Urbanization and industrialization have led to the coexistence of winter haze and summer heat island in some cities in northern China, but the mitigation effect of ventilation corridors is lack of quantitative evaluation. This paper introduces circuit theory into urban climate research. Taking Shenyang as a case study, it comprehensively employs three-dimensional urban landscape pattern indices (including SVF, FAD, and Z0) to guide ventilation corridor construction, establishes an analytical framework for PM2.5 and LST, and quantifies the environmental benefits of ventilation corridors. The results show that the corridor generated by circuit theory can make 65.14% of path PM lower than the average level of the city; Among the 7 exit paths of wind corridors, the surface temperature of 4 channels is lower than the average level of the city. FAD is positively correlated with Z0 (R2 = 0.7) and negatively correlated with SVF (R2 = 0.61). Meanwhile, the circuit theory model identifies eight pinch points along ventilation paths. CFD software is employed to simulate atmospheric environments for six typical building layouts to guide subsequent urban planning. Therefore, the reasonable layout of urban morphology indicators and the construction of reasonable ventilation corridors can effectively control the atmospheric particulate pollution and the heat island effect in summer.

Landscape genetic studies often rely on contemporary landscape snapshots, potentially underestimating the cumulative and time-lagged effects of historical landscapes on contemporary spatial genetic structure (SGS). We aim to assess isolation-by-resistance (IBR) of Siberian roe deer across 1995, 2000, 2005, 2010, 2015, and 2020 throughout the Lesser Xing’an Mountains, northeastern China, and identify landscape features driving gene flow and the effective time-lag interval. We also aim to quantify the cumulative impact of these six landscapes and to partition the genetic variation into cumulative IBR and isolation-by-distance (IBD). We characterized the SGS of Siberian roe deer using 13 microsatellite loci and assessed single-year IBR with ResistanceGA. We then constructed an isolation-by-temporal-cumulative-resistance (IBtcR) model by weighting and integrating resistance distances across six temporal points, and partitioned genetic variation using redundancy analysis. IBR was influenced by topographical roughness, nighttime light, slope, and landscape type. Model selection identified a 25-year (1995–2020) time-lag interval, within which six historical landscapes contributed to SGS, with the 2005 landscape showing the strongest association (35.66

Urbanization significantly reshapes regional precipitation patterns by altering the local hydrological cycle, drawing increasing attention to the associated hydro-meteorological risks. Urban Precipitation Anomaly (UPA), Urban Extreme Precipitation Anomaly (UEPA), and the Relative Contribution of Urban Expansion to Precipitation (USPA) were constructed to analyze the urban precipitation patterns of 31 provincial capital cities in mainland China from 2000 to 2020 based on multi-source remote sensing data. By integrating the Gradient Boosting Decision Tree (GBDT) model with the Shapley Additive Explanations (SHAP) method, this study systematically elucidated the non-linear contributions and interaction mechanisms of 12 driving factors related to urban morphology, climatic context, and human activities on urban precipitation anomalies. The results indicated that from 2000 to 2020, the precipitation in urban areas of humid cities was substantially higher than that in rural areas, exhibiting a typical urban rain island effect (UPA > 0), conversely, arid and semi-arid regions exhibited an urban dry island effect (UPA < 0). The urban precipitation enhancement effect was most intense in summer, with over 60% of cities exhibiting positive UPA values. Notably, UEPA was particularly pronounced during this season; specifically, values in the Middle-Lower Yangtze River Plain exceeded 200 mm, indicating that urbanization markedly exacerbates the intensity of extreme precipitation in this region. Furthermore, USPA revealed a gradient effect where higher city tiers were associated with a stronger precipitation enhancement in urban expansion areas. According to the threshold analysis of GBDT-SHAP, land surface temperature (LST) and urban morphology were identified as dominant drivers of UPA. When LST exceeds 17.3 °C, building volume exceeds 5.2 m3, building density exceeds 0.032 count/km2, and patch density falls below 2.5 count/km2, the rain enhancement effect transitions to suppression. In contrast, UEPA exhibits higher sensitivity to building height, showing a substantial positive contribution when the index exceeds 8, emphasizing the critical role of vertical dynamic structures in triggering extreme precipitation. Robust interaction effects were observed. Specifically, the synergistic coupling between LST and building height, and between patch density and aerosol optical depth, jointly regulated the formation of precipitation anomalies. This study not only quantified the spatiotemporal patterns of urban precipitation in China but also revealed the nonlinear coupling characteristics of thermal and dynamic processes, providing a scientific basis for optimizing urban spatial planning and enhancing climate resilience.

Reclamation induces habitat fragmentation and hydrological disconnection, impairing nitrogen (N) and phosphorus (P) adsorption in wetland soils. Concurrently, accelerated decomposition and reduced plant photosynthesis suppress carbon accumulation. These processes weaken the synergistic coupling between carbon storage (CS) and water purification (WP) function. Despite established evidence of reclamation impacts, critical knowledge gaps persist in diagnosing threshold-dependent functional relationship shifts. Focusing on the coastal wetlands of the Liaohe River Delta (LRD)—a globally significant estuarine system in China. This study integrates multi-source remote sensing and field data, coupling the InVEST model, local hotspot analysis, and segmented quantile regression to dissect nonlinear response of multi-dimensional reclamation on the CS–WP functional synergies. Results quantified a stark functional contrast: Croplands maintain moderate to high CS but exhibit the weakest WP function, as indicated by the highest N P export load (e.g., 0.697 kg/ha for dry farmland and 0.750 kg/ha for paddy fields). In contrast, reed wetlands demonstrate a synergistic relationship, sustaining superior CS (20.29 t/ha) concurrently with effective water purification. A set of optimal thresholds were identified to sustain synergy, including population density of less than 129 people/km², NDVI of 0.16–0.26, agricultural intensity of 0.017–0.072, and distance to roads of 1.415–5.337 km. Aquaculture intensity should be kept below 0.16 when purification equipment is used. Applying these results enables a spatially tailored management framework for regulating ecological functional relationships in areas with high population density, agricultural and aquaculture intensity, and impervious surface coverage.

Urbanization radically alters the climatic environment and landscape patterns of urban areas, but its impact on the carbon sequestration capacity of vegetation remains uncertain. Given the limitations of current small-scale ground-based in situ experiments, the response of vegetation carbon sequestration capacity to urbanization and the factors influencing it remain unclear at the global scale. Using multisource remote sensing data, we quantified and differentiated the direct and indirect impacts of urbanization on the carbon sequestration capacity of vegetation in 508 large urban areas globally from 2000 to 2020. The results revealed that the direct impacts of urbanization were generally negative. However, 446 cities experienced an indirect enhancement in vegetation carbon sequestration capacity during urbanization, averaging 19.6 % globally and offsetting 14.7 % of the direct loss due to urbanization. These positive indirect effects were most pronounced in environments with limited hydrothermal conditions and increased most in densely populated temperate and cold regions. Furthermore, indirect impacts were closely related to urbanization intensity, human footprint, and level of urban development. Our study enhances the understanding of how the carbon sequestration capacity of vegetation dynamically responds to changes in the urban environment, which is crucial for improving future urban vegetation management and building sustainable cities.

Urban non-point source (NPS) pollution is an important risk factor that leads to the deterioration of urban water quality, affects human health, and destroys the ecological balance of the water environment. Reasonable risk prevention and control of urban NPS pollution are conducive to reducing the cost of pollution management. Therefore, based on the theory of “source–sink” in landscape ecology, combined with the minimum cumulative resistance (MCR) model, this study considered the influence of geographic-environment factors in Shenyang’s built-up area on pollutants in the process of entering the water body under the action of surface runoff, and evaluated its risk. The results indicated that the highest pollution loads are generated by road surfaces. High-density residential zones and industrial zones are the main sources of urban NPS pollution. Impervious surface ratios and patch density were the dominant environmental factors affecting pollutant transport, with contributions of 56% and 40%, respectively. The minimum cumulative resistance to urban NPS pollution transport is significantly and positively correlated with the distance from water bodies and roads. Higher risk areas are mainly concentrated in the center of built-up areas and roads near the Hun River. Green spaces, business zones, public service zones, development zones, and educational zones demonstrate the highest average risk index values, exceeding 29. In contrast, preservation zones showed the lowest risk index (7.3). Compared with the traditional risk index method, the method proposed in this study could accurately estimate the risk of urban NPS pollution and provide a new reference for risk assessments of urban NPS pollution.

Clarifying the spatio-temporal characteristics of urban vegetation phenology (UVP) is essential for understanding the potential effects of future climate change on vegetation growth. However, current studies lack quantitative analysis of urbanization impacts on spatio-temporal characteristics in UVP. This study measured UVP (including start, end and length of the growing season: SOS, EOS and GSL) changes in both long-term (from 2003 to 2020) and inner-outer city (between inner and outer city areas) in 365 Chinese cities using multi-source data. SHapley Additive exPlanations (SHAP) and accumulated local effect models were used to identify the key drivers and their changing patterns. The results indicated that UVP changed more consistently in inner-outer changes than in long-term changes. SOS in 75 % of cities advanced by -15.96+10.30 days in long-term changes, while GSL and EOS in 74 % and 72 % of cities extended by 23.89+13.81 days and delayed by 11.93+8.99 days, respectively. SOS in 80 % of cities advanced in inner-outer changes (-12.81+9.33 days), and GSL and EOS in 85 % and 73 % of cities extended and delayed (18.28+13.17 and 10.91+7.59 days), respectively. Long-term and inner-outer changes in UVP showed significant differences across city sizes. In the climate zones from south to north, there was a clear gradient in UVP in long-term changes, but little change in UVP in inner-outer changes. SHAP analyses revealed that temperature is the most important factor affecting UVP, followed by night lighting, radiation, and these drivers showed complex non-linear relationships with UVP. This study clarified the spatiotemporal characteristics of UVP, which contributed to a better understanding of the carbon sink potential of urban vegetation.

Urban residents face serious health issues owing to air pollution, especially from particulate matter (PM). The dynamic exposure risk of PM exhibits intricate spatiotemporal fluctuations influenced by resident activity and urban patterns. Therefore, high spatiotemporal resolution assessments and researches are needed. In this study, high-resolution dynamic exposure risk was assessed using mobile monitoring of three types of PM (PM1, PM2.5, and PM10) and cell phone signaling data in the center of Shenyang, China, combined with geographically weighted regression model and dynamic exposure risk model. And influencing factors of dynamic exposure risks were explored by boosted regression tree model. The results showed that high-risk areas were concentrated along the main roads. Residents suffered greater risks during the morning peak than evening peak, and weekday than weekend. The dynamic exposure risk was significantly affected by the speed of population mobility (relative influence>55.49), surpassing the effect of POI (Point of Interest) density (relative influence<36.55), except during the weekday morning peak. POI density more pronounced affected on dynamic exposure risk of PM2.5, except during the weekend evening peak. Leveraging diverse data with model simulations to independently analyses based on human activity enables a cost-effective assessment and better understanding of the spatiotemporal variability of dynamic exposure risks.

Urban expansion irreversibly alters the structure and function of terrestrial ecosystems, yet the specific impact on the carbon sequestration capacity of vegetation (characterized by net ecosystem productivity, NEP) in and around urban areas remains unclear. Previous studies confined to urban boundaries may have underestimated the full ecological impacts of urban expansion; therefore, this study explores the impact of urban expansion on the NEP at multiple scales across urban boundaries for 2904 cities globally using multiple global remote sensing datasets. The results indicated that differences within urban areas lead to significant heterogeneity in the interannual changes in NEP. There were insignificant changes in urban areas, a slow upward trend in old urban, insignificant changes in new urban, and a slow upward trend in suburban. When further differentiated by country level, NEP shows a clear upward trend in all urban areas of developed cities, whereas in developing cities, no urban area shows a clear upward trend. The impact of urban expansion extends significantly beyond urban boundaries, with megacities and supercities exhibiting a radius of influence of 70-80 km, compared to only 30-40 km for large, medium, and small cities. The negative impacts of global urban expansion caused a significant decrease in NEP over an area of 320,350 km2, while the positive impacts increased NEP over an area of 86,710 km2, offsetting the negative effects by approximately 27.1 %. The negative impacts of urban expansion on NEP increase, while the positive impacts decrease, with decreasing city size. The dominant factors for negative and positive impacts vary by city size: for negative impacts, the main drivers are POP, HF, and UI in megacities and supercities, and UI, POP, and HF in large, medium, and small cities; for positive impacts, the main drivers are GDP, UI, and POP in megacities and supercities, and POP, GDP and EVI in medium and small cities. This study lays the foundation for future cross-regional ecological management and sustainable planning, and holds significant scientific value in promoting human-land harmony and addressing global change.

Urban non‐point source (NPS) pollution has become an important issue affecting water quality, but current research has focused mainly on local scales and has lacked systematic evaluations at large spatial scales. Here, a meta‐analysis was conducted to explore the characteristics of runoff pollution indicators (TSS: total suspended solids, TN: total nitrogen, TP: total phosphorus, and COD: chemical oxygen demand) on the roads and roofs in 41 Chinese cities, and a boosted regression tree model was used to reveal the geographical differences in pollution levels and the contribution rates of their influencing factors. The results revealed that the average event mean concentrations (EMCs) of TSS (326 mg/L), TP (0.6 mg/L), and COD (160 mg/L) were significantly greater in road runoff than in roof runoff. Among them, the TSS concentrations were nearly four times greater than those in roof runoff, whereas the TP and COD concentrations were 3.2 and 2.3 times greater, respectively. Urban NPS pollution is severe in China, and the concentrations of runoff pollutants far exceed those in the USA, Germany, and France. There were significant geographical differences in urban NPS pollution due to the influences of air quality (35% relative contribution), climate conditions (15%), and human activities (45%). Prominent pollution from road runoff was observed in the Central region, more severe pollution from roof runoff was observed in the Northern region, and relatively light pollution was observed in the Southern and North‐Eastern regions. This study provides the first synthesis of NPS road and roof runoff pollution levels in Chinese cities on a large spatial scale, resulting in scientific guidance for urban stormwater management and NPS pollution control.

Urbanization has intensified in recent decades, raising concerns about increasing flood exposure in cities. Here, we assess urban flood exposure in terms of built-up area, population, and economic activity located within zones affected by 1-in-100-year river flood events. We use global historical data from 2000 to 2020 and future projections from 2030 to 2100 under the Shared Socioeconomic Pathways. From 2000 to 2020, global urban flood exposure increased substantially, with the most severe impacts in East Asia and the fastest growth in Africa. Future exposure continues to rise, especially under high-risk development scenarios. From 2030 to 2100, flood-exposed urban area, population, and economy in Global South are more than twice, nearly five times, and over twice those in Global North, respectively. Inequality in exposure is greater within developing regions than developed ones. These disparities are projected to widen, highlighting the urgent need for targeted, region-specific strategies to reduce flood risks.

Under rapid urbanization, the urban heat island (UHI) effect is increasing, which poses a serious threat to human settlements. Changes in neighborhood land surface temperature (LST) reflect the UHI effect at a finer scale, with implications for the thermal comfort of residents. Landsat images were used to analyze the distribution of the urban neighborhood heat/cool island (UNHI/UNCI) within the fourth ring area of Shenyang City. Three-dimensional buildings and the urban functional zones (UFZs) were combined to explore the relationships with the UNHI and UNCI. Using boosted regression trees to analyze the relative importance of UFZs in the UNHI and UNCI, the results showed a significant lowering effect on the neighborhood LST with increased building height, which may be due to the fact of more architectural shadows generated by higher buildings. As the size of the green space patches increased, the cooling amplitude and the influence distance had an increasing trend. Industrial and public service zones had the most significant effect on the UNHI, with influences of 30.46% and 19.35%, respectively. In comparison, green space zones and water contributed the most to the UNCI effect, with influences of 18.75% and 11.95%, respectively. These results will provide urban decision-makers with crucial information on mitigating UHI problems through urban planning.

Land use/cover change (LUCC) and climate change have important influences on ecosystem services (ESs) and their interactions, particularly in regions with rapid socioeconomic development. However, little research has distinguished the impacts of these 2 factors on ES interactions. Therefore, the impacts of LUCC and climate change on water-related ecosystem service (WES) trade-offs in the Yangtze River Economic Belt (YREB) were measured by combining spatial analysis with ES valuation. These results indicated that water yield, soil retention, and water purification presented varying degrees of increase from 1990 to 2020, with rates of 4.53%, 21.80% and 5.40%, respectively. The order of the mean WES trade-offs in the upstream, midstream, and downstream regions remained stable at the grid scale from 1990 to 2020, while there were important changes at the county scale. Climate change had a greater impact on WES trade-offs than LUCC, with climate change dominating 88.17% of the total area. The effect of climate change on WES trade-offs across areas was in the order of downstream > midstream > upstream, while the effect of LUCC was not obvious. The relative importance of drivers on WES trade-offs from 1990 to 2020 was dominated by climate dominance (71.50%), followed by LUCC to climate dominance (13.66%) and least by LUCC dominance (0.65%). This research emphasized the importance of LUCC and climate change on ES trade-offs in heavily disturbed areas, providing important guidance for multi-objective land management that sustainably provides ESs.

PM2.5, as a major air pollutant, remains unclear as to what factors influence it and the magnitude of the influence. Ten influencing factors, including socioeconomic, natural and landscape indicators, were chosen, and the effects of these factors on PM2.5 concentration was examined through Pearson correlation analysis and the boosted regression tree model. The findings indicate that PM2.5 concentration was most affected by GDP, NDVI and precipitation. The GDP imposed the most notable positive effect in China. The temperature imposed the greatest negative effect in East China. Northeast, North and Northwest China were the most negatively affected by the NDVI. Southwest and South-Central China were the most negatively affected by the relative humidity. More than half of the areas were affected by the main positive effects of GDP and more than a third of the areas were affected by the main negative effects of RH. This study systematically studied the correlations between PM2.5 concentrations and their influencing factors from a spatial perspective over a long time series. The findings could contribute to a more comprehensive understanding of the factors influencing PM2.5 and offer a theoretical basis for zonal PM2.5 pollution management.

As urbanization accelerates and global warming intensifies, urban heat risks are increasingly becoming a critical issue and addressing this challenge to foster more harmonious and livable urban environments is essential. In our study, multisource remote sensing and point-of-interest (POI) data were applied to assess the urban heat risks in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2001 to 2020. The results indicated that the heat hazard, exposure, and sensitivity were notably greater in the southeastern of the BTH. Conversely, the heat adaptability was lower in the southeast, leading to a severe high heat risk in this region. From 2001 to 2020, the heat risk index (HRI) values for these cities generally displayed an upward trend, peaking in 2015. Among the cities, Handan had the highest heat risk, followed by Baoding and Beijing, with HRIs of 0.32, 0.32, and 0.31, respectively. In urban areas, commercial zones had the highest average HRI at 0.3, while public management zones had the lowest at 0.28. These findings highlight significant variations in heat risk, indicating the need for targeted urban planning and heat mitigation strategies in the most affected areas of the BTH.

The vertical expansion of urbanization has increased the morphological heterogeneity of the urban landscape, affecting the physical and emotional wellbeing of urban dwellers by obstructing the view of greenery. In this study, multisource spatial data was used to calculate the building green view index (BGVI). Baidu Street View (BSV) images were collected for comparison with the corresponding BGVI results. A random forest model was used to analyze the contributions and marginal effects of multiple influencing factors on BGVI. The results indicated that approximately 76.10% of the sampled sites had a higher BGVI than the street view green view index, indicating buildings' superiority of visible greenery in height. The western edge of the research region frequently had the highest BGVI. Meanwhile, the hotspot regions were primarily located in the west, which was more consistent with the distribution of high value zones. The green area within the maximum visible distance, the maximum visible distance, and average height were the most influential factors of BGVI according to marginal effects analysis with the highest IncNodePurity. As a quantitative measure of urban dwellers’ visual accessibility to green space, the BGVI will contribute to urban green planning and the development of landscape architecture.

With the development of urbanization, the urban heat island (UHI) effect has gradually increased. The UHI footprint (FP) is an important indicator of the UHI effect and can quantitatively characterize its spatial pattern, it is essential to investigate the state of UHI FP for mitigating UHI problem. Therefore, the logistic model was applied in this study to assess the spatiotemporal characteristics and evolutionary trends of the surface UHI (SUHI) FP of three cities of different sizes (Beijing, Shijiazhuang and Cangzhou) from 2001 to 2020. The results indicated that the SUHI FP area tended to increase as the city size increased. The average areas of daytime SUHI FPs in Beijing, Shijiazhuang and Cangzhou were 2192.69 km2, 832.32 km2 and 222.83 km2, respectively. During the daytime, the fractal dimension index (FRAC) increased with city size and time, and the footprint expansion intensity index (FEII) increased with decreasing city size. However, for the FEII of the normalized SUHI FP, the values for super huge city were generally negative. And there was a significant positive correlation between the SUHI FP and built-up area (BA), population (POP). The study is required to provide comprehensive references for sustainable urban planning and development.

Accurate and timely urban boundaries can effectively quantify the spatial characteristics of urban evolution and are essential for understanding the impacts of urbanization processes and land-use changes on the environment and biodiversity. Currently, there is a lack of long time-series, high-resolution, nationally consistent Chinese urban boundary data for urban research. In this study, the city clustering algorithm was used to generate urban settlement boundaries in China based on the local density, size, and spatial relationships of impervious surfaces. The results showed that both the area and the number of urban settlements in China revealed an upward trend from 1985 to 2020, with East China (EC) being much higher than other regions and South China showing the most significant growth rate. The average area ratio of urban green space in China was 41.68%, with the average area ratio in EC being higher than in other regions. Meanwhile, Zipf’s law was used to verify the universality of urban settlement rank–size; the changes in the Zipf index from 1985 to 2020 also revealed that China’s urban size tended to be concentrated, and the development of large urban settlements was relatively prominent. The urban definition method we propose in this study can divide urban boundaries efficiently and accurately, identify urban expansion hotspots, and promote research on farmland loss and ecological land degradation, further exploring the impacts of urbanization on food security, biodiversity, and carbon sequestration. By coupling big data such as economy, energy, and population with urban evolution patterns, urban managers can analyze current and future problems in urban development, thereby providing scientific decision-making for urban sustainability.

Habitat quality (HQ) has been progressively degrading worldwide in recent decades due to rapid climate change and intensive human activities. These changes not only threaten biodiversity and ecosystem functions, but also impact socio-economic development. Therefore, a few studies have focused on the dynamics of HQ and its natural and anthropogenic drivers. However, many contributions have failed to reveal how these factors interact to impact HQ, especially in ecologically fragile areas. We estimated HQ in the Songnen Plain of Northeast China, an ecologically fragile area, from 2000 to 2020 using the InVEST model and explored the response of HQ to the interactions of natural factors (topography, climate, NDVI) and anthropogenic factors (nighttime light index, population density) influencing HQ using Structural Equation Modelling (SEM). The results showed that 1) HQ decreased constantly from 2000 to 2018, and then increased slightly from 2018 to 2020. 2) In terms of spatial distribution, HQ appeared to be highly heterogeneous with a pattern of 'high HQ in the east - low HQ in the center - high HQ in the west' at each time point. The high-HQ areas were significantly clustered in the eastern parts with dense forests, while the low-HQ areas in the central parts were dominated by a large number of man-made patches of agriculture and towns or cities. 3) The spatial patterns of HQ are mainly affected by the interactions of factors including the natural environment and human disturbance. Natural factors had a greater impact on HQ than human disturbance, and human disturbance factors had significant negative impact among all these factors at 4 time points. Furthermore, the intensity of the impact of various influencing factors on habitat quality, as well as the positive or negative effects of these drivers on habitat quality, changed over time. The most important influencing factor was temperature in 2000 and topography in 2010, 2018, and 2020. This study can provide important suggestions for future ecological protection and restoration in similar ecologically fragile areas.