Pub Date : 2026-06-01Epub Date: 2025-08-06DOI: 10.1016/j.jnlssr.2025.100236
Mujeeb Ali Khan , Weiguo Song , Abbas Khan , Mazhar Ali , Rehmat Karim , Jun Zhang
Fire disasters in urban areas, including homes, offices, and industrial facilities, have increased significantly over the past decade, causing extensive damage and loss of life. Integrating intelligent fire detection systems with machine learning (ML) is crucial for providing early warnings and facilitating effective response coordination. In this research, a novel hybrid dynamic best model selection (HDBMS) ML-based algorithm is proposed for internet of things (IoT)-enabled fire detection in buildings, which outperforms traditional static models by providing higher accuracy and adaptability across diverse fire scenarios. The proposed algorithm employs feature selection pre-processing techniques, followed by the synergistic integration of five classifiers: support vector classifier (SVC), logistic regression, random forest, Gaussian Naïve Bayes (Gaussian NB), and decision tree, aiming to enhance prediction accuracy and robustness across various fire scenarios. This system dynamically selects the optimal classifier based on real-time performance metrics such as precision, accuracy, F1-score, and recall. This approach was rigorously validated using our developed dataset and real-time sensor data to monitor smoke, temperature, and humidity under various fire scenarios. Following algorithm validation, a laboratory-constructed multi-sensor fire detection node prototype wirelessly feeds sensor data to the ThingSpeak cloud platform for real-time data analysis and communication with ML algorithms in the back-end. The system's improved precision, accuracy, and root mean squared error (RMSE) confirm its effectiveness. The results demonstrate that the proposed approach achieves superior classification accuracy compared with existing methods in the literature. Furthermore, this study's novelty lies in the dynamic selection of the most effective model in real-time, a characteristic that is currently lacking in fire detection systems, thereby enhancing the system's flexibility and efficiency in various fire scenarios.
{"title":"Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings","authors":"Mujeeb Ali Khan , Weiguo Song , Abbas Khan , Mazhar Ali , Rehmat Karim , Jun Zhang","doi":"10.1016/j.jnlssr.2025.100236","DOIUrl":"10.1016/j.jnlssr.2025.100236","url":null,"abstract":"<div><div>Fire disasters in urban areas, including homes, offices, and industrial facilities, have increased significantly over the past decade, causing extensive damage and loss of life. Integrating intelligent fire detection systems with machine learning (ML) is crucial for providing early warnings and facilitating effective response coordination. In this research, a novel hybrid dynamic best model selection (HDBMS) ML-based algorithm is proposed for internet of things (IoT)-enabled fire detection in buildings, which outperforms traditional static models by providing higher accuracy and adaptability across diverse fire scenarios. The proposed algorithm employs feature selection pre-processing techniques, followed by the synergistic integration of five classifiers: support vector classifier (SVC), logistic regression, random forest, Gaussian Naïve Bayes (Gaussian NB), and decision tree, aiming to enhance prediction accuracy and robustness across various fire scenarios. This system dynamically selects the optimal classifier based on real-time performance metrics such as precision, accuracy, F1-score, and recall. This approach was rigorously validated using our developed dataset and real-time sensor data to monitor smoke, temperature, and humidity under various fire scenarios. Following algorithm validation, a laboratory-constructed multi-sensor fire detection node prototype wirelessly feeds sensor data to the ThingSpeak cloud platform for real-time data analysis and communication with ML algorithms in the back-end. The system's improved precision, accuracy, and root mean squared error (RMSE) confirm its effectiveness. The results demonstrate that the proposed approach achieves superior classification accuracy compared with existing methods in the literature. Furthermore, this study's novelty lies in the dynamic selection of the most effective model in real-time, a characteristic that is currently lacking in fire detection systems, thereby enhancing the system's flexibility and efficiency in various fire scenarios.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 2","pages":"Article 100236"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2025-07-16DOI: 10.1016/j.jnlssr.2025.100230
Xiaoyu Sun , Yifei Ding , Zhichao He , Xinyan Huang
Fire, a globally significant hazard, has a detrimental impact on lives, property, and socio-economic development. Public attention to fires is crucial for building fire safety under the current disaster-driven regulation development. This study employs Chinese social media (Sina Weibo) data to analyze several major domestic and overseas fire incidents and explore the corresponding public awareness. Case studies analyze three groups, (1) building facade fire: the 2010 Shanghai Jing’an fire and the 2017 Grenfell Tower fire of London, (2) ancient building fire: the 2014 Shangri-La fire and the 2019 Notre-Dame de Paris fire, and (3) wildland-urban interface (WUI) fire: the 1987 Daxing’anling wildfire and the 2025 Southern California wildfire. The results show that these major fires attract varying levels of attention due to factors like fire location, losses, associated social issues, and subsequent developments. Comparison between fire events in China and overseas reveals that the Chinese internet pays more attention to overseas fire events. Compared to earthquakes and floods, major fire incidents generally receive less attention due to the scale of their influence and the intensity of media coverage. Social media data and analysis also suggest that measures such as publishing articles and conducting educational activities can increase public attention to past fires and pre-disaster fire safety awareness. This work helps understand the impact of major fire incidents on social media and the public’s evolving reactions and offers practical measures and suggestions for stakeholders to improve fire safety and mitigate fire losses.
{"title":"Awareness analysis via Weibo social media data of major fire disasters","authors":"Xiaoyu Sun , Yifei Ding , Zhichao He , Xinyan Huang","doi":"10.1016/j.jnlssr.2025.100230","DOIUrl":"10.1016/j.jnlssr.2025.100230","url":null,"abstract":"<div><div>Fire, a globally significant hazard, has a detrimental impact on lives, property, and socio-economic development. Public attention to fires is crucial for building fire safety under the current disaster-driven regulation development. This study employs Chinese social media (Sina Weibo) data to analyze several major domestic and overseas fire incidents and explore the corresponding public awareness. Case studies analyze three groups, (1) building facade fire: the 2010 Shanghai Jing’an fire and the 2017 Grenfell Tower fire of London, (2) ancient building fire: the 2014 Shangri-La fire and the 2019 Notre-Dame de Paris fire, and (3) wildland-urban interface (WUI) fire: the 1987 Daxing’anling wildfire and the 2025 Southern California wildfire. The results show that these major fires attract varying levels of attention due to factors like fire location, losses, associated social issues, and subsequent developments. Comparison between fire events in China and overseas reveals that the Chinese internet pays more attention to overseas fire events. Compared to earthquakes and floods, major fire incidents generally receive less attention due to the scale of their influence and the intensity of media coverage. Social media data and analysis also suggest that measures such as publishing articles and conducting educational activities can increase public attention to past fires and pre-disaster fire safety awareness. This work helps understand the impact of major fire incidents on social media and the public’s evolving reactions and offers practical measures and suggestions for stakeholders to improve fire safety and mitigate fire losses.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 2","pages":"Article 100230"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2025-09-06DOI: 10.1016/j.jnlssr.2025.100255
Tong Xu , Xiaole Dai , Kai Wang , Fazheng Chong , Fengju Shang , Chang Liu
A series of experiments was conducted to explore the influence of cable spacing on combustion characteristics in real cable installation, considering various external radiation intensities (30, 50 kW/m2) and cable spacings (0, 2.5, 5 cm). A comparative analysis of the combustion characteristics (such as heat release rate (HRR), combustion gas, and mass loss) was conducted, and the HRR calculation for cables that were not fully filled in the sample tray was revised as well. It could be found that with the increasing radiation intensities, the peak concentrations of CO and CO₂ increased (O₂ concentration decreased), and the interval between the two peaks shortened. The HRR curves of cables with different spacings all presented two peaks under two radiation intensities. The max-peak HRR occurs at Dd = 2.5 cm, and the double max-peak HRRs are 582 kW/m2 and 407 kW/m2 under radiation intensities of 50 kW/m2 and 35 kW/m2, respectively. This is because when Dd = 0 cm, the thermal feedback effect between cables is relatively enhanced, while the air entrainment between the cables is weakened. When Dd = 5 cm, the thermal feedback effect is weakened. When Dd = 2.5 cm, both the air entrainment and the thermal feedback are strengthened, and the peak HRR occurs. The above results could provide data to support fire safety design and the emergency response to cable laying.
{"title":"Study on combustion characteristics of cables with spacing arrangement under different external radiation conditions","authors":"Tong Xu , Xiaole Dai , Kai Wang , Fazheng Chong , Fengju Shang , Chang Liu","doi":"10.1016/j.jnlssr.2025.100255","DOIUrl":"10.1016/j.jnlssr.2025.100255","url":null,"abstract":"<div><div>A series of experiments was conducted to explore the influence of cable spacing on combustion characteristics in real cable installation, considering various external radiation intensities (30, 50 kW/m<sup>2</sup>) and cable spacings (0, 2.5, 5 cm). A comparative analysis of the combustion characteristics (such as heat release rate (HRR), combustion gas, and mass loss) was conducted, and the HRR calculation for cables that were not fully filled in the sample tray was revised as well. It could be found that with the increasing radiation intensities, the peak concentrations of CO and CO₂ increased (O₂ concentration decreased), and the interval between the two peaks shortened. The HRR curves of cables with different spacings all presented two peaks under two radiation intensities. The max-peak HRR occurs at <em>D<sub>d</sub></em> = 2.5 cm, and the double max-peak HRRs are 582 kW/m<sup>2</sup> and 407 kW/m<sup>2</sup> under radiation intensities of 50 kW/m<sup>2</sup> and 35 kW/m<sup>2</sup>, respectively. This is because when <em>D<sub>d</sub></em> = 0 cm, the thermal feedback effect between cables is relatively enhanced, while the air entrainment between the cables is weakened. When <em>D<sub>d</sub></em> = 5 cm, the thermal feedback effect is weakened. When <em>D<sub>d</sub></em> = 2.5 cm, both the air entrainment and the thermal feedback are strengthened, and the peak HRR occurs. The above results could provide data to support fire safety design and the emergency response to cable laying.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 2","pages":"Article 100255"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2025-09-11DOI: 10.1016/j.jnlssr.2025.100265
Qian Yang, Feiyue Wang, Zihuan Wang, Bo Ma, Jiajie Lu, Leiwei Li
After a disaster, due to transportation constraints, transportation priority setting, emergency resource shortage and imprecise assessment of emergency resources, it is easy to cause an inaccurate match between the types of supply and demand for emergency resources. It aggravates the psychological pain of the victims. To address this gap, combining descriptive analysis, correlation analysis, and regression analysis, the mechanisms and internal pathways of imprecise emergency material supply-demand type on psychological pain were comprehensively examined. Based on Maslow's hierarchy of needs theory, four priority levels for emergency material types are proposed. A survey questionnaire was designed using the Numerical Rating Scale (NRS), which included 12 scenarios. Nine typical waterlogging sites in Changsha, Hunan Province, China, were selected as field survey sites, and 162 valid samples were collected through face-to-face interviews. The results show that: (1) Emergency material types have a significant impact on psychological pain, and this impact is related to the priority level of the material types. (2) The impact of the matching degree of emergency material supply-demand types on psychological pain is moderated by the priority level of the demanded material types, exhibiting a reverse compensation effect. (3) The impact of demographic factors on the degree of psychological pain experienced by victims shows complex differences. Most surprisingly, the higher the monthly income is, the more difficult it is to accept the inaccurate supply-demand of emergency supplies. Because high-income people have higher expectations for quality of life, their psychological pain is more obvious.
{"title":"The impact of inaccurate supply-demand types for emergency supplies on the psychological pain of victims: Data from flood disasters in China","authors":"Qian Yang, Feiyue Wang, Zihuan Wang, Bo Ma, Jiajie Lu, Leiwei Li","doi":"10.1016/j.jnlssr.2025.100265","DOIUrl":"10.1016/j.jnlssr.2025.100265","url":null,"abstract":"<div><div>After a disaster, due to transportation constraints, transportation priority setting, emergency resource shortage and imprecise assessment of emergency resources, it is easy to cause an inaccurate match between the types of supply and demand for emergency resources. It aggravates the psychological pain of the victims. To address this gap, combining descriptive analysis, correlation analysis, and regression analysis, the mechanisms and internal pathways of imprecise emergency material supply-demand type on psychological pain were comprehensively examined. Based on Maslow's hierarchy of needs theory, four priority levels for emergency material types are proposed. A survey questionnaire was designed using the Numerical Rating Scale (NRS), which included 12 scenarios. Nine typical waterlogging sites in Changsha, Hunan Province, China, were selected as field survey sites, and 162 valid samples were collected through face-to-face interviews. The results show that: (1) Emergency material types have a significant impact on psychological pain, and this impact is related to the priority level of the material types. (2) The impact of the matching degree of emergency material supply-demand types on psychological pain is moderated by the priority level of the demanded material types, exhibiting a reverse compensation effect. (3) The impact of demographic factors on the degree of psychological pain experienced by victims shows complex differences. Most surprisingly, the higher the monthly income is, the more difficult it is to accept the inaccurate supply-demand of emergency supplies. Because high-income people have higher expectations for quality of life, their psychological pain is more obvious.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"7 2","pages":"Article 100265"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-08DOI: 10.13227/j.hjkx.202501278
Yi Han, Zhen-Mei Hou
As China's 2030 carbon peak target approaches, carbon emission reduction efforts have become increasingly urgent and crucial. Resource-based cities, characterized by their reliance on high-carbon industries, play a pivotal role in the nation's carbon peak progress. This study focuses on 108 resource-based cities from 2000 to 2022, employing the RF-RFECV algorithm to identify key factors influencing carbon emissions in these cities and utilizing the SHAP algorithm to evaluate feature importance. Furthermore, a BO-CNN-BiLSTM-attention prediction model is constructed, combined with scenario analysis to simulate the dynamic pathways of carbon peaking in resource-based cities under low-carbon, baseline, and high-speed scenarios. The results indicate the following: ① From the perspective of influencing factors, energy consumption was the most critical driver of carbon emissions in resource-based cities, reflecting their dependence on energy-intensive industries. The GDP of the primary industry and population density had a negative impact on carbon emissions, while the other six variables exerted a positive influence. ② In terms of city types, the impact of energy consumption on regenerative cities gradually declined, the development of secondary industries varied in its influence across different city types, and urbanization levels had the most significant impact on growing resource-based cities. ③ According to the peak scenario simulations, under the baseline and high-speed scenarios, carbon emissions in resource-based cities will continue to rise before 2040, whereas under the low-carbon scenario, emissions are projected to peak by 2034. Based on these findings, resource-based cities should achieve low-carbon transformation and sustainable development by improving energy efficiency, developing renewable energy, advancing green finance, adjusting industrial structures, and establishing carbon emission trading markets.
{"title":"[Analysis of Carbon Emission Impact Factors and Peak Scenario Simulation for Resource-based Cities in China Based on RF-RFECV Feature Selection and BO-CNN-BiLSTM-attention].","authors":"Yi Han, Zhen-Mei Hou","doi":"10.13227/j.hjkx.202501278","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501278","url":null,"abstract":"<p><p>As China's 2030 carbon peak target approaches, carbon emission reduction efforts have become increasingly urgent and crucial. Resource-based cities, characterized by their reliance on high-carbon industries, play a pivotal role in the nation's carbon peak progress. This study focuses on 108 resource-based cities from 2000 to 2022, employing the RF-RFECV algorithm to identify key factors influencing carbon emissions in these cities and utilizing the SHAP algorithm to evaluate feature importance. Furthermore, a BO-CNN-BiLSTM-attention prediction model is constructed, combined with scenario analysis to simulate the dynamic pathways of carbon peaking in resource-based cities under low-carbon, baseline, and high-speed scenarios. The results indicate the following: ① From the perspective of influencing factors, energy consumption was the most critical driver of carbon emissions in resource-based cities, reflecting their dependence on energy-intensive industries. The GDP of the primary industry and population density had a negative impact on carbon emissions, while the other six variables exerted a positive influence. ② In terms of city types, the impact of energy consumption on regenerative cities gradually declined, the development of secondary industries varied in its influence across different city types, and urbanization levels had the most significant impact on growing resource-based cities. ③ According to the peak scenario simulations, under the baseline and high-speed scenarios, carbon emissions in resource-based cities will continue to rise before 2040, whereas under the low-carbon scenario, emissions are projected to peak by 2034. Based on these findings, resource-based cities should achieve low-carbon transformation and sustainable development by improving energy efficiency, developing renewable energy, advancing green finance, adjusting industrial structures, and establishing carbon emission trading markets.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1433-1448"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147459551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-08DOI: 10.13227/j.hjkx.202502011
Yang Chen, Wen-Ge Liu
As pivotal pillars of China's national energy security strategy, resource-based cities can leverage the data-sharing capabilities, real-time transmission features, and low marginal cost advantages inherent in digital-real integration to forge new pathways for overcoming the "resource curse" and "transition inertia" dilemmas. Based on panel data of China's resource-based cities from 2011 to 2022, this study constructs a multidimensional econometric framework incorporating two-way fixed effects models, mediation and moderation effect models, and threshold regression analysis to systematically deconstruct the operational impacts and mechanistic drivers of digital-real integration in propelling green and low-carbon urban transitions. The results showed that: ① Digital-real integration demonstrated statistically significant positive effects on green low-carbon transition in resource-based cities, with robustness confirmed through multiple empirical tests. ② Mechanism tests revealed that digital-real integration significantly facilitated green and low-carbon transition in resource-based cities through innovation-driven effects and environmental regulation effects, whereas industrial optimization effects demonstrated no significant driving force. Concurrently, government intervention exhibited a negative moderating effect on this transition process driven by digital-real integration. ③ Heterogeneity tests revealed significant differential effects across three dimensions: typology of resource-based cities, economic development levels, and digital technology innovation capacities. ④ Threshold effect tests confirmed that higher digital economy policy supply levels intensified the green and low-carbon transition effects of digital-real integration.
{"title":"[Analysis of Digital-real Economy Integration Driving Green and Low-carbon Transition in Resource-Based Cities].","authors":"Yang Chen, Wen-Ge Liu","doi":"10.13227/j.hjkx.202502011","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502011","url":null,"abstract":"<p><p>As pivotal pillars of China's national energy security strategy, resource-based cities can leverage the data-sharing capabilities, real-time transmission features, and low marginal cost advantages inherent in digital-real integration to forge new pathways for overcoming the \"resource curse\" and \"transition inertia\" dilemmas. Based on panel data of China's resource-based cities from 2011 to 2022, this study constructs a multidimensional econometric framework incorporating two-way fixed effects models, mediation and moderation effect models, and threshold regression analysis to systematically deconstruct the operational impacts and mechanistic drivers of digital-real integration in propelling green and low-carbon urban transitions. The results showed that: ① Digital-real integration demonstrated statistically significant positive effects on green low-carbon transition in resource-based cities, with robustness confirmed through multiple empirical tests. ② Mechanism tests revealed that digital-real integration significantly facilitated green and low-carbon transition in resource-based cities through innovation-driven effects and environmental regulation effects, whereas industrial optimization effects demonstrated no significant driving force. Concurrently, government intervention exhibited a negative moderating effect on this transition process driven by digital-real integration. ③ Heterogeneity tests revealed significant differential effects across three dimensions: typology of resource-based cities, economic development levels, and digital technology innovation capacities. ④ Threshold effect tests confirmed that higher digital economy policy supply levels intensified the green and low-carbon transition effects of digital-real integration.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1576-1585"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147459843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-08DOI: 10.13227/j.hjkx.202502145
Jie Tan, Si-Yu Fan, De Yu, Hui-Ting Deng, Zhao-Yang Liao, Qin Liu
<p><p>The dissection of the spatiotemporal evolution characteristics and future development trends of the "production-living-ecology" space in the Great Lake Economic Zone is a fundamental basis for territorial spatial planning and governance. It can provide strategic support for the orderly utilization, renovation, and restoration of regional territorial space, thereby illuminating the direction for constructing a high-quality regional territorial spatial pattern. In this study, a multi-source indicator identification system was utilized to analyze the evolutionary characteristics of the spatial pattern of "production-living-ecology" space in the Dongting Lake Eco-economic Zone from 2014 to 2022. The spatial conflict comprehensive index model and the IM model were employed to measure the evolutionary characteristics of spatial conflict. Subsequently, the PLUS model was applied to forecast the potential development trends of the spatial pattern of "production-living-ecology" space in the Dongting Lake Eco-economic Zone by 2030 under three scenarios: natural development, economic priority, and ecological priority. The results showed that: ① The "production-living-ecology" space in the Dongting Lake Eco-economic Zone featured a distribution characterized by "three parts mountain, one part water, and five parts farmland." Living space, with the existing urban areas as the central point, continuously expanded linearly outward. There was frequent conversion between production space and ecological space, resulting in a significant reduction of ecological space. Particularly in the northern, western, and southwestern parts of the East Dongting Lake area, as well as in the Hong Lake area, there were clear instances of production space encroaching on ecological space. ② The conflict pattern changes of "production-living-ecology" space in the Dongting Lake Eco-economic Zone were significant. Slight conflicts were the main type of conflicts in the region, which showed a continuous increasing trend, while the number of severe conflict types accounted for the lowest proportion of the total conflict types, showing a trend of rising first and then declining. Concurrently, the systemic tendency was always the main transformation tendency of regional conflict types, with the overall conflict level of the region aggregating towards slight conflicts, and the fragmentation of "production-living-ecology" space landscape exhibited an increasing trend. ③ Under the natural development scenario, economic development priority scenario, and ecological development priority scenario for the Dongting Lake Eco-economic Zone, there were significant spatial concentration changes in the distribution of various types of spatial transformations. The mutual conversion between production space and ecological space primarily occurred in the mountainous forests surrounding the region and in the low-lying river and lake areas. The increase in living space showed little variation among t
{"title":"[Characteristics and Prediction of \"Production-Living-Ecology\" Space Conflict in the Dongting Lake Eco-economic Zone].","authors":"Jie Tan, Si-Yu Fan, De Yu, Hui-Ting Deng, Zhao-Yang Liao, Qin Liu","doi":"10.13227/j.hjkx.202502145","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502145","url":null,"abstract":"<p><p>The dissection of the spatiotemporal evolution characteristics and future development trends of the \"production-living-ecology\" space in the Great Lake Economic Zone is a fundamental basis for territorial spatial planning and governance. It can provide strategic support for the orderly utilization, renovation, and restoration of regional territorial space, thereby illuminating the direction for constructing a high-quality regional territorial spatial pattern. In this study, a multi-source indicator identification system was utilized to analyze the evolutionary characteristics of the spatial pattern of \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone from 2014 to 2022. The spatial conflict comprehensive index model and the IM model were employed to measure the evolutionary characteristics of spatial conflict. Subsequently, the PLUS model was applied to forecast the potential development trends of the spatial pattern of \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone by 2030 under three scenarios: natural development, economic priority, and ecological priority. The results showed that: ① The \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone featured a distribution characterized by \"three parts mountain, one part water, and five parts farmland.\" Living space, with the existing urban areas as the central point, continuously expanded linearly outward. There was frequent conversion between production space and ecological space, resulting in a significant reduction of ecological space. Particularly in the northern, western, and southwestern parts of the East Dongting Lake area, as well as in the Hong Lake area, there were clear instances of production space encroaching on ecological space. ② The conflict pattern changes of \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone were significant. Slight conflicts were the main type of conflicts in the region, which showed a continuous increasing trend, while the number of severe conflict types accounted for the lowest proportion of the total conflict types, showing a trend of rising first and then declining. Concurrently, the systemic tendency was always the main transformation tendency of regional conflict types, with the overall conflict level of the region aggregating towards slight conflicts, and the fragmentation of \"production-living-ecology\" space landscape exhibited an increasing trend. ③ Under the natural development scenario, economic development priority scenario, and ecological development priority scenario for the Dongting Lake Eco-economic Zone, there were significant spatial concentration changes in the distribution of various types of spatial transformations. The mutual conversion between production space and ecological space primarily occurred in the mountainous forests surrounding the region and in the low-lying river and lake areas. The increase in living space showed little variation among t","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1845-1857"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under the dual carbon goals, reducing carbon emissions in the planting industry is crucial for achieving green and low-carbon transformation in agriculture. This study focuses on the planting industry in Heilongjiang Province, utilizing the LMDI model, an extended STIRPAT model, and ridge regression to measure carbon emissions from 2002 to 2021, identify influencing factors, and predict future carbon emissions. The results indicate that: ① From 2002 to 2021, carbon emissions showed an overall fluctuating upward trend, divided into four phases: a slow growth period from 2002 to 2004, an accelerated growth period from 2004 to 2016, a fluctuating decline period from 2016 to 2019, and a stable growth period from 2019 to 2021. ② Economic level and agricultural structure promoted carbon emissions, while production efficiency and labor scale inhibited them. ③ Future carbon emissions will maintain a slow growth trend. By 2031, carbon emissions were projected to reach 10.832 million tons, an increase of 593 500 tons compared to that in 2021, with an average annual growth rate of 0.53%. Although Heilongjiang Province has made initial progress in carbon emission reduction, future challenges remain. It is recommended to further develop practical carbon reduction strategies.
{"title":"[Research on Influencing Factors and Forecasting of Carbon Emissions from Planting Industry in Heilongjiang Province].","authors":"Yun Teng, Tian-Lei Yan, Rui-Xiang Ge, Li Ma, Ya-Lin Wang, Hui Xu","doi":"10.13227/j.hjkx.202502021","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502021","url":null,"abstract":"<p><p>Under the dual carbon goals, reducing carbon emissions in the planting industry is crucial for achieving green and low-carbon transformation in agriculture. This study focuses on the planting industry in Heilongjiang Province, utilizing the LMDI model, an extended STIRPAT model, and ridge regression to measure carbon emissions from 2002 to 2021, identify influencing factors, and predict future carbon emissions. The results indicate that: ① From 2002 to 2021, carbon emissions showed an overall fluctuating upward trend, divided into four phases: a slow growth period from 2002 to 2004, an accelerated growth period from 2004 to 2016, a fluctuating decline period from 2016 to 2019, and a stable growth period from 2019 to 2021. ② Economic level and agricultural structure promoted carbon emissions, while production efficiency and labor scale inhibited them. ③ Future carbon emissions will maintain a slow growth trend. By 2031, carbon emissions were projected to reach 10.832 million tons, an increase of 593 500 tons compared to that in 2021, with an average annual growth rate of 0.53%. Although Heilongjiang Province has made initial progress in carbon emission reduction, future challenges remain. It is recommended to further develop practical carbon reduction strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1521-1531"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an important indicator for measuring the carbon sequestration capacity of ecosystems，carbon storage is of great significance for alleviating global climate change. By taking advantage of machine learning and ecosystem service models，an integrated analysis framework based on the InVEST-Ridge Regression-PLUS model was constructed to conduct a quantitative analysis of the spatio-temporal evolution characteristics and driving mechanisms of carbon storage in the Yunnan-Guizhou Plateau from 2000 to 2020，and future scenarios were designed to predict the changing trends of regional carbon storage under different land use paths. The results show that：Firstly，from 2000 to 2020，the carbon storage in the Yunnan-Guizhou Plateau generally presented a slow growth trend，and the growth rate continued to decline，showing a distribution pattern of "higher in the south and lower in the north." Secondly，vegetation coverage was a crucial determining factor for carbon storage in this area，and the conversion between different land use types affected the spatial distribution of carbon storage. Thirdly，in the future scenario simulation，the carbon storage under the carbon sink enhancement scenario performed best，effectively verifying the effects of ecological projects such as the conversion of farmland to forest and grassland restoration，providing a scientific basis for the dynamic assessment and optimization of carbon storage in the Yunnan-Guizhou Plateau and similar karst areas.
{"title":"[Spatiotemporal Evolution of Carbon Storage and Multi-scenario Prediction in the Yunnan-Guizhou Plateau Based on the InVSET-Ridge Regression-PLUS Model].","authors":"Yuan Li, Yu-Ling Peng, Hao-Na Peng, Wei-Ying Cheng","doi":"10.13227/j.hjkx.202501105","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501105","url":null,"abstract":"<p><p>As an important indicator for measuring the carbon sequestration capacity of ecosystems，carbon storage is of great significance for alleviating global climate change. By taking advantage of machine learning and ecosystem service models，an integrated analysis framework based on the InVEST-Ridge Regression-PLUS model was constructed to conduct a quantitative analysis of the spatio-temporal evolution characteristics and driving mechanisms of carbon storage in the Yunnan-Guizhou Plateau from 2000 to 2020，and future scenarios were designed to predict the changing trends of regional carbon storage under different land use paths. The results show that：Firstly，from 2000 to 2020，the carbon storage in the Yunnan-Guizhou Plateau generally presented a slow growth trend，and the growth rate continued to decline，showing a distribution pattern of \"higher in the south and lower in the north.\" Secondly，vegetation coverage was a crucial determining factor for carbon storage in this area，and the conversion between different land use types affected the spatial distribution of carbon storage. Thirdly，in the future scenario simulation，the carbon storage under the carbon sink enhancement scenario performed best，effectively verifying the effects of ecological projects such as the conversion of farmland to forest and grassland restoration，providing a scientific basis for the dynamic assessment and optimization of carbon storage in the Yunnan-Guizhou Plateau and similar karst areas.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1928-1940"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-08DOI: 10.13227/j.hjkx.202501218
Fen Hu, Qiao-Hong Chen, Lie Jiang, Lian-Wei Yang
In order to explore the characteristics and sources of heavy metal pollution in the surrounding soil of a coal mine concentration area in Pingxiang, Jiangxi Province, based on the test results of six heavy metal indicators (Cd, Hg, Pb, Cu, Ni, and Zn) and pH values of 127 sampling sites, the geo-accumulation index and Nemerow integrated pollution index methods were used to compare and evaluate the pollution characteristics of soil heavy metals. On the basis of correlation analysis, APCS-MLR and PMF models were used to quantitatively analyze the sources and contributions of soil heavy metal pollution. The results showed that the average content of heavy metals in six types of soils was higher than the soil environment background value in Jiangxi Province. There were five types of soil heavy metals, Cd, Hg, Cu, Ni, and Zn, with contents exceeding the risk screening value in GB 15618-2018, and some samples had Cd content exceeding the control value of GB 15618-2018. The spatial distribution characteristics of Pb, Cu, and Ni were highly similar. Cd was highly enriched in the downstream area of coal mine concentration. The spatial distribution and correlation coefficient of Hg indicated that its source may have been different from other indicators. The high-value area of Zn was significantly smaller than that of other indicators. The pollution assessment showed that the overall soil in the study area was moderately polluted or above, with Cd, Hg, and Ni as the main pollution indicators. The heavy metal pollution in the coal gangue accumulation area was more severe than in other surrounding areas. The APCS-MLR model analyzed two known sources and one unknown source, with contribution rates of 65.69% from a mixture of industrial dust, natural causes, transportation, and coal mine pollution; 8.02% from agricultural activities; and 26.29% from unknown sources. The PMF model identified five pollution sources, agricultural activity sources, industrial dust sources, coal mine pollution sources, transportation sources, and naturally occurring sources, with corresponding contribution rates of 23.61%, 16.84%, 22.74%, 25.85%, and 10.96%, respectively. The source analysis results were consistent with the actual situation in the study area, which can provide theoretical support for the prevention, control, and remediation of soil heavy metal pollution in the study area.
{"title":"[Contamination Characteristics and Source Apportionment of Heavy Metals in Soil around the Agricultural Land in Coal Mining Concentration Areas Based on APCS-MLR and PMF Models].","authors":"Fen Hu, Qiao-Hong Chen, Lie Jiang, Lian-Wei Yang","doi":"10.13227/j.hjkx.202501218","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501218","url":null,"abstract":"<p><p>In order to explore the characteristics and sources of heavy metal pollution in the surrounding soil of a coal mine concentration area in Pingxiang, Jiangxi Province, based on the test results of six heavy metal indicators (Cd, Hg, Pb, Cu, Ni, and Zn) and pH values of 127 sampling sites, the geo-accumulation index and Nemerow integrated pollution index methods were used to compare and evaluate the pollution characteristics of soil heavy metals. On the basis of correlation analysis, APCS-MLR and PMF models were used to quantitatively analyze the sources and contributions of soil heavy metal pollution. The results showed that the average content of heavy metals in six types of soils was higher than the soil environment background value in Jiangxi Province. There were five types of soil heavy metals, Cd, Hg, Cu, Ni, and Zn, with contents exceeding the risk screening value in GB 15618-2018, and some samples had Cd content exceeding the control value of GB 15618-2018. The spatial distribution characteristics of Pb, Cu, and Ni were highly similar. Cd was highly enriched in the downstream area of coal mine concentration. The spatial distribution and correlation coefficient of Hg indicated that its source may have been different from other indicators. The high-value area of Zn was significantly smaller than that of other indicators. The pollution assessment showed that the overall soil in the study area was moderately polluted or above, with Cd, Hg, and Ni as the main pollution indicators. The heavy metal pollution in the coal gangue accumulation area was more severe than in other surrounding areas. The APCS-MLR model analyzed two known sources and one unknown source, with contribution rates of 65.69% from a mixture of industrial dust, natural causes, transportation, and coal mine pollution; 8.02% from agricultural activities; and 26.29% from unknown sources. The PMF model identified five pollution sources, agricultural activity sources, industrial dust sources, coal mine pollution sources, transportation sources, and naturally occurring sources, with corresponding contribution rates of 23.61%, 16.84%, 22.74%, 25.85%, and 10.96%, respectively. The source analysis results were consistent with the actual situation in the study area, which can provide theoretical support for the prevention, control, and remediation of soil heavy metal pollution in the study area.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"2007-2019"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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