Warning Levels Research Articles

Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm

Background: Tsunami caused by volcanic collapse are categorized as non-seismic uncommon events, unlike tsunamis caused by earthquakes, which are common events. The traditional tsunami early warning based on the seismic sensor (e.g. earthquake detectors) may not be applicable to volcanic tsunamis because they do not generate seismic waves. Consequently, these tsunamis cannot be detected in advance, and warnings cannot be issued. New methods should be explored to address these non-seismic tsunamis caused by volcanic collapse. Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for non-seismic tsunamis, specifically volcanic tsunamis. The Anak Krakatau volcano event in Indonesia was used as a case study. Methods: This study generated a database of 160 collapse scenarios using numerical simulation as input sequences. A classification model was constructed by defining the worst tsunami elevation and its arrival time at the coast. The database was supervised by labeling the warning levels as targets. Subsequently, a decision tree algorithm was employed to classify the warning levels. Results: The results demonstrated that the classification model performs very well for the Major Tsunami, Minor Tsunami, and Tsunami classes, achieving high precision, recall, and F1-Score with very high accuracy of 98%. However, the macro average indicates uneven performance across classes, as there are instances of ‘No Warning’ in some coastal gauges. Conclusion: To improve the model performance in the ‘No Warning’ class, it is necessary to balance the dataset by including more ‘No Warning’ scenarios, which can be achieved by simulating additional scenarios involving very small-volume collapse. Additionally, exploring additional collapse parameters such as dip angle and outlier volume could contribute to developing a more robust classification model. Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Tree

Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China

Regional forecasting of the landslide rainfall threshold is complex, and the empirical prediction of the rainfall threshold relies on historical statistical data. To obtain the intensity I ∼ duration (D) rainfall threshold, Lanzhou City was selected as the research object, and a field investigation, 3S technology, mathematical statistics and a Python language environment were employed to develop a set of automatic rainfall threshold calculation programs. Factor elimination, the information value method, weight of evidence, the frequency ratio, the density method and receiver operating characteristic (ROC) curve were applied to obtain landslide susceptibility, and the I ∼ D rainfall threshold (temporal probability) and landslide susceptibility (spatial probability) were combined to construct regional landslide rainfall threshold warning levels (red, orange, yellow, and blue). The results showed the rainfall duration of historical landslides ranged from 0.99 to 216 h, thereinto, it was accounted for 92.78 % with that of between 6 and 96 h, and the rainfall intensity was between 0.11 and 23.6 mm/h, and the α and β of the rainfall threshold curve was gradually increased with an increase in the rainfall threshold probability. Additionally, when the rainfall condition probability was the same, landslides occurrence was more frequent in Chenguan District, where there are large areas of bare land, and slope and annual rainfall are less than 10 °and 300 mm, respectively. Fifteen, nine, and six factors were used to obtain landslide susceptibility, and the accuracy of the ROC ranged from 0.78 to 0.80 under different methods and different factor combinations. The spatial distribution of landslide susceptibility was very similar. There were 35, 12, 25, and 108 landslides associated with red, orange, yellow, and blue levels, respectively, under the spatial–temporal probability, but 30, 26, 54, and 70, respectively, under temporal probability. Under the different conditions, there were 89, 69, and 22 landslide points with unchanged, increased, and decreased warning levels, respectively. The ability and accuracy of rainfall threshold warning and prediction were effectively improved by integrating the temporal and spatial probabilities of the rainfall threshold.

TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation.

Concept drift arises from the uncertainty of data distribution over time and is common in data stream. While numerous methods have been developed to assist machine learning models in adapting to such changeable data, the problem of improperly keeping or discarding data samples remains. This may results in the loss of valuable knowledge that could be utilized in subsequent time points, ultimately affecting the model's accuracy. To address this issue, a novel method called time segmentation-based data stream learning method (TS-DM) is developed to help segment and learn the streaming data for concept drift adaptation. First, a chunk-based segmentation strategy is given to segment normal and drift chunks. Building upon this, a chunk-based evolving segmentation (CES) strategy is proposed to mine and segment the data chunk when both old and new concepts coexist. Furthermore, a warning level data segmentation process (CES-W) and a high-low-drift tradeoff handling process are developed to enhance the generalization and robustness. To evaluate the performance and efficiency of our proposed method, we conduct experiments on both synthetic and real-world datasets. By comparing the results with several state-of-the-art data stream learning methods, the experimental findings demonstrate the efficiency of the proposed method.

FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm

Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.

Development of a Graded Early Warning Index System and Identification of Critical Temperatures for Coal Spontaneous Combustion Using Composite Gas Characteristics.

Conventional single gas alarm method and coal spontaneous combustion three-stage alarm method have become increasingly inadequate to meet complex underground conditions. To address this issue, this study focuses on the coal in the goaf of the Z109 working face in the Donggucheng Mine as the research object. Through program-controlled heating experiments, the production of carbon monoxide and hydrocarbon gases during the coal oxidation process was determined, and the variation characteristics of gas ratios with temperature were further analyzed. The coal spontaneous combustion process was subdivided into seven small stages, and a quantitative composite parameter coal spontaneous combustion grading warning system was formulated. Based on its characteristics, measures to be taken under different warning levels were proposed, and it was determined that 120, 140, and 160 °C are the key temperatures for coal spontaneous combustion prevention and control in the Z109 working face of Donggucheng Mine. By using numerical simulation, the optimal nitrogen injection position for the working face was determined and the on-site fire prevention and extinguishing measures were optimized, providing insights into the establishment of a coal mine spontaneous combustion warning system.

A real-time early warning classification method for natural gas leakage based on random forest

Serious natural gas leakage explosion accidents have brought seriously threatening to people's lives and properties. Efficient warning classifications is of great significance to make rapid response, thus reducing the losses caused by accidents. This paper describes a novel early warning classification method for natural gas leakage based on a multi-classification random forest (RF) model, which allows evaluating the level of early warning of gas accidents timely and accurately, assisting monitoring department and gas company in timely rapid decision and scientific disposal. Fully considering the laws of natural gas leakage and the change of comprehensive risks in underground spaces adjacent to natural gas pipeline, an early warning classification index system was established, and multiple warning factors features were extracted from recorded warning events of natural gas leakage. Then the early-warning level labels of the warning events was gained by K-mean clustering and experts scoring methods. The extracted warning features and the associated early-warning level labels were used to train and validate the proposed model. The effectiveness and feasibility of this model is further verified by comparing with other popular approaches. Furthermore, the verified model is loaded into real time module, which can achieve the real time warning classification. The research results demonstrated that the proposed method can timely and accurately classify the levels of the early warning events. The prediction accuracy of the natural gas leakage early warning classification model based on the RF algorithm is 88.02 %. For real time warning events, rapid decision can be made according to the characteristics of early-warning grades, and the emergency disposal can be guided more effectively based on the warning classification results.

Determination of River Ecological Flow Thresholds and Development of Early Warning Programs Based on Coupled Multiple Hydrological Methods

In order to safeguard the health of river ecosystems and maintain ecological balance, it is essential to rationally allocate water resources. This study utilized continuous runoff data from 1967 to 2020 at the Zhouqu Hydrological Station on the Bailong River. Five hydrological methods, tailored to the hydrological characteristics of the Zhouqu hydrological cross-section, were employed. These methods included the improved dynamic calculation method, the NGPRP method, the improved monthly frequency computation method, the improved RVA method, and the Tennant method. Ecological flow calculations were conducted to determine the ecological flow, with analysis carried out through the degree of satisfaction, economic benefits, and the nonlinear fitting of the GCAS model. We established an ecological flow threshold and early warning program for this specific hydrological cross-section. Ecological flow values calculated using different methods for each month of the year were compared. The improved RVA method and Tennant method resulted in small values ranging from 4.05 to 36.40 m3/s and 7.65 to 22.94 m3/s, respectively, with high satisfaction levels and economic benefits, but not conducive to ecologically sound development. In contrast, the dynamic calculation method, NGPRP method, and improved monthly frequency calculation method yielded larger ecological flow values in the ranges of 21.79–97.02 m3/s, 23.90–137.00 m3/s, and 28.50–126.00 m3/s, respectively, with poor fulfillment and economic benefits. Ecological flow thresholds were determined using the GCAS model, with values ranging from 16.72 to 114.58 m3/s during the abundant water period and from 5.03 to 63.63 m3/s during the dry water period. A three-level ecological warning system was proposed based on these thresholds, with the orange warning level indicating optimal sustainable development capacity for the Zhouqu Hydrological Station. This study provides valuable insights into the scientific management of water resources in the Bailong River Basin to ensure ecological security and promote sustainable development.

Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices

Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time.

A Big Data-Driven Approach for Early Warning of Enterprise Emissions Alignment with Carbon Neutrality Targets: A Case Study of Guangxi Province

Achieving the target of carbon neutrality has been an important approach for China to mitigate global climate change. Enterprises are major carbon emitters, and a well-designed early warning system is needed to ensure that their emissions align with carbon neutrality goals. Therefore, this study utilized electricity big data to construct an early warning model for enterprise carbon emissions based on carbon quota allocation. Taking key carbon-emitting enterprises in Guangxi as a case study, we aim to provide insights to support China’s dual carbon goals. Firstly, we established the Carbon Quota Allocation System, enabling carbon quota allocation at the enterprise levels. Secondly, we developed the Enterprise Carbon Neutrality Index, facilitating dynamic warnings for carbon emissions among enterprises. The main conclusions are as follows: (1) In 2020, Guangdong received the highest carbon quota of 606 million tons, representing 5.72% of the national total, while Guangxi only received 2.63 billion tons. (2) Only 39.34% of enterprises in Guangxi are able to meet the carbon neutrality target, indicating significant emission reduction pressure faced by enterprises in the region. (3) Over 90% of enterprises in Guangxi receive Commendation and Encouragement warning levels, suggesting that enterprises in Guangxi are demonstrating a promising trend in emission reduction efforts.

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.

Research on Gas Multi-indicator Warning Method of Coal Mine Working Face Based on MOA-Transformer.

The gas emission from the coal mine working face is influenced by multiple factors, resulting in the real-time value, fluctuation, and trend changes of gas concentration being relatively independent and interrelated. This paper establishes a gas multi-indicator warning method that can comprehensively warn the status of real-time value, fluctuation, and trend changes of gas concentration from the working face. This paper proposes six basic indicators and includes two main research contents: intelligent threshold partition and gas multi-indicator warning. First, this paper proposes an intelligent threshold partition algorithm based on GF-KMeans (genetic fixed-centered K-means), which combines a genetic algorithm (GA) and an FC-KMeans (fixed-centered K-means) algorithm to dynamically partition the threshold range corresponding to the gas warning level. The GA solves the local optimal problem in the traditional K-Means algorithm, enhancing its stability and predictability. The FC-KMeans algorithm achieves a more precise control in the initial clustering center selection. Second, this paper researches a gas multi-indicator warning method based on a multihead optimal attention (MOA)-Transformer. By using the multihead optimization attention mechanism to represent classification features and utilizing Transformer's encoder structure to classify gas warning. The experimental result shows that the accuracy of the MOA-Transformer method is 86.17%, which is 3.45% higher than that of the Transformer method. The precision of the MOA-Transformer method is 88.78%, which is 3.75% higher than that of the Transformer method. The recall of the MOA-Transformer method is 85.23%, which is 4.70% higher than that of the Transformer method. The macro-F1 of the MOA-Transformer method is 86.96%, which is 4.39% higher than that of the Transformer method. The results fully demonstrate the superiority of the MOA-Transformer method in gas warning tasks.

Financial warning for coal mining investments: Evidence from the fruit fly optimisation algorithm with backpropagation neural networks

Venture capital firms may be unable to withstand the high investments and risks associated with coal mining exploration due to limitations in the funding scale. Risk control capability is also a challenge for venture capital companies. Finding a balance between high-risk and high-return coal mining exploration projects is a problem that venture capital companies must face. We propose a financial risk-warning model for coal mining investment enterprises based on the fruit fly optimisation algorithm (FOA) with the backpropagation neural network (BPNN). The fusion of standardised and dimensionless sample data through factor analysis reduces the input dimension of the BPNN and improves its stability. The financial warning algorithm of the model has been effectively validated through experiments. The research results indicate that its accuracy has reached a high level of financial warning, reaching a level of 95%.

영향한계강우량을 고려한 삼척시 호우특보 기준에 관한 연구

Heavy rain has a short duration of occurrence, causing many casualties and property damage. However, the heavy rain warning criteria are a single warning criterion that does not reflect local characteristics. Therefore, this study aims to study and propose heavy rain warning criteria for Samcheok City that consider the threshold rainfall to supplement the uniform decision-making system for heavy rain disasters. By analyzing the basic statistical values of the threshold rainfall grid for each advisory area in Samcheok City, the criteria for heavy rain warning for each advisory area were selected in four levels, and the applicability was examined by applying it to past rainfall events. As a result, this study visibly forecasted the expected risk level that changes depending on the rainfall event by advisory area, and categorized the advisories and warnings of heavy rain warning from the KMA were categorized into Warning and Danger levels. The results of this study are expected to be useful in securing more golden time for responding to heavy rain disasters and preparing efficient measures against heavy rain disasters.

Design and application of coastal erosion indicators using satellite and drone data for a regional monitoring program

Coastal erosion is a complex issue that affects landforms at various spatio-temporal scales, with concerns rising due to the impending threats of climate change. Given the increasing availability of coastal shoreline data, we identify a gap in present coastal management methods: the need for a simple and cost-effective mechanism to track and compare shoreline trends, to inform high-level reporting, down to local management scales. This study introduces simplified, automated erosion indicators, demonstrated for 20 morphologically diverse beach sites across the temperate coast of Victoria, Australia. The methods utilise coastal drone surveys (2018–2023; 560 surveys) and satellite-derived shorelines (Digital Earth Australia Coastlines, 1988 to 2021). Two tools are introduced: (1) the Erosion Warning Indicator, for high-level reporting, reducing multiple aspects of shoreline change (long-term vs. short-term; widespread vs. hotspot) to a simplified scoring system allowing overall site assessment and comparison across sites; and (2) the Erosion Hotspot Detector, to aid local managers to automatically identify and analyse emerging erosion areas. Results for a 2-year period (2021-22) indicate one site with a high erosion warning level, and four sites with a moderate level. Tracking the indicator over time (2018–2022) identifies two sites with persistently high erosion warning levels (Apollo Bay and Marengo). Importantly, the indicators also highlight areas where recent erosion trends differ from longer-term patterns (e.g., Inverloch), allowing re-evaluation of short-term expenditure on management interventions, without impacting longer-term adaptation pathways. The erosion indicators presented here hold value for coastal managers in many parts of the world. If comparable approaches are introduced and shared at scale, we will collectively be better equipped and informed to respond to future challenges to our coastlines.

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

In light of the worsening global climate, providing predictive models for surface temperature and energy consumption is crucial for formulating effective climate action strategies. Initially, a Bi-directional Long Short-Term Memory (BiLSTM) network model is established to predict the maximum surface temperatures over the next century, with the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model serving as a benchmark. To assess the risk levels of climate security, the k-means clustering algorithm is utilized to classify the growth rates of carbon dioxide emissions, enabling the construction of a three-tier climate security early warning index. Subsequently, a hybrid classification model based on Support Vector Machine (SVM) and Random Forest (RF) takes the energy consumption growth rates as inputs and the warning indices as outputs to construct a climate security early warning system. The BiLSTM model is employed to predict the energy consumption growth rates for the upcoming decade, and these rates are input into the SVM-RF model to forecast future warning levels. The study demonstrates that the model can effectively predict the maximum surface temperatures and provide a three-tier safety warning system for future climate risk management. The intent of this research is to offer a novel tool for global climate prevention and to deliver practical application value to policymakers in finance, energy, and environmental sectors.

Design and Development of a Flood Detection Device for Drainage Systems Utilizing Float Switch Water Level Sensors

Indonesia ranks second in Asia Pacific in the list of the highest number of deaths due to natural disasters. In a year, more than 300 flood events occur, resulting in large losses, environmental damage and even loss of life. To overcome this problem, it is necessary to design a tool for monitoring water levels and rainfall. This research aims to design a flood detection system using the fuzzy method and using a float switch water level sensor. The results of the research show that this system can monitor water levels and rainfall using a web server. In the first test, it produces a fuzzy warning level in a safe condition. In the second test, the water level reached 51 cm with rainfall of 0.47 mm, and the fuzzy warning level was in a safe condition. Finally, in the third test, the average water level reached 99 cm with rainfall of 0.69 mm, resulting in a fuzzy warning level in an alert state. It can be concluded that this system needs to be improved in quality by adding items to the system.

Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons

Outdoor solutions aiding the navigation of visually impaired individuals can seamlessly transition to indoor environments. Take, for instance, the adaptation of special lanes and configurations on the floor. However, these existing solutions fall short when it comes to addressing obstacles above ground level, such as open windows, as highlighted in a previous article on the use of ultrasonic glove for visually impaired users. In response, the present proposal is a user-friendly, cost-effective solution that is capable of detecting elevated obstacles. Importantly, this solution aligns with a user’s language preferences, eliminating the need for learning new languages or possessing IT skills. Users simply specify their desired language for the prototype to communicate in, ensuring a personalized experience. The system alerts users to the presence of obstacles through varying levels of warning, calculated based on the distance between the obstacle and the user’s current position. This approach not only enhances safety but also prioritizes accessibility and ease of use.

Analysis and Warning Prediction of Tunnel Deformation Based on Multifractal Theory

To better analyze the fluctuation characteristics and development law of tunnel deformation data, multifractal theory is applied to tunnel deformation analysis. That is, the multifractal detrended fluctuation analysis (MF-DFA) model is first utilized to carry out the multifractal characterization of tunnel deformation data. Further, Mann–Kendall (M–K) analysis is utilized to construct the dual criterion (∆α indicator criterion and ∆f(α) indicator criterion) for the tunnel deformation early warning study. In addition, the particle swarm optimization long-short-term memory (PSO-LSTM) prediction model is used for predicting tunnel settlement. The results show that, in reference to the tunnel warning level criteria and based on the Z-value results of the indicator criterion, the warning level of all four sections is class II. At the same time, through the analysis of tunnel settlement predictions, the PSO-LSTM model has a better prediction effect and stability for tunnel settlement. The predicted results show a slow increase in tunnel settlement over the next 5 days. Finally, the tunnel warning level and the predicted results of tunnel settlement are analyzed in a comprehensive manner. The deformation will increase slowly in the future. Therefore, monitoring and measurement should be strengthened, and disaster preparedness plans should be prepared.

Research on Intelligent Management System of Gas Pipeline with Multi-source Data Fusion

Abstract Aiming at the current challenges of enormous scale, complex structure, difficult control and frequent accidents of city gas high-pressure pipeline network, there are still three aspects of difficulties in the risk monitoring and control of China’s city gas high-pressure pipeline network, namely, rough data, shallow assessment, and lack of power. This paper proposes an intelligent management system for gas pipelines based on C/S model and J2EE enterprise-level framework, in which the failure warning models of gas leakage, Gaussian plume diffusion, and fire and explosion are established. And the Kalman filter algorithm improved by DS evidence theory is used for intelligent fusion of Multi-source data, analyzing and screening the unified adequate information on data types, extracting state characteristics, classifying warning levels, and developing an integrated and visualized pipeline remote diagnosis and warning platform. In the simulation of the intelligent management system of gas pipeline, when the wind speed is 1.5m/s in winter, the ground surface is a safe area within 12.15m of the gas pipeline. When the maximum wind speed is 10m/s, the upper limit distance of the gas leading to fire and explosion is only 2.43m, and the hazardous range of the gas pipeline jet fire is within 12.69m. Relying on the gas high-pressure pipeline network in L city for practical experiments and applications, it provides technical support and decision-making basis for the construction of intelligent pipeline network, comprehensively improves the risk control capability of city gas high-pressure pipeline network, and has reference significance for the risk control of national city gas high-pressure pipeline network.

Evaluation of microplastic bioaccumulation capacity of mussel (Perna viridis) and surrounding environment in the North coast of Vietnam

This study aimed to identify the presence of microplastics in green mussels (Perna viridis), surface seawater, and beach sediment on the North Coast of Vietnam. The average concentration of MPs in mussels was 3.67 ± 1.20 MPs/g wet weight and 25.05 ± 5.36 MPs/individual. Regarding surface seawater and beach sediments, the MPs concentration was found at 88.00 ± 30.88 MPs/L and 4800 ± 1776 MPs/kg dry weight, respectively. The dominant microplastics shape was fragment with the fractions ranging from 69.86 to 82.41 %. In addition, the size distribution of MPs was mostly in the range of smaller than 50 μm and 1–150 μm (34.17 % and 45.62 % in mussels; 29.65 % and 43.20 % in surface seawater and 40.22 % and 39.40 % in beach sediment, respectively). Polyethylene terephthalate was the major polymer types 49.93–58.44 % of the detected MPs. The risk assessment results based on the polymer types indicated a warning level in several sites.