Pub Date : 2024-09-10DOI: 10.1016/j.ecolind.2024.112577
Ye Su, Longlong Zhao, Xiaoli Li, Hongzhong Li, Yuankai Ge, Jinsong Chen
Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.
森林火险预测是维护森林生态安全的关键环节。机器学习因其强大的非线性建模能力，已被广泛应用于森林火险预测研究。然而，现有研究在构建特征空间时往往只关注多个环境因子特征所提供的直接信息，而忽略了特征交叉相关所传递的深层信息。此外，火灾风险预测主要依赖单一模型预测，模型的泛化和稳定性略显不足。模型融合算法（MFA）可以结合多种模型的优势来弥补这一局限。本研究提出了一种结合特征交叉（FC）和模型融合的机器学习框架 FC-StackGNB。该框架采用 FC 方法分析影响火灾发生的各种环境因素的时间趋势，构建了多个季节交叉特征（SCF），能够有效捕捉环境因素与时间之间的非线性关系。此外，该框架还开发了高斯奈维贝叶斯（GNB）优化堆叠 MFA，以充分发挥不同 ML 算法的优势。结果表明，SCF 的引入有效提高了六个机器学习模型的预测性能，五个评价指标（准确率、精确度、召回率、F1-分数和 ROC_AUC）的平均值提高了 1.58% 至 6.30%。基于 StackGNB 算法构建的融合模型能有效处理特征的多重共线性问题，其预测性能明显优于单一模型，特别是在提高召回率指标方面（与排名前两位的单一模型相比，召回率分别提高了约 3% 和 5%），这表明该模型具有预测正样本（即高风险火灾区域）的能力。所提出的建模框架有效地提高了模型的稳健性和预测性能，为后续研究提供了新的建模思路。这项研究对于提高森林火灾风险预警水平具有重要意义。
{"title":"FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm","authors":"Ye Su, Longlong Zhao, Xiaoli Li, Hongzhong Li, Yuankai Ge, Jinsong Chen","doi":"10.1016/j.ecolind.2024.112577","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112577","url":null,"abstract":"Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1016/j.ecolind.2024.112562
Yuan Zhou, Jing Yao, Pengyao Li, Bei Li, Yushu Luo, Shunbin Ning
With rapid urbanization, the conflicts between economic development and environmental protection have become increasingly complex and exhibit multiscale characteristics. Constructing a green space ecological network (EN) is an effective approach to alleviate urban ecological issues and maintain regional ecological security. However, traditional EN research often focuses on a single scale, neglecting multiscale nesting and collaborative optimization of landscape elements, particularly the importance of spatial granularity. To address this deficiency, this study proposed a multiobjective, multiscale nested green space EN framework for Chengdu, China. Using landscape pattern index fitting functions, morphological spatial pattern analysis, the connectivity index, circuit theory, hydrological analysis model, scale nesting, and hierarchical transmission theories, a multiscale coupled synergistic and hierarchical optimization regional green space ecological security pattern (ESP) was developed. Analysis of the derived results revealed the following. ① With increasing granularity, the landscape pattern indices showed overall increasing, overall decreasing, and overall fluctuating trends. The optimal granularity threshold for large, medium, and small scales was 9, 6, and 3 m, respectively. ② Overall, 92, 66, and 88 ecological sources; 403, 278, and 321 ecological corridors; 72, 77, and 47 pinch points; 96, 94, and 88 barriers; and 182, 120, and 87 ecological nodes were identified in the city, central city, and old city areas, respectively. ③ The ENs demonstrated reasonable hierarchical nesting characteristics, essential for interscale material and energy circulation. There were 9 overlapping ecological sources with total area of 18.34 km and 47 overlapping corridors with total length of 53.37 km. These overlapping areas were key regions for ensuring stability in the overall regional ESP and continuity in biological processes, and should be given priority protection. In addition, there were also 19 overlapping pinch points, 12 overlapping barriers, and 4 ecological nodes that accounted for 26.39 %, 24.68 %, and 40.43 % of the total pinch points; 12.50 %, 12.77 %, and 13.64 % of the total barriers; and 2.20 %, 3.33 %, and 4.60 % of the total ecological nodes in the city area, central city area, and old city area, respectively. The high overlap of ecological strategic points across scales indicated cross-scale continuity of biological processes. ④ Enhancing the habitat quality of land types such as forest land, green spaces, grassland, and water bodies is crucial for restoration of ecological corridors and strategic points. Constructing a multiscale hierarchical linkage framework and formulating cross-scale pattern optimization and coordination schemes can effectively address ecological issues. The multiscale nested and synergistic green space ESP, constructed from the “source–corridor–strategic-point–network” framework, enhances the connectivity of regional
{"title":"Multilevel green space ecological network collaborative optimization from the perspective of scale effect","authors":"Yuan Zhou, Jing Yao, Pengyao Li, Bei Li, Yushu Luo, Shunbin Ning","doi":"10.1016/j.ecolind.2024.112562","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112562","url":null,"abstract":"With rapid urbanization, the conflicts between economic development and environmental protection have become increasingly complex and exhibit multiscale characteristics. Constructing a green space ecological network (EN) is an effective approach to alleviate urban ecological issues and maintain regional ecological security. However, traditional EN research often focuses on a single scale, neglecting multiscale nesting and collaborative optimization of landscape elements, particularly the importance of spatial granularity. To address this deficiency, this study proposed a multiobjective, multiscale nested green space EN framework for Chengdu, China. Using landscape pattern index fitting functions, morphological spatial pattern analysis, the connectivity index, circuit theory, hydrological analysis model, scale nesting, and hierarchical transmission theories, a multiscale coupled synergistic and hierarchical optimization regional green space ecological security pattern (ESP) was developed. Analysis of the derived results revealed the following. ① With increasing granularity, the landscape pattern indices showed overall increasing, overall decreasing, and overall fluctuating trends. The optimal granularity threshold for large, medium, and small scales was 9, 6, and 3 m, respectively. ② Overall, 92, 66, and 88 ecological sources; 403, 278, and 321 ecological corridors; 72, 77, and 47 pinch points; 96, 94, and 88 barriers; and 182, 120, and 87 ecological nodes were identified in the city, central city, and old city areas, respectively. ③ The ENs demonstrated reasonable hierarchical nesting characteristics, essential for interscale material and energy circulation. There were 9 overlapping ecological sources with total area of 18.34 km and 47 overlapping corridors with total length of 53.37 km. These overlapping areas were key regions for ensuring stability in the overall regional ESP and continuity in biological processes, and should be given priority protection. In addition, there were also 19 overlapping pinch points, 12 overlapping barriers, and 4 ecological nodes that accounted for 26.39 %, 24.68 %, and 40.43 % of the total pinch points; 12.50 %, 12.77 %, and 13.64 % of the total barriers; and 2.20 %, 3.33 %, and 4.60 % of the total ecological nodes in the city area, central city area, and old city area, respectively. The high overlap of ecological strategic points across scales indicated cross-scale continuity of biological processes. ④ Enhancing the habitat quality of land types such as forest land, green spaces, grassland, and water bodies is crucial for restoration of ecological corridors and strategic points. Constructing a multiscale hierarchical linkage framework and formulating cross-scale pattern optimization and coordination schemes can effectively address ecological issues. The multiscale nested and synergistic green space ESP, constructed from the “source–corridor–strategic-point–network” framework, enhances the connectivity of regional","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1016/j.ecolind.2024.112571
Han Gao, Yunhao Chen, Kangning Li, Shengjun Gao
Sufficient and equitable exposure to blue-green spaces enhances human well-being. Existing studies on people’s exposure to blue-green spaces have not adequately considered the different contributions of various components in the blue-green spaces. We proposed an improved model to estimate people’s exposure to blue-green spaces and inequality by distinguishing the contributions of blue-green space components via ecosystem services value. The results showed that people’s exposure to blue-green spaces in Chinese cities was low (0.067). Spatially, the exposure was higher in the south and lower in the north, while higher in the west and lower in the east. Compared with 2001, the exposure declined by 23% in 2020. Additionally, the spatial distribution and the component configuration of the blue-green spaces were not reasonable. Meanwhile, the exposure among the population exhibited extremely high inequality, with an average Gini coefficient of 0.845. During 2001–2020, the overall inequality continued to worsen (+3%). The intensification trend demonstrated spatial differences, with a more pronounced pattern in eastern cities. Further research confirmed that inequality existed among different genders and age groups. Finally, spatial correlation analysis showed that cities with high exposure and equality tended to be in southern China. Low exposure was more pronounced in northern cities, and inequality was a more pressing issue in southern cities. We hope this study can provide a reference for blue-green space planning and boost the sustainability of cities.
{"title":"People’s exposure to blue-green spaces decreased but inequality increased during 2001–2020 across major Chinese cities","authors":"Han Gao, Yunhao Chen, Kangning Li, Shengjun Gao","doi":"10.1016/j.ecolind.2024.112571","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112571","url":null,"abstract":"Sufficient and equitable exposure to blue-green spaces enhances human well-being. Existing studies on people’s exposure to blue-green spaces have not adequately considered the different contributions of various components in the blue-green spaces. We proposed an improved model to estimate people’s exposure to blue-green spaces and inequality by distinguishing the contributions of blue-green space components via ecosystem services value. The results showed that people’s exposure to blue-green spaces in Chinese cities was low (0.067). Spatially, the exposure was higher in the south and lower in the north, while higher in the west and lower in the east. Compared with 2001, the exposure declined by 23% in 2020. Additionally, the spatial distribution and the component configuration of the blue-green spaces were not reasonable. Meanwhile, the exposure among the population exhibited extremely high inequality, with an average Gini coefficient of 0.845. During 2001–2020, the overall inequality continued to worsen (+3%). The intensification trend demonstrated spatial differences, with a more pronounced pattern in eastern cities. Further research confirmed that inequality existed among different genders and age groups. Finally, spatial correlation analysis showed that cities with high exposure and equality tended to be in southern China. Low exposure was more pronounced in northern cities, and inequality was a more pressing issue in southern cities. We hope this study can provide a reference for blue-green space planning and boost the sustainability of cities.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1016/j.ecolind.2024.112582
Ruiwu Zhang, Jun Ying, Yiqi Zhang, Zhi Li, Xinao Zhou
Urban green infrastructure (GI), a pivotal element of urban ecosystems, enhances carbon sequestration and sustainability. However, current research has not adequately addressed changes in the spatial pattern of GI and their implications for future carbon sequestration benefits. This study focuses on Hangzhou’s main urban areas, analyzing the GI’s spatial pattern from 2002 to 2020. Utilizing climate data provided for two future scenarios (SSP126-SSP370lu and SSP370-SSP126lu) by CMIP6, predictions up to 2060 were made using a backpropagation neural network. Gross primary productivity (GPP) was employed to assess carbon sequestration benefits. The impact of the GI spatial pattern on GPP from 2002 to 2060 was examined through a spatiotemporal geographically weighted regression model. Sensitivity analysis and Geodetector were used to evaluate the uncertainty and interactive effects of changes in the GI spatial pattern on GPP. The findings suggest that under the SSP126- SSP370lu scenario, a decrease in GI area and increased fragmentation by 2060 could reduce average GPP to 0.592 gC/m. Under the SSP370- SSP126lu scenario, an increase in GI area and enhanced compactness will increase the average GPP to 0.641 gC/m. The GI spatial pattern significantly boosts GPP yet exhibits complex fluctuations in future scenarios, particularly regarding GI area, number, and density. A comprehensive consideration of various factors and their optimal control can effectively enhance the explanatory power of the GI spatial pattern on GPP. Based on these results, this research proposes optimized strategies for the GI spatial pattern under both future scenarios, providing scientific evidence and data support for urban planners to enhance urban carbon sequestration benefits and sustainability through optimized GI layouts.
{"title":"Future carbon sequestration Benefits: The role of urban green Infrastructure’s spatial patterns","authors":"Ruiwu Zhang, Jun Ying, Yiqi Zhang, Zhi Li, Xinao Zhou","doi":"10.1016/j.ecolind.2024.112582","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112582","url":null,"abstract":"Urban green infrastructure (GI), a pivotal element of urban ecosystems, enhances carbon sequestration and sustainability. However, current research has not adequately addressed changes in the spatial pattern of GI and their implications for future carbon sequestration benefits. This study focuses on Hangzhou’s main urban areas, analyzing the GI’s spatial pattern from 2002 to 2020. Utilizing climate data provided for two future scenarios (SSP126-SSP370lu and SSP370-SSP126lu) by CMIP6, predictions up to 2060 were made using a backpropagation neural network. Gross primary productivity (GPP) was employed to assess carbon sequestration benefits. The impact of the GI spatial pattern on GPP from 2002 to 2060 was examined through a spatiotemporal geographically weighted regression model. Sensitivity analysis and Geodetector were used to evaluate the uncertainty and interactive effects of changes in the GI spatial pattern on GPP. The findings suggest that under the SSP126- SSP370lu scenario, a decrease in GI area and increased fragmentation by 2060 could reduce average GPP to 0.592 gC/m. Under the SSP370- SSP126lu scenario, an increase in GI area and enhanced compactness will increase the average GPP to 0.641 gC/m. The GI spatial pattern significantly boosts GPP yet exhibits complex fluctuations in future scenarios, particularly regarding GI area, number, and density. A comprehensive consideration of various factors and their optimal control can effectively enhance the explanatory power of the GI spatial pattern on GPP. Based on these results, this research proposes optimized strategies for the GI spatial pattern under both future scenarios, providing scientific evidence and data support for urban planners to enhance urban carbon sequestration benefits and sustainability through optimized GI layouts.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-07DOI: 10.1016/j.ecolind.2024.112578
Song Zhang, Linlin Zhang, Qingyan Meng, Chongchang Wang, Jianjun Ma, Hong Li, Kun Ma
Agricultural non-point source pollution threatens the quality of the ecological environment, human health, and safety. This study took the Sixth Drainage Ditch of the Yellow River Irrigation Area in Ningxia as the research area, set up a runoff water quality monitoring network, and comprehensively constructed an agricultural non-point source pollution monitoring model by combining the “source-sink” landscape theory, high-resolution remote sensing technology, and soil and water assessment tool (SWAT). The results showed that the simulation results of the flow and total nitrogen met the accuracy requirements. The values of total nitrogen in the calibration and validation periods were both > 0.8, and was > 0.9. The regional applicability of the model was good. Based on the simulation results, the following conclusions were drawn. (1) The temporal distribution of the pollution load was concentrated in May–October, with peaks in June and August, which is consistent with the irrigation period. (2) Spatially, the pollution load was mainly distributed in sub-basins 1 and 5. The area is dominated by cultivated land and has poor conditions that are prone to nitrogen and phosphorus loss. (3) By quantitatively identifying pollution sources, the results showed that agricultural irrigation accounted for approximately 92.88 % of total pollutants. Compared with traditional methods, the monitoring method proposed in this study systematically evaluates the potential for non-point source pollution in the region and builds a relatively complete real-time monitoring network, improving data quality and model reliability. In addition, the relationship between river network density and catchment area threshold was used to optimize the catchment area threshold in the SWAT model, and non-point source pollution parameters suitable for the basin were obtained, providing a data basis and theoretical support for the large-scale application of the model.
{"title":"Evaluating agricultural non-point source pollution with high-resolution remote sensing technology and SWAT model: A case study in Ningxia Yellow River Irrigation District, China","authors":"Song Zhang, Linlin Zhang, Qingyan Meng, Chongchang Wang, Jianjun Ma, Hong Li, Kun Ma","doi":"10.1016/j.ecolind.2024.112578","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112578","url":null,"abstract":"Agricultural non-point source pollution threatens the quality of the ecological environment, human health, and safety. This study took the Sixth Drainage Ditch of the Yellow River Irrigation Area in Ningxia as the research area, set up a runoff water quality monitoring network, and comprehensively constructed an agricultural non-point source pollution monitoring model by combining the “source-sink” landscape theory, high-resolution remote sensing technology, and soil and water assessment tool (SWAT). The results showed that the simulation results of the flow and total nitrogen met the accuracy requirements. The values of total nitrogen in the calibration and validation periods were both > 0.8, and was > 0.9. The regional applicability of the model was good. Based on the simulation results, the following conclusions were drawn. (1) The temporal distribution of the pollution load was concentrated in May–October, with peaks in June and August, which is consistent with the irrigation period. (2) Spatially, the pollution load was mainly distributed in sub-basins 1 and 5. The area is dominated by cultivated land and has poor conditions that are prone to nitrogen and phosphorus loss. (3) By quantitatively identifying pollution sources, the results showed that agricultural irrigation accounted for approximately 92.88 % of total pollutants. Compared with traditional methods, the monitoring method proposed in this study systematically evaluates the potential for non-point source pollution in the region and builds a relatively complete real-time monitoring network, improving data quality and model reliability. In addition, the relationship between river network density and catchment area threshold was used to optimize the catchment area threshold in the SWAT model, and non-point source pollution parameters suitable for the basin were obtained, providing a data basis and theoretical support for the large-scale application of the model.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study assessed the service efficacy of Nanjing’s urban open space (UOS) system, emphasizing the complex structural characteristics of UOS services, specifically, the impact of interactions between different subsystems on the overall service efficacy of the UOS system. The objective was to understand whether and how a UOS system can effectively and efficiently meet urban needs under constrained conditions. The dimensions of UOS services and the interrelationships between services offered by different subsystems were examined, and a structural model for assessing the service efficacy of the UOS system was developed. A multidimensional analytical approach was applied to quantify and characterize the relationships between subsystems and identify and determine the weighting of subsystem analysis indicators. The findings indicate that subsystems and their respective indicators differ in importance across dimensions, influence each other, and collectively form the overall service efficacy of a UOS system. Furthermore, while service efficacy across different administrative districts in Nanjing’s main urban areas generally remains moderate, significant differences in evaluation values exist among districts and subsystems, offering direction for future urban planning and resource allocation. This study offers detailed insights into the efficiency and optimization of UOS services, helping urban planners improve UOSs globally.
{"title":"Integrated evaluation of service efficacy of the urban open space system in Nanjing, China: A system structure perspective","authors":"Penghao Song, Bing Qiu, Minghui Li, Zhe Wang, Jinguang Zhang","doi":"10.1016/j.ecolind.2024.112561","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112561","url":null,"abstract":"This study assessed the service efficacy of Nanjing’s urban open space (UOS) system, emphasizing the complex structural characteristics of UOS services, specifically, the impact of interactions between different subsystems on the overall service efficacy of the UOS system. The objective was to understand whether and how a UOS system can effectively and efficiently meet urban needs under constrained conditions. The dimensions of UOS services and the interrelationships between services offered by different subsystems were examined, and a structural model for assessing the service efficacy of the UOS system was developed. A multidimensional analytical approach was applied to quantify and characterize the relationships between subsystems and identify and determine the weighting of subsystem analysis indicators. The findings indicate that subsystems and their respective indicators differ in importance across dimensions, influence each other, and collectively form the overall service efficacy of a UOS system. Furthermore, while service efficacy across different administrative districts in Nanjing’s main urban areas generally remains moderate, significant differences in evaluation values exist among districts and subsystems, offering direction for future urban planning and resource allocation. This study offers detailed insights into the efficiency and optimization of UOS services, helping urban planners improve UOSs globally.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The green belts are indispensable constituents of urban spatial structure, exerting substantial impact on the urban eco-environmental quality. “Nature-based Solutions (NbS)” measures provide a new means to addressing urban sustainability challenges. Taking Beijing green belts as the research object, this study analyzed the change of eco-environmental quality and its driving factors, with a particular focus on the driving effect of NbS measures. Our analyses revealed several key findings. As data from 2005 to 2020 indicate, the process of land use change exhibits a spreading trend from inside to outside, reflecting the characteristics of industrialization-driven urbanization. The eco-environmental quality of the green belts demonstrated a U-shaped trajectory, initially declining before showing signs of recovery. Notably, the first green belt experienced a relatively lower eco-environmental quality, with a persistent decline throughout the studied timeframe. From the perspective of ecological contribution rate, the negative effect of land use change outweighted the positive effect, resulting in an overall decline in eco-environmental quality. This study confirmed the driving effect of NbS measures on eco-environmental quality improvement. Specifically, Green Infrastructure showed a significant driving effect of the two green belts, while Ecological Infrastructure only demonstrated a significant driving effect in the second green belt. In light of these findings, future implementations of NbS measures can be more precisely targeted to optimize the resilience and sustainable development capacity of cities.
{"title":"Change and driving factors of eco-environmental quality in Beijing green belts: From the perspective of Nature-based Solutions","authors":"Hao Zhang, Qingping Zhou, Jianzan Yang, Huawei Xiang","doi":"10.1016/j.ecolind.2024.112581","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112581","url":null,"abstract":"The green belts are indispensable constituents of urban spatial structure, exerting substantial impact on the urban eco-environmental quality. “Nature-based Solutions (NbS)” measures provide a new means to addressing urban sustainability challenges. Taking Beijing green belts as the research object, this study analyzed the change of eco-environmental quality and its driving factors, with a particular focus on the driving effect of NbS measures. Our analyses revealed several key findings. As data from 2005 to 2020 indicate, the process of land use change exhibits a spreading trend from inside to outside, reflecting the characteristics of industrialization-driven urbanization. The eco-environmental quality of the green belts demonstrated a U-shaped trajectory, initially declining before showing signs of recovery. Notably, the first green belt experienced a relatively lower eco-environmental quality, with a persistent decline throughout the studied timeframe. From the perspective of ecological contribution rate, the negative effect of land use change outweighted the positive effect, resulting in an overall decline in eco-environmental quality. This study confirmed the driving effect of NbS measures on eco-environmental quality improvement. Specifically, Green Infrastructure showed a significant driving effect of the two green belts, while Ecological Infrastructure only demonstrated a significant driving effect in the second green belt. In light of these findings, future implementations of NbS measures can be more precisely targeted to optimize the resilience and sustainable development capacity of cities.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-07DOI: 10.1016/j.ecolind.2024.112568
Sakshi Dange, Kumaraguru Arumugam, Sai Saraswathi Vijayaraghavalu
The pollution of groundwater by heavy metals is becoming an increasingly urgent problem due to the rapid growth of industrialization and urbanization. A detailed geochemical investigation of physicochemical properties and heavy metal contamination was conducted on 140 groundwater samples in the Vellore district around the Palar River, Tamil Nadu, India, to assess the metal index risk in groundwater. The water quality is significantly contaminated with Cr, Co, Cu, Cd, and Fe, and moderately contaminated with Mn, Ni, and Zn, according to the groundwater assessment using the heavy metal index, contamination factor, and Igeo index. This contamination is associated with various health problems, including mental illness, stomach and skin cancer, liver failure, and kidney damage. Based on contour maps created with GIS, most of the region is heavily contaminated with heavy metals, rendering the groundwater unfit for human consumption. The Health Risk Assessment Index (HRAI) rose to 9028.6, indicating an extremely high health risk. In addition to identifying the extent of contamination, the study also proposes sustainable solutions to mitigate these risks. These include the adoption of advanced water treatment technologies, the promotion of green chemistry practices in local industries, the establishment of stricter regulatory frameworks, and the implementation of community-based water management strategies. These measures are essential to ensuring safe drinking water and achieving Sustainable Development Goal 6 (SDG 6). The findings underscore the urgent need for comprehensive monitoring and proactive remediation strategies in industrially contaminated regions.
{"title":"Geochemical Insights into Heavy Metal Contamination and Health Hazards in Palar River Basin: A Pathway to Sustainable Solutions","authors":"Sakshi Dange, Kumaraguru Arumugam, Sai Saraswathi Vijayaraghavalu","doi":"10.1016/j.ecolind.2024.112568","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112568","url":null,"abstract":"The pollution of groundwater by heavy metals is becoming an increasingly urgent problem due to the rapid growth of industrialization and urbanization. A detailed geochemical investigation of physicochemical properties and heavy metal contamination was conducted on 140 groundwater samples in the Vellore district around the Palar River, Tamil Nadu, India, to assess the metal index risk in groundwater. The water quality is significantly contaminated with Cr, Co, Cu, Cd, and Fe, and moderately contaminated with Mn, Ni, and Zn, according to the groundwater assessment using the heavy metal index, contamination factor, and Igeo index. This contamination is associated with various health problems, including mental illness, stomach and skin cancer, liver failure, and kidney damage. Based on contour maps created with GIS, most of the region is heavily contaminated with heavy metals, rendering the groundwater unfit for human consumption. The Health Risk Assessment Index (HRAI) rose to 9028.6, indicating an extremely high health risk. In addition to identifying the extent of contamination, the study also proposes sustainable solutions to mitigate these risks. These include the adoption of advanced water treatment technologies, the promotion of green chemistry practices in local industries, the establishment of stricter regulatory frameworks, and the implementation of community-based water management strategies. These measures are essential to ensuring safe drinking water and achieving Sustainable Development Goal 6 (SDG 6). The findings underscore the urgent need for comprehensive monitoring and proactive remediation strategies in industrially contaminated regions.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Maritime Silk Road presents not only great opportunities for development but also enormous challenges to marine ecological security. While, there lacks a systematic assessment of the state of marine ecological security at the regional multi-country scale, which leads to the limited understanding of the marine ecological security from integrated dimensions. This study considered 30 countries along the Maritime Silk Road, constructed marine ecological security assessment indicator system based on Environment-Economy-Society (EES) model, and combined with fuzzy object element model, assessed the spatial and temporal changes of marine ecological security of each country from 2013 to 2019. The results showed that Tanzania has the best state of marine environmental security and Bahrain has the worst state of marine environmental security in terms of marine environmental security. In terms of marine economic security, from 2013 to 2019, there is an upward trend in marine economic security in South Asia. In terms of marine social security, Philippines has the best marine social security, with a security index of 0.787. From the comprehensive assessment results, the marine ecological security situation of the countries along the Maritime Silk Road generally shows an upward trend during the 2013–2019 period. Factors such as the construction of marine protected areas, marine industry, port construction, coastal population numbers, and the number of foreign tourists have significant impacts on marine ecological security. Countries along the Maritime Silk Road should take measures in the areas of marine infrastructure construction, marine ecological restoration and protection, and transboundary cooperation and management in order to promote the sustainable development of marine ecological security in the future.
{"title":"Spatial and temporal change assessment of marine ecological security in regions along the Maritime Silk Road","authors":"Jingxuan Liu, Juanle Wang, Chen Xu, Jiacheng Jiang","doi":"10.1016/j.ecolind.2024.112576","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112576","url":null,"abstract":"The Maritime Silk Road presents not only great opportunities for development but also enormous challenges to marine ecological security. While, there lacks a systematic assessment of the state of marine ecological security at the regional multi-country scale, which leads to the limited understanding of the marine ecological security from integrated dimensions. This study considered 30 countries along the Maritime Silk Road, constructed marine ecological security assessment indicator system based on Environment-Economy-Society (EES) model, and combined with fuzzy object element model, assessed the spatial and temporal changes of marine ecological security of each country from 2013 to 2019. The results showed that Tanzania has the best state of marine environmental security and Bahrain has the worst state of marine environmental security in terms of marine environmental security. In terms of marine economic security, from 2013 to 2019, there is an upward trend in marine economic security in South Asia. In terms of marine social security, Philippines has the best marine social security, with a security index of 0.787. From the comprehensive assessment results, the marine ecological security situation of the countries along the Maritime Silk Road generally shows an upward trend during the 2013–2019 period. Factors such as the construction of marine protected areas, marine industry, port construction, coastal population numbers, and the number of foreign tourists have significant impacts on marine ecological security. Countries along the Maritime Silk Road should take measures in the areas of marine infrastructure construction, marine ecological restoration and protection, and transboundary cooperation and management in order to promote the sustainable development of marine ecological security in the future.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1016/j.ecolind.2024.112574
Yangyang Yan, Hao Hou, Yuji Murayama, Ruci Wang, Tangao Hu
Urban wetlands are important blue–green spaces in cities and, hence, play a pivotal role in regulating local urban ecological environments and thermal conditions. However, despite their significance, studies on the cooling effects of urban wetlands, as well as the influencing factors, remain limited. This study used multi-ring buffer analysis and random forest (RF) model to calculate the significant and potential cooling scales and intensities in urban wetlands. More specifically, we introduced four indicators, integrated patch diversity and proximity (IPDP), integrated wetland proximity and shape (IWPS), patch aggregation (PA), and logarithmic area (LA), to enhance urban wetland characteristic representation, and conducted correlation analyses to investigate their relationships with the cooling effects. The results revealed significant cooling scale and cooling intensity ranges across the 13 urban wetlands. Similarly, potential cooling scales varied from 10,284 to 44,408 m, with potential cooling intensities ranging from 0.35 to 1.81 ℃. Notably, factors such as IWPS, number of patches (NP), and PA significantly influenced the cooling effects, whereas LA emerged as a key factor affecting potential cooling effects. This study highlights the importance of urban wetlands in reducing urban thermal conditions, and advances the understanding of their cooling effects.
{"title":"How do landscape patterns affect cooling intensity and scale? Evidence from 13 primary urban wetlands in China","authors":"Yangyang Yan, Hao Hou, Yuji Murayama, Ruci Wang, Tangao Hu","doi":"10.1016/j.ecolind.2024.112574","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112574","url":null,"abstract":"Urban wetlands are important blue–green spaces in cities and, hence, play a pivotal role in regulating local urban ecological environments and thermal conditions. However, despite their significance, studies on the cooling effects of urban wetlands, as well as the influencing factors, remain limited. This study used multi-ring buffer analysis and random forest (RF) model to calculate the significant and potential cooling scales and intensities in urban wetlands. More specifically, we introduced four indicators, integrated patch diversity and proximity (IPDP), integrated wetland proximity and shape (IWPS), patch aggregation (PA), and logarithmic area (LA), to enhance urban wetland characteristic representation, and conducted correlation analyses to investigate their relationships with the cooling effects. The results revealed significant cooling scale and cooling intensity ranges across the 13 urban wetlands. Similarly, potential cooling scales varied from 10,284 to 44,408 m, with potential cooling intensities ranging from 0.35 to 1.81 ℃. Notably, factors such as IWPS, number of patches (NP), and PA significantly influenced the cooling effects, whereas LA emerged as a key factor affecting potential cooling effects. This study highlights the importance of urban wetlands in reducing urban thermal conditions, and advances the understanding of their cooling effects.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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