Urban Characteristics Research Articles

Feature-Selection-Based Unsupervised Transfer Learning for Change Detection from VHR Optical Images

Accurate understanding of urban land use change information is of great significance for urban planning, urban monitoring, and disaster assessment. The use of Very-High-Resolution (VHR) remote sensing images for change detection on urban land features has gradually become mainstream. However, most existing transfer learning-based change detection models compute multiple deep image features, leading to feature redundancy. Therefore, we propose a Transfer Learning Change Detection Model Based on Change Feature Selection (TL-FS). The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images. A change feature selection algorithm is then designed to filter relevant change information. Subsequently, these change features are combined into a vector. The Change Vector Analysis (CVA) is employed to calculate the magnitude of change in the vector. Finally, the Fuzzy C-Means (FCM) classification is utilized to obtain binary change detection results. In this study, we selected four VHR optical image datasets from Beijing-2 for the experiment. Compared with the Change Vector Analysis and Spectral Gradient Difference, the TL-FS method had maximum increases of 26.41% in the F1-score, 38.04% in precision, 29.88% in recall, and 26.15% in the overall accuracy. The results of the ablation experiments also indicate that TL-FS could provide clearer texture and shape detections for dual-temporal VHR image changes. It can effectively detect complex features in urban scenes.

Exploring patterns of surface urban heat island intensity: a comparative analysis of three Greek urban areas

One of the most pressing issues of our time is the destabilization of the global climate as a result of rapid urbanization. In Europe, according to United Nations reports, almost 73% of the population lives in cities and this percentage is expected to rise to 85% by 2050 (United Nations and Department of Economic and Social Affairs, ‘World Urbanization Prospects 2018 Highlights’, 2018). This observed urban development and its trends pose significant challenges to our urban environments, therefore it is critical to improve our understanding and assess its impact by measuring and analyzing its patterns. This study presents a comprehensive five-year analysis of microclimatic parameters that can contribute to this analysis, such as land surface temperature (LST) and surface urban heat island (SUHI) intensity, using remotely sensed data over three cities in Greece. Specifically, the selected cities are the city of Thessaloniki, which is the second largest city situated in the northern part of Greece, the city of Larissa on the mainland, and finally the city of Tripoli in the Peloponnese peninsula in southern Greece. Each city represents urban areas with different physical and urban characteristics. For this purpose, the vegetation index-based technique for LST extraction was applied within the Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) calculations were also performed over a five-year period. The results highlighted the spatio-temporal variations and trends that occur in the studied urban areas, providing valuable insights into the annual patterns of the SUHI phenomenon in different geographical contexts within Greece. This is particularly relevant in countries like Greece, where the density of World Meteorological Organization (WMO) stations providing reliable temperature data is low, usually limited to one per city. Consequently, LST derived from satellite data can provide more accurate intra-urban results of the SUHI effect, demonstrating the value of remotely sensed data as a cost effective and supportive tool for decision-making in environmental and urban planning policies.

Investigating urban morphological drivers of household water use in developing economies: A structural equation model approach

Urban populations are snowballing, increasing household (HH) water consumption. The growing housing density, the expanding infrastructure, and altered land use patterns place increasing pressure on the water supply and usage. This makes it harder for urban HHs to obtain clean water and worsens water scarcity, with infrastructural and environmental implications for highly populated metropolitan areas. This study aims to quantitatively evaluate the impact of urban morphology on household water use in a developing economy using a Structural Equation Model (SEM), specifically within the growing urban centres surrounding Kolkata in South Bengal. The primary hypothesis projects an increase in urban morphological indicators and HH characteristics that significantly affect water use. The findings reveal that increases in urban morphological indicators are associated with a 34.2% decrease in HH water use, highlighting the importance of urban planning strategies that emphasize compact and diverse urban forms. This work highlights new ways of building cities that consider morphological aspects and allow cities to expand in a way that conserves HH water while being resilient to rapid urban change.

Evolution Process and Land Use/Land Cover Response of Urban–Rural Space in Wuhan under Polycentric Structure

Polycentric development facilitates urban–rural spatial reshaping and land use/land cover (LULC) protection. Previous studies have predominantly focused on urban areas, with spatial delineation methods biased towards the macro-level, lacking a holistic perspective that situates them within the urban–rural spatial framework. This study proposes a spatial delineation framework that is applicable to the polycentric structure, taking into account the social, economic, and natural characteristics of urbanization. It employs semivariance analysis and spatial continuous wavelet transform (SCWT) to analyze the effects of polycentric development on the urban–rural space of Wuhan from 2012 to 2021 and applies a land use transition matrix, landscape indices, and bivariate spatial autocorrelation to quantify the responses and differences of LULC within urban–rural space. The results indicate that 600m×600m is the best scale for exhibiting the multidimensional characterization of urbanization. The polycentric structure alleviates the compact development of the central city, and it drives rapid expansion at the urban–rural fringe, exacerbating the spatial heterogeneity in LULC change pattern, spatial configuration, and urbanization response within urban–rural spaces. The overall effects of urbanization on LULC are relatively weak along the urban–rural gradient, experiencing a transition from positive to negative and back to positive. This study employs a novel spatial delineation framework to depict the polycentric transformation of metropolitan areas and provides valuable insights for regional planning and ecological conservation in the urban–rural fringe.

The Urban Dimensions of Mountain Society in Late-First Millennium bc Italy: Monte Vairano in Samnium

The mountain communities of late-first millennium bc Italy have been regarded as non-urban societies that reverted to city life mainly owing to Roman intervention. A growing body of archaeological evidence is uncovering the diversity of settlement forms and dynamics in the region's pre-Roman past, which included sites encompassing a range of functions and social agents. This article presents an in-depth, microscale analysis of one such site, Monte Vairano in Samnium, drawing on perspectives from comparative urbanism. Monte Vairano developed urban characteristics such as a complex socioeconomic profile and political cohesion, as well as potentially more unique features such as an apparently balanced distribution of wealth. These results can shed further light on the diversity of ancient urbanization and its sociopolitical implications in late-first millennium bc Italy and the Mediterranean.

ASSESSING URBAN HEAT ISLAND IMPACT AND IDENTIFYING VULNERABILITY ZONES THROUGH GEOSPATIAL AND GEO-STATISTICAL TECHNIQUES

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018.

THE MOTIF OF THE URBAN ENVIRONMENT'S INFLUENCE ON THE INDIVIDUAL

The article analyzes the motif of the influence of urban life on the characters (the fate of people) of Tolen Abdikov’s story “The right hand” and Didar Amantay’s story “I miss you”. Also, issues such as loneliness, misunderstanding within the family, lack of time in the city, the density of the life schedule, people’s envy and jealousy, that are part of city life were comprehensively analyzed through examples from the works. The influence of city life on the consciousness, mood and soul of a person, conflict situations in communication between people were revealed based on a psychoanalytic analysis of the heroes’ characters and analyzed with examples of situations which took place in the personal practice of the scientist K. Jung who studied the psychology of creativity. The hermeneutic method is employed for the interpretation of the text of a literary work, while the psychoanalytic method is utilized for discerning the author's unconscious imagination and analyzing the unconscious actions of characters within the literary piece. Furthermore, the article sets forth the task of analyzing changes in the psychology of urban characters in relation to societal transformations. The conclusions drawn from this study can be applied as additional educational material in preparation for lectures and practical classes in the disciplines “Theory of Literature”, “Modern Kazakh Literature”, “Literary Analysis and Literary Creativity” which are taught in universities and also during studying the formation of new directions in literary criticism.

Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments

Traditional methods for assessing urban thermal environments (UTEs) often rely on GIS and remote sensing data, suffering from data limitations in coverage, accuracy, and availability. To address these challenges, we established a novel framework developing visual intelligence simulated indices (VISIs) for improved UTE characterization. This framework combines GIS spatial data and pre-trained large-scale and local-trained GeoAI models to generate visual intelligence, extracting GIS-like visual elements from remote sensing imagery. These GIS-like VISIs comprehensively represent land surface temperature (LST) and urban fabric for cost-effective UTE analysis. We applied our framework to Zhengzhou, China, a city with diverse landscapes. Comparative experiments among Random Forests (RF), Neural Networks (NN), and Convolutional Neural Networks (CNN) demonstrated that RF models exhibited superior performance in LST representation (0.7 test R2 and 0.9 training R2). Interestingly, CNNs performed better than RFs and NNs when less cloud cover (<6 %) was present, suggesting that data diversity is essential for CNN to learn generalizable features for LST representation. For urban fabric representation, feature importance analysis indicated that VISIs (37 %) derived from large-scale GeoAI models outperform spectral bands (20 %). Furthermore, both Pearson correlation and Shapley values showed that VISIs (e.g., building features) better characterize urban heat islands than traditional remote sensing ecological indices (RSEIs) such as NDBI, underscoring the cost-effectiveness of our visual intelligence approach. In summary, our visual intelligence method effectively consolidates UTE seasonality characterization into a predictable scenario even given the unpredictable availability of remote sensing and GIS data, revealing novel perspectives on sensing unseen details for UTEs.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Answering new urban questions: Using eXplainable AI-driven analysis to identify determinants of Airbnb price in Dublin

This paper presents a novel approach for leveraging an eXplainable artificial intelligence approach to examine the price determinants of Airbnb in Dublin, Ireland, mainly focusing on urban characteristics. We employ an interpretable machine learning (ML) approach on publicly accessible Airbnb data to identify optimal Airbnb rental features, predict price trends, and understand Airbnb’s growth in Dublin. Our analysis also integrates features from three main categories: physical geography (e.g., elevation), social geography (e.g., Electoral Division), and infrastructure & economic geography based on housing/rental market theories (e.g., distance from the city centre). This integration of geographical attributes with ML algorithms provides localised insights into Airbnb pricing dynamics, addressing gaps in previous research that often overlooked the impact of geographical features on price prediction. By applying the TreeSHAP utility (tree-based attribute for SHapley Additive exPlanations), we establish the significant role of these geographical features in predicting Airbnb rental prices, thereby reinforcing theories like Alonso’s Bid-Rent Theory. We further validate the robustness of our models through testing procedures. Our study offers considerable value to urban planners, policymakers, urban researchers, and market stakeholders, deepening the understanding of Airbnb’s impact on urban landscapes and contributing to the broader discourse on the Sharing Economy.

Can government-led urban expansion simultaneously alleviate pollution and carbon emissions? Staggered difference-in-differences evidence from Chinese firms

The outward expansion of city centers is a typical feature of rapid urbanization in developing countries, leading to complex environmental pollution effects due to evolving urban spatial patterns. This study examines the impact of government-led urban expansion on pollution and carbon emissions by firms, utilizing firm and city-level data. Employing the staggered difference-in-differences (DID) method and taking the county-to-district conversion (CDC) as a quasi-natural experiment, the study finds that CDC reduced SO2 emissions by 80.95% and CO2 emissions by 13.12%. The key to this synergistic emission reduction lies in a series of source control strategies, including enhancing energy efficiency and innovation, industrial restructuring, and production reduction. Furthermore, the study reveals that CDC has a more significant effect on emission reduction for NOEs and small firms. Additionally, large cities, cities with high fiscal self-sufficiency, and cities with high per capita GDP exhibit greater environmental regulatory strength, leading to more effective emissions reduction. This paper seeks to explore potential gaps in research on the environmental impacts of government-led urban expansion, and it may provide empirical support for relevant policies in developing countries.

Reshaping landscape factorization through 3D landscape clustering for urban temperature studies

As urban populations grow and cities expand, the challenge of managing urban heat and its environmental impacts becomes increasingly critical. Traditional methods for analyzing urban temperature dynamics often fall short in precisely capturing the complexity of urban landscapes. This paper introduces the 3D Landscape Clustering (3LC) framework, a new tool designed to analyze urban temperature dynamics by factoring in landscape variables. It clusters landscapes into homogeneous groups using high-resolution 3D land cover maps. The 3LC adopts a clustering mechanism to enhance flexibility and objectivity in landscape categorization, thereby enhancing the depth and accuracy of urban climate studies and moving beyond traditional classification frameworks such as the Local Climate Zone (LCZ). Case studies demonstrate its capability to provide detailed insights into the relationships between urban landscape features and temperature variations. Additionally, the paper details how the framework can excel in multi-city analyses and outlines advanced analytical techniques. Promising research opportunities and limitations are identified. This research reshapes our approach to landscape categorization, advancing our understanding of the interactions between landscape and climate dynamics, and contributing to more sustainable, climate-resilient cities.

Effects of the Western Pacific Subtropical High on the urban heat island characteristics in the middle and lower reaches of the Yangtze River, China

The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.

Street photography and the flâneur: reflections on Australian city narratives

This original photographic work investigates the intersection of street photography and the concept of the flâneur within an Australian urban context. By transforming cityscapes into visual texts, the study captures urban narratives through the absence and reflection of inhabitants, rather than their direct representation. The photographic work employs window reflections as a key technique, introducing multiple visual dimensions to enrich street scenes and overall narratives. Drawing on Baudelaire's notion of the flâneur and Tomanić-Trivundža's three-stage journey of the flâneur (perambulating observation, critical reflection, and production of communicable texts), this work synthesises street photography with flâneur-like observation. It employs a multi-layered visual approach to city streets, representing a less-explored facet of lens-based practice in Australia. While the use of window reflections and the flâneur in street photography are well-established in European and North American contexts, their application in Australian settings remains less explored. This work aims to contribute to the street photography canon by translating these established techniques to Australian cities, offering insights into unique local urban characteristics. By exploring the interplay between global photographic traditions and local realities, this work seeks to provide a fresh perspective on Australian city life through the flâneur lens and reflective urban surfaces, thus expanding the geographical and cultural scope of this photographic tradition.

Nonlinear impact of automobile industry agglomeration on CO2 emission: Incorporating urban characteristics in China

Despite the increasing economic benefits generated by the cluster effect of the automotive industry, CO2 emissions have become a serious challenge in the “3060” dual-carbon context. There are significant uncertainties regarding the impact of automotive industry agglomeration on urban CO2 emissions, including quantifying impact thresholds and understanding the sensitivity of different agglomeration patterns. This study explores how automotive industry agglomeration impacts CO2 emissions across 278 Chinese cities, and uncover the impact patterns based on varying city characteristics. The results show that the technology agglomeration of the automobile industry exhibits a nonlinear “U-shaped” impact on CO2 emissions with a threshold value of 0.4267, while the scale agglomeration shows a nonlinear inverted “U-shaped” impact with a threshold value of 0.1206. In low-carbon demonstration cities, technology agglomeration has the most significant impact, with fluctuations ranging from −14.65% to 16.12%. In contrast, transitional cities are most affected by scale agglomeration, with a fluctuation range of −18.1%–11.19%. Besides, the results indicate that China's automobile industry development programs tend to relocate economic activities from highly agglomerated low-carbon demonstration cities to potential development cities with lower levels of agglomeration and other transitioning cities. Lastly, the results reconfirm that developing automotive industry development models based on cities' characteristics can enhance the environmental benefits of industrial agglomeration.

Revealing the role of land-use features on macrolitter distribution in Swiss freshwaters

Macrolitter, especially macroplastics, (> 0.5 cm) negatively impact freshwater ecosystems, where they can be retained along lake shores, riverbanks, floodplains or bed sediments. Long-term and large-scale assessments of macrolitter on riverbanks and lake shores provide an understanding of litter abundance, composition, and origin in freshwater systems. Combining macrolitter quantification with hydrometeorological variables allows further study of leakage, transport, and accumulation characteristics. Several studies have explored the role of hydrometeorological factors in influencing macrolitter distribution and found that river discharge, runoff, and wind only partially explains its distribution. Other factors, such as land-use features, have not yet been thoroughly investigated. In this study, we provide a country-scale assessment of land-use influence on macrolitter abundance in freshwater systems. We analyzed the composition of the most commonly found macrolitter items (referred to as ‘top items’, n = 42,565) sampled across lake shores and riverbanks in Switzerland between April 2020 and May 2021. We explored the relationship between eleven land-use features and macrolitter abundance at survey locations (n = 143). The land-use features included buildings, city centers, public infrastructure, recreational areas, forests, marshlands, vineyards, orchards, other land, and rivers and canals. The majority of top items are significantly and positively correlated with land-use features related to urban coverage, notably roads and buildings. Over 60% of top items were found to be correlated with either roads or buildings. Notably, tobacco, food and beverage-related products, as well as packaging and sanitary products, showed strong associations with these urban land-use features. Other types of items, however, did not exhibit a relationship with land-use features, such as industry and construction-related items. Ultimately, this highlights the need to combine measures at the local and regional/national scales for effective litter reduction.

Virtual Assessment of Physical Activity-Related Built Environment in Soweto, South Africa: What Is the Role of Contextual Familiarity?

Understanding how urban environments shape physical activity is critical in rapidly urbanizing countries such as South Africa. We assessed the reliability of virtual audits for characterizing urban features related to physical activity in Soweto, South Africa. We used the Microscale Audit of Pedestrian Streetscapes Global tool to characterize pedestrian-related features from Google Street View images in four neighborhoods of Soweto. Neighborhoods were selected to represent different levels of deprivation. Inter-rater reliability was analyzed according to the rater's familiarity with the local area. The results show a higher inter-rater reliability was observed among auditors with greater contextual familiarity. Many measurements however generated inconclusive results due to either low variability in the raters' responses or the absence of the features in the streets. It is evident from our findings that virtual audits are efficient tools that can be used to assess the built environment. However, to ensure meaningful use of these tools in diverse settings, we recommend that auditors comprise of people with contextual familiarity.

Assessing urban water-energy nexus characteristics in China and the US

Abstract The Water-Energy Nexus (WEN) provides a comprehensive concept for the cooperative management of resources. Although the WEN system in cities is intricately connected to socioeconomic activities, relationship between WEN and economic systems remains understudied. This study introduces a tri-dimensional Nexus Pressure Index (NPI) to assess the pressure on WEN system. Gross Domestic Product (GDP) per capita and city tiers in the urban agglomeration were used to assess the relationship between the characteristics of WEN and economic system. We conducted a case study of 296 cities in China and 1330 counties in the United States from 2012 to 2019. During the 9 year study period, on average, pressure on WEN system have relieved by 22% in China and 27% in the United States, measured by NPI. Cities with most ideal characteristics (low pressure in all dimensions) rank merely in the middle of all eight classes, with GDP per capita 74% and 85% of the highest-GDP-per-capita class in China and the US respectively. Well-performing WEN system does not yield best economic outcomes. High water pressure correlates with better economic performance in the US, while high-energy-pressure cities had GDP per capita about 50% and 70% of the class with highest GDP per capita in China and the US, respectively, suggesting stronger economic constraints from energy stress. Urban agglomeration analysis revealed a negative relationship between WEN and economic performance. NPI in emerging cities is 0.6–1 lower than NPI in regionally-central cities in China, while 0.2–0.5 lower in the US. These results underscore the contradiction between preferred WEN characteristics and higher economic performance, and underpin the resource curse hypothesis at city-level in the two considered giants. A sustainable approach to harmonize WEN and economic system is in urgent need.

PM10 Organic Aerosol Fingerprints by Using Liquid Chromatography Orbitrap Mass Spectrometry: Urban vs. Suburban in an Eastern Mediterranean Medium-Sized Coastal City

This study compares the PM10 (particulate matter of diameter smaller than 10 μm) organic aerosol composition between urban and suburban stations in Heraklion, Crete, during winter 2024 in order to highlight the impact of local anthropogenic activities on urban atmospheric particulate matter pollution. Using an HPLC-ESI-MS Orbitrap analyzer (High Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry) in full MS scan mode at a resolution of 140,000, 48 daily aerosol filter extracts were analyzed in both positive and negative modes, resulting in the detection of 2809 and 3823 features, respectively. Features with at least five times higher intensity in the urban environment compared to the suburban, and p &lt; 0.05, were deemed significant. A correlation with black carbon (r &gt; 0.6) was observed for 71% of significant urban features in positive mode. These features showed a predominance of low O:C ratios (&lt;0.2) and the majority were classified as intermediate volatility organic compounds (IVOCs), indicating fresh primary emissions. A clear urban–suburban distinction was shown by PCA of positive mode features, unlike the negative mode features. Regarding the total intensity of the features, urban samples were on average 55% higher than suburban samples in positive mode and 39% higher in negative mode. This study reveals the molecular profile of locally emitted combustion related organics observed in positive mode in an urban environment.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.