Area Of Urban Green Space Research Articles

A GIRS-based analysis of urban green space losses with land-use changes and its relationship with surface urban heat island in the city of Tabriz

This research investigates the relationship between urbanization, land-use land-cover (LULC) changes, and surface urban heat islands (SUHIs) in Tabriz, Iran. Using Landsat satellite data from 2000 to 2021, the study analyzed changes in urban green space (UGS) and their impact on SUHIs. The maximum likelihood classification algorithm was employed to assess LULC changes, while the perceptron neural network model and cellular automaton Markov chain model were used to simulate and predict future LULC dynamics. Various indices, including the UGS Change Intensity Index (CII), Land Use Dynamic Degree Index (LUDD), and UGS Land Index, were used to quantify urban expansion, green space changes, and per capita green space (PCG) availability. The findings revealed a significant increase in built-up area and a subsequent decline in green space. While green space initially increased, it later decreased, leading to a reduction in both UGS area and PCG. Surprisingly, the analysis of heat islands associated with green space and built-up areas showed that built-up areas had a more pronounced mitigating effect on SUHIs compared to green space. Consequently, the green space heat island effect intensified, while the built-up area heat island effect decreased over time.

Larger cities host richer bee faunas, but are no refuge for species with concerning conservation status: Empirical evidence from Western Europe

In the context of worldwide biodiversity and wild bee decline, it is increasingly important to better understand the effect of land-use changes on wild bee communities at a global scale. To do so, we studied the effect of city area and urban green spaces layout on wild bee species richness and community composition, as well as on wild bee species with an unfavorable UICN conservation status. This study was based on a large European dataset encompassing 20 cities from France, Belgium and Switzerland. We found a mean wild bee species richness in cities of 96 ± 48 (SD), showing that this species richness was highly variable among cities. The main factor positively influencing wild bee species richness in cities was the area of the city. Conversely, species richness was not significantly related to the total area of urban green spaces in a given city, measured as the spatial extent of urban parks, wastelands and other semi-natural habitats, excluding urban private gardens. Species with conservation status were quite scarce in urban environments, especially when compared to the European Red List of Bees, and we could not link their presence to either city or urban green space area. Dissimilarities in wild bee species community compositions were not associated with any of the studied characteristics of cities. We found that the dissimilarity of wild bee community composition among cities was mainly driven by the rarest species, as the most common ones were found in a majority of the cities sampled. Overall, these results emphasize that larger cities host more wild bee species, but are no refuge for the ones with concerning conservation status. Thus, stakeholders are encouraged to design their cities in favor of biodiversity to better support wild bee communities, and perhaps mitigate the established effect of the urban ecological filter.

Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998–2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

Area-Based Hedonic Pricing of Urban Green Amenities in Beijing: A Spatial Piecewise Approach

AbstractThis study explores a spatial piecewise approach for the hedonic valuation of the area of urban green space at different distances from a property, using a rich census dataset collected from Beijing. We explore three novel empirical strategies that improve the identification of the spatial boundary or threshold distance within which green space is capitalised into housing prices. We first delineated a series of concentric circles surrounding each property and measured the area of green space within each doughnut-shaped ring. We next estimated the hedonic price using three methods. The first is a regression spline model combined with a machine learning type of model selection procedure which objectively selects the exact location of the threshold distance that optimises the model’s predictive performance. The second is a novel matching algorithm that minimises covariate imbalance for a continuous treatment variable (i.e., the area of green space) to provide stronger causal evidence on the hedonic prices of green space at different distances. The third is a spatial difference-in-differences approach that further accounts for endogeneity bias associated with unobserved factors. For our dataset, we found that housing prices are more likely to be affected by green space within a 1 km radius.

Estimation and Differential Analysis of the Carbon Sink Service Radius of Urban Green Spaces in the Beijing Plain Area

Enhancing the carbon sink capacity of urban green spaces is considered an effective means of reducing carbon dioxide concentration. This study, employing xCO2 as a key indicator and utilizing buffer analysis, estimated the carbon sink service radius of urban green spaces. Using spatial zoning and multifactor analysis, this research statistically analyzed 15 indicators, exploring the differences in carbon sink service radius from both the dimensions of urban green spaces and urban zones. The findings indicate that the carbon sink service radius is a result of the combined effect of urban green spaces and adjacent urban areas. Urban green space area, the NPP (net primary productivity) of urban zones, forest proportion, and grassland proportion are positively correlated with the carbon sink service radius, and the correlation degree is 0.12, 0.095, 0.121, and 0.125, respectively. The proportion of grassland and the proportion of impervious area in the city have a significant negative correlation with the carbon sink service radius, and the correlation degree is −0.074 and −0.081, respectively. This research holds significant implications for enhancing the carbon sink capacity of urban green spaces, adjusting land use patterns, and promoting the sustainable development of cities.

Urban green space area mitigates the accumulation of heavy metals in urban soils

Despite that the heavy metals in urban soils pose a threat to public health, the critical factors that influence their concentrations in urban soils are not well understood. In this study, we conducted a survey of surface soil samples from urban green spaces in Shanghai, to analyze the concentrations of the key heavy metals. The results showed that Zn was the most abundant metal with an average concentration of 122.99 mg kg−1, followed by Pb (32.72 mg kg−1) and Cd (0.23 mg kg−1). All concentrations were found to be below the risk screening values defined by the National Environmental Quality Standards for soils of development land in China (GB36600-2018), indicating no current risk in Shanghai. However, there was a clear accumulation of heavy metals, as the mean concentrations were significantly higher than the background values. Furthermore, we explored the relationships between key heavy metals with population density, GDP and green space area. Both Spearman correlation and Random Forest analysis indicated that per capita green space area (pGSA) and population density were the most crucial factors influencing the status of heavy metals in urban soils, unlike edaphic factors e.g. SOM content in farmland soils. Specifically, there was a significantly positive linear correlation between heavy metal concentrations and population density, with correlation coefficients ranging from 0.3 to 0.4. However, the correlation with pGSA was found to be non-linear. The nonlinear regression analysis revealed threshold values between heavy metals concentrations and pGSA (e.g Zn 22.22 m2, Pb 24.92 m2, and Cd 25.92 m2), with a sharp reduction in heavy metal concentrations below the threshold and a slow reduction above the threshold. It suggests that an increase in per capita green space area can mitigate the accumulation of heavy metals caused by growing population density, but the effect is limited after the threshold. Our findings not only provide insights into the distribution patterns of heavy metals in the urban soils at the local scale, but also contribute to the urban greening at the global scale and offer guidance for city planning in the face of increasing population densities over the coming decades.

Quantification of Urban Greenspace in Shenzhen Based on Remote Sensing Data

Rapid urbanization has led to the expansion of Shenzhen’s built-up land and a substantial reduction in urban greenspace (UG). However, the changes in UG in Shenzhen are not well understood. Here, we utilized long-time-series land cover data and the Normalized Difference Vegetation Index (NDVI) as a proxy for greenspace quality to systematically analyze changes in the spatio-temporal pattern and the exposure and inequality of UG in Shenzhen. The results indicate that the UG area has been decreasing over the years, although the rate of decrease has slowed in recent years. The UG NDVI trend exhibited some seasonal variations, with a noticeable decreasing trend in spring, particularly in the eastern part of Shenzhen. Greenspace exposure gradually increased from west to east, with Dapeng and Pingshan having the highest greenspace exposure regardless of the season. Over the past two decades, inequality in greenspace exposure has gradually decreased during periods of urban construction in Shenzhen, with the fastest rate of decrease in spring and the slowest rate of decrease in summer. These findings provide a scientific basis for a better understanding of the current status of UG in Shenzhen and promote the healthy development of the city. Additionally, this study provides scientific evidence and insights for relevant decision-making institutions.

Trends in all causes and cause specific mortality attributable to ambient particulate matter pollution in China from 1990 to 2019: A secondary data analysis study.

Particularly fine particulate matter (PM2.5) has become a significant public health concern in China due to its harmful effects on human health. This study aimed to examine the trends in all causes and cause specific morality burden attributable to PM2.5 pollution in China. We extracted data on all causes and cause specific mortality data attributable to PM2.5 exposure for the period 1990-2019 in China from the Global Burden of Disease 2019. The average annual percent change (AAPC) in age-standardized mortality rates (ASMR) and years of life lost (YLLs) due to PM2.5 exposure were calculated using the Joinpoint Regression Program. Using Pearson's correlation, we estimated association between burden trends, urban green space area, and higher education proportions. During the period 1990-1999, there were increases in mortality rates for All causes (1.6%, 95% CI: 1.5% to 1.8%), Diabetes mellitus (5.2%, 95% CI: 4.9% to 5.5%), Encephalitis (3.1%, 95% CI: 2.6% to 3.5%), Ischemic heart disease (3.3%, 95% CI: 3% to 3.6%), and Tracheal, bronchus and lung cancer (5%, 95% CI: 4.7% to 5.2%). In the period 2010-2019, Diabetes mellitus still showed an increase in mortality rates, but at a lower rate with an AAPC of 1.2% (95% CI: 1% to 1.4%). Tracheal bronchus and lung cancer showed a smaller increase in this period, with an AAPC of 0.5% (95% CI: 0.3% to 0.6%). In terms of YLLs, the trends appear to be similar. Our findings highlight increasing trends in disease burden attributable to PM2.5 in China, particularly for diabetes mellitus, tracheal, bronchus, and lung cancer.

High‐resolution in situ stable isotope measurements reveal contrasting atmospheric vapour dynamics above different urban vegetation

AbstractWe monitored stable water isotopes in liquid precipitation and atmospheric water vapour (δv) using in situ cavity ring‐down spectroscopy (CRDS) over a 2 month period in an urban green space area in Berlin, Germany. Our aim was to better understand the origins of atmospheric moisture and its link to water partitioning under contrasting urban vegetation. δv was monitored at multiple heights (0.15, 2 and 10 m) in grassland and forest plots. The isotopic composition of δv above both land uses was highly dynamic and positively correlated with that of rainfall indicating the changing sources of atmospheric moisture. Further, the isotopic composition of δv was similar across most heights of the 10 m profiles and between the two plots indicating high aerodynamic mixing. Only at the surface at ~0.15 m height above the grassland δv showed significant differences, with more enrichment in heavy isotopes indicative of evaporative fractionation especially after rainfall events. Further, disequilibrium between δv and precipitation composition was evident during and right after rainfall events with more positive values (i.e., values of vapour higher than precipitation) in summer and negative values in winter, which probably results from higher evapotranspiration and more convective precipitation events in summer. Our work showed that it is technically feasible to produce continuous, longer‐term data on δv isotope composition in urban areas from in situ monitoring using CRDS, providing new insights into water cycling and partitioning across the critical zone of an urban green space in Central Europe. Such data have the potential to better constrain the isotopic interface between the atmosphere and the land surface and to thus, improve ecohydrological models that can resolve evapotranspiration fluxes.

Urban Green Space Analysis and its Effect on the Surface Urban Heat Island Phenomenon in Denpasar City, Bali

The Urbanization process in Indonesia’s big cities causes adverse environmental impacts such as climate change and land cover change. Urban climate change causes the warming of urban areas compared to rural areas; it is called Urban Heat Island phenomenon. Loss of vegetation due to urban development is one of several causes that contribute to urban heat islands. This study examines the availability of green spaces and their effects on the surface urban heat island in Denpasar city. This study used the spatial approach for Urban Green space mapping with digitizing methods. Landsat 8's thermal band is used for land surface temperature mapping and to conduct a spatial pattern analysis of the SUHI phenomena. The Global Moran’s Index and Local Indicator of Spatial Association (LISA) were used to determine the correlation between urban green space and SUHI. The study result shows that Denpasar City's urban green space area covers 28.22 km2. That's equal to 22.1% of the Denpasar City Administrative area. Denpasar Selatan district has the largest urban green space cover, with 14.19 km2 covered, or 50.27% of all the green space in Denpasar City. The majority of Denpasar is affected by UHI occurrences, except the northern region of North Denpasar and the southern region of South Denpasar. The maximum UHI level reaches 4-5°C, located on the east side of South Denpasar, especially in the Sanur coastal area. According to the spatial pattern study, the association between urban green space and SUHI only exists on the north side of Denpasar. The correlation between low-SUHI intensity clusters and high cover of green space is shown in the same area. However, the association between High-UHI intensity and low green space cover has not significantly happened. It indicated that other factors besides green space could affect the land surface temperature.

Response of habitat quality to urban spatial morphological structure in multi-mountainous city

Revealing the relationship between urban spatial morphological structure (USMS) and habitat quality (HQ) is of great significance for high-quality urban development. In this study, the central urban area of a typical multi-mountainous city (Guiyang, China) was taken as research area. Two-dimensional (2D) and three-dimensional (3D) characteristic indicators system of USMS was constructed. The InVEST model was used to evaluate the HQ in the study area, and the spatial pattern characteristics of USMS and HQ in the multi-mountainous city were analyzed. Finally, the response relationship of HQ to USMS was revealed through Pearson correlation analysis and curve fitting. The results showed that: (1) The average level of HQ in the study area is low and the spatial aggregation effect is obvious; the spatial distribution difference between the high value area and the low value area is significant; the high value area is mainly distributed in the area where the large area of urban green space (UGS) and the urban remnant mountains (URMs) are located, while the low value area is mainly distributed in the high-intensity urban construction area. (2)The URMs have a significant impact on the USMS within the city, the intelligibility(I), spatial permeability, spatial aggregation degree, and spatial traffic attraction potential were poor; the buffer zone within 4 km of the urban center is the core area of urban development, and the buffer zone within 15 km is the rapid urban development area. (3)Among the USMS indicators, Choice(C) was significantly positively correlated with the HQ, while the Connectivity(Con), I, Average height(AVH), and Otherness(O) were significantly negatively correlated with the HQ. (4)Stepwise regression results showed that USMS could explain the difference of HQ in the study area well, and the values of fitting coefficient R2 were 0.975, 0.837 and 0.945 in 2008, 2013 and 2018, respectively. (5)There was a quadratic function relationship between HQ and USMS principal component factor f, and there was a threshold effect, and the thresholds were 0.798 in 2008, 0.905 in 2013 and 0.474 in 2018, respectively. This study confirmed the response relationship of HQ to USMS and demonstrates the existence of a threshold, which has important reference significance for urban biodiversity conservation and high-quality urban development.

Green space settlement landscape optimization strategy under the concept of ecological environment restoration

This article focuses on the urban development strategy and urban overall planning under the concept of ecological environment restoration, and determines planning guidance, planning principles, and planning goals. First of all, the urban green space settlement landscape design based on the ecological concept is jointly carried out from the three aspects of natural landscape, artificial landscape and human landscape. Secondly, in the research on the structural characteristics of urban green space and its temporal and spatial evolution, based on the four phases of remote sensing images, the application of remote sensing and GIS and other spatial information technologies, with the assistance of basic data and sample plot surveys, has achieved a survey. Comprehensive analysis and evaluation of the evolution of the quantity structure of urban green space, the evolution of vegetation coverage, and the evolution of vegetation structure are given. The results of the experiment found that the total area of urban green space has shrunk during the study period. Through summarizing and summarizing, the basic design process and elements are derived: the natural landscape emphasizes its ecological diversity from the green space settlement landscape form, slope protection, topography and planting design. In summary, the green space settlement landscape design of natural landscape, artificial landscape, and human landscape is more scientific and complete. Based on the analysis and evaluation of the natural conditions, social economy, urban nature and development planning, the current situation of the urban water system and other background data in the planned area, with the theoretical guidance of landscape ecology and environmental behavior, the case study combines the landscape ecology. The restoration design is used to test the application methods of ecological landscape restoration.

Evaluation of Systems and Technologies Of Underground Pipeline Gallery, Haikou

As the urban areas become denser, the built environment and land resources have limited the scope for further development of the urban areas. Haikou, as one of ten pilot cities to develop underground space, is chosen as the case in this study. Haikou Underground Pipe Gallery is a current underground building structure system that maximizes the utilization rate of land. This study investigates and analyzes the most useful and valuable technologies and systems of the Underground Pipe Gallery by survey and research. The result shows that U-shape construction technology is the most beneficial one and the electrical system is the largest and most widely used one. There are four high-tech applications in the electrical system: load power supply, construction of electrical distribution, power supply cable, and lighting facilities. In addition, a combination of green (sponge project), blue (water control) and gray (underground pipe network) infrastructure is utilized to create an optimal drainage system. It has made good use of the large area of urban green space and rainwater storage facilities.

Intelligent Identification of Building Patches and Assessment of Roof Greening Suitability in High-density Urban Areas: A Case Study of Chengdu

With the expansion of a city, the urban green space is occupied and the urban heat island effect is serious. Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment. To provide effective data support for urban green space planning, this paper used high-resolution images to (1) obtain accurate building spots on the map of the study area through deep learning assisted manual correction; and (2) establish an evaluation index system of roof greening including the characteristics of the roof itself, the natural environment and the human society environment. The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process (AHP). The suitable green roof locations were evaluated by spatial join, weighted superposition and other spatial analysis methods. Taking the areas within the Chengdu city's third ring road as the study area, the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment. The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction, with an accuracy of 86.58%. The roofs suitable for greening account for 48.08%, among which, the high-suitability roofs, medium-suitability roofs and low-suitability roofs represent 45.32%, 38.95% and 15.73%. The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu. This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.

Research on Intelligent Control System of Indoor Greening Based on Wireless Sensor Network

With the acceleration of urbanization, people living in cities have a fast pace of life, great work pressure, and difficult to form a regular work and rest. As an emerging industry, indoor greening began to develop rapidly, which is of great significance to increase the area of urban green space and improve the living environment in a specific range. Aiming at the contradiction between urban resources and demand, the existing indoor greening automatic management system is studied, and an indoor greening intelligent management system based on wireless sensor network is designed. After careful study of indoor greening and wireless sensor technology, the design scheme is obtained by comprehensively considering the factors of intelligent management and resource saving. The hardware and software are designed from four parts: acquisition end, control end, gateway, and host computer. The plant growth environment parameters such as light intensity are obtained in real time. Using analytic hierarchy process and fuzzy comprehensive evaluation method to evaluate and analyze the plant growth environment, it can collect the relevant data of light, temperature, humidity, gas, and soil in real time and transmit the collected data to the host computer through wireless sensor network. The quantitative environmental suitability index is put forward, and the intelligent management of indoor greening plants is realized through the feedback control system. This topic combines wireless sensor technology with indoor greening, which can provide a new application field for the existing indoor greening. Once mature products are launched into the market, it can save time and energy for people to raise plants and better beautify and optimize the living environment.

PERENCANAAN URBAN GREEN SPACE DALAM MERESPON COVID-19

Bendosari Eduwisata Fruit Garden is a natural laboratory assisted by the Center for Development and Community Service, a work unit under the auspices of the Research and Community Service Institute (LPPM) of UIN Sunan Kalijaga Yogyakarta. This research is a guide in the further development of the master plan for the Bendosari Eduwisata Fruit Garden in the design development phase, before entering the final phase, namely the final master plan in the form of construction documents. The methodology used is descriptive qualitative research method. The initial stage is carried out by conducting interviews and focus group discussions with garden managers and the surrounding community. Then proceed with conducting a site analysis and study of precedents. Based on the collected data, a schematic master plan is then designed which is composed of site analysis and programming, adjusting to the needs and surrounding conditions. The Bendosari area is prepared to become an urban green space area that is resilient or able to withstand any situation such as the current Covid-19 pandemic. This design focuses on community empowerment through a participatory approach and learning together which aims to prosper the community around the orchard area, both in terms of economy and environmental health.

Residents’ Spatial Image Perception of Urban Green Space through Cognitive Mapping: The Case of Beijing, China

The fundamental purpose of future urban development is to meet residents’ yearning for a better city life with the rapid development of urbanization. This study uses a multinomial logit model and cognitive map to evaluate residents’ spatial image perception of urban green space. A field study and data collection were conducted from July to August 2019, using the typical urban green space area in Beijing as the research object. Based on 375 valid questionnaires and 139 cognitive maps, the study analyzed and evaluated the image characteristics and differences of residents to the urban green space under different conditions. The results show the following. First, there is a close relationship between residents’ preference and the characteristics of urban green spaces, especially the working and living environment and characteristics will have a great influence on it. Second, the cognitive map drawn by Beijing residents can be divided into sequential and spatial cognitive maps, and the image perception shows diversified characteristics. However, the perception is relatively superficial overall, and most are simple line maps. Besides, according to the analysis of the elements of the cognitive map, the advantages and disadvantages of each type of urban green space are closely related to their geographical location and internal structure. This study has two key findings. First, the construction of urban green spaces in various cities should be carried out according to local conditions, considering the scientific basis and reasonableness of urban green space in terms of structural setting. Second, the multinomial logit model and cognitive map can effectively quantify the subjective evaluation of respondents’ spatial perceptions in a relatively simple manner, which can be further expanded in the application system design of the method.

Inequalities of urban green space area and ecosystem services along urban center-edge gradients

Rising inequality threatens the improvement of human well-being. As an important type of green infrastructure within cities, urban green spaces provide ecosystem services and contribute to human health. The inequalities of per capita urban green space area and ecosystem services are critical environmental justice and public health issues but are not well understood. Based on the spatial patterns of green spaces, land rent, and the population of 20 cities in China, we assessed the inequalities of per capita green space area and ecosystem services by using the Gini coefficient. Results showed that, (1) the Gini coefficient (an indicator to measure inequality) of per capita available ecosystem services was 0.430, which was greater than that of per capita green space area (0.357), (2) the inequality of per capita ecosystem services had a negative relationship with city size measured by population and GDP, and (3) the inequality of green space area was negatively related to the subjective quality of life, while the inequality of ecosystem services was negatively related to the economic competitiveness of cities. We suggest that urban planners comprehensively consider multiple indicators (such as per capita green space area, Gini coefficients of per capita green space area and ecosystem services) to simultaneously evaluate the efficiency and equality of green space construction.

Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach

Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design.