Urban Green Coverage Research Articles

Analysis and Comprehensive Evaluation of Urban Green Space Information Based on Gaofen 7: Considering Beijing’s Fifth Ring Area as an Example

Urban green spaces constitute a vital component of the ecosystem. This study focused on urban green spaces located within the Fifth Ring Road of Beijing, using Gaofen 7 (GF-7) as the primary data source for analysis. The main objective was to develop a system for extracting and classifying urban green spaces in Beijing by applying deep learning and machine learning algorithms, and further, the results were validated with ground survey samples. This study provides detailed extraction and classification of urban green space coverage by creating a comprehensive evaluation system. The primary findings indicate that the deep learning algorithm enhances the precision of green space information extraction by 10.68% compared to conventional machine learning techniques, effectively suppresses “pretzel noise”, and eventually aids in extracting green space information with complete edges. The thorough assessment of green spaces within the study area indicated favorable outcomes showing the high service capacity of park green spaces. The overall classification accuracy of the final extraction results was 94.31%. Nonetheless, challenges, such as unequal distribution of green zones and a significant fragmentation level throughout the study area, were still encountered. Consequently, the use of GF-7 high-resolution imagery, in conjunction with the collaborative application of deep learning and machine learning techniques, enabled the acquisition of highly accurate information regarding urban green zone coverage. According to the established grading standards of evaluation indices, the landscape pattern of urban green spaces within the study area was comprehensively assessed. This evaluation offers essential data support for monitoring urban green spaces and planning landscape patterns, thereby contributing to the achievement of sustainable development objectives related to urban greening and ecological conservation.

Spatial-temporal dynamics and influencing factors of city level carbon emission of mainland China

Urban areas are major sources of carbon emissions, making it crucial to understand their emission characteristics for effective carbon reduction and sustainable development. Using carbon emission data from mainland China (2001–2021), we analyzed the spatio-temporal dynamics and future trends of city-level emissions and explored influencing factors using machine learning methods. Results indicate significant fluctuations in carbon emissions, with over 40 % of mainland Chinese cities experiencing a doubling in total emissions. Geospatially, cities in South Coast (SC), East Coast (EC), Northeast (NE), and NorthCoast (NC) show stronger intensity and increasing trends in carbon emissions compared to other regions, with over 80 % of cities in these regions experiencing high or higher increases. Additionally, a continued rise in carbon emissions was detected in most Chinese cities, with an average Hurst index of 0.64, indicating persistent trends. Using the XGBoost method, factors such as population density, built-up area, urban green coverage rate, and GDP were found to strongly correlate with urban carbon emissions, exhibiting significant spatial heterogeneity. This research uncovers the characteristics and influencing factors of urban-scale carbon emissions, offering valuable insights for policymakers to tailor carbon reduction strategies to the specific needs and conditions of various urban areas.

The green promotion back to digitization: Research on air quality, urban green coverage and digital innovation

We examine the relationship between air quality, urban green coverage, and the digital innovation of companies in this paper. Using a sample of Chinese A-share listed companies spanning from 2006 to 2016, we discover a positive effect of air quality on companies' digital innovation. Furthermore, urban green coverage enhances this positive relationship through the environmental effect and signaling effect. In mechanism analysis, we find that improved air quality correlates with digital investment attraction, digital human resource immigration, and digital R&D efficiency enhancement channels, subsequently contributing to the advancement of digital innovations among companies. The positive impact of air quality on digital innovation is particularly pronounced in non-state-owned enterprises and under stricter government air policy regulations. Additionally, our study shows that while air quality significantly boosts total innovation by enhancing digital innovation, it has a minimal effect on non-digital innovation. Our findings underscore the importance of paying attention to air quality and promoting green environmental initiatives in the digital era.

Study on temperature regulation function of green spaces at community scale in high-density urban areas and planning design strategies

In recent years, the frequency of extreme high-temperature events has gradually increased. To better understand the impact of urban green space coverage's spatial pattern on land surface temperature (LST), this study selected three sample areas along the urban-rural gradient in Shanghai. Using LST inversion and resampling methods, LST data for different grid sizes were obtained for spring, summer, and winter in the 2010s, 2015s, and 2020 s. A boosting regression tree model was employed to determine the key indicators affecting LST and effective cooling thresholds. The impact of green quality, structure, and pattern at the community scale on LST was discussed, providing feasible suggestions for green space planning and design in different urban spaces. The study found significant differences in the spatial patterns of green areas and LST among the three sample regions. The quantity and integrity of green spaces in the Huangpu, Minhang, and Songjiang sample areas have progressively improved, with overall green coverages of 19 %, 38 %, and 43 %, respectively. As grid size increases, the relative influence of fractional vegetation coverage (FVC) on LST generally decreases, whereas the relative influence of green structure and patterns gradually increases. Taking the summer of the 2020 s as an example, the influence of FVC on the three study sample areas was 80.79, 83.36, and 87.18 at a 30 m grid size, which decreased to 48.87, 40.59, and 47.64 at a 120 m grid size. The green structure's impact rose from 13.09, 15.22, and 10.6–30.01, 51.82, and 38.5; the influence of green patterns increased from 6.12, 1.42, and 2.21–21.13, 7.6, and 13.86. Key indicators affecting LST include FVC (Fractional Vegetation Coverage), AREA (Green Patch Area), PD (Patch Density), COHESION (Patch Cohesion Index), and ED (Edge Density). High temperatures in summer are one of the ecological issues that need special attention in Shanghai's green space design. Setting the green space proportion to 35 % while avoiding fragmentation and low vegetation coverage can achieve effective cooling. This study's main advancement lies in utilizing machine learning algorithms to identify the principal green spatial pattern impact factors and key thresholds influencing LST at the community scale in Shanghai. The related results and proposed strategies provide a research framework and strong basis for special regulations in urban green space system planning concerning urban thermal environments. They offer references for urban renewal and new town planning to address climate change and urban high-temperature issues, such as clear requirements for optimal green space area at the community scale, community park spatial layout and quantity regulations, and urban park planning design suggestions. This study also highlights obstacles in the practical application of planning and design strategies, which can help avoid difficulties in implementing planning policies. It attempts to set goals for community-scale vegetation planning in China from a policy perspective and provides relevant recommendations.

Eco-Efficiency Evaluation of Integrated Transportation Hub Using Super-Efficiency EBM Model and Tobit Regressive Analysis – Case Study in China

The transportation industry is a key area for ecological civilisation construction and low-carbon development. As the core support of the national integrated transportation system, the ecological development level of integrated transportation hub (ITH) is crucial for enhancing the sustainable development capacity of the national integrated transportation. An eco-efficiency evaluation index system of ITH is established in this study and the eco-efficiencies of twenty international ITHs in China are comprehensively evaluated based on the super-efficient epsilon-based measure (EBM) model. Then the panel Tobit regression model is adopted to analyse the influencing factors of eco-efficiency. The results show that the average eco-efficiency of ITHs in China during 2011–2021 declines first and then rises, with a relatively high level overall but not efficient yet, and there is an obvious gradient distribution characteristic in all eco-efficiencies. Among them, Guangzhou ranks first, followed by Haikou, and Harbin ranks last. It is found that integrated transportation efficiency, urban green coverage, level of opening-up and economic development improve eco-efficiency significantly, while urbanisation rate, industrial structure and technology input have a negative impact. The results are consistent with the actual situation, verifying the practicality of models, and can be used to promote the sustainable development of integrated transportation.

Spatial–temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5)

ObjectiveThis study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011–2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China.MethodsThis study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression.ResultsPCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas.ConclusionPM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

Research on urban three-dimensional greening design from the perspective of climate change—a case study of Beilin District, Xi’an, Shaanxi Province, China

Climate change is an important issue for cities today and in the future. At present, China has a large population and complex climate conditions, and cities are also vulnerable to the adverse effects of climate change (Tian, Environ Sustain Dev 6: 153-155 2020). Three-dimensional greening can not only improve the green space system of a city but also have a far-reaching impact on the ecology, image, and economic benefits of a city. Therefore, the study of urban three-dimensional greening is an effective means to deal with climate change strategies. By exploring the influence of traditional greening and three-dimensional greening on Local Climate in Beilin District of Xi’an, Shaanxi Province, the mechanism of three-dimensional greening on urban ecological environment was discussed, and the ecological theory, urban three-dimensional greening theory, and urban local climate zone (LCZ) were referred to. Based on the methods of national climate monitoring, ENVI-met simulation, and field independent measurement, this paper selected a research sample site in the east section of Jianshe Road, Beilin District, Xi’an City, Shaanxi Province, China, and applied ENVI-met software to simulate the thermal stress relationship among building exterior surfaces, plants, and air in the street; quantified the overall ecology of the area; and used measuring instruments. The influence of different types of greening in the base on the site temperature, humidity, CO2 (carbon dioxide) concentration, wind speed, and other climate factors data was, respectively, measured and analyzed. The grid analysis was used to compare the traditional greening and three-dimensional greening, then the numerical differences of each impact factor were sorted out, and the effect of three-dimensional greening on the improvement of urban ecological environment was discussed by analyzing the climate factors with greater impact. The results show that (1) three-dimensional greening plus traditional greening is the most beneficial mode; (2) in the same environment, according to the parameter of 1.5 m from the ground in the model environment, it can be seen that the temperature of the space treated with three-dimensional greening of buildings is reduced by 3.5–3.6 ℃ compared with the control group, the relative humidity is different by 7–8%, the CO2 concentration is reduced by about 5%, and the spatial wind speed is relatively small. (3) When the urban green coverage rate is more than 40%, the improvement of temperature is more obvious, if it reaches 50%, the cool phenomenon in summer can be fundamentally changed. From the perspective of human perception, the PMV index increased by 0.27 on average. This paper discusses and analyzes the three-dimensional greening of urban streets in Beilin District, Xi’an City, Shaanxi Province, China, and studies its influence on urban ecology to different degrees. The conclusions are as follows: Different types of greening have different degrees of influence on urban climate. Meanwhile, the experimental results of this paper show that in cities like Xi’an, Shaanxi Province, China, where summer is hot, adding three-dimensional greening to traditional street greening can significantly improve the environmental microclimate, which is an effective means to cope with climate change, improve the site environment, and stabilize the urban ecosystem.

UNRAVELING COMORBIDITY PREVALENCE AND PM2.5 EXPOSURE AMONG MIDDLE-AGED AND ELDERLY: A SPATIAL-TEMPORAL ANALYSIS

Abstract Objective This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China. Methods This study utilized CHARLS follow-up data for ≥45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression. Results PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas. Conclusion PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

What are public preferences for air quality improvement policies? Additional information from extended choice models

Effectively assessing public preferences for air quality improvement policies is extremely important to environmental policy formulation, but developing policies that cater to public tastes is a great challenge. Although the random parameters logit (RPL) model in the choice experiment is widely used in relevant studies, it remains limited in revealing additional preference heterogeneity. Given this, the study applies two extended models in exploring public preference heterogeneity for air quality policies. An RPL model with heterogeneity in means and variances (RPL-HMV) and an RPL model with correlated random parameters (RPL-CRP) are used to provide more beneficial insights for policy analysis. The study shows that better-educated groups are more willing to pay for increasing urban green coverage, and income increases the randomness of such preferences’ distribution among groups. From the perspective of preferences, reducing heavy pollution days is positively associated with decreasing morbidity of respiratory diseases caused by outdoor air pollution and negatively correlated with improving urban green coverage. In addition, compared to the RPL-CRP model, the willingness to pay in the RPL model is overestimated by 14.72%. The study further clarifies public preferences for air quality policies, and the extra information revealed by extended models provides more valuable references for policy-making.

Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method

Ground-level ozone (O3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O3 pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient O3 pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level O3 (PWGLO3) concentrations, representing population exposure to ambient O3, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in PWGLO3 pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on PWGLO3 concentrations have partial spatial spillover effects. The PWGLO3 concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and PWGLO3 concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but PM2.5 pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage.

Urban Economic Efficiency, Environmental Factors, and Digital Finance: Impacts on Sustainable Development in Chinese Cities

This study investigates the potential of digital finance to mitigate the environmental impact of economic development. Utilizing panel data from 290 Chinese cities between 2011 and 2019, we employ a two-way fixed effects model to control for unobservable city characteristics and time-invariant macro factors. Our findings reveal that cities with higher proportions of value-added tax and secondary industries (mainly for manufacturing) tend to experience increased air pollution. Conversely, cities characterized by higher economic efficiency exhibit better air quality, underscoring the significance of prioritizing technology-intensive sectors during economic development. Pollution control technologies, such as centralized wastewater treatment systems and harmless treatment systems for household waste, also play positive roles. Moreover, technological and educational investments correlate with increased urban green coverage and lower air pollution, highlighting the pivotal role of technology development and residents’ literacy in fostering sustainable practices. Although we initially observed a positive association between the index of digital financial technology and air pollution, which could be attributed to the influence of economic growth, our study employs instrumental variable regression to unveil their true correlation. The results suggest the application of digital financial technology may enhance environmental awareness, contributing to the mitigation of air pollution.

Impact of City Forests on Haze Reduction—Implementation of the National Forest City Policy in China

The construction and development of China’s forest cities revolves around the goal of building an environmentally friendly society and achieving harmonious development between human beings and nature, which is essentially the implementation of the concept of green development and helps promote the sustainable development of sustainable cities. Based on the panel data of 263 prefecture-level cities in China from 2001 to 2020, the National Forest City Policy (NFCP) is used as a quasi-natural experiment to assess the effect of NFCP on haze pollution management and their heterogeneity using a time-varying DID (difference-in-differences) model, and a mediating effect model is used to analyze the mechanism of the effect of NFCP on haze pollution. The research found that (1) the NFCP can reduce urban haze pollution, and this finding remained robust after placebo tests and the replacement of explanatory variables; (2) the NFCP is more conducive to reducing haze pollution in the Pearl River Delta urban agglomeration, Type II large cities, cities in the eastern region, and cities east of the Hu Huanyong line; (3) the NFCP will improve urban green space coverage, raise residents’ awareness of environmental protection, and promote the development of tertiary industries, thereby promoting urban haze reduction.

Will information interventions affect public preferences and willingness to pay for air quality improvement? An empirical study based on deliberative choice experiment

Environmental information is a prerequisite for public participation in air quality improvement, and the choice of such participation behavior is influenced by the intervention of environmental information. Nonetheless, there has been insufficient analysis of how information interventions affect public preferences and willingness to pay for air quality improvement. The combination of deliberative and choice experiment is used to explore the importance of information interventions for public participation in air quality improvement, and the changes in public preferences and willingness to pay for air quality improvement before and after information interventions are compared to analyze the impact of information interventions on evaluation results of air quality value. The results suggest that information interventions do alter the preferences and willingness of the public to pay for air quality improvement, significantly increasing the choice certainty of respondents and decreasing the protest response. In addition, women and high-income groups showed a stronger willingness to improve air quality after the information interventions, with 35.15 CNY, 44.07 CNY and 46.75 CNY increases in willingness to pay for improved urban green coverage rate, fewer haze days and reduced morbidity. The combination of deliberative information interventions and choice experiment will help improve the effectiveness of air quality value evaluation, stimulate public environmental awareness and willingness to participate, and the results will aid government environmental management.

Impact of green space connectivity on urban tick presence, density and Borrelia infected ticks in different habitats and seasons in three cities in southern England

Understanding the effects of local habitat and wider landscape connectivity factors on tick presence, nymph density and Borrelia species (spp.) prevalence in the tick population is important for identifying the public health risk from Lyme borreliosis. This multi-city study collected data in three southern England cities (Bath, Bristol, and Southampton) during spring, summer, and autumn in 2017. Focusing specifically on urban green space used for recreation which were clearly in urbanised areas, 72 locations were sampled. Additionally, geospatial datasets on urban green space coverage within 250 m and 1 km of sampling points, as well as distance to woodland were incorporated into statistical models. Distance to woodland was negatively associated with tick presence and nymph density, particularly during spring and summer. Furthermore, we observed an interaction effect between habitat and season for tick presence and nymph density, with woodland habitat having greater tick presence and nymph density during spring. Borrelia spp. infected Ixodes ricinus were found in woodland, woodland edge and under canopy habitats in Bath and Southampton. Overall Borrelia spp. prevalence in nymphs was 2.8%, similar to wider UK studies assessing prevalence in Ixodes ricinus in rural areas. Bird-related Borrelia genospecies dominated across sites, suggesting bird reservoir hosts may be important in urban green space settings for feeding and infecting ticks. Whilst overall density of infected nymphs across the three cities was low (0.03 per 100 m2), risk should be further investigated by incorporating data on tick bites acquired in urban settings, and subsequent Lyme borreliosis transmission.

Abstract 13139: Explainable Machine Learning Model for Prediction of Cardiovascular Disease With Routinely Collected Clinical Data Combined With Publicly Available Data on Environmental Risk Factors

Introduction: Little is known about the performance benefit of combining these data with publicly available environmental risk factors for CVD prediction models. Hypothesis: We aimed to test whether routinely collected clinical data (age, sex, systolic blood pressure, total cholesterol, cigarette smoking) in combination with data on environmental risk factors could improve the performance of CVD prediction using an explainable machine learning (ML) technique. Methods: We identified individuals without previous history of CVD from the National Health Insurance Service, 2002-2015 in the Republic of Korea, linked to publicly available data on annual average of fine particulate matter (PM 10 ) exposure and urban green space coverage according to the administrative code for residence in each individual. Random forest (RF) model and SHapley Additive exPlanations (SHAP) value were used for prediction for newly diagnosed CVD and explanation for contribution of each component to the model output, respectively. Performance of the prediction models were evaluated using area under the curve (AUC). Results: Among 151,936 individuals included, there were 2,837 subsequent CVD events. The AUCs for the RF model with the clinical data only and the model with environmental risk factors (high annual average PM 10 and low UGS coverage) in addition to the clinical data were 0.731 and 0.733, respectively. The SHAP values showed that adding these environmental risk factors did not have significant impact on the model output (SHAP summary plot). However, the clinical data comprised of traditional CVD risk factors had notable contribution to the performance of prediction model. Conclusions: This study showed that adding publicly available data on environmental risk factors to the routinely collected clinical data had only marginal improvement in the prediction of CVD outcomes.

Assessment of spatiotemporal distribution pattern of land surface temperature with incessant urban sprawl over Khulna and Rajshahi City Corporations

Urban sprawl and urban forest fragmentation are the dominating driving factors of land use land cover changes in city areas and changes in land surface temperature subsequently. Khulna and Rajshahi City Corporations, the major divisional city corporations of Bangladesh, were purposively selected in this study because of their difference in geography and demographic pattern. Khulna is the southwestern city of the country and is near to the Sundarbans, whereas Rajshahi is situated at the northwestern side. The population trend of Khulna has been decreasing day by day after 2001, but an increasing trend is observed in Rajshahi. The present study investigates the urban sprawl, change in urban greenspace, and spatiotemporal pattern of land surface temperature over these two cities from 1989 to 2021 at an interval of 16 years. Based on season, having the least cloud cover and shade-free images of Landsat 4–5 Thematic Mapper (TM), and 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) were collected from USGS Earth Explorer. Images of November and April, which had closer dates along with the required criteria, were downloaded for analysis. Land use land cover change was detected by using images from November, and land surface temperatures were retrieved from images of November and April to compare the temperature variation in winter and summer. The result of the study revealed that in 1989, urban green coverage was 21.07% in Khulna City Corporation (KCC) and 21.29% in Rajshahi City Corporation (RCC). The percentage was more than the threshold level of green coverage (20% of total land) required by plain lands. But these proportions reduced severely by 2021, to 11.86% and 8.56%, respectively. Urban sprawl was found as the major driver for destroying greenspace, which increased land surface temperature in both cities, but in Rajshahi City Corporation, it is comparatively more rapid than in Khulna City Corporation. The urban land cover increased by 32.40% in Khulna and by 71.92% in Rajshahi City Corporations during 1989–2021. The Kappa coefficients were found satisfactory for the years 1989, 2005, and 2021 in both cities – 93.03%, 86.3%, 93.15% for KCC, and 92.48%, 78.87%, 92.83% for RCC, respectively. In the time between 1989 and 2021, the overall land surface temperature increased by 2.72 °C in Khulna and 4.76 °C in Rajshahi City Corporations. In KCC and RCC, the total temperature decreased in winter season by 2.14 °C and 0.90 °C, correspondingly. The year 2005 was the warmest year in both cities among all the studied years. The summer season is getting warmer, and the winter is getting colder with the passing of decades. This significant change in temperature must be a sign of global warming. The results of the study will be scientific evidence to the policymakers, planners, city developers, city dwellers, as well as researchers.

Regional heterogeneity and driving factors of road runoff pollution from urban areas in China.

Due to the multiple influences of natural and anthropogenic factors, stormwater runoff from urban roads generally presents heterogeneous pollution among cities. The identification of regional heterogeneity and related driving factors of road runoff pollution is of significance for the optimal management of road runoff pollution according to the local circumstances. In this study, the regional heterogeneity of urban road runoff pollution from fourteen representative cities in China is analyzed for four typical pollutants including total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). The results show wide variations in TSS, COD, TN and TP pollution among cities, with the average event mean concentrations ranging from 77.0 to 1347.9, 31.4 to 488.1, 0.81 to 8.46, 0.139 to 1.930mg/L, respectively. One-way ANOVA analyses demonstrate significant differences in road runoff pollution among cities. The TSS pollution is significantly heavier for northern and northwestern inland cities than that for eastern and southern cities. Pearson correlation analysis and Stepwise linear regression analysis are performed to identify and rank the influence of climate, population, economy, industry structure, traffic and environmental quality. Direct relationships of road runoff pollution are detected with PM2.5, PM10, secondary industry, tertiary industry, annual rainfall, and urban green coverage, among which PM10 and urban green coverage are the most important and common factors exerting positive and negative influences on road runoff pollution, respectively. Based on the findings of this work, improvement of atmospheric particulate pollution and increase in urban greenness are recommended measures to manage the road runoff pollution. Furthermore, the traffic-related emissions accompanying the upgrading of industry structure should be effectively controlled to attenuate the TSS and COD pollution in road runoff.

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

Expanding urban green space with superblocks

The Barcelona superblock is an urban neighbourhood transformation strategy, with which car-based traffic is redirected away from streets that are inside a 3 × 3 urban block configuration. This urban design concept promises opportunities for alternative street use such as the transformation of street space into urban green space. The potential to foster urban green space due to superblock design is however unclear, particularly outside of the original Barcelonan context. Concerning urban densification, fostering urban green space is particularly relevant to achieving dense and green cities. In this study, potential superblocks are identified in all major Swiss cities and the current urban green space is quantified. This analysis reveals distinct differences of superblock opportunities and urban green space between different cities and allows pinpointing opportunities for expanding urban green space. Considering the nine largest Swiss cities, between 3% and 18% of the current street network was simulated to be potentially suitable for superblock implementation to transform the streetscape and increase urban green space. The variation of current urban green space coverage across all potential individual superblock sites that have high urban density ranges considerably from a few percentages to above 30%. The presented results allow the exploration of urban green space opportunities at street and block-level arising from superblocks in Switzerland. For example, when considering a citywide superblock implementation scenario, the potential street area is relatively high for Basel whilst at the same time showing relatively low current availability of urban green space. For creating detailed design or transformation strategies based on the explored superblock sites, more integrated analysis to evaluate potential benefits as well as disservices of such urban transformation is required.

NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index (UGSI) and Per Capita Green Space (PCGS) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.