Search Results (553)

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. Full article

Urbanization transforms landscapes from natural ecosystems to configurations of impervious surfaces and green spaces, leading to urban heat island effects that impact health and ecosystem sustainability. This study in Xiamen City, China, categorizes urban areas into functional zones, employs Random Forest and Stepwise Regression models to assess thermal differences, and proposes optimization measures for the building–green space landscape. The optimization involves altering the characterization of the building–green space landscape pattern. Results indicate: (1) due to the spatial heterogeneity of the building–green space landscape pattern in different functional zones, the surface temperature also shows strong spatial heterogeneity in different functional zones; (2) different optimization measures for the building–green space pattern are needed for different functional zones; taking the urban residential zone as an example, the Normalized Difference Vegetation Index (NDVI) in the hot spot area can be adjusted according to the value range of the cold spot area; (3) considering the solar radiation process, Sun View Factor (S_unVF) plays an important role in indicating the change in surface temperature in the commercial service area, and as S_unVF increases, the surface temperature of the functional zone tends to rise. This research offers insights into urban thermal environment improvement and landscape pattern optimization. Full article

The Huang-Huai-Hai Plain, a vital agricultural area in China with a significant amount of arable land, plays a pivotal role in influencing grain production, ecological carbon cycles, and global climate change through its shifts in land use. Within this research, we have employed the ArcGIS tool and the In-VEST-Geodetector-PLUS methodology to scrutinize the shifts in carbon storage from the year 2000 to 2020, determine the pivotal influences behind these shifts, and anticipate the projected carbon storage for 2030. Although there has been a slight increase in forested areas as a result of environmental policies, the conversion of cropland to impervious surfaces due to urbanization has led to a persistent decrease in carbon storage, with a cumulative loss of 272.79 million metric tons over the two decades. The Normalized Difference Vegetation Index (NDVI), Night-Time Lights (NTL), Gross Domestic Product (GDP), and Population (POP) are critical factors impacting carbon storage, reflecting the intricate connection between socio-economic development and natural ecosystems. The multi-scenario simulations for 2030 suggest that the least reduction in carbon storage would occur under the scenario of protecting arable land, while the most significant decrease would be under the urban expansion scenario, highlighting the impact of urbanization. The study’s results emphasize the critical need to harmonize agricultural land conservation with economic progress for the enduring growth of the Huang-Huai-Hai region. Full article

Increasing urbanisation is occurring in Australia’s major cities and in almost every country in the world. This creates a challenge for the urban water sector, which not only needs to provide traditional water services (i.e., wastewater, domestic water) for a rapidly growing population, but also to service potential additional demands to contribute to enhanced amenity, and to do so in the context of climate change. This paper is focused on stormwater management controls for the develop of new greenfield urban sites in the three major east coast Australian cities—Melbourne, Sydney and Brisbane. While stormwater management in all three cities is focused on the protection of community values of the waterways, including environment (ecology), amenity and recreation, the scale or type of the waterways considered is considerably different—Melbourne has adopted a regional waterway strategy, while the Sydney and Brisbane approach is more localised. Pollution load reduction targets (TSS, TP, TN and litter) from new urban areas have been enforced in all three cities for many years, although there is concern that these targets primarily aimed at protecting the values of downstream bays (e.g., Port Phillip Bay, Sydney Harbour and Morton Bay) will not necessarily protect the values of the contributing waterways. However, targets to control stormwater volumes entering waterways are proving to be considerably more difficult to both develop and implement. These targets are typically expressed as volumes of stormwater to be harvested and/or infiltrated for every additional hectare of directly connected impervious (DCI) surface created as a result of urban development. The three cities have approached the setting of stormwater flow targets somewhat differently, as is apparent from the details provided in the paper. Additionally, we argue that there is a need for the development of new targets related to the reuse of stormwater and its integration with wastewater and domestic water management. Full article

This study investigates land-cover changes along riparian zones in South Carolina, focusing on intermittent and perennial streams to assess the impact of urbanization, forest loss, and impervious surface expansion on sensitive ecosystems. South Carolina’s diverse geography, ranging from coastal marshes to the Blue Ridge Mountains, and subtropical humid climate, offers a rich context for understanding environmental changes. The research utilizes various geospatial datasets, including the National Land Cover Database (NLCD), National Hydrography Dataset (NHD), and National Agricultural Imagery Program (NAIP) imagery, to evaluate changes in forest cover, urbanization, and impervious surfaces from 2011 to 2021 as a decade of transition. The study areas were divided into buffer zones around intermittent and perennial streams, following South Carolina’s riparian management guidelines. The results indicate significant land-cover transitions, including a total of 3184.56 hectares of non-urban areas converting to forest within the 100 m buffer around intermittent streams. In contrast, 137.43 hectares of forest transitioned to urban land in the same buffer zones, with Spartanburg and Greenville leading the change. Intermittent stream buffers exhibited higher imperviousness (4.6–5.5%) compared to perennial stream buffers (3.3–4.5%), highlighting the increased urban pressure on these sensitive areas. Furthermore, tree canopy loss was significant, with counties such as Greenwood and Chesterfield experiencing substantial reductions in canopy cover. The use of high-resolution NAIP imagery validated the land-cover classifications, ensuring accuracy in the results. The findings emphasize the need for effective land-use management, particularly in the riparian zones, to mitigate the adverse impacts of urban expansion and to safeguard water quality and biodiversity in South Carolina’s streams. Full article

Impervious areas are one of the important indicators for evaluating the urbanization process, while surface temperature is one of the reference factors for evaluating the urban environment. In order to investigate whether the spatial distribution of an impervious surface has any influence on urban surface temperature, Xuzhou City was selected as the study area, and the impervious surface information was extracted based on the maximum likelihood classification method for Xuzhou City for the period of 2013–2022, and surface temperature inversion was performed using Landsat 8 remote sensing imagery and nighttime lighting data. In order to reduce the confusion between bare soil and impervious surfaces, the study area was divided into built-up and non-built-up areas for the selection of impervious and pervious surface samples using nighttime lighting data, and, finally, the maximum likelihood classification method was used to realize the extraction of impervious surfaces. The experimental results show that, by extracting the impervious surface of Xuzhou City, the impervious surface of Xuzhou City continued to increase from 2013 to 2022, in which the growth rate was faster in 2014–2016 and 2019–2021, and slower in 2017–2018 and 2021–2022, after performing surface temperature inversion as well as temperature grading. The results of impervious surface extraction and surface temperature inversion were subjected to overlay analysis and linear regression analysis. It was found that most of the impervious surface area is in high-temperature areas, and the density of the impervious surface is proportional to the surface temperature in the impervious surface and its surrounding area. Therefore, it can be concluded that the expansion of impervious surfaces is one of the reasons for the increase in urban surface temperature. Full article

In urban areas prone to extreme weather, it has become crucial to implement effective strategies to improve living conditions for residents reliant on medical and educational facilities. This research highlighted the importance of urban green spaces in cooling European cities and examined the planning and maintenance of these areas alongside economic losses due to water consumption during heatwaves. Key findings using an SEM (structural equation model) showed that hot summer days indirectly impacted water prices by increasing cumulative temperature days. The confidence interval (0.015, 0.038) confirmed this effect. Additionally, tropical nights indirectly impacted water prices, as shown by the cooling degree days, which indicated the need for air conditioning. The increased use of energy for cooling resulted in higher water prices due to the water required for power generation. This effect was statistically significant, with an estimated value of 0.029 (p < 0.001). A generalized linear model (GLM) indicated an inverse relationship between urban green space and impervious surfaces (slope: −0.69996 ± 0.025561, intercept: 53.675 ± 0.97709, p < 0.01), which was important for reducing impervious surfaces and improving water management, ultimately leading to cooler urban temperatures. Practical recommendations for decision-makers, urban planners, and residents are provided to adapt to changing extreme weather conditions. These include improving the soil environment in current locations and increasing access to green spaces, which can enhance well-being and address health issues. Full article

Urbanization increases the number of impervious surfaces in watersheds, reducing infiltration and evapotranspiration, which increases runoff volumes and the risks of flooding and the pollution of water resources. Nature-based solutions (NBS) mitigate these effects by managing water volume and quality, restoring the hydrological cycle, and creating sustainable livelihoods that can promote socioeconomic equity by providing green space. In light of the aforementioned information, this study analyzes the hydrological response of NBS in La Guapil, a densely populated and socioeconomically vulnerable area of Costa Rica with approximately 80% impervious surfaces, focusing on their effectiveness in stormwater management and improving hydrological conditions. Field data from the study area’s storm drainage system, as well as hydrological analyses, were collected and processed to evaluate RCP8.5 climate change scenarios using the Clausius–Clapeyron (CC) relationship. Three scenarios were proposed: (1) the “status quo”, reflecting current conditions, (2) green roofs and green improvements, and (3) detention ponds and green improvements, evaluated using the SWMM, with the latter scenario also using the Iber model. Simulations showed that Scenario 2 achieved the greatest reduction in peak flow (53.74%) and runoff volume (57.60%) compared to Scenario 3 (peak: 28.37%; volume: 56.42%). Both scenarios demonstrate resilience to climate change projections. The results of this study provide a foundation for further research into NBS in Costa Rica and other comparable regions. Full article

The Yangtze River Economic Belt (YREB), spanning nine provinces and cities in eastern, central, and western China, is a key region for China’s urbanization. This study utilizes the Google Earth Engine (GEE) platform to integrate four land cover and impervious surface datasets, constructing built-up area datasets for the YREB at five-year intervals from 1985 to 2020. The employed random forest model achieved an overall accuracy (OA) and kappa coefficient both exceeding 90%, demonstrating high reliability and precision in the generated datasets. Using this dataset, we then calculated the United Nations Sustainable Development Goal 11.3.1 (SDG11.3.1) index for the YREB and its nine constituent provinces, which includes the land consumption rate (LCR), population growth rate (PGR), and ratio of land consumption rate to population growth rate (LCRPGR). The results show that the LCRPGR index for the entire region over the 35-year period is −0.006, 4.84, 0.44, 0.77, 5.15, 0.09, and 2.13, respectively. These values suggest that the land consumption rate significantly outpaced the population growth rate during 1990–1995, 2005–2010, and 2015–2020, reflecting periods of rapid urban development. This study offers important insights into urban expansion in the YREB, offering valuable data to inform sustainable urbanization practices. Full article

Land use change and energy consumption caused by human activities is the primary source of carbon emissions and a driver of climate change. The study focused on the Yangtze River Delta (YRD), using the China Land Cover Dataset (CLCD) to calculate the region’s carbon emissions from 1990 to 2020. Based on the Natural Segment Method, the spatial distribution of carbon emissions in the YRD region were constructed by dividing them into three categories: heavy, medium, and light. The results indicate that: (1) Carbon emissions of the YRD region was 594.02 million tons at the end of 2020, an increase of 468.53 million tons compared with that of 1990. The impervious surface was the major source of carbon emissions, accounting for more than 98.51% of the total, and woodland was the most important carbon sink, accounting for more than 91.32% of the total carbon uptake. (2) The carbon emissions increase rate over the 30-year period has risen from rapid to gradual, with the fastest rate of increase occurring between 2000 and 2010. (3) Differences in economic development and land type lead to spatial variability in carbon emissions. Regions with substantial emissions were predominantly located in coastal areas, indicating a trend toward shifting inland. The assessment of carbon emissions is helpful for designing emissions mitigation policies. Full article

Urbanization has significantly altered the ecological resources, functions, and services, thereby imposing specific constraints on particulate matter (PM) mitigation through green spaces. To investigate the effect of green spaces on mitigating PM_10,2.5 under multiple urban stressors, this study employed combined remote sensing imagery and small-scale quantitative measurements to identify the PM within green space and street tree, and their PM differences with the square underlying surface according to a continuous scale of 60~3000 m. The results indicated that urban stressors significantly influenced air PM₁₀ and PM_2.5 mitigation, with stressors LST (land surface temperature) and RD (traffic road density) as key stressors on air PM₁₀, while LST, ISA (impervious surface area), BH (building height), NDVI (normalized difference vegetation index), GA (green space area), and WA (water body area) were key stressors on air PM_2.5. Furthermore, stressors exhibited a significant scale effect on air PM_10,2.5 mitigation; for air PM_2.5, stressors ISA, RD, BH and BD (building density) had a notable impact on air PM_2.5 mitigation at 1500~3000 m scales, while NDVI, GA, and WA showed a significant impact at 450~600 m. For air PM₁₀, stressors ISA, BH, NDVI, and GA revealed a continuous scale effect, with the key scales occurring at 450 m and 3000 m. In summary, urbanization stressors can combine to affect air PM₁₀ and PM_2.5 mitigation by green spaces, especially at different spatial scales, to provide practical guidance for urban planning. Full article

The urban heat island (UHI) effect, marked by higher temperatures in urban areas compared to rural ones, is a key indicator of human-driven environmental changes. This study aims to identify the key morphological parameters that primarily contribute to the development of surface urban heat island intensity (SUHII) and investigates the relationship between SUHII and urban morphology using land surface temperature (LST) data from the Sentinel-3 satellite. The research focuses on Milan and Lecce, analyzing how urban geometry affects SUHII. Factors such as building height, aspect ratio, sky visibility, and surface cover are examined using approximately 1000 satellite images from 2022 and 2023. The study highlights seasonal and diurnal variations in SUHII, with particular emphasis on HW periods. Through multicollinearity and multiple regression analyses, the study identifies the main morphological drivers influencing SUHII in the two cities, specifically the Impervious Surface Fraction (ISF) and Mean Building Height (HM). Milan consistently exhibits higher SUHII, particularly during HWs, while Lecce experiences a negative SUHII, especially during the summer, due to lower urban density, more vegetation, and the low soil moisture around the urban area. Both cities show positive SUHII values at night, which are slightly elevated during HWs. The heat wave analysis reveals the areas most susceptible to overheating, typically characterized by high urban density, with ISF and HM values in some cases above the 90th percentile (0.8 and 13.0 m, respectively) compared to the overall distribution, particularly for Milan. The research emphasizes the importance of urban morphology in influencing SUHII, suggesting that detailed morphological analysis is crucial for developing climate adaptation and urban planning strategies to reduce urban overheating and improve urban resilience to climate change. Full article

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA-ConvLSTM). This paper takes the Wuhan urban circle of China as the research object, establishes a carbon stock AI prediction model, constructs a carbon stock change evaluation system, and investigates the correlation between carbon stock change and land use change during urban expansion. The results demonstrate that (1) the overall accuracy of the ConvLSTM and SA-ConvLSTM models improved by 4.68% and 4.70%, respectively, when compared to the traditional metacellular automata prediction methods (OS-CA, Open Space Cellular Automata Model), and for small sample categories such as barren land, shrubs, and grassland, the accuracy of SA-ConvLSTM increased by 17.15%, 43.12%, and 51.37%, respectively; (2) from 1999 to 2018, the carbon stock in the Wuhan urban area showed a decreasing trend, with an overall decrease of 6.49 × 10⁶ MgC. The encroachment of arable land due to rapid urbanization is the main reason for the decrease in carbon stock in the Wuhan urban area. From 2018 to 2023, the predicted value of carbon stock in the Wuhan urban area was expected to increase by 9.17 × 10⁴ MgC, mainly due to the conversion of water bodies into arable land, followed by the return of cropland to forest; (3) the historical spatial error model (SEM) indicates that for each unit decrease in carbon stock change, the Single Land Use Dynamic Degree (SLUDD) of water bodies and impervious surfaces will increase by 119 and 33 units, respectively. For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. This study demonstrates that we can use deep neural networks as a new method for predicting land use expansion, revealing the key impacts of land use change on carbon stock change from both historical and future perspectives and providing valuable insights for policymakers. Full article

Urban green spaces (UGSs) are integral to urban ecosystems, providing multiple benefits to human well-being. However, previous studies mainly focus on the quantity or quality of UGSs, with less emphasis on a comprehensive analysis. This study systematically examined the spatiotemporal UGS dynamics in the Pearl River Delta urban agglomeration (PRDUA) in China from the perspectives of the area, spatial configuration, and quality, using the high spatial resolution (30 m) Landsat-derived land-cover data and Normalized Difference Vegetation Index (NDVI) data during 1985–2021. Results showed the UGS area in both the old urban districts and expanded urban areas across all nine cities in the PRDUA has experienced a dramatic reduction from 1985 to 2021, primarily due to the conversion of cropland and forest into impervious surfaces. Spatially, the fragmentation trend of UGSs initially increased and then weakened around 2010 in nine cities, but with an inconsistent fragmentation process across different urban areas. In the old urban districts, the fragmentation was mainly due to the loss of large patches; in contrast, it was caused by the division of large patches in the expanded urban areas of most cities. The area-averaged NDVI showed a general upward trend in urban areas in nearly all cities, and the greening trend in the old urban districts was more prevalent than that in the expanded urban areas, suggesting the negative impacts of urbanization on NDVI have been balanced by the positive effects of climate change, urbanization, and greening initiatives in the PRDUA. These findings indicate that urban greening does not necessarily correspond to the improvement in UGS states. We therefore recommend incorporating the three-dimensional analytical framework into urban ecological monitoring and construction efforts to obtain a more comprehensive understanding of UGS states and support effective urban green infrastructure stewardship. Full article

Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta urban agglomeration and explores its driving factors using a Geographically Weighted Random Forest model. The results demonstrate a transition from “point expansion” to “infill development”, while also revealing a gradual southward shift in the developmental focus of the Yangtze River Delta urban agglomeration. Although expansion intensity has decreased, spatial clustering has intensified. Based on the expansion patterns of impervious surface areas, we propose a novel regional classification method, dividing the Yangtze River Delta urban agglomeration into three zones: “A-Development Decline Zone”, “B-Development Core Zone”, and “C-Development Ascendance Zone”. Socio-economic factors are the primary drivers of this expansion, followed by science and education, and then the ecological environment, while physical geography factors have the least impact. The study reveals differentiated regional development characteristics and further refines the sub-regions within the urban agglomeration, providing a new perspective for future regional coordinated development policies. Full article