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About the Journal

Superintended by：China North Industries Group Corporation Limited

Sponsoredy：Beijing Institute of Technology, Chinese Scoiety for Environmental Sciences, China Occupational Safety and Health Association

Edited & Published by: Editorial Department of Journal of Safety and Environment

Issues per year: 12

ISSN 1009-6094

CN 11-4537/X

Current Previous Issues Online First Citation Ranking Download Ranking

Issue 06,2025

Research on key causes identification and prediction of ground proximity warnings during the approach phase at plateau airports

QI Yannan;ZHAO Jiaxing;WU Zuoyu;ZHAO Yu;

To ensure the safe operation of aircraft during the final descent phase, this paper investigates the key causes and predictive factors associated with Ground Proximity Warnings at plateau airports. First, parameters are extracted from four aspects—environment, aircraft state, aircraft control, and trajectory—to establish a preliminary set of causal factors. The similarity characteristics among these parameters are assessed by calculating mutual information values, which allows for the elimination of redundant parameters. Additionally, the FP-Growth association rule analysis algorithm is employed to identify key causes. Finally, to tackle the high dimensionality of flight sample data and the black box nature of machine learning prediction processes, a fusion of Convolutional Neural Networks(CNN) and decision tree algorithms is utilized to develop a ground proximity warning prediction model. The key causes that trigger ground proximity warnings, along with the prediction model, were validated using Quick Access Record(QAR) data from Aspen Airport, a plateau airport in the United States. The research identified six association rules related to ground proximity warnings: FATA-1 and VRTG-1—Pattern 1; W＿h-1 and IVV-1—Pattern 1; FATA-1 and SPLG-1—Pattern 2A; VRTG-1 and SPLG-1—Pattern 2B; IVV-1 and CW-1—Pattern 4; and W＿h-1 and ELEV-0—Pattern 5. All of these rules represent key causes involving combinations of multiple indicators. The prediction algorithm was validated using flight data that had been subject to data augmentation. The prediction results demonstrate that the combined model can effectively predict ground proximity warnings based on the operational parameters during the initial descent phase, achieving an average accuracy of 86.1%. The analysis and verification of randomly selected samples predicted for each mode revealed that the positive samples identified by the model indeed corresponded to instances where Ground Proximity Warnings were triggered in actual flights. In comparison to other prediction models, the model presented in this paper demonstrates superior performance in terms of accuracy, recall, F₁ score, False Alarm Rate(FAR), and Critical Success Index(CSI), further validating its reliability and effectiveness.

Issue 06 ,2025 v.25 ; 国家重点研发计划项目(2022YFB4300904); 国家自然科学基金委员会与中国民用航空局联合资助项目(U1633124)
[Downloads: 189 ] [Citations: 0 ] [Reads: 2 ] PDF Cite this article

Identification of critical risk factors and their interaction mechanisms in aircraft apron operational safety

LI Mingjie;YIN Tiance;FENG Sirong;

This study aims to improve the safety of apron operations by identifying and analyzing critical risk factors. To accomplish this, we compared several existing methods for risk factor identification and ultimately chose grounded theory due to its effectiveness in extracting meaningful insights from qualitative data. Using this method, we analyzed incidents from 56 transportation airports and selected 17 representative cases for in-depth study. From these cases, we identified 11 key risk factors that pose a threat to apron safety. These factors encompass human behavior, equipment conditions, management practices, and environmental influences. We subsequently developed an enhanced Decision-Making Trial and Evaluation Laboratory(DEMATEL) model that integrates Interval Type-2 Fuzzy Sets(IT2FS) with the Ordered Weighted Averaging(OWA) operator. This model effectively addresses uncertainties in expert evaluations by capturing hesitant and fuzzy judgments more accurately. Expert opinions were initially assessed using the original DEMATEL method and subsequently reanalyzed with the improved model. The results indicate that the enhanced model more accurately reflects real-world complexities and aligns closely with expert intentions, thereby enhancing the accuracy and reliability of the findings. Our analysis identifies four critical factors: S₁(personnel quality), S₁₁(supervision), S₂(operational behavior), and S₉(training management). These factors received the highest centrality scores, measuring 10.34, 9.96, 9.94, and 9.90, respectively. These results underscore the pivotal roles these factors play in ensuring apron safety. Prioritizing them can significantly reduce risks associated with apron operations. This study illustrates the advantages of incorporating advanced fuzzy techniques into risk analysis models, as the enhanced DEMATEL model improves the robustness and applicability of expert evaluations. Furthermore, identifying high-centrality factors lays a solid foundation for developing training protocols, revising supervision standards, and enhancing operational practices specifically tailored to the apron environment. Overall, our findings offer actionable insights for creating targeted safety strategies in aviation apron operations. This framework serves as a practical tool for enhancing both safety and operational efficiency within the aviation industry.

Issue 06 ,2025 v.25 ; 民航机场智慧运营与运维四川省工程研究中心项目(JCZX2023ZZ03)
[Downloads: 16 ] [Citations: 0 ] [Reads: 0 ] PDF Cite this article

Dynamic coupling analysis of risk factors in ship self-sinking accidents using the N-K-DBN model

CUI Xiufang;ZENG Jiexi;SHAO Zhipeng;AN Nannan;

Maritime accidents in China occur frequently, and when multiple factors are dynamically coupled, the risk of exceeding threshold values increases, potentially leading to accidents that result in casualties, economic losses, and environmental hazards. Therefore, this study quantitatively analyzes the dynamic coupling relationship governing the evolution of ship self-sinking risks and identifies the key factors contributing to these accidents. This paper first introduces the N-K model and dynamic Bayesian networks to investigate the dynamic coupling characteristics of ship self-sinking accident risks. Using 146 self-sinking accident cases published by the Maritime Safety Administration, the risk factors are classified, and their coupling mechanisms are explored. Next, the N-K model is constructed to quantify the coupling values and analyze their relationships. The secondary factors are utilized as parent nodes to construct a Bayesian Network(BN) correlation network represented by a directed acyclic graph. The coupling value is incorporated into the BN network to optimize its probabilities and minimize subjectivity. Finally, a dynamic coupling model of Dynamic Bayesian Network(DBN) risk is established by incorporating time slices into the Bayesian Network framework. This model facilitates risk probability analysis, sensitivity analysis, forward reasoning, reverse diagnosis, and uncertainty analysis to identify key and catalytic factors influencing dynamic risk correlations. The goal is to achieve dynamic control of coupling risks in navigation, leading to the development of risk management strategies and preventive measures aimed at enhancing maritime safety. The results indicate a positive correlation between ship sinking accidents and the dynamic coupling value of risks, meaning that as the coupling factors increase, the risk level also rises, reflecting a stronger interaction among these factors. In the early stages of an accident, subjective behavioral elements such as human and management factors are identified as key contributors to ship sinking incidents, with their cross-coupling resulting in a heightened level of risk. As time progresses, the influence of objective factors, such as ship-specific characteristics, on accidents gradually increases. These factors are more likely to cross-couple with human factors, leading to heightened dynamic risks. Adverse weather conditions serve as a catalyst that facilitates the coupling of ships with other factors, increasing the likelihood of multi-factor cross-coupling risks and consequently raising the probability of accidents. The study identified several key dynamic risk coupling factors that contribute to self-sinking accidents, including weak safety awareness, inadequate company management, ship failure, unseaworthiness, improper ship management, and company failures. Additionally, severe weather conditions were recognized as a significant catalytic factor in these dynamic risk couplings.

Issue 06 ,2025 v.25 ;
[Downloads: 45 ] [Citations: 0 ] [Reads: 2 ] PDF Cite this article

Application of an improved N-K model for coupling analysis of construction safety risks in prefabricated buildings

YANG Wenan;XU Biqin;YAN Yuxin;

To identify the key safety risk factors in prefabricated construction and explore the coupling mechanism between these factors, an improved N-K model is developed for coupling analysis. First, based on on-site interviews, expert consultations, policy organization, and literature analysis, a safety risk indicator system is established using grounded theory. This system addresses five key aspects: personnel, technology, management, equipment, and environment. Secondly, the Decision-Making Trial and Evaluation Laboratory(DEMATEL) method and the entropy weight method are used to calculate both subjective and objective weights. Game theory is then introduced to compute the comprehensive weights of indicators through combination weighting. Based on the centrality ranking derived from the DEMATEL method, the key safety risk factors in the secondary indicators are identified. Next, based on the risk coupling mechanism, an improved N-K model is constructed. In this model, the probability values typically derived from statistical accident cases in traditional coupling models are replaced with the comprehensive weights of secondary indicators. The model then calculates the risk coupling information interaction values between each primary indicator. Finally, engineering examples are used to demonstrate the applicability and reliability of the improved N-K model in the coupling analysis of safety risk factors in prefabricated construction. The results show that key safety risk factors include personnel safety awareness, on-site construction safety management levels, and non-standard behaviors of personnel. The coupling T-value of risks associated with personnel and management factors is relatively high, indicating that these factors significantly impact the coupling of various risk elements. Furthermore, an increase in the number of risk coupling factors correlates with a higher value of risk coupling interaction information, which increases the likelihood of safety accidents occurring. Relevant measures should be implemented to focus on controlling key safety risk factors and to prevent the coupling of multiple risk factors. The findings of this research offer valuable insights for further analyzing the coupling mechanism of safety risk factors in prefabricated construction.

Issue 06 ,2025 v.25 ; 湖南省自然科学基金项目(2021JJ30744)
[Downloads: 453 ] [Citations: 0 ] [Reads: 1 ] PDF Cite this article

Application of an enhanced entropy weight method combined with the grey cloud model for safety evaluation of bridge crane hoisting mechanisms

WANG Quanwei;XU Kun;CHEN Zewei;LIU Yilin;WEN Hao;

To tackle the challenges posed by uncertain indicator information and complex grade attribution in the safety assessment of bridge crane hoisting mechanisms, this study proposes a novel safety evaluation methodology that integrates combined weighting with grey cloud modeling. Beginning with an analysis of the structural composition and accident causation related to bridge crane hoisting mechanisms, this paper systematically identifies potential safety risk factors and establishes a comprehensive safety evaluation index system based on pertinent safety and technical standards. For weight assignment, the Fuzzy Analytic Hierarchy Process(FAHP) and an improved entropy method are utilized to calculate subjective and objective weights, respectively. A game theory-based combination method is then applied to integrate these dual weights, effectively balancing the advantages of both approaches. In terms of evaluation methodology, we introduce an innovative grey-cloud model that synergizes grey theory with cloud models, thereby overcoming the limitations of each individual method in addressing fuzziness and randomness. A systematic safety evaluation procedure based on the grey-cloud model is subsequently proposed. Using a specific bridge crane hoisting mechanism as a case study, we first conduct quantitative processing of the evaluation indicators. The combined weighting method calculates comprehensive indicator weights, which are then used in the grey-cloud model computation to assess safety levels for both individual components and the entire system. The comprehensive grey-cloud clustering coefficients for the hoisting mechanism are(0, 0, 0.2770, 0.4943, 0.2356). According to the maximum membership principle, the coefficient “0.494 3” corresponds to the “mildly hazardous” classification. A comparative analysis with the fuzzy comprehensive evaluation method shows consistent conclusions at the system level, both indicating a “mildly hazardous” status. However, the proposed method exhibits superior accuracy in assessing individual components. Case verification and model comparison confirm that this methodology objectively and accurately reflects the actual safety conditions of hoisting mechanisms and their components. It provides a viable technical approach for the safety evaluation of bridge crane hoisting mechanisms. The integrated approach effectively addresses information uncertainty and grading complexity present in traditional evaluation processes, offering valuable theoretical guidance and methodological references for equipment safety management.

Issue 06 ,2025 v.25 ; 山西省重点研发计划项目(202003D111009); 山西省高等学校科技创新项目(2020L0360)
[Downloads: 705 ] [Citations: 0 ] [Reads: 1 ] PDF Cite this article

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Investigation of recovery efficiency considering the cascade effect of infrastructure failure under extreme precipitation: a case study of Gulou District， Nanjing

WANG Peng;WU Xizhen;SI Zhiyuan;ZHI Hongyu;LI Chuanxun;

This study develops an efficiency evaluation framework for urban road recovery by integrating input-output factors during the restoration process， specifically in the context of road failures caused by extreme precipitation events. Using the SW2D–GPU hydrodynamic model， a simulation of a 100-year extreme precipitation event was conducted in Gulou District， Nanjing， China， to analyze road inundation patterns and resulting damages. The proposed framework incorporates restoration costs as input variables， while output variables include quantitative indicators reflecting cascading effects. The restoration costs associated with roads during extreme precipitation encompass drainage， maintenance， congestion， and inspection expenses. A particular focus was placed on quantifying cascading effects， wherein localized road failures propagate disruptions throughout the transportation network. These cascading impacts were measured using two metrics: the number of congested road segments resulting from the cascade and the number of critical infrastructure sites affected. To evaluate recovery efficiency， a Slack-Based Measure (SBM) model with super-efficiency modification was utilized. The results indicate that 26.8% of the roads in the study area became dysfunctional due to inundation， necessitating post-recovery rehabilitation for 97 segments. The total recovery cost amounted to 1.94 million CNY， with repair costs accounting for the largest share at 61.8% (1.203 million CNY)， followed by drainage costs at 21.9% and inspection costs at 15.2%. Efficiency analyses identified 14 roads that should be prioritized for restoration. Furthermore， the spatial distribution of road restoration efficiency revealed that roads experiencing deeper inundation depths generally had lower recovery efficiency， underscoring the need for targeted and effective restoration strategies. These findings highlight the importance of incorporating cascading effect mitigation and cost-benefit analysis into post-disaster recovery planning. The proposed methodology offers valuable insights for optimizing resource allocation and prioritizing rehabilitation activities during extreme weather events. By integrating these insights into urban planning and disaster preparedness initiatives， cities can bolster their resilience and mitigate the negative impacts of extreme precipitation events. However， the cost calculation and efficiency assessment framework presented in this study has its limitations and will require refinement in future research to enhance post-disaster road recovery outcomes.

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Rapid assessment of seismic resilience in substation systems using machine learning

WANG Kaige;ZHENG Shansuo;YANG Feng;ZHOU Lin;TIAN Qi;CAI Guowei;

This study tackles the computational inefficiency of Monte Carlo (MC) simulations in evaluating substation system resilience. We propose a machine learning-based approach for the rapid assessment of seismic resilience in substations. In this framework， the substation is modeled as a topological network that integrates both structural and functional units. Additionally， we analyze the correlation between physical damage to electrical equipment and the occurrence of cascading failures within the substation. Service path information was captured using transitive closure， and uncertainties in equipment recovery times were incorporated into the modeling process. To expedite the MC simulations， a surrogate modeling approach was introduced. The functional assessment was reformulated as a multi-class classification problem， considering physical-functional constraints. Support Vector Machine (SVM) and logistic regression models were developed to map the damage function and predict system performance. Stochastic damage scenarios， incorporating uncertainties in equipment failures， were constructed as feature inputs. Supervised learning was then conducted using eleven discrete operational states of the substation as training labels to train the surrogate model. The method was validated on a 220 kV double-bus electrical main connection system. The results indicate that the system resilience index converges once the sample size of damage-function states reaches 3 000. The linear kernel SVM and logistic regression models were trained using 5 000 sample sets under varying Peak Ground Acceleration (PGA) conditions， achieving prediction accuracies of 96.6% and 96.9%， respectively. The trained surrogate models were then applied to the resilience assessment， showing that the robustness and recovery time distributions of the substation under different PGAs closely match those obtained from MC simulations. The resilience deviations remained below 0.009， thereby validating the reliability of the proposed method. Additionally， a comparative analysis of time complexity reveals that the Warshall algorithm requires O(n³) for an n-node system， while the time complexity of the surrogate model， relying on linear kernel SVM or logistic regression during prediction， operates at O(n). This demonstrates the computational efficiency of the proposed method. The results confirm that this approach significantly enhances the efficiency of substation resilience assessment without compromising accuracy.

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A multidimensional dynamic risk assessment method for fires at large-scale auto shows

LUAN Tingting;ZHANG Ye;CHEN Wentao;TAO Li;LI Xiaoyun;ZHANG Lijia;

To tackle the complex array of fire risk factors and the dynamic fluctuations in risk states during large-scale auto shows， this study introduces a dynamic risk assessment method for auto show fires. This method integrates fuzzy set theory， an improved Dempster-Shafer (D–S) evidence theory， and Dynamic Bayesian Network (DBN)， building upon traditional fault tree analysis and static Bayesian networks. First， a multidimensional fire risk evolution path model is developed， and a fault tree model for large-scale auto show fires is established. Expert assessments of fire risk factors are collected， and fuzzy set theory is applied to quantify these assessments. Next， an improved evidence theory is used to fuse the expert evaluation results， minimizing the uncertainty introduced by subjective judgments and calculating the weights of each risk factor. Based on these weight calculations， a dynamic Bayesian model is then constructed. The DBN model enables the capture of dynamic changes in fire risks and facilitates the prediction of risk trends across different time slices. Using a large-scale auto show in Beijing as a case study， a quantitative and dynamic risk assessment is conducted. The weight fusion and node probabilities are calculated， and through bidirectional reasoning and sensitivity analysis of the model， the results reveal that the risk probabilities of nodes such as electrical equipment failure， combustion， exceeding personnel load， and insufficient fire compartment design gradually increase over time. Notably， the risk probability of electrical equipment failure and combustion exhibits the fastest rate of change. Reverse diagnosis reveals that electrical equipment failure and combustion， inadequate firefighting personnel， and insufficient safety management personnel are the most likely causes of auto show fires. Sensitivity analysis further identifies that electrical equipment failure and combustion， inadequate firefighting personnel， and insufficient emergency preparedness are the key risk factors contributing to accidents. By controlling these critical risk factors， the likelihood of fire accidents is significantly reduced. The research findings offer valuable theoretical support for dynamic risk assessment and the daily risk prevention of large-scale events， such as auto shows.

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Research on small object detection algorithms for cigarette butts in park environments

BAI Yateng;LIU Runyi;

Detecting cigarette butts in industrial parks is crucial for enhancing ecological quality and mitigating fire hazards. However， the small size and indistinct features of cigarette butts in park environments present significant challenges. To develop a more effective small object detection model for identifying cigarette butts in these settings， this paper proposes enhancements and validations based on the original YOLOv9 model. Firstly， this article introduces a new lightweight feature extraction module， P–RepELAN4， to tackle the challenges of convolutional layer stacking and computational redundancy. By incorporating partial convolution， the module minimizes redundant feature extraction from similar feature maps. Specifically， certain convolutional layers (PConv) are integrated into the RepNCSSPELAN4 module. Positioning the P–RepELAN4 module within the CBFuse layer facilitates efficient gradient information propagation across different semantic levels， enhancing the extraction of target features from the network while reducing computational complexity. This article also presents a feature reconstruction method based on a context prediction sampling kernel to mitigate detail loss and the checkerboard effect during the upsampling process. By employing a feature recombination upsampling technique， this method aggregates contextual information over a large receptive field. Additionally， it facilitates instance-specific， content-aware processing that dynamically generates adaptive kernels. Finally， comparative and ablation experiments were conducted on the cigarette butt dataset to assess the impact of encoding and reassembly on average accuracy and to validate the effectiveness of the proposed improvements. The results indicate that the detection performance and efficiency have been enhanced compared to the original YOLOv9 model. Specifically， the average accuracy of the algorithm reached 87.4%， reflecting an increase of 0.3 percentage point. Additionally， the FLOPs count was reduced to 198.7 GB， a decrease of 37.9 GB， while the parameter count was lowered to 83.8 MB， resulting in a reduction of 19 MB. This study aims to develop an effective cigarette butt detection model using deep learning techniques， thereby promoting the establishment of smoke-free environments in public areas of parks.

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[Downloads: 33 ] [Citations: 0 ] [Reads: 0 ] HTML PDF Cite this article

Fire smoke temperature prediction using an enhanced sparrow search algorithm for optimized gated recirculation units

MA Hongyan;WANG Shun;CAO Yanxi;YANG Liyao;

The corridor serves as a critical pathway for personnel evacuation. The spread of smoke within building corridors can significantly jeopardize the safety of evacuees， leading to injury or even fatalities. However， obtaining real-time smoke distribution data during a fire poses significant challenges. To enhance evacuation efficiency and predict smoke temperature distribution， we propose a smoke temperature prediction model utilizing an optimized gated recurrent unit. Initially， we develop fire smoke simulation models based on fire dynamics for an office building. A total of 160 fire scenarios were simulated， considering various fire factors such as four fire source intensities， five fire source locations， four different fire rooms， and varying corridor ventilation conditions. Thermocouples were strategically placed at multiple locations and heights within the corridor to collect smoke temperature data. The results of these simulations were compiled to construct a comprehensive fire smoke dataset. Next， we developed the Improved Sparrow Search Algorithm-Gated Recurrent Unit (ISSA–GRU) model， which integrates an improved sparrow search algorithm with the GRU framework to predict fire smoke temperature. Enhancements to the SSA algorithm were made by incorporating circular chaotic mapping and nonlinear weighting， which improved modeling accuracy and effectiveness. The reliability of the model was validated using data from the fire smoke temperature test set. The proposed ISSA–GRU model demonstrated superior generalization ability and higher accuracy compared to both the traditional GRU model and the Convolutional Neural Networks-Gated Recurrent Unit (CNN–GRU) model. Notably， the relative error of the predictions across various fire conditions remained within 8%. To assess the accuracy of the ISSA–GRU model in predicting corridor smoke temperature， we varied the ventilation conditions within the corridor to evaluate the model's performance. The results indicate that the overall accuracy of fire smoke temperature predictions exceeds 90.9% under complex ventilation scenarios， demonstrating the model's robustness. This model significantly enhances fire early warning capabilities and supports effective firefighting decision-making.

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Study on the evaluation and treatment of lake eutrophication by means of algae biology

KUANG Qi-jun~1, MA Pei-ming~(1,2), HU Zheng-yu~1,ZHOU Guang-jie~(1,2)(1 Institute of Hydrobiology, The Chinese Academy of Sciences, Wuhan 430072, China; 2 Graduate School, The Chinese Academy of Sciences, Beijing 100039, China)

The present paper aims to introduce the authors' research on the method of evaluation and treatment of lake eutrophication by means of algae biology. As a matter of fact, with the fast industrialization tendency, lake eutrophication in China is still getting more and more exacerbated for a number of reasons, both human activities and natural factors. Luckily, algae is one of the important organisms in lake eco-system, whose growth is favorable for metabolism and species constitution. In the paper, the authors give a general outline on the application of algae in biologica1 assessment on lake trophic status with detailed information about the indicator of algae in the water bodies of hypertrophication, eutrophication, mesotrophication and olipotrophication. In addition, an example for giving an algal growth potential test, and the corresponding AGP test results. In addition, the authors have also discussed some algal species diversity and synthesis indexes as well as their evaluated standards. In order to figure out the key problems on nutrients excess and algal overgrown in lake eutrophication, a brief introduction about the recent progress on the study of nutrients and algae removal, whose research results about the research field conducted by the authors are also given. In order to protect our lakes against the eutrophication, it is important to draw up a more detailed and functional management rules and regulations on water resources, to strengthen the ecological environment protection of valleys and water areas, and to prohibit strictly the discharge of the wastewater with high concentration of phosphorus and nitrogenous compounds into water bodies.

Issue 02 ,2005 ; 国家 8 63项目 (2 0 0 2 AA60 10 2 1) ;; 国家 973项目(2 0 0 2 CB412 3 0 9)
[Downloads: 5,471 ] [Citations: 671 ] [Reads: 64 ] HTML PDF Cite this article

Economic growth and its effects on carbon emission in China

WANG Zhong-ying 1,2,WANG Li-mao1(1 Institute of Geographical Sciences and Natural Resources research,Chinese Academy of Sciences,Beijing 100101,China;2 Graduate School of Chinese Academy of Sciences,Beijing 100039,China)

This paper intends to present the authors' analysis of the relationship between the GDP growth and carbon emission in China today with the correlative analytical method(R2=0.958 1).The authors' conclusion is that the present economic development pattern mainly dependent on the investment and structural expansion of secondary industry has strong impact on the emission increase of greenhouse gas.The investment rate of China exceeds 35%-40% of GDP during the period of 1980 to 2003,which is higher than other countries.As a result,China has brought about tremendous development of its heavy industry with ever increasing consumption of energy resources.Such a consequence,on the one hand,has led to higher proportion of secondary industry to the whole macro-economy(over 50%).On the other hand,its energy consumption has to be dependent heavily on coal(over 60%),which emits large amounts of gases than oil or natural gas.Thus,it will be inevitable to consume more energy and increase the emission of greenhouse gases in future.However,under the background of globalization,our economic growth should depend more on technological improvement,scientific and institutional innovation.With the high efficient use of renewable energy and environment-friendly technology,China can and is sure to make contributions to global efforts to prevent global warming.Therefore,it can be predicted that the carbon emission intensity will gradually slow down with the economic development and the adjustment of its industrial structural system.

Issue 05 ,2006 ; 中国科学院知识创新工程重大项目(KZCX1-SW-01-18);; 国家自然科学基金项目(70473103)
[Downloads: 5,012 ] [Citations: 509 ] [Reads: 45 ] HTML PDF Cite this article

Cd, Zn and Se content of the polished rice samples from some Chinese open markets and their relevance to food safety

ZHEN Yan-hong, CHENG Yan-jun, PAN Gen-xing, LI Lian-qing(Institute of Resources, Ecosystem and Environment of Agriculture, Nanjing Agricultural University, Nanjing 210095, China)

This paper is aimed at reporting its investigation over the content of heavy metals in different sorts of rice so far available on the Chinese markets by sampling 91 sorts of rice including 47 of Japonica and 44 of Indica. The motive of our research is closely related to the health hazards due to food exposure for Chinese people. It is well known that rice has been a predominant diet source in our country. The samples of ours have just been randomly chosen from some open markets over China in 2005. Their respective content rates of Cd, Zn and Se are then determined by using atomic absorption spectrometry (AAS) and atomic fluorescence spectrometry (AFS) individually with the mutual relationship among these elements analyzed. The results of our investigation show that about 10% of the samples had a Cd content beyond the State Food Security Standards (0.2 mg·kg-1), while 70% of the above samples is likely to exert potential health hazards due to the exposure of Cd if the RfD ( USEPA, 2000) value is taken as a reference. In addition, the content levels of Zn and Se in the rice samples prove generally rather low, failing to meet the health need regulation through the regular dietary intake. On the other hand, it is found from our research that there indeed exists positive correlation between the content of Cd and that of Zn, as well as between Se content and that of Zn ratio, which indicates a slight accumulation of Zn in relation to Cd and Se. However, on the whole, 90% of the rice samples has a lower Cd/Zn ratio than the critical value of 0.015 proposed by the American standard. In addition, samples with high Cd content may not be necessarily beyond the regular high Cd/Zn ratio. The fact is that the rice samples of Indica may imply much higher Cd and Se content level than that of Japonica, with the former suggesting a far higher Cd exposure hazard than the latter. Therefore, the present research can be used as a valuable reference to further studies for the potential effects of the toxic heavy metal content in rice on the human health as well as their relation to the soil-crop-food security.

Issue 01 ,2008 No.43 ; 教育部高等学校博士点专项研究基金项目(B200613)
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TECHNOLOGICAL IMPLICATIONS OF PHYTOREMEDIATION AND ITS APPLICATION IN ENVIRONMENT PROTECTION

ZHOU Qi xing & SONG Yu fang (Open Laboratory of Terrestrial Ecological Process, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China)

As a safe and reliable technology for treating contamination of soil, phytoremendiation is explored in the present paper.The roots of special plants and their special rhizospheric effects through releasing natural organic matter to promote the degradation of organic pollutants and stabilization of heavy metals are emphasized.The processes of the degradation of organic pollutants by the plants are illustrated and summarized as the biodegrading processes of rhizospheres,the interactions between roots and leaves and pollutants,the excretive processes of polutants by roots and leaves,the hydroxylating processes of plants and the enzyme oxidation degradation.The relationships between types and concentrations of pollutants and metabolic degradation,action of plant cellular structure are also discussed in the present paper.Some of ecological factors influencing the security and the validity of phytoremediation are disussed.For example,plant roots and their enzyme systems,activity of soil microorganisms,soil types and physiochemical properties and climate conditions.Finally,the view of the application and the developmnt of phytoremediation,and the problems to be solved during the development of phytoremediation are pointed out.

Issue 03 ,2001 ; 中国科学院知识创新工程资助项目 (ZKCX- 4 0 1 );; “污染生态化学”百人计划资助项目
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Common factors leading to accidents and behavior type research based on prevention

FU Gui, LI Xuandong, LI Jun(School of Resources and Safety Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China)

This paper is focused on the study of the accidental common causes leading to accidents and the corresponding accident prevention strategy. At the very beginning of this paper, the authors introduces a few basic principles for safety science proposed by Heinrich and other safety scientists, (i.e.,) all the accidents have their own leading factors, which may be either the unsafe conditions or unsafe behaviors or the both. And the frequency of the accidents changes with its severity within an “accident triangle”. The first aim of this paper is to find all the common factors leading to the accidents. According to the basic principles, the author has categorized analytically and statistically all the factors discovered into 10 major classes as to the gas explosion accidents that have occurred in China's mines during 20002001. Then, we conclude that worker's unsafe working habits have been the main cause for the accidents. Then the authors examined a medical accident that was caused by spoiling children and a severe food poisoning involving 367 students, and the other two accidents caused due to the lack of safety knowledge and safety awareness. Based on the above statistics, the authors summarized all the common factors leading to the various accidents, which can be sequentially ordered as unsafe habit, lack of safety knowledge and safety awareness. Among the three, unsafe condition or unsafe behavior can be put together as one, with the other two being different by nature. As to the second aim of this paper, the author categorized a 3-level factor, that is, the safety management frame, the safety climate survey, and the behavior-based safety programs in the across-industry accident prevention map. And finally, the authors introduced some leading edge progress of the key elements in the prevention map in practical cases, such as Wilton-Olefines case and DuPont case for reader's information. The most important conclusion of this paper is that all the accidents share common factors. To fight against all the accidents, a common prevention strategy (map) can be developed with the key elements of the map developed and supplied by reference to that popularly used in some European and North American countries.

Issue 01 ,2005 ;
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Summary of research on account of carbon emission in building industry and analysis of its influential factors

LI Xiao-dong;ZHU Chen;

This paper is aimed at making a review of the recent research situation in-situ on the carbon emission( CE) account and analysis of the influential factors( IFs) through summing up the applications and relevant analysis methods. And,then,a brief review has to be made on the research status and trends of the aforementioned aspects in the field of building industry so as to provide references for further studies on the energy conservation and carbon emission reduction. To achieve the above mentioned purposes,the paper of ours has briefly introduced 3 general methods of CE accounting especially by highlighting the accounting manner of the single building CE and macro-building embodiment and operational CE. The CE accounting method for the individual building evaluation should be mainly done on the basis of the framework of the life cycle assessment,e. g. BEES,ENVEST and BEPAS. And,in regard to the macroscopic features,it would be possible for us to classify or categorize the accounting ideas of building embodiment and operational CE into methods of top-down approach and bottom-up one with their differences being illustrated. The bottom-up approach seems to be more detailed,visible as well as accurate or precise,yet,it is more complex and complicated to fulfil their calculations and acquire a lot of data investigations. On the contrary,the topdown approach just goes opposite by nature. In terms of CE-IFs analysis,there can be found 3 main types of methods( i. e.IPAT series model,index decomposition analysis and the structural decomposition analysis). And,we have also illustrated and elaborated their advantages,disadvantages as well as the application procedures in addition to a vivid comparison. Focusing on the building of CE IFs,we have also summarized a few key issues in this way:( 1) The research scopes can be mainly focused on the CE/IFs of the building industry or the building operations;( 2) The model of IPAT series has been widely adopted in combination with LMDI;( 3) The key IFs are mainly related to the society,economics and building operations. Thus,in final analysis,the paper has mainly illustrated the drawbacks of the current studies and presented prospects for their further research in 3 aspects: building CE accounting scope, the accounting methodologies and perspectives of IFs analysis.

Issue 01 ,2020 v.20;No.115 ; 国家重点研发计划项目(2018YFC0704400)
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LIGHT POLLUTION AND PREVENTION COUNTERMEASURES

WANG Ya-jun (State Key Lab of Prevention and Control of Explosion Disasters, Beijing Institute of Technology, Beijing 100081, China)

The present paper aims to introduce the problem of light pollution. Light pollution is a new kind of environmental pollution, with the development of industrialization and civilization. It could be classified into three types, white light pollution, artificial daylight, and colorful light pollution. Light pollution is harmful to people's health, and it could disrupt ecology, increase the traffic accident, disturb the astronomical observation, bring much trouble to people's life, and waster energy. It is necessary to take measures to prevent the problems caused by light pollution. To build relative laws and regulations, to strengthen constructive layout and management are the good solutions and suggestions.

Issue 01 ,2004 ;
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Study on the evaluation and treatment of lake eutrophication by means of algae biology

Issue 02 ,2005 ; 国家 8 63项目 (2 0 0 2 AA60 10 2 1) ;; 国家 973项目(2 0 0 2 CB412 3 0 9)
[Downloads: 5,471 ] [Citations: 671 ] [Reads: 64 ] HTML PDF Cite this article

Economic growth and its effects on carbon emission in China

Characteristics and mechanism of road traffic accidents

GAO Yinan;GONG Jianqiang;

To study the characteristics and mechanism of road traffic accidents, the cause, form, time, perpetrator, motor vehicle, casualties, and other characteristics were analyzed based on the data of 1.09 million road traffic accidents in 2015-2019, and then causative factors of road traffic accidents were abstracted. The Decision Making Trial and Evaluation Laboratory(DEMATEL) method was used to calculate the correlation of complex system, and the influence degree, affected degree, cause degree, and center degree of causative factors were calculated to determine the impact of each causative factor on the formation of road traffic accidents. A hierarchical structure diagram of causing factors of road traffic accidents was established. The mechanism and essential causative factors of road traffic accidents were obtained theoretically. Based on the analysis of the characteristics and causes of road traffic accidents, suggestions on road traffic safety management in line with national conditions were proposed. 86.29% of the accidents were caused by motor vehicle violations. Among them, failure to give way according to regulations was the main reason, illegal loading was a high-risk factor, and speeding accidents were frequent and harmful. 70.21% of the accidents were multi-vehicle accidents. Side impact was a high-frequency accident form, and falling and rolling were high-risk accident forms. 17:00-19:59 was the accident-prone period. 3:00-5:59 was the period of high injury accidents. Accidents caused by production and operation vehicles were accompanied by high hazards, among which tourism passenger transport, transport of dangerous chemicals, and general freight transport caused the highest degree of damage. Pedestrians were more vulnerable to fatal injuries and men were more likely to be injured than women in accidents. 77.38% of the fatal causes of accidents were brain injuries. 3.63% of the motor vehicles involved in accidents had mechanical failures, the probability of poor braking was the highest, and the danger of excessive tire wear was the greatest. The analysis results of the mechanism show that speeding, overloaded transportation, insufficient technology or lagging standards and regulations are the essential factors. These factors can be used as key points to reduce accident risk.

Issue 11 ,2023 v.23 ; 交通运输部公路科学研究所(院)科技创新专项资金项目(2018-D0007,2019-I401)
[Downloads: 4,759 ] [Citations: 74 ] [Reads: 31 ] HTML PDF Cite this article

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Summary of research on account of carbon emission in building industry and analysis of its influential factors

LIGHT POLLUTION AND PREVENTION COUNTERMEASURES

Study on the evaluation and treatment of lake eutrophication by means of algae biology

Economic growth and its effects on carbon emission in China

Characteristics and mechanism of road traffic accidents

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