Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method

Cui, Peng; Dai, Chunyu; Zhang, Jun; Li, Tingting

doi:10.3390/su14052618

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…The decision tree analysis results are shown in Figure 5. The first line of each node is the name of the attribute used to split the node, i.e., the splitting indicator (X [9] is the land use intensity, X [1] is the comprehensive porosity, X [16] is the ratio of the height to the total floor area, X [2] is the urban canopy resistance, X [4] is the vegetation cover, X [7] is the spatial congestion rate, X [6] is the building density, and X[0] is the average building plot ratio). The second line is the Gini index, which is a measure for assessing the purity.…”

Section: Decision Tree Analysismentioning

confidence: 99%

“…Ref. [16] investigated the correlation between PM2.5 distribution and urban form parameters in a cold climate city in China using gradient boosted regression trees, and quantitatively analyzed the influencing factors and found that building density had a greater impact on PM2.5 concentration. However, research on the urban spatial morphology lacks a set of comprehensive quantitative analysis methods, which cannot be applied in the optimization of urban spaces.…”

Section: Introductionmentioning

confidence: 99%

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Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

Wang,

Dai,

Gong

et al. 2024

Atmosphere

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During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process.

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Section: Decision Tree Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

Wang,

Dai,

Gong

et al. 2024

Atmosphere

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“…The gradient boosting regression tree is derived from the integrated learning boosting algorithm and has upgraded it [26]. The basic principle is to construct M different base learners through several iterations to generate strong learners to achieve the final combination.…”

Section: Principle Of Model Computationmentioning

confidence: 99%

“…Traditional machine learning is not good at dealing with nonlinear problems. Aiming at tackling this problem, this paper proposes a gradient boosting regression tree (GBRT) prediction model [26], which has high prediction accuracy and efficiency in dealing with high-dimensional nonlinear problems, as an interpretable machine learning theory can make the model transparent, allowing designers to understand the decision-making process and build trust between people and the model. On this basis, by introducing explainability into the dynamic light environment prediction in waiting halls, the interpretation of inductive preferences of the model is added to the harvest regression model, and the different contributions of design factors to light environment metrics are summarized [27].…”

Section: Introductionmentioning

confidence: 99%

Research on Light Comfort of Waiting Hall of High-Speed Railway Station in Cold Region Based on Interpretable Machine Learning

Xie¹,

Song

Zhang

2023

Buildings

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Upon the need for sustainability and natural lighting performance simulation for high-speed railway station waiting halls in cold regions, a new prediction method was proposed for the quantitative analysis of their natural lighting performance in the early design stage. Taking the waiting hall of Harbin West Railway Station as the prototype, the authors explore the optimization design of green performance-oriented waiting halls in this paper. To maximize daylight and minimize visual discomfort, and with the help of Rhinoceros and Grasshopper and Ladybug, and Honeybee platform simulation programs, spatial elements such as building orientation, shape and windowing were simulated through optimizing target sDA, UDI and DGPexceed, respectively, based on natural lighting performance. Additionally, a dataset covering several light environment influencing factors was constructed by parametric simulations to develop a gradient boosted regression tree (GBRT) model. The results showed that the model was valid; that is, the coefficient of determination between the predicted value and the target one exceeds 0.980 without overfitting, indicating that the interpretability analysis based on the GBRT prediction model can be used to fully explore the contribution of related design parameters of the waiting hall to the indoor light environment indexes, and to facilitate more efficient lighting design in the early design stage without detailed analysis. In addition, the GBRT prediction model can be used to replace the traditional one as the effective basis for decision support. To conclude, the skylight ratio played a significant role in UDI, while the section aspect ratio (SAR) and plan aspect ratio (PAR) served as the key design parameters for sDA and DGPexceed, respectively. At the same time, the building orientation had the least degree of influence on the natural lighting of the waiting hall.

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“…The differentiation studies identified, include global environment and resources, urban vitality, urban Spatiotemporal, big data, etc. (Zhang et al, 2020;Shi et al, 2021a;Luo et al, 2021;Cui et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Using spatial syntax and GIS to identify spatial heterogeneity in the main urban area of Harbin, China

Sun

Meng

2022

Front. Earth Sci.

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The discourse about urban planning, urban design, urban management, and many other basic urban planning issues are related to identifying the spatial heterogeneity in the main urban area (MUA). Road networks are important infrastructure for social and economic development and also an important driving factor for urban land use. Therefore, the topological characteristics of the urban road network can be used as an important basis for the space of the division of the MUA. This study takes the MUA space in the Harbin Ring Expressway as the research object. The findings of this research establish a city model to identify the spatial heterogeneity, through a method of “Spatial essential attribute—Spatial autocorrelation analysis—Establishment of urban model.” In this model, spatial essential attributes are obtained from the analysis of space syntax. The spatial autocorrelation analysis is a method to calculate spatial clustering, and building the urban model is a kind of computer visualization method. Using this method, this research constructs a spatial heterogeneity model (SHM) of the urban built-up area in Harbin, which consists of three layers of spatial circles and two prominent regional structures. Then, it analyzes the spatial distribution of infrastructure and population agglomeration through nuclear density. We gain three conclusions: first, in addition to educational and greening facilities, the spatial distribution of infrastructure is correlated with the regional structure of SHM in Harbin’s MUA. Second, there are differences in the spatial attraction of population agglomeration between the different areas in the SHM of Harbin’s MUA. Finally, the new identification model of the SHM is scientific, but it has some advantages and demerits. Using spatial topological values to construct the SHM, it provides a new city model in urban spatial heterogeneity region recognition. Through the research results, the identification method of the SHM improves the traditional method which identifies urban heterogeneity areas according to people’s subjective wishes in urban planning. Therefore, the SHM helps relevant practitioners and scholars practice urban planning, urban analysis, urban protection, and urban management.

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Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method

Cited by 8 publications

References 33 publications

Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

Research on Light Comfort of Waiting Hall of High-Speed Railway Station in Cold Region Based on Interpretable Machine Learning

Using spatial syntax and GIS to identify spatial heterogeneity in the main urban area of Harbin, China

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