Urban Metrics Research Articles

Global comparison of urban bike-sharing accessibility across 40 cities

The global expansion of bike-sharing networks offers a cost-effective and environmentally friendly transportation alternative that complements public transit and promotes active, healthy lifestyles. Despite significant research, most studies focus on individual locations, specific environmental or health impacts, or infrastructure elements like bike lanes. Instead, this paper elaborates a comprehensive global comparison of bike-sharing systems by introducing a novel database that aggregates data from 40 cities worldwide. Our study integrates this data with population data and urban metrics to classify these networks topologically and assess their effective coverage concerning the population served and their relation with existing public transit systems. We introduce the “Bike-Share Service Accessibility Index” (BSAI), a new metric to evaluate and compare the performance of bike-sharing networks. Our findings provide valuable insights for urban planners and policymakers, offering data-driven strategies to enhance sustainable urban mobility through better-integrated and more spatially equitable bike-sharing systems.

Deciphering the effects of 2D/3D urban morphology on diurnal cooling efficiency of urban green space

Urban green spaces (UGS) have emerged as a main nature-based solution proven to effectively cool urban areas, and cooling efficiency (CE) serves as a prominent indicator for assessing the performance of UGS. Yet few studies have explored the diurnal dynamics of CE and the comprehensive effects of both 2D and 3D urban morphology on CE over a 24-h period. In this study, we employed diurnal land surface temperature (LST) data from Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to delve into the diurnal CE of UGS within the urban core area of megacity Paris, France. Meanwhile, using high-resolution land cover data, digital height model, and explainable machine learning models, we elucidated the intricate relationship between various 2D and 3D urban morphology metrics and CE during a 24-h cycle. In summary, city-scale continuous monitoring revealed diurnal CE variations, with values ranging from 0.008 °C to 0.13 °C and a standard deviation of 0.04 °C. On an average of a day, the relative importance of the 2D and 3D urban form indicators was 77.25 % and 22.75 %, respectively. Enhancing the dispersion of trees or improving the integrity of non-tree vegetation patches proves to be an effective strategy for augmenting CE. The results can be further used in urban planning to improve urban thermal resilience and adaptability.

Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression

Purpose This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices. Design/methodology/approach The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation. Findings The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker. Originality/value This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

The cooling capacity of urban vegetation and its driving force under extreme hot weather: A comparative study between dry-hot and humid-hot cities

Extreme heat events in cities necessitate closer examination of urban vegetation’ capacity to mitigate extreme hot weather. However, the cooling capacity of vegetation (CCV) and its driving forces of 2D and 3D landscape metrics under extreme hot weather conditions in dry-hot and humid-hot cities, particularly within different functional zones is still not well understood. The results indicate that the CCV is more pronounced in humid-hot cities than in dry-hot cities, particularly during periods of extreme hot weather. This CCV varied significantly across different functional zones, being the lowest in commercial zones and the highest in green space zones. Moreover, residential zones in humid-hot cities have higher CCV when the weather is more extreme. In dry-hot and humid-hot cities, urban vegetation landscape configuration metrics (such as patch and edge densities) contribute significantly to the CCV, and landscape composition (vegetation coverage) also contributes, with a significant increase during periods of extreme hot weather, particularly in humid-hot cities. Additionally, the 3D building morphology plays a crucial role in moderating the CCV; however, its impact diminishes during extreme heat. We also found that the CCV does not increase further after vegetation coverage is greater than 30–40 % in dry-hot cities, compared to approximately 60–80 % in humid-hot cities. The CCV increased nonlinearly with increasing urban vegetation largest patch index and decreasing urban vegetation patch density in dry-hot and humid-hot cities, with different thresholds of vegetation coverage and largest patch index under normal and extreme hot weather. A higher building height increased the CCV in humid-hot cities and reduced the CCV in dry-hot cities, particularly under extreme hot weather, and sparse high-rise buildings were better suited to humid-hot cities for increasing urban CCV. These findings provide valuable insights for urban planners to enhance the urban CCV, contributing to long-term urban resilience and public health protection during extreme heat events.

The spatial heterogeneity of urban green space distribution and configuration in Lilongwe City, Malawi.

Urban green spaces provide several benefits related to the quality of urban life. The existence and spatial arrangement of these spaces within neighbourhoods and functional land uses have significant implications for the well-being of urban dwellers. Previous studies on green spaces in urban areas of Malawi have focused on a broader and macro-level perspective, offering insightful information on general trends in different cities. However, there is a significant research shortage in localised understanding, which requires carrying out micro-level assessments concentrating on land use zones and neighbourhoods within these cities. In this study, we used remote sensing data and landscape metrics to understand the distribution and configuration of urban green spaces in the city's neighbourhoods and functional land uses and their relationship with urban form. The study revealed that 20% of neighbourhoods fail to meet the WHO-recommended standard of 9 m2 of green space per person, with a predominant concentration of these undersupplied areas in high-density and quasi-residential zones. In addition, 56.2% of Lilongwe City's total green area was contained under functional land uses. Particularly, high-rise residential, medium-density residential, low-density residential, quasi-residential, high-rise flat area, commercial class, high-rise commercial, heavy industry, light industry, and government land use zones contained 17.3%, 12.0%, 22.2%, 12.0%, 4.1%, 6.4%, 6.1%, 5.0%, 1.6%, and 13.3% of the total green spaces in functional land uses, respectively. Importantly, this research found significant correlations between urban form metrics, namely building coverage, building density, building perimeter area ratio, road density, and the distribution and configuration of urban green spaces. This necessitates an integrated approach to urban planning and design, emphasising the importance of balancing development with green space preservation.

Assessing the effects of urban green spaces metrics and spatial structure on LST and carbon sinks in Harbin, a cold region city in China

In the face of accelerated urbanization, urban green spaces (UGSs) are crucial in alleviating urban heat island effects. This study investigates the link between UGS landscape patterns, plant community characteristics, Land surface temperature (LST), and Annual carbon sequestration of plant community (ACSPC) across different seasons and scales in Harbin by remote sensing and field measurements. The results show that (1) The LST in urban built-up areas shows an increasing trend, and its correlation with UGS exhibits seasonal fluctuations. (2) Landscape pattern metrics significantly relate to LST, particularly at 1500 and 1260 m window scales. (3) In summer and winter, Aggregation index (AI) , Mean patch area (AREA_MN), Edge density (ED), Percentage of landscape (PLAND), Mean patch shape index (SHAPE_MN), Mean diameter at breast height (MDBH), Mean canopy diameter (MCD), Average carbon sequestration capacity(ACSC), and Leaf area index (LAI) are positively correlated, whereas Sky view factor (SVF) is negatively correlated with ACSPC. (4) LAI, SVF and MCD (winter) significantly affect LST. MDBH, MCD, and SVF in summer significantly influence ACSPC. This research elucidates the effect of UGS landscape patterns and plant community characteristics on urban heat islands and carbon sinks, providing vital insights for urban ecological improvement.

A cost-and-effect simulation model for compact city approaches: A case study in Japan

Japan's compact-city policy addresses rapid population decline and aging to ensure sustainable development. However, its implementation requires significant time, financial resources, and complex stakeholder collaborations. Faced with these challenges, we asked: How can the cost-and-effect of planning be simply and automatically recognized by stakeholders? To address this, a simulation model was developed for evaluating urban planning strategies within the compact-city framework. Our model integrates pseudo-people-flow data and includes three fundamental modules and five sub-models to simulate and predict urban metrics, including population, administrative costs, visitors, and traffic volume from 2015 to 2040. Four scenarios-baseline, location optimization, new road and new station installation-were analyzed. Findings suggest that 1) new station project could mitigate population decline, 2) both location optimization and new station projects improve public services efficiency and sustainability, 3) urban vitality could be enhanced by new station construction, generating a new urban core, and 4) compact city policy may also reduce and concentrate traffic volume, shortening car trips. The model's efficiency and uncertainty were examined, and user experiments highlighted the potential of digital tools to democratize urban planning, making it accessible for non-expert stakeholders and fostering informed participation. This research advances the use of technology in participatory urban planning.

Stereoscopic urban morphology metrics enhance the nonlinear scale heterogeneity modeling of UHI with explainable AI

In urban heat island (UHI) effect modeling, traditional 3D morphological indexes often homogenize elements within units, leading to a loss of spatial detail. Here we introduce detailed stereoscopic urban morphology indices, including the Height Variation Coefficient, the Height Gravity Index, the Mixed Degree, and the Aggregation Degree, using urban building and vegetation height data to represent the 3D structures within urban units. We employed ensemble learning algorithms to explore nonlinear effects and statistical correlations across 20 buffer radii (100 m to 2000 m) related to the UHI effect. The integration of these inhomogeneous indexes substantially improved model accuracy, with XGBoost achieving an R2 of 0.95. The optimal scale for calculating such indexes was found to be between 300 m and 400 m. Our findings suggest optimizing UHI mitigation by lowering the Building Height Coefficient in high-density areas and increasing it in low-density regions. Furthermore, we propose tailoring the Vegetation Height Coefficient for green spaces, advocating higher values for larger parks and lower values for smaller ones. This approach offers practical insights for mitigating urban heat effects through customized morphological strategies, emphasizing the importance of precise urban planning and environmental management.

Tracing trajectories and co-evolution of metropolitan urbanization in the United States, Europe, and China

Metropolization has emerged as a prominent feature of 21st-century urbanization. To gain a comprehensive understanding of global urbanization patterns and pathways, as well as to interpret their consequences and implications, we aimed to provide a comparative characterization on urban transformation in metropolitan areas of developed and developing countries. Here, we quantified and compared the urban growth rates, growth modes, urban landscape metrics, and the co-evolution of urban land and population in 21 representative metropolitan areas across the United States, Europe, and China from 1985 to 2020, using remotely sensed impervious-surface dynamic dataset and patch-based analyses. The results showed that each metropolitan area has experienced substantial urban expansion with different scales and growth rates. Developing China possessed a relatively lower urbanization level but urbanized faster compared to the developed counterparts. Spatially, with infilling expansion increasing and even dominating, American and European metropolitan areas developed into more compact central urban cores, demonstrating the coalesced trajectories. Chinese metropolitan areas showed point-axis urban development mainly via edge-expansion in concentric rings. Furthermore, the horizontal co-evolution of urban area and population generally showed stable economies of scale in the United States and Europe whereas transitioned from diseconomy to economy of scale in China, evidencing that the century-long urbanization journey traversed by developed countries can be completed by emerging-developing ones within several decades. Temporally, the urban expansion greatly outpaced population growth in all metropolitan areas except London, signifying that urban land use efficiency is still a grand challenge in the Metropolitan Century. Effective metropolitan governance should be tailored to spatial configuration for efficient urban land use and intercity coordinated development towards a sustainable urban future, particularly for developing countries with emerging metropolization.

Measuring Urban and Landscape Change Due to Sea Level Rise: Case Studies in Southeastern USA

As a consequence of global climate change, sea level rise (SLR) presents notable risks to both urban and natural areas located near coastlines. For developing effective strategies to mitigate and adapt to these risks, it is essential to evaluate the potential impacts of SLR in coastal areas. While substantial research has been conducted on mapping the broad-scale impacts of SLR based on scenarios of Global Mean Sea Level (GMSL), consideration of regional scenarios, systematic classification, and distinct stages of SLR have been largely overlooked. This gap is significant because SLR impacts vary by region and by the level of SLR, so adaptations, planning, and decision-making must be adapted to local conditions. This paper aims to precisely identify the landscape and urban morphology changes caused by the impact of SLR for each foot of elevation increase based on remote sensing technologies, focusing on St. Johns County, Florida, and Chatham County, Georgia. These two counties are both situated along the southeastern coastline of the United States but with completely different urban forms due to distinct historical and cultural developments. Regional forecasting SLR scenarios covering the period from 2020 to 2100 were utilized to assess the landscape transformation and urban changes, incorporating selected landscape and urban metrics to calculate quantitative data for facilitating comparative analyses. This study investigated gradual alterations in urban morphology and green infrastructure both individually and in combination with the effect on wetlands due to SLR. The mapping outcomes of this research were generated by employing comprehensive remote sensing data. The findings of this research indicated that, when the sea level rose to 3 feet, the wetlands would experience notable alterations, and the level of fragmentation in urban built areas would progressively increase, causing most of the metric data to exhibit a pronounced decline or increase.

Predicting and analyzing crime—Environmental design relationship via GIS‐based machine learning approach

AbstractCorrelation between burglary crime and urban environmental characteristics is crucial for understanding the causes of crime events. Mathematical relationships can be linked between crime and crime‐causing events with the help of the machine learning (ML) model and geographic information system (GIS). The main objective of this research is to analyze and predict burglary crime events by applying ML‐based GIS models for Trabzon and Turkey. Random forest regression (RFR) and support vector regression (SVR) were implemented to predict crime. Correlation between crime and urban physical environmental metrics was used in the prediction model. Due to the result of the analysis, the R2 value was measured as 0.78 with the RFR and 0.71 with the SVR algorithm. The height of the building, the proportion of floor area, the density of buildings, and the density of intersection of streets are the four most important variables that affect the burglary crime rate positively. Conversely, the variable with the lowest effect on burglary crime is the ratio of the park to the residential area.

Harnessing Regression Analysis and Machine Learning to Unveil the Market Dynamics and Environmental Impact of New Energy Vehicles

This study leverages regression analysis and machine learning algorithms to systematically investigate the growth patterns of the global New Energy Vehicle (NEV) market, evaluate the effects of international policy frameworks on NEVs, and quantify the environmental advantages of NEVs in urban settings. Through the analysis of worldwide market data, case studies on international policies, and urban air quality metrics, this research delineates the pivotal contributions of NEVs to mitigating urban pollution and advancing environmental sustainability. The findings provide empirical evidence and strategic recommendations for policymakers, the automotive industry, and environmental organizations, aimed at promoting the sustainable development of the global NEV market.

Impact of Urban Expansion on Carbon Emissions in the Urban Agglomerations of Yellow River Basin, China

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin.

Spatial coupling relationship between architectural landscape characteristics and urban heat island in different urban functional zones

The process of urbanization exacerbates the urban heat island phenomenon. In order to reasonably identify urban functional zones, and reveal the influence of buildings on land surface temperature (LST). Guiyang, a multi-mountainous city in China, was taken as the research area, Open Street Map (OSM) and land use type data were used to identify urban functional zones. Subsequently, the correlation between urban building morphology metrics and LST was analyzed using Pearson correlation and boosted regression tree (BRT) model. The results showed that: (1) There were significant differences in LST among different urban functional zones. (2) The relative contribution value of building density (BD) to land surface temperature was high, and it significantly exacerbated the urban heat island effect across all urban functional zones, with the maximum impact on LST reaching 2.5 °C in Utility zone. (3) Mean building height (MBH), Southerly wind speed (S_Wind speed) and Landscape shape index of building (LSI_Building) had the effect of alleviating thermal environment, and the mitigation degree of land surface temperature was the highest in Residential zone and Ecological zone, reaching 0.6 °C, 0.8 °C and 2.0 °C, respectively. This study enhances our understanding of the relationship between urban functions and LST, providing valuable insights for mitigating the urban thermal environment and promoting sustainable development.

Investigating the neighborhood effect of urban morphological metrics on summertime land surface temperature variations in Istanbul, Turkey

Investigating the neighborhood effect of urban morphological metrics on summertime land surface temperature variations in Istanbul, Turkey

Investigating the nexus between energy, socio-economic factors and environmental pollution: A geo-spatial multi regression approach

The Yellow River Basin (YRB) and Yangtze River Basin (YZRB) stand as pivotal regions in China, holding paramount importance in both economic development and environmental security. However, the rapid pace of climate change and extensive human activities have dramatically reshaped these areas, leading to substantial alterations in natural landscapes and urban ecosystems. To ensure sustainable socio-economic growth, a profound understanding of the intricate interplay between socioeconomic factors and the emission of fine particulate matter (FPM) is imperative, along with an exploration of the underlying mechanisms governing these relationships. n this comprehensive study, we conducted spatial autocorrelation and spatial panel regression analyses, leveraging panel data encompassing the years from 2002 to 2021, derived from provincial-level administrative units within the YZRB and YRB. By adopting a holistic approach that considers comprehensive features and spatial effects, our research contributes substantively to the existing literature concerning the YZRB and YRB areas. Our analysis unveiled a notable decline in pollutant emissions over the course of the study period, yet it became evident that socioeconomic and energy-related factors continued to exert significant influence on FPM levels. Furthermore, we identified pronounced positive spatial autocorrelations in FPM emissions, suggesting a need for regionally tailored environmental management strategies. Employing various statistical tests, we rigorously examined the spatial autocorrelation patterns among the regions. Results from our random effect regression model and Geographically Weighted Regression (GWR) approach underscored the significant impact of socioeconomic and natural factors on FPM concentrations. Importantly, the magnitudes of these impacts exhibited variations contingent upon the specific river basin type. Within the YZRB, our findings emphasize the relevance of urbanization metrics, such as urban population, urban green space, Gross Domestic Product (GDP), and economic spending, which displayed positive and statistically significant relationships with FPM concentrations. Conversely, in the YRB, the utilization of energy resources and natural assets emerged as pivotal determinants.

The influence of the urban model on civic involvement and public time. A study applied to the commuting population of the Greater Metropolitan Area of San José, Costa Rica

A major problem for policymakers is to ensure that levels of citizen participation reflect the society in its diversity, but they must also ensure that those citizens who want to get involved have the means to do so. Despite empirical evidence of the role of urban planning in health, well-being, and livability, studies have not delved into the implications of how low-density sprawled urban forms influence civic involvement. This paper adds new evidence regarding the significance of urban form, built environment characteristics and more in general city planning, as intermediary factors between public and private life connected to civic engagement and public space use. Based on surveys and geographic data from the Greater Metropolitan Area of San José, in Costa Rica, the article investigates how the results of citizen participation change depending on whether residents are from a compact or dispersed study area, relating to urban rhythms and mobility metrics. Findings suggest that inhabitants of group A (lower-density neighborhoods) differ significantly from group B (compact-city areas) in four variables: means of mobility, commuting time, public space use, and civic activism, while having similar sociodemographic composition. Compared with residents of group A, group B report higher levels of use and knowledge of public spaces and civic participation. These two aspects constitute, the Urban Civic Participation Index, which, related to self-reported free time, forms what we define as Public Time. The main characteristics of the compact city (higher population and building density, high street connectivity, grid presence and proximity of services, mixed land use) were found to be positively related to the outcomes of accessibility to public space and community participation. In contrast, there are no significant differences in the availability of free time between the two groups. This study, along with others on the compact city, shows that there are other reasons to reduce daily commuting distances in cities and the need of urban time policies to redesign urban society's temporal dimension besides the known environmental impacts. It also raises the question of how low urban density affects people's time and disposition to participate in public life.

Investigating consistent effects of the urban built environment and human mobility on COVID-19 outbreaks: A comprehensive meta-analysis

The COVID-19 pandemic has prompted strategies for mitigating its transmission across various disciplines and raised questions regarding the impact of urban density on spread. To build sustainable cities that protect against the pandemic, it is imperative to assess the impact of urban built environments and human mobility. However, current research on this topic presents mixed and conflicting results and has not confirmed consistent trends or quantified effect sizes. We conducted a meta-analysis of 50 studies to address this issue and determine a consistent trend in the effects of urban built environments on COVID-19 morbidity and mortality. A sensitivity analysis revealed that population density, road density, and human mobility significantly impact COVID-19 outbreaks, whereas other urban density metrics have minimal effects. The findings highlight the greater importance of human gatherings and active movement over static density, such as housing and building density or land use, in infectious disease transmission. These insights provide a comprehensive perspective on enhancing urban resilience to infectious diseases by rethinking urban density and implementing effective measures. Understanding the complex dynamics between urban built environment factors and disease transmission can inform policymakers and urban planners in creating healthier and more resilient cities.

Developing a 15-minute city: A comparative study of four Italian Cities-Cagliari, Perugia, Pisa, and Trieste

The pandemic and the environmental crisis raise the question of a radical transformation of cities to foster inclusion, sustainability, participation, and quality of life. The 15-minute city concept prefigures a reorganization of urban spaces, structures, and functions aimed at increasing residents’ access to essential services, favoring sustainable modes of transportation, and transforming public urban spaces into multifunctional places. The proposed study develops a set of indicators derived from the spatial and configurational analysis to evaluate the compliance of urban systems with the 15-minute city concept. The selected areas of study are the cities of Cagliari, Perugia, Pisa, and Trieste, in Italy. The study investigates four issues: i) defining a set of relevant, reproducible, and comparable indicators for measuring density, proximity, and diversity; ii) investigating the influence of distinct urban planning concepts, manifested by distinct spatial configurations, on density, proximity, and diversity; iii) defining urban scale metrics to measure levels of spatial injustice in terms of unequal conditions of access to essential services and iv) measuring the correlation between configurational factors and access to services. The analysis underlines the gap in access to essential services among central compact districts and dispersed outer areas, thus enabling the identification of inequalities in the distribution of spatial capital. Moreover, the study underlines the relationship between the urban environment structure and the location of functions. As a result, the study emphasizes that the proposed set of indicators is instrumental to understanding the urban environment's potential to meet urban populations' needs and facilitating informed decisions in urban planning.

Modeling the impacts of 2D/3D urban structure on PM2.5 at high resolution by combining UAV multispectral/LiDAR measurements and multi-source remote sensing images

Understanding the impacts of 2D/3D urban structure on PM2.5 is critical for protecting resident health and sustainable development. However, accurate understanding is limited by the lack of high-resolution PM2.5 simulation in cities with sparse monitoring stations and the simultaneously consideration of different landscape types both in 2D and 3D directions. Using Yinchuan City as a case study, an integrated simulation model combining satellite AOD, NDVI and meteorological data was developed using random forest (RF) algorithm to simulate city-scale PM2.5 variation at 30 m resolution. Then, the most critical 2D/3D urban metrics in altering PM2.5 and their impacts were explored through RF analysis. Finally, optimal urban structure was identified using Bayesian Network and multi-scenario analysis. The results indicated that 1) the established RF model could effectively simulate within-city variation of PM2.5 at 30 m with an R2 of 0.75. Thus, it is feasible to use the abundant temporal information and high-resolution AOD to map city-scale PM2.5 and overcome the limitation of spare monitoring stations. 2) The emission-related metric of distance to polluting enterprise and city center had the largest impacts on PM2.5, followed by NDVI, building height, impervious surface area and shape, and UGS green volume, area, shape and connectivity. Optimized 3D urban structure for reducing PM2.5 was proposed from the three aspects of emission source location, UGS pattern and impervious surface. This study deepens the understanding of how 2D/3D urban structure impact PM2.5 and provides scientific references for optimization possibility of urban air quality in both horizontal and vertical directions.