The Regulating Effect of Urban Large Planar Water Bodies on Residential Heat Islands: A Case Study of Meijiang Lake in Tianjin
Abstract
:1. Introduction
- (1)
- (2)
- The mechanism of thermal environment changes with respect to the spatial characteristics between water bodies and the surrounding urban areas. Currently, most research on the impact mechanism of water bodies on the thermal effects of urban spaces is conducted at the macroscale, including studies on urban clusters [28] and city scales [29].
- (1)
- There is a lack of research on the water body cool island effect at the residential area scale and with respect to layout patterns. Most theories concerning the thermal environment regulation effects of water bodies on human living spaces concentrate on macroscopic scales, such as cities and regions [41,42], with a scarcity of research focusing on the direct regulatory effects on residential areas [43].
- (2)
- There is a lack of research on the thermal environment of northern Chinese cities. Residential communities in northern Chinese cities, represented by Tianjin, have unique architectural layout patterns and classifications. Moreover, there are significant differences in the thermal environment regulation requirements of cities in different climatic zones. Research on the impact patterns of the water body cooling effect under summer climate conditions in cold regions of China is insufficient.
2. Materials and Methods
2.1. Study Site
2.2. Data Collection
2.3. Inversion of Surface Temperature and the Calculation of Water Body Temperature Regulation Range
2.4. Classification of Residential Areas and the Selection of Factors
- The distance between residential areas and water (WD): The microclimate effect of a water body gradually decreases relative to the area far away from the water body [56]. The length from the nearest point of the bank to the vertical line of the bank is taken as the waterfront distance of the block in this study.
- Average building height (H): The variation in building height affects the roughness variation of an urban area and thus affects the heat island effect. In addition, the vortex and wind shadow regions formed around high-rise buildings will also affect regional ventilation and heat dissipation. The average building height in the block is calculated using Equation (6):
- H—average building height in the residential area;
- hi—the height of each building;
- Ai—the floor area of each building;
- n—number of buildings in the residential area.
- 3.
- Average building surface width (L): The average value of the long side of all buildings in the residential area is one of the metrics that reflect the scale and form of buildings.
- 4.
- Building density (BD): Open spaces are crucial factors that influence the microclimate, and the land coverage ratio is employed to describe the proportion of non-building open spaces within a site’s total area. This ratio can be substituted with building density as a metric. Higher building densities tend to reduce regional ventilation efficiency, exacerbating the urban heat island effect. Empirically, a building density ranging from 25% to 35% is optimal and recommended for waterfront areas, favoring an approach that is characterized as “sparse in front, dense in back, and well structured“ [57]. The calculation method for building density within residential areas is outlined in Equation (7):
- BD—building density in the residential area;
- Ai—the floor area of each building;
- AT—total land area of the residential area;
- n—number of buildings in the residential area.
- 5.
- Floor area ratio (FAR): FAR reflects the intensity of urban block development. Research has suggested that architectural waterfront clusters with lower FAR, coupled with spacious and well-ventilated corridors, tend to exhibit lower temperatures [58]. The influence of FAR on microclimatic patterns requires further exploration, and its calculation method is detailed in Equation (8):
- Si—total building area of the residential area;
- AT—total land area of the residential area;
- n—number of buildings in the residential area.
- 6.
- Normalized difference vegetation index (NDVI): NDVI is employed to ascertain he vegetation coverage and growth on a land patch. Some studies have demonstrated its significant correlation with urban surface temperatures [59,60]. NDVI calculation utilizes the spectral reflectances from Landsat 8 [61], and values in urban range from −1 to 1. Its calculation method is detailed in Equation (9):
- RNIR—NIR band (near-infrared band) of Landsat 8;
- Rred—Red band of Landsat 8.
3. Results
3.1. Regulation Range of Thermal Water Environments in Meijiang Lake
3.2. Thermal Environment Characteristics of Residential Waterfront Areas in Different Layout Modes
3.3. Correlation between Spatial Form and the Thermal Environment of Residential Waterfront Areas
4. Discussion
4.1. Factors Affecting the ΔLST in Different Combination Models of Residential Areas and Water Bodies
4.2. Factors Affecting ΔLST in Different Building Layout Categories
4.3. Uncertainties and Limitations
5. Conclusions
- The water surface studied in this paper, located within the Meijiang Lake area, spans an impressive 2 square kilometers. Water bodies of such magnitudes can significantly and directly reduce the temperature of their surrounding areas. The highly efficient cooling range extends up to 130 m from the water’s edge, with temperature reductions decreasing from 14.44% to 6.05%, representing the optimal zone for harnessing the cooling benefits of water bodies. It is crucial to give special attention to urban planning and architectural layout within this range, designating it as a priority control zone. Furthermore, the effective cooling range radiating from the water body can extend up to 810 m. Within the 130 to 810 m range, the cooling effect diminishes and gradually stabilizes. It can be designated as a general control zone. Different control standards should be established based on regional control intensity. Efforts should be made to position residential areas as close to the water as possible while simultaneously reducing the building’s density and increasing vegetation cover. Diminishing the width of buildings can moderately enhance the thermal environment, although its effectiveness is not as pronounced as the previously mentioned method. These approaches maximize the efficient utilization of cooling effects provided by the water body.
- To maximize the effective utilization of the thermal environmental regulation effects of water bodies in residential waterfront areas, it is imperative to ensure that residential areas are in direct adjacency to water bodies and minimize the intervening distance. Notably, once residential areas are already adjacent to the water, whether it is adjacent on one side, two sides, or multiple sides, this specific adjacency pattern does not emerge as the primary determinant that influences the thermal environment. The primary factor affecting the thermal environment in such areas is building density. Residential areas with internal landscaped water features exhibit some cooling effects, yet these are markedly less significant than those experienced by zones adjoining larger bodies of water. Conversely, residential areas that are not directly adjacent to water bodies exhibit the poorest thermal environmental conditions, with an average surface temperature approximately 5.3% higher than that of residential areas that are directly adjacent to water bodies.
- Waterfront distance, vegetation coverage, residential area building density and building width are the factors influencing the thermal environment of residential areas. The explanatory power of the independent variables relative to the dependent variable reaches 55.6%. And the most crucial spatial factors are the building density and waterfront distance. Furthermore, in terms of residential area-oriented metrics, although the floor area ratio serves as a commonly utilized metric in China in controlling development intensity, it is an indirect measure derived from building density and building height calculations and does not exhibit a correlation with the thermal environment. Therefore, when formulating standards pertinent to waterfront areas, building density should be considered as the primary metric for regulating the thermal environment.
- In light of several prevalent urban layout patterns in northern Chinese cities, distinct control standards should be devised based on their respective characteristics. For low parallel (A) residential areas, which are characterized by relatively lower surface temperatures, effectively regulating building densities represents a viable approach to creating a favorable thermal environment. In the case of parallel (B) residential areas, which typically exhibit the poorest thermal conditions, an average surface temperature that is approximately 5.58% higher than that of dispersed residential areas results. While increasing the vegetation coverage and reducing the waterfront distance, the construction of such residential areas on the waterfront should also be minimized. Conversely, dispersed (C) residential areas, characterized by lower average building densities and higher average building heights, stand to benefit from reducing the distance between the residential area and the waterfront in order to ameliorate the thermal environment effectively.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Layout of Building | Plan Description | 3D Description | Case Selection | |
---|---|---|---|---|
Low parallel (villa) (A) | ||||
Parallel (B) | ||||
Dispersed (C) | ||||
Mixed (D) | Mixed-I (D-I) Dispersed + parallel | |||
Mixed-II (D-II) Parallel + Low parallel |
Group | LST (°C) | F 1 | p 2 | Post Hoc |
---|---|---|---|---|
CM-1 (n = 17) | 37.38 ± 1.33 | 8.341 | <0.05 | CM-1 < CM-3 ***3 CM-2 < CM-3 *** |
CM-2 (n = 11) | 37.34 ± 0.98 | |||
CM-3 (n = 14) | 39.34 ± 1.35 | |||
CM-4 (n = 15) | 38.22 ± 0.51 |
WD | BD | H | L | FAR | NDVI | |
---|---|---|---|---|---|---|
ΔLST | 0.469 ** | 0.551 ** | −0.410 ** | 0.334 * | −0.120 | −0.401 ** |
p 1 | 0.001 | 0.000 | 0.004 | 0.018 | 0.422 | 0.005 |
Mode | Unstandardized Coefficients | Standardized Coefficients | t | Significance | Collinearity Diagnostics | |||
---|---|---|---|---|---|---|---|---|
B | Standard Error | Tolerance | VIF | |||||
1 | (Constant) | 6.973 | 1.278 | 5.455 | 0.000 | |||
WD | 0.002 | 0.001 | 0.347 | 3.076 | 0.004 | 0.757 | 1.321 | |
BD | 8.365 | 2.502 | 0.380 | 3.343 | 0.002 | 0.749 | 1.336 | |
L | 0.029 | 0.012 | 0.269 | 2.352 | 0.023 | 0.736 | 1.358 | |
NDVI | −12.408 | 4.474 | −0.322 | −2.774 | 0.008 | 0.717 | 1.395 |
Mode | Unstandardized Coefficients | Standardized Coefficients | t | Significance | Collinearity Diagnostics | |||
---|---|---|---|---|---|---|---|---|
B | Standard Error | Tolerance | VIF | |||||
2 | (Constant) | 5.474 | 0.507 | 10.797 | 0.000 | |||
BD | 10.293 | 2.531 | 0.624 | 4.067 | 0.000 | 1.000 | 1.000 |
Mode | Unstandardized Coefficients | Standardized Coefficients | t | Significance | Collinearity Diagnostics | |||
---|---|---|---|---|---|---|---|---|
B | Standard Error | Tolerance | VIF | |||||
3 | (Constant) | 14.859 | 1.167 | 12.728 | 0.000 | |||
NDVI | −28.149 | 5.614 | −0.772 | −5.014 | 0.000 | 1.000 | 1.000 |
Mode | Unstandardized Coefficients | Standardized Coefficients | t | Significance | Collinearity Diagnostics | |||
---|---|---|---|---|---|---|---|---|
B | Standard Error | Tolerance | VIF | |||||
4 | (Constant) | 2.879 | 1.539 | 1.870 | 0.120 | |||
BD | 19.770 | 6.181 | 0.820 | 3.198 | 0.024 | 1.000 | 1.000 |
Mode | Unstandardized Coefficients | Standardized Coefficients | t | Significance | Collinearity Diagnostics | |||
---|---|---|---|---|---|---|---|---|
B | Standard Error | Tolerance | VIF | |||||
5 | (Constant) | 14.678 | 1.178 | 12.458 | 0.000 | |||
WD | 0.002 | 0.001 | 0.351 | 2.491 | 0.026 | 0.992 | 1.008 | |
NDVI | −30.554 | 5.774 | −0.745 | −5.292 | 0.000 | 0.992 | 1.008 |
Mode | Unstandardized Coefficients | Standardized Coefficients | t | Significance | Collinearity Diagnostics | |||
---|---|---|---|---|---|---|---|---|
B | Standard Error | Tolerance | VIF | |||||
6 | (Constant) | 6.688 | 0.361 | 18.540 | 0.000 | |||
WD | 0.002 | 0.001 | 0.566 | 2.276 | 0.044 | 1.000 | 1.000 |
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Wang, L.; Wang, G.; Chen, T.; Liu, J. The Regulating Effect of Urban Large Planar Water Bodies on Residential Heat Islands: A Case Study of Meijiang Lake in Tianjin. Land 2023, 12, 2126. https://doi.org/10.3390/land12122126
Wang L, Wang G, Chen T, Liu J. The Regulating Effect of Urban Large Planar Water Bodies on Residential Heat Islands: A Case Study of Meijiang Lake in Tianjin. Land. 2023; 12(12):2126. https://doi.org/10.3390/land12122126
Chicago/Turabian StyleWang, Liuying, Gaoyuan Wang, Tian Chen, and Junnan Liu. 2023. "The Regulating Effect of Urban Large Planar Water Bodies on Residential Heat Islands: A Case Study of Meijiang Lake in Tianjin" Land 12, no. 12: 2126. https://doi.org/10.3390/land12122126