theory to uncover the potential topological features of the wind corridor network. In this analysis, wind corridor intersections and pathways are represented as the nodes and edges of the topological network structure, respectively, creating an undirected network graph. The following metrics were chosen to measure the overall network structure features: average degree, average path length, clustering coefficient, and the number of triangles. The average degree is the average number of connecting edges for all nodes in the network and is a metric of overall connectivity of wind corridor network. Average path length denotes the average shortest path length between two nodes in the network, where a smaller value indicates reduced energy loss during airflow propagation [55]. The clustering coefficient indicates the degree of gathering between nodes, with a higher value indicating more closely connected node groups in the network. The number of triangles reflects the frequency of triangle formation among nodes in the network, with a higher value indicating a more stable network structure [56]. Additionally, eigencentrality, closeness centrality, and eccentricity are selected to evaluate the importance of nodes within the network’s structure and function. Eigencentrality indicates the importance of a node, with higher values suggesting that the node is a critical area connecting multiple vital ventilation paths. Closeness centrality reflects the proximity of a node, indicating the “accessibility” of the node in promoting urban ventilation. Eccentricity refers to the shortest path length from a node to the farthest node in the network. The lower the value, the more crucial the node is for maintaining the connectivity of the network [25].

3.6. Construction Strategy for Urban Three-Level Wind Corridor System

This study constructs a multi-level UVC system by integrating results from various analyses, including dominant wind directions in summer and winter, functional and compensation space locations, urban ventilation potential, wind corridors and critical ventilation nodes identified by circuit theory, and findings from kernel density and complex network analysis (Table 6). The primary UVC is designed to traverse the entire study area, with the most suitable areas selected based on the analysis results as predetermined channel locations. To ensure the continuity of the wind corridors and the cleanliness of their source areas, we avoided open spaces occupied by large industrial lands. Drawing on research from Stuttgart, Germany, and the Beijing region [57], we ensured that the width of the primary wind corridor exceeds 500 m for adequate ventilation capacity and air improvement effects. To extend the influence of wind energy to a larger portion of the city, the corridor widths were adjusted as necessary. Secondary UVCs serve as auxiliary pathways to the primary UVC, compensating for areas with lower urban ventilation potential that are not traversed by the primary UVC. Based on previous studies, we set the width of the secondary UVCs at 200–300 m [58]. Tertiary UVCs supplement secondary UVCs; the former rely on urban spaces with better local ventilation potential, thereby contributing to the overall improvement of the urban ventilation environment. In comparison to primary and secondary corridors, tertiary corridors have lower requirements for urban ventilation capacity and exert a smaller global impact on urban airflow. Therefore, strict limitations are not imposed on the analysis of kernel density and topological characteristics in these areas.

4. Result and Analysis

4.1. Functional and Compensative Spaces

The functional and compensatory spaces of the urban ventilation system are distributed in a mosaic pattern (Figure 6). Overall, the southeast has a higher number of functional spaces and fewer compensation spaces. The functional spaces are relatively concentrated, mainly located in areas with high heat and severe air pollution, such as industrial zones, logistics parks, high-density commercial areas, and transportation hubs. The compensatory spaces are relatively dispersed and primarily located on the city’s outskirts. Most of the compensation spaces are parks, green spaces, and water that offer good ventilation and cooling effects. The city’s rivers, such as the Jin, Fu, Nan, and Sha Rivers, along with their protective green spaces, form strip-like spaces that serve as important ventilation corridors linking the north and south of the city.

4.2. Urban Ventilation Potential Analysis

  • Building Morphology
Figure 7 illustrates the spatial distribution of various building morphology indicators in different blocks. The results show that building density is higher within the First and Second Ring Roads, whereas in the Third Ring Road, it is higher in the west and lower in the east. This outcome is primarily influenced by urban functional positioning and land use differences, with the western region containing more high-density residential areas. Blocks with higher average building heights often exhibit inconsistent distribution patterns with building density and tend to show small-scale aggregation in commercial zones. In the area outside the Second Ring Road in the eastern region, high-rise buildings are arranged in a curved shape along the Sha–Jin River, which can easily block the prevailing SSE wind from entering the interior of the city during the summer. The spatial distribution of the plot ratio is similar to that of building density, with higher values concentrated within the Second Ring Road and lower values beyond the Fourth Ring Road. The FAI exhibits a characteristic of being “high inside and low outside”. A statistical analysis of the average FAI values across the four ring roads indicates that the average values for the first to fourth ring roads are approximately 0.22, 0.19, 0.12, and 0.05. Roughness length varies significantly between different blocks, with areas exceeding 14 m in height exhibiting a scattered distribution. SVF displays a characteristic of being “low inside and high outside”. The average values for the First, Second, Third, and Fourth Ring Roads are 0.72, 0.74, 0.85, and 0.94, respectively.
2.
Terrain, Land Cover, and Road Traffic
The overall terrain of the study area is relatively flat (Figure 8a), with higher elevations in the north and lower elevations in the south. Such terrain not only facilitates the penetration of prevailing SSE winds into the city during summer but also provides some barrier against the prevailing NE winds during winter. The overall NDVI in the study area is relatively low (Figure 8b), with higher values concentrated in the northeast part, including the Chengdu Panda Base, Botanical Gardens, and Phoenix Hill Park. The spatial distribution of water extracted from land use data is shown in Figure 8c. Road openness is represented by grid values ranging from 0 to 20 (Figure 8d), with higher values indicating greater openness, which is more conducive to airflow. Areas with higher road openness are mainly distributed within the densely trafficked Third Ring Road, facilitating the flow of urban winds.
3.
VRC
Ventilation resistance patches are spatially aggregated, gradually decreasing from the urban center towards the periphery (Figure 8e). In the urban center, especially within the Second Ring Road where commercial and residential areas are densely populated, VRC tends to be higher because of high building density, average building height, and limited open spaces. Areas with lower VRC are mainly located outside the Third Ring Road, where vegetation coverage in parks and ecological protection areas is more extensive. The southeast part of the city exhibits higher VRC, encountering greater resistance when prevailing SSE winds enter the city during summer. This resistance disrupts airflow, reduces wind speed and impedes the formation and connectivity of UVCs. Overall, the VRC of the study area remains relatively high, indicating suboptimal ventilation potential within the urban interior. Therefore, the scientific planning and implementation of UVC strategies are urgently needed to promote more effective airflow and improve the urban microclimate.
Figure 9 shows the distribution of ventilation potential indicator within Chengdu’s First to Fourth Ring Roads. In the radar chart, the values in each direction represent the ratio of actual values to the maximum values for each ring road indicators. By comparing the changes in different indicators with the distance from the ring road, the following patterns were observed: As the distance from the ring road increases, the values of building density, average building height, plot ratio, FAI, and roughness length gradually decrease. SVF, elevation, and road openness increase as the ring road expands outward, indicating that the spatial morphology of suburban areas is more conducive to improving the ventilation and alleviating the UHIE. Water does not show a significant variation pattern. The NDVI value is highest within the Fourth Ring Road, followed by the Third Ring Road. The NDVI value of the First Ring Road is slightly higher than that of the Second Ring Road because of the relatively larger number of parks and green spaces.

4.3. Determination and Verification of UVC

4.3.1. The Prevailing Wind Direction

The wind rose statistical results (Figure 10) show that in Chengdu City, the prevailing wind direction throughout the year is northeast (NE, wind frequency of 12.2%), the predominant wind direction in summer is south–southeast (SSE, wind frequency of 9.47%), and in winter is NE (wind frequency of 15.47%), followed by east (E, wind frequency of 10.79%). After determining the prevailing wind directions in Chengdu, circuit modeling is employed to simulate the UVCs along the prevailing wind directions for both summer and winter, with the winter easterly wind primarily serving as an auxiliary for the construction of the NE-oriented ventilation corridor system.

4.3.2. Wind Corridor Network Simulation Results Based on Circuit Theory

The simulation results (Figure 11) indicate that UVCs mostly rely on urban parks and green spaces along linear areas with lower ventilation resistance, such as city roads and rivers, connecting inlet and outlet areas. However, covering the densely built city center is difficult for UVCs. A total of 186 sets of inlet and outlet areas were identified, with 49 sets corresponding to SSE winds in summer and 71 and 66 sets corresponding to NE and E winds in winter, respectively. This difference reflects the varying ability of the city to regulate the urban microclimate under different prevailing winds. Winter benefits from fewer building obstacles and more green spaces in the NE part of the study area, resulting in more opportunities for wind penetration into the city. By contrast, in summer, higher building heights and densities in the southeast part increase resistance, limiting wind penetration. The pinch points in the wind corridor network are areas with higher current density identified by circuit theory and are crucial for urban ventilation. We identified a total of 143 crucial ventilation nodes in the study area, emphasizing the significance of prioritizing these areas to maintain the connectivity of the wind corridor system. Our study indicates that NE and E corridors in winter are more effective in reducing urban pollutant accumulation and improving air quality. By contrast, the SSE corridors in summer need further enhancement of urban ventilation capacity through urban planning and building design.

4.3.3. Validation of the Ventilation Potential

The reliability of the UVCs is validated using both LST and field measurement data. The LST data, derived from Landsat remote sensing imagery, are detailed regarding their source and date in Section 2.2, while the method for LST inversion is described in Section 3.3. Since changes in LST are mainly governed by the amount of solar radiation received and surface heat balance, ventilation effect is not the main factor. Therefore, LST alone cannot serve as the sole basis for evaluating the effectiveness of UVCs. To improve the reliability of the validation, we further verified the results using field measurements. The data collected included maximum air temperature and maximum wind speed recorded at ten-minute intervals.
1.
LST Verification
Thermal differences in LST reflect the effectiveness of a UVC [59,60]. Studies have shown that LST significantly influences urban ventilation: as LST decreases, ventilation volume increases [39]. According to the basic principles of heat transfer, when fluids at different temperatures (such as wind) come into contact with solid surfaces, convective heat exchange occurs. As wind flows over different surfaces in the city, the heat exchange caused by the temperature difference can significantly affect the urban microclimate. Previous studies in cities such as Beijing [39], Dalian [61], and Hong Kong [34] have utilized LST as an indirect method to for assessing the performance of wind corridors. Areas traversed by wind corridors generally have lower LST compared with non-corridor areas. In our analysis, LST within the identified corridors was used as the experimental group, while buffer zones of 100 m and 200 m were established as control groups to compare the average LST differences between corridor and non-corridor areas. The experimental results (Figure 12) show that UVCs identified based on circuit theory are effective in reducing LST. The average LST within the corridors is generally lower than that in non-corridor areas, particularly in summer, where the cooling effect of the UVC is more pronounced. The SSE wind corridor in summer reduces the average LST by 1.29 °C, whereas the NE and E wind corridors in winter reduce LST by 0.72 °C and 0.61 °C, respectively. This seasonal difference is primarily attributed to the varying intensity of solar radiation between summer and winter, resulting in differences in the heat absorption by surfaces. Consequently, the LST difference between the summer ventilation corridor and its surrounding areas is greater than that observed in winter.
2.
Verification of Field Measurements
Field measurements can reflect the real changes in the climatic environment and are an important method for validating computer numerical simulations. In this study, simultaneous experimental observations were used to compare the climate differences between experimental points inside potential wind corridors and control points located a certain distance on either side outside the corridors. To ensure that the test points reflect the general characteristics of different types of minimum resistance, three major categories of nodes were selected: large park-type (Figure 13A,B)), river corridor-type (Figure 13C,D), and major traffic-type (Figure 13E,F). There are a total of 18 observation points, including 6 experimental points (A1–F1 in Figure 13) and 12 control points (A2–F2 and A3–F3 in Figure 13). The instruments used for the experiment were the Testo 405i anemometer (manufactured by Testo Instruments Shenzhen Co., Ltd., Guangdong, China) and the Kestrel NK5500 handheld weather station (manufactured by Kestrel USA) from the United States. The observation period was from 10:00 to 18:00 on 11 May 2024, during which the city experienced overcast weather, with a southwest wind direction, no sustained wind of level 2, a maximum temperature of 25 °C, and a minimum temperature of 17 °C.
Differences in wind speed and temperature between the internal and external observation points of the ventilation corridors were observed. Wind speeds at experimental points inside the corridors were generally higher than those at the external control points, and temperatures were lower (Figure 14). The experimental results from field measurements confirmed the reliability of the UVC identified based on ventilation potential and the circuit model. The river corridor-type node C in the southern part of the study area exhibited the greatest climatic differences between the inside and outside of the corridor, with the maximum wind speed difference being 0.39 m/s and the temperature difference reaching 1.67 °C. The southerly wind on that day and the relatively open space around the Nan river test point C1 contributed to the highest average wind speed of about 1.75 m/s and the lowest average maximum temperature of about 23.25 °C. Control points C2 and C3, influenced by the surrounding high-density residential and business district built environments, had poorer ventilation and higher temperatures. The river corridor-type node D displayed poor climatic characteristics because it is located in the city center with a high building density and low vegetation cover, making it difficult to effectively utilize the natural ventilation effect of the river. The Qilong Lake Wetland Park node B benefited from a better ecological environment, showing a clear climatic advantage. The Phoenix Hill Park test point A1 had lower wind speeds than control points A2 and A3 due to high vegetation coverage obstructing wind at pedestrian height, but it had lower temperatures, indicating effective cooling by the vegetation. The climatic differences were smallest at the major traffic-type nodes E and F. Since the overpass itself is elevated, the test points, unobstructed, exhibited a slightly better climate than the built areas at the control points on either side.

4.4. Kernel Density Analysis of Wind Corridor Network

Line density analysis reflects the density of wind flow paths in the network, whereas point density analysis represents areas with higher wind flow frequency and ventilation potential. Combining the analysis results of Figure 15a,b, a notable spatial imbalance in the wind corridor network distribution becomes apparent. High line density areas are located outside the Third Ring Road, forming a dual-axis distribution along roads or water that connect urban parks, green spaces, and other open areas in a northeast–southwest direction. The northern axis links key nodes such as the Chengdu Panda Base, Phoenix Hill Park, Qingshui River Park, and Jiedai Temple Overpass. The southern axis connects important nodes such as North Lake Ecological Park, Duobao Temple Park, Donghu Park, and Chengdu New Frontier Golf Club. Point density forms a gap in the city center, with high values mainly distributed in the northeast. The Chengdu Panda Base, Phoenix Hill Park, Botanical Gardens, and North Lake Ecological Park are important nodes. The second-highest values are found at the outlet areas of the NE wind, where there are related parks and are also part of the Chengdu Greenway.

4.5. Analysis of the Topological Characteristics of the Wind Corridor Network

The topological structure of the wind corridor network was abstracted, containing a total of 466 nodes and 688 edges (Figure 16). Nodes in the wind corridor network that share similar ventilation characteristics and are closely connected will form a community, with wind flowing more frequently within the same community. Using complex network modeling for topological abstraction of the wind corridor network, we identified 16 communities with different ventilation characteristics. Identifying these communities is helpful in formulating effective ventilation strategies for specific areas. For example, as shown in Figure 17, community 9 serves as a key inlet area for SSE winds. Its relatively low average degree value (Figure 16) indicates that this community lacks sufficient internal connections, which may lead to insufficient wind penetration into the city, affecting the overall urban ventilation efficiency. The average degree of the wind corridor network is 2.92, indicating that each node in the network is connected by approximately three edges, and the connectivity of the entire wind corridor network is appropriate [56]. The network’s average clustering coefficient is 0.115, the number of triangles is 2, and the average path length is 13.401. Based on the analysis results from existing research on complex networks [25,55], it can be concluded that the wind corridor network does not exhibit small-world characteristics, as evidenced by the low clustering coefficient at network intersections and the large average path length. The small number of triangles indicates relatively weak stability in the network, suggesting that wind flow within the network may experience some stagnation.
The differences in topological indicators indicate that the wind corridor nodes play different roles in the network structure (Figure 17). Community 1, located in the northern part of the network and consisting of significant areas such as Phoenix Mountain Park and Happy Valley, demonstrates high eigencentrality (Figure 17a) and closeness centrality (Figure 17b) among its nodes, indicating their crucial role within the wind corridor network. The eccentricity (Figure 17c) of the nodes is generally low, ranging from 0.01 and 0.28, which means that the connections between nodes are relatively compact, facilitating interconnection through shorter paths. The spatial distribution of nodes’ comprehensive importance (Figure 17d) indicates that community 9, located in the southeastern wind inlet area of the city, exhibits relatively low overall significance. Due to differences in ventilation conditions, the importance of the nodes in the northwest is higher than that of the nodes in the southeast (Figure 17d). At the edge of the city, the inlet and outlet nodes have higher eccentricity, lower eigencentrality, and lower closeness centrality, suggesting that the ventilation potential in these areas needs further improvement.

4.6. Urban Three-Level Wind Corridor System

To maximize the ventilation potential of the city, we planned six primary, six secondary, and several tertiary seasonal UVCs (Figure 18). Combining the primary control points traversed by the corridors, the planned wind corridors mainly rely on parks, green spaces, and water. They possess high ventilation potential and climatic environmental value. Primary UVCs are key to urban ventilation, designed according to seasonal dominant wind directions. They aim to transport clean air from the SSE and NE parts into the inner urban areas. To maintain the effectiveness of the wind corridors, we recommend strengthening the protection and expansion of primary corridors, managing along the corridor routes according to the ecological construction restriction zone standards. Secondary UVCs, which serve as channels extending more broadly into the inner urban areas, act as branch channels for wind flow and exhibit a spatial distribution pattern of connecting internally and externally. Owing to urban ventilation resistance, secondary UVCs also struggle to penetrate the city center. Therefore, future urban renewal projects need to further adjust and transform these corridors in suitable locations, reserving and widening corridor spaces to extend as deeply into the city center as possible. Tertiary UVCs are mainly distributed on the urban outskirts, serving as auxiliary channels that penetrate local areas of the city and help improve local thermal pressure and air circulation. For these spaces, we recommend avoiding increasing construction intensity and moderately expanding open and green spaces through “planning subtraction”.

5. Discussion

5.1. Enhancing UVC Research through Comprehensive Analytical Methods

This study uses GIS spatial analysis techniques combined with circuit theory, kernel density, and complex network analysis to develop a multi-level ventilation system plan for Chengdu. This comprehensive analytical approach enables cross-verification between different model results, avoiding biases from partial models and fragmented data. It provides a more comprehensive and accurate guidance for the planning and implementation of UVCs. We demonstrated the reliability of wind corridors identified by the circuit model in improving urban ventilation and reducing temperatures, using both LST and field-measured data. Compared with the traditional LCP method, the circuit model showed some progress in our study: (1) It can fully consider the complexity of the urban environment, identifying optimal ventilation paths and crucial ventilation areas. (2) It also overcomes the limitations of the LCP method, which subjectively presets inlets and outlets based on dominant wind directions and urban boundary characteristics. By simulating current flow across the urban ventilation resistance surface, circuit theory provides a concrete analysis of the city’s actual geographical features and building layout, resulting in the identification of inlets and outlets that better align with the city’s actual conditions. Using kernel density analysis to measure the spatial distribution pattern of wind corridor paths and intersections allows for more intuitive identification of areas with high and low wind flow frequency and ventilation potential within the spatial scope. This provides a direct reference for the classification of wind corridors. Moreover, compared with existing UVC research, our work not only focuses on the construction techniques and methods of UVCs but also applies complex network theory for the first time to analyzes the topological structure characteristics of wind corridor network. By examining both the overall network and node characteristics, this approach offers a new perspective for diagnosing the connectivity of wind corridor networks and the importance of nodes. This aids urban planners in understanding areas that obstruct air flow, thereby facilitating the optimized design of wind corridor networks. In summary, comprehensive analysis is beneficial for enhancing the scientific validity and practicality of wind corridor planning in Chengdu.

5.2. Implications for Urban Planning

This study found that, compared with the winter wind corridors, the average LST inside the summer wind corridors is lower. This is because the summer wind corridors mainly pass through open spaces such as parks, green areas, and water bodies, which contribute to better cooling effects. However, further analysis shows that the summer wind corridors suffer from severe congestion and lack ecological nodes that guide wind flow into the city’s interior. For instance, the urban ventilation potential analysis demonstrates that the southeastern region has a relatively high VRC, resulting in fewer identified ventilation corridors and wind inlets. This is disadvantageous for the penetration of the prevalent SSE wind into the internal areas of the city during summer. A typical example is that field measurements indicate that the river corridor-type node D has lower wind speeds and higher temperatures than node C. The results of the kernel density analysis revealed that the spatial distribution of the corridors primarily follows a northeast–southwest orientation. Additionally, complex network analysis identified communities in the southeastern part with lower average degree and node importance, indicating that there are fewer crucial ventilation nodes and lower connectivity in the southeastern direction. In these key areas that determine whether background winds can smoothly flow into the city, their ventilation potential is particularly important to the city’s overall ventilation environment. Therefore, in planning and implementing wind corridors, cities should not only consider the cooling effect of the corridors but also focus on their connectivity. Proposed regional planning goals and mitigation strategies for improving Chengdu’s ventilation:
  • Attention should primarily focus on the development and optimization of peripheral urban areas to enhance the ventilation potential of air inlets and outlets. Urban planners must implement targeted measures to tackle the severe ventilation blockages in Chengdu’s southeastern regions. Given the impracticality of large-scale demolitions and reconstructions within built-up areas, it is recommended that urban renewal projects be utilized to optimize the land use structure and building layout in the southeastern area. Adjustments to building heights and densities, particularly in densely packed high-rise zones are recommended. Furthermore, the orientation of buildings should be strategically designed to facilitate the smooth flow of air through ventilation corridors aligned with the prevailing wind direction.
  • Increase green spaces and open areas, distributing then along major ventilation corridors to simultaneously enhance urban airflow and provide residents with quality recreational spaces. In addition, ecological nodes and their connectivity should be increased. By introducing additional green belts and wetland areas along ventilation corridors, the connections between different sections of the corridors can be strengthened, thereby improving both the city’s ventilation capacity and the overall stability of its ecosystem.

5.3. Limitations and Future Research

This study made progress in constructing UVC systems; however, the proposed methodology has several limitations: First, the method proposed in this study provides a static evaluation and analysis of urban ventilation conditions, lacking rigorous aerodynamic analysis. Consequently, it faces limitations in dynamically simulating wind speed. This limitation is evident in two main aspects: The calculation of urban form parameters involves a numerical simulation of the ventilation potential in specific areas. While this approach demonstrates a strong linear relationship between ventilation capacity and factors such as buildings, land cover, roads, and terrain [26], these indices only generalize urban spatial morphology. Consequently, there is no aerodynamic analysis foundation to support this method. When simulating potential ventilation paths using the circuit model, the approach assumes a static, unidirectional airflow from high-pressure to low-pressure zones, failing to fully account for the complexity of turbulence diffusion and momentum transport in real wind environments. In reality, wind speed is influenced by factors such as surface friction from buildings, resulting in a loss of kinetic energy. Future research should integrate dynamic simulation methods, such as turbulence models and aerodynamic analysis, to improve accuracy in wind speed and momentum transport predictions. This will contribute to a more comprehensive understanding of the urban ventilation environment. Second, the proposed method for constructing ventilation corridors improves upon existing GIS-based numerical simulation approaches for analyzing urban wind environments. While it is well-suited for large-scale urban ventilation analysis, its application to finer spatial scales, such as blocks or individual building, remains underexplored. Unlike wind tunnel experiments and CFD simulations that provide precise modeling of airflow around buildings, GIS-based urban ventilation analysis methods rely on empirical models that correlate urban form parameters with experimental wind data. This characteristic helps to circumvent the complex aerodynamic calculations typically required in urban ventilation planning. By using local 3D building databases, urban planners can easily integrate knowledge of natural ventilation with urban planning requirements. Third, considering the homogeneity of buildings within streets, this study uses blocks as the basic evaluation unit. However, at this resolution, it is challenging to comprehensively capture ventilation corridors within specific streets and squares in urban studies. Future research could consider using higher-resolution data to improve the accuracy of ventilation corridor identification. Lastly, the ventilation potential indicators used in this study are primarily based on the context of city-scale research and a summary of the existing literature. However, these indicators may not be applicable to all aspects of this study and may not fully capture the complexity of urban ventilation. Future research could consider incorporating additional relevant parameters and exploring their applicability at different scales to gain a more comprehensive understanding of the factors influencing urban wind environments.

6. Conclusions

This study focused on the area within Chengdu’s Ring Expressway and proposed a UVC system framework combining multi-source data and multi-model analysis. First, the city’s dominant wind direction and comprehensive ventilation potential were analyzed. Subsequently, based on circuit theory, UVCs were extracted, and important ventilation nodes, inlet, and outlet areas were identified. Next, kernel density and complex network analyses were used to reveal the spatial distribution and internal structural characteristics of the wind corridor network. Finally, by overlaying the analysis results, a multi-level UVC system was constructed. This study’s findings are as follows:
  • The prevailing wind direction in Chengdu is NE throughout the year, with SSE winds in summer and NE and E winds in winter. The functional and compensatory spaces are distributed in a mosaic pattern, with functional spaces primarily located in urban areas such as industrial zones, logistics parks, high-density commercial areas, and transportation hubs. In contrast, compensatory spaces are mostly made up of parks and green areas on the city’s outskirts. The city’s VRC is generally high and shows spatial aggregation characteristics, gradually decreasing from the city center to the outskirts. High-rise buildings densely line the Sha–Jin River area, severely obstructing the prevailing SSE winds during summer from reaching the city’s interior.
  • A total of 143 important ventilation areas were identified, which are beneficial for maintaining the overall connectivity of the wind corridor network. The number of inlet and outlet points for the SSE direction (49 sets) during the summer is lower than for the NE (71 sets) and E directions (66 sets) during the winter, resulting in fewer opportunities for summer winds to penetrate into the city’s interior. However, the cooling effect within the summer wind corridors is significantly better than in the winter. The high line density of the wind corridors network exhibits a northeast–southwest double-axis feature, whereas high point density areas are mainly located at the NE wind inlets. The wind corridor network as a whole has connectivity but lacks small-world characteristics, showing congestion and instability. In the southeastern part, there are communities with lower average degrees and node importance.
  • The UVC system in the study area comprises six primary corridors, six secondary corridors, and several tertiary wind corridors. These corridors mainly rely on parks, green spaces, and other high vegetation coverage open spaces in the city and are distributed along urban roads and rivers, forming effective ventilation pathways. The primary wind corridors are used to directly introduce fresh air from the suburbs into the city center, the secondary corridors to bring wind into a broader range of the city’s interior, and the tertiary corridors to optimize local air circulation and the thermal environment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land13101671/s1.

Author Contributions

Conceptualization, X.X. and L.J.; Methodology, L.J.; Software, L.J.; Validation, K.O., X.L. (Xiuying Liu), X.L. (Xuewen Liang), Y.Z. and B.L.; Formal analysis, X.X. and Y.Z.; Investigation, K.O., X.L. (Xiuying Liu), X.L. (Xuewen Liang) and B.L.; Resources, L.J., X.L. (Xiuying Liu), X.L. (Xuewen Liang), Y.Z. and B.L.; Data curation, L.J. and K.O.; Writing—original draft, L.J.; Writing—review & editing, X.X. and L.J.; Visualization, L.J. and K.O.; Supervision, X.X.; Project administration, X.X.; Funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Open Fund of State Key Laboratory of Urban and Regional Ecology (Grant Number: SKLURE2022-2-6), Soft Science Project of Sichuan Provincial Department of Science and Technology (Grant Number: 2022JDR0061), Philosophy and Social Sciences Research Fund Project of Chengdu University of Technology in 2021 (Grant Number: YJ2021-YB023), and Scientific Research Project of Sichuan Provincial Department of Culture and Tourism (Grant Number: 2023YB27).

Data Availability Statement

The original contributions presented in the study are included in the article and Supplementary Materials, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location map of the study area. (a): location of Chengdu City in China; (b): location of the central city of Chengdu; (c): location of the Ring Expressway in the central city; (d): extent of the Ring Expressway with building distribution and Ring Roads shown.
Figure 1. Location map of the study area. (a): location of Chengdu City in China; (b): location of the central city of Chengdu; (c): location of the Ring Expressway in the central city; (d): extent of the Ring Expressway with building distribution and Ring Roads shown.
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Figure 2. Changes in monthly air temperature, rainfall, and wind speed in Chengdu from 2010 to 2023 (the wind speed data were measured at a height of 10 m above the ground).
Figure 2. Changes in monthly air temperature, rainfall, and wind speed in Chengdu from 2010 to 2023 (the wind speed data were measured at a height of 10 m above the ground).
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Figure 3. Framework for urban wind environment assessment and multi-level wind corridor system construction.
Figure 3. Framework for urban wind environment assessment and multi-level wind corridor system construction.
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Figure 4. Basic evaluation units for urban building ventilation.
Figure 4. Basic evaluation units for urban building ventilation.
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Figure 5. Principles of wind corridor simulation configuration under different dominant wind directions.
Figure 5. Principles of wind corridor simulation configuration under different dominant wind directions.
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Figure 6. (a) Functional spaces and (b) compensative spaces in the ventilation system.
Figure 6. (a) Functional spaces and (b) compensative spaces in the ventilation system.
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Figure 7. Spatial distribution of building morphology indicators. (a): building density; (b): building height; (c): plot ratio; (d): FAI; (e): roughness length; (f): SVF.
Figure 7. Spatial distribution of building morphology indicators. (a): building density; (b): building height; (c): plot ratio; (d): FAI; (e): roughness length; (f): SVF.
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Figure 8. Spatial distribution of terrain, land cover, road traffic indicators, and VRC. (a): elevation; (b): NDVI; (c): water; (d): road openness; (e) VRC.
Figure 8. Spatial distribution of terrain, land cover, road traffic indicators, and VRC. (a): elevation; (b): NDVI; (c): water; (d): road openness; (e) VRC.
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Figure 9. Radar distribution of ventilation potential indicators for different urban ring roads. (a): building morphology indicators; (b): terrain, land cover, and road traffic indicators.
Figure 9. Radar distribution of ventilation potential indicators for different urban ring roads. (a): building morphology indicators; (b): terrain, land cover, and road traffic indicators.
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Figure 10. Prevailing wind environment information of Chengdu City. (a): location of Chengdu in Sichuan Province; (b): wind rose diagrams for 14 meteorological stations; (c): annual average prevailing wind frequencies in 16 directions; (d): prevailing wind frequencies in 16 directions for summer and winter seasons.
Figure 10. Prevailing wind environment information of Chengdu City. (a): location of Chengdu in Sichuan Province; (b): wind rose diagrams for 14 meteorological stations; (c): annual average prevailing wind frequencies in 16 directions; (d): prevailing wind frequencies in 16 directions for summer and winter seasons.
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Figure 11. Simulation results of the wind corridor network under prevailing summer and winter wind directions.
Figure 11. Simulation results of the wind corridor network under prevailing summer and winter wind directions.
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Figure 12. Statistics of internal and external LST of UVCs under different prevailing wind directions.
Figure 12. Statistics of internal and external LST of UVCs under different prevailing wind directions.
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Figure 13. Selection of experimental and control point locations.
Figure 13. Selection of experimental and control point locations.
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Figure 14. Field measurements of average maximum wind speed and air temperature inside and outside the UVC.
Figure 14. Field measurements of average maximum wind speed and air temperature inside and outside the UVC.
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Figure 15. Kernel density analysis of the wind corridor network. (a): analysis of linear elements; (b): analysis of point elements.
Figure 15. Kernel density analysis of the wind corridor network. (a): analysis of linear elements; (b): analysis of point elements.
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Figure 16. Undirected wind corridor network constructed based on complex networks (nodes of the same color belong to the same community, and the average degree for each module is shown in brackets).
Figure 16. Undirected wind corridor network constructed based on complex networks (nodes of the same color belong to the same community, and the average degree for each module is shown in brackets).
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Figure 17. Variations in topological indices of different nodes in the wind corridor network. (a): eigencentrality; (b): closeness centrality; (c): eccentricity; (d): comprehensive importance. The colors of the nodes correspond to the communities identified in Figure 16.
Figure 17. Variations in topological indices of different nodes in the wind corridor network. (a): eigencentrality; (b): closeness centrality; (c): eccentricity; (d): comprehensive importance. The colors of the nodes correspond to the communities identified in Figure 16.
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Figure 18. Structure of the three-level wind corridor system. (a): summer wind corridors; (b): winter wind corridors.
Figure 18. Structure of the three-level wind corridor system. (a): summer wind corridors; (b): winter wind corridors.
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Table 1. Details of all data.
Table 1. Details of all data.
DataData SourceYearFunction
Monthly air temperature, precipitation, and wind speed dataNational Ground Meteorological Stations in China2010–2023Analyzing the urban background climate
Hourly meteorological data
(wind speed and direction)		2005–2015Analyzing the prevailing winds
Building data (footprint and floors) Amap2023Used to calculate the impact of building morphology on VRC
Road dataOpenStreetMap2023Used to calculate the impact of road traffic on VRC
Digital elevation model Geographic Spatial Data Cloud–Used to calculate the impact of terrain on VRC
Land use datahttp://www.globallandcover.com/
(accessed on 20 June 2023)		2020Used to calculate the impact of land use types VRC
Remote sensing image (Landsat 9)United States Geological Survey 2022Used to retrieve LST to identify functional and compensative space, and verify UVC
Chengdu Land and Space Master Plan (2021–2035)https://mpnr.chengdu.gov.cn/
(accessed on 25 January 2024)		2021–2035Used to identify the functional and compensative space
Table 2. Grading standard for urban heat island [38].
Table 2. Grading standard for urban heat island [38].
GradesDetailed ZoningConditions *
1High-temperature zone (HTZ)LST > μ + std
2Sub-high-temperature zone (SHTZ)μ + 0.5 std < LST < μ + std
3Medium-temperature zone (MTZ) μ − 0.5 std < LST < μ + 0.5 std
4Sub-medium-temperature zone (SMTZ) μ − std < LST < μ − 0.5 std
5Low-temperature zone (LTZ)LST < μ − std
* μ: mean LST; std: standard deviation of LST.
Table 3. Typical evaluation indicators for urban ventilation potential research.
Table 3. Typical evaluation indicators for urban ventilation potential research.
ResearcherIndicator DomainIndicatorsResearcherIndicator Domain Indicators
(J.Y.) [41]BuildingBuilding density(Y.X.) [42]TerrainElevation
Plot ratioBuildingSky view factor
Building heightRoughness length
(Y.M.) [43]TerrainElevationFrontal area index
SlopeBuilding height
Thermal environmentHeat island intensityBuilding density
BuildingBuilding density(Q.H.) [44]TerrainElevation
Building heightSlope
Urban green spaceAngle between green space and dominant wind directionAspect
Striped green space widthRoad networkRoad grade
Block green space areaBuildingFrontal area index
RoadRoad widthWaterWater
WaterWaterGreen spaceVegetation coverage
Cold sourceGreen source grades(X.L.) [23]Land useVegetation
(D.L.) [45]BuildingBuilding densityWater body
RoadRoad network lengthRoadOpen space
WaterWater body lengthRoad density
Land useVegetation coverageRoad connectivity
(F.Y.) [46]BuildingBuilding densityBuildingBuilding height
Building heightBuilding density
Sky view factorFrontal area density
Roughness lengthTerrainElevation
Table 4. Road system assignment.
Table 4. Road system assignment.
Road ClassAverage Width of Road Red Lines (m)Score
Main roads707
Secondary roads606
Tertiary roads404
Highways303
Railways202
Table 5. Ventilation influencing factors and contribution directions and corresponding weights.
Table 5. Ventilation influencing factors and contribution directions and corresponding weights.
Indicator TypeIndicator NameIndex PropertiesWeights 1 *Weights 2 *
Building morphologyFAINegative0.20.4
SVFPositive0.2
Building heightNegative0.15
Building densityNegative0.15
Plot ratioNegative0.15
Roughness lengthNegative0.15
Road traffic Road opennessPositive-0.25
Land coverNDVIPositive0.50.25
WaterPositive0.5
TerrainElevationPositive-0.1
* Weights 1: indicator weights; Weights 2: indicator type weights.
Table 6. Guidance of the scientific basis for planning multi-level UVCs.
Table 6. Guidance of the scientific basis for planning multi-level UVCs.
CriteriaPrimary UVCSecondary UVCTertiary UVC
Angle with dominant wind≤30°≤45°No angle specified
Corridor width500–1000 m200–300 mNo width specified
Ventilation potentialGood or moderateModerateModerate or poor
Land use typeGreen space, water, open spaces, and other ecological areasAreas with rivers, roads, and well-maintained greeneryAreas with rivers, roads, and well-maintained greenery
High current density areasYesPartialPartial
High kernel density areasYesPartialNo specified
Important topological feature areasYesPartialNo specified
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Xia, X.; Jian, L.; Ouyang, K.; Liu, X.; Liang, X.; Zhang, Y.; Li, B. Assessment of Ventilation Potential and Construction of Wind Corridors in Chengdu City Based on Multi-Source Data and Multi-Model Analysis. Land 2024, 13, 1671. https://doi.org/10.3390/land13101671

AMA Style

Xia X, Jian L, Ouyang K, Liu X, Liang X, Zhang Y, Li B. Assessment of Ventilation Potential and Construction of Wind Corridors in Chengdu City Based on Multi-Source Data and Multi-Model Analysis. Land. 2024; 13(10):1671. https://doi.org/10.3390/land13101671

Chicago/Turabian Style

Xia, Xiaojiang, Ling Jian, Kaiji Ouyang, Xiuying Liu, Xuewen Liang, Yang Zhang, and Bojia Li. 2024. "Assessment of Ventilation Potential and Construction of Wind Corridors in Chengdu City Based on Multi-Source Data and Multi-Model Analysis" Land 13, no. 10: 1671. https://doi.org/10.3390/land13101671

APA Style

Xia, X., Jian, L., Ouyang, K., Liu, X., Liang, X., Zhang, Y., & Li, B. (2024). Assessment of Ventilation Potential and Construction of Wind Corridors in Chengdu City Based on Multi-Source Data and Multi-Model Analysis. Land, 13(10), 1671. https://doi.org/10.3390/land13101671

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