Effective Mitigation Measures Research Articles

Geological survey techniques and carbon storage: Optimizing renewable energy site selection and carbon sequestration

Geological survey techniques play a crucial role in optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage to mitigate climate change. This abstract provides an overview of how geological survey techniques can be used to achieve these objectives. Renewable energy development, particularly solar and wind power, requires careful site selection to maximize energy generation efficiency and minimize environmental impacts. Geological surveys are instrumental in assessing factors such as subsurface geology, topography, soil composition, and hydrological conditions. These surveys help identify suitable locations with optimal wind or solar resources and geologic conditions for infrastructure development. Additionally, geological surveys are essential for identifying suitable sites for carbon storage, a critical component of carbon capture and storage (CCS) technologies aimed at reducing greenhouse gas emissions. Geological formations, such as deep saline aquifers, depleted oil and gas reservoirs, and unmineable coal seams, can serve as storage reservoirs for captured carbon dioxide (CO2). Geological surveys help characterize these formations to assess their suitability for long-term CO2 storage, considering factors such as porosity, permeability, and sealing integrity. Optimizing site selection for renewable energy projects and carbon storage requires a comprehensive understanding of subsurface geology and environmental conditions. Advanced geological survey techniques, such as seismic imaging, remote sensing, and geophysical surveys, are essential for acquiring detailed subsurface data. These techniques enable scientists and engineers to assess site suitability, evaluate risks, and design effective mitigation measures. In conclusion, geological survey techniques are invaluable tools for optimizing site selection for renewable energy projects and identifying suitable locations for carbon storage. By leveraging these techniques, stakeholders can make informed decisions that promote sustainable energy development and mitigate the impacts of climate change.

Evaluating the effectiveness of mitigation measures in environmental impact assessments: A comprehensive review of development projects in Korea

Rapid urbanization and development projects in Korea have posed significant threats to biodiversity; thus, effective mitigation measures are required to preserve natural habitats. Nevertheless, the factors underlying variations in mitigation measure effectiveness according to the disturbance level and surrounding environmental conditions have not been clarified. This study evaluated the effectiveness of mitigation measures implemented in environmental impact assessments (EIAs) of development projects in Korea, with a focus on their effectiveness with respect to the disturbance level and surrounding environmental conditions. A review of 288 EIA reports from selected projects that implemented all 10 mitigation measures classified according to the Wildlife Conservation Comprehensive Plan was conducted. Using the biodiversity tipping point framework, the effects of mitigation measures on biodiversity were categorized into four levels and analyzed. Analysis of variance and redundancy analysis were then performed to discern the variance in mitigation measure effectiveness in terms of the disturbance level, surrounding environment, and species. The results revealed significant variations in the effectiveness of mitigation measures depending on the surrounding environment and disturbance level. Linear projects exhibited a clear impact on various species as the disturbance level increased, whereas area-based projects did not exhibit such pronounced effects. All species demonstrated a negative relationship with development duration, development area, and distance from urban centers. Notably, avian and amphibian species showed a strong negative correlation with the digital elevation model while reptiles and mammals exhibited a strong positive relationship with pre-development biodiversity and distance from protected areas, respectively. Mitigation measures play a key role in alleviating the adverse effects of development projects; therefore, our findings indicate the need for spatially tailored mitigation plans to augment their effectiveness.

The Risk of SARS-CoV-2 Transmission in Community Indoor Settings: A Systematic Review and Meta-Analysis.

Quantifying the risk of SARS-CoV-2 transmission in indoor settings is crucial for developing effective non-vaccine prevention strategies and policies. However, summary evidence on the transmission risks in settings other than households, schools, elderly care and healthcare facilities is limited. We conducted a systematic review to estimate the secondary attack rates (SARs) of SARS-CoV-2 and the factors modifying transmission risk in community indoor settings. We searched Medline, Scopus, Web of Science, WHO COVID-19 Research Database, MedrXiv, and BiorXiv from January 1, 2020, to February 20, 2023. We included articles with original data for estimating SARS-CoV-2 SARs. We estimated the overall and setting-specific SARs using the inverse variance method for random-effects meta-analyses. We included 34 studies with data on 577 index cases, 898 secondary cases, and 9173 contacts. The pooled SAR for community indoor settings was 20.4% (95% confidence interval [CI] 12.0-32.5%). The setting-specific SARs were highest for singing events (SAR 44.9%, 95% CI 14.5-79.7%), indoor meetings and entertainment venues (31.9%, 10.4-65.3%), and fitness centers (28.9%, 9.9-60.1%). We found no difference in SARs by index case, viral, and setting-specific characteristics. The risk of SARS-CoV-2 transmission was highest in indoor settings where singing and exercising occurred. Effective mitigation measures such as assessing and improving ventilation should be considered to reduce the risk of transmission in high-risk settings. Future studies should systematically assess and report the host, viral, and setting-specific characteristics that may modify the transmission risks of SARS-CoV-2 and other respiratory viruses in indoor environments.

Ammonia – an assessment of potential environmental impacts including prevention and mitigation measures

Ammonia (chemical formula NH3) is one of the most widely produced chemicals for industrial and agricultural uses. Ammonia has gained attention as a potential hydrogen and energy carrier, and low carbon substitute fuel for rail and maritime transport. The potential of ammonia for new use cases may lead to an increased footprint of potential environmental impacts. The physical and chemical properties of ammonia offer significant benefits for transportation and storage, but challenges remain due to its highly toxic nature and uses outside of dedicated industrial precincts. Ammonia is typically liquefied to increase density for ready storage and transportation. Unplanned releases of liquid ammonia will result in immediate environmental impacts. Unplanned releases may adversely affect receptor species in aquatic and terrestrial environments, human health, as well as the reputation of the responsible organisation. Dispersion of ammonia into the environment depends on factors that include mixture density, temperature, humidity, wind and wave action, and release mode and direction. Understanding the fate of ammonia is fundamental to adoption of effective mitigation measures. Consequently, it is necessary to understand the behaviour of ammonia in the environment, how unplanned releases can be minimised, and how they can be combatted, in order to appraise its suitability as a low carbon liquid transport fuel. This paper assesses the risks posed by ammonia spills, its fate in aquatic environments, and the applicability of available spill models. It also addresses management measures for the prevention and mitigation of spills noting the highly soluble and toxic nature of the chemical.

Oil Spillage Monitoring in Nigeria and the Role of Remote Sensing - An Overview

Oil spillage poses a persistent challenge in Nigeria most especially in oil-rich Niger Delta region, necessitating effective monitoring and mitigation measures. This study provides an overview of oil spill monitoring in Nigeria, emphasizing the pivotal role of remote sensing technology. Leveraging data from the NOSDRA Oil Spill Monitoring website, spanning 2016 to 2020, variations in spill occurrences among oil companies are analyzed. In 2016, a total of 42,741.25 barrels were spilled across 689 incidents, with NPDC emerging as the top spillage contributor. Similarly, 2017 witnessed 35,076.62 barrels spilled across 604 incidents, with NPDC and SPDC maintaining dominance. The trend continued in 2018, with SPDC recording the highest volume of spills among the top six contributing companies. However, in 2019, spillage increased, led by SPDC, followed closely by Heritage Energy Operational Services Limited and Aiteo. Conversely, 2020 saw a decrease in spillage, albeit SPDC retained its lead. These findings underscore the critical need for enhanced monitoring and regulatory enforcement to mitigate the environmental and socio-economic impacts of oil spills in Nigeria. However, proactive measures are needed to strengthen regulatory oversight, improve spill response capabilities, and promote transparency and accountability within the petroleum industry.

Impact of heavy metals on aquatic life and human health: a case study of River Ravi Pakistan

Heavy-metal contamination in river and ocean is a critical environmental issue that endangers marine ecosystems and human health. Therefore, conducting extensive research to devise effective mitigation measures is imperative. Sediment samples were taken randomly throughout the study area. Analysis was done to determine the presence of different metals, including arsenic, cadmium, chromium, nickel, copper, zinc, lead, and manganese. The assessment of different pollution levels was done by using various pollution indicators including “geo-accumulation index (Igeo), contamination factor (CF), enrichment factor (EF)” for accuracy. The Igeo measurement for Cd indicated varying pollution, ranging from moderate to significantly polluted, while Mn revealed no contamination. Elements such as Ni, Cr, Cu, and Zn showed a moderate level of contamination. The contamination factor values exhibited a range of 0.436 (Pb) to 7.637 (Cd), with average values spanning from 0.9176 (Mn) to 4.9714 (Cd), suggesting significant regional variation. EF exhibits a pattern of contamination comparable to that of Igeo. The noncarcinogenic risk associated with exposure to Cd and As exceeded the higher limit (HI > 1) for children and adults. Furthermore, the carcinogenic risk presented by pollutants such as copper (Cu), arsenic (As), nickel (Ni), cadmium (Cd), and chromium (Cr) was found to exceed the limits in children. In adults, only arsenic (As) and copper (Cu) were shown to represent a higher risk of cancer than the limit of 10−4. The PCA analysis revealed that two (PCs) accounted for more than 65% of the total variance in the River Ravi, as determined by eigenvalues greater than 1. This study underscores the importance of the ongoing monitoring and management of heavy-metal pollution to ensure sustainable marine ecosystem development and public health protection.

Assessing Desertification Vulnerability and Mitigation Strategies in Northern Nigeria: A Comprehensive Approach.

Desertification constitutes a grave threat to the environmental and socio-economic stability of desertification frontline states in Northern Nigeria. From 2003 to 2020, this research comprehensively analyzes desertification vulnerability, integrating parameters such as NDVI, LST, TVDI, MSAVI, and Albedo. Key factors contributing to land degradation are identified, along with the spatial patterns and trends of desertification over the two-decade period. The consequences are profound, with Northern Nigeria's ecosystem experiencing a steady decline in vegetation cover. Agriculture, vital to the region's economy, faces increased aridity and reduced arable land, jeopardizing food security. Diminishing water resources exacerbates scarcity issues, placing additional strain on communities. These environmental changes lead to severe socio-economic implications, including displacement, loss of livelihoods, and heightened vulnerability to climate-related risks. Urgent, comprehensive, and strategic interventions are imperative. Policy recommendations underscore revising and enforcing land use regulations, promoting sustainable agricultural practices, and establishing monitoring systems to guide decision-making. This research contributes practical strategies to enhance the resilience of desertification frontline states, safeguard livelihoods, and align with Nigeria's sustainable development objectives. Findings from the study indicate that only a tiny percentage (6.7%) of the study area remains unaffected by desertification. Moreover, 13.3% exhibit light vulnerability, 20% demonstrate moderate exposure, and 60% fall into the severe (26.7%) and compelling (33.3%) vulnerability categories. These statistics underscore the gravity of desertification in the study area, emphasizing the urgent need for effective mitigation measures to address its impact comprehensively.

Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment

Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure, highlighting the crucial need for monitoring PM levels to develop and implement effective risk mitigation measures. Notwithstanding, accurate measurement of PM concentration relies on expensive and cumbersome equipment. Despite the rising popularity of low-cost alternatives, their reliability remains questionable, given their sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. This article proposes a novel approach to efficient correction of low-cost PM sensors. The primary calibration model is a feedforward artificial neural network (ANN), which directly renders predicted output of the corrected sensor based on environmental variables such as temperature, humidity, and atmospheric pressure. The ANN hyper-parameters are identified by aligning time series of prior reference and low-cost sensor readings, which enables the network to learn typical temporal changes of the sensor outcome as a function of the aforementioned parameters as well as operational relationships between the sensor and the reference device. The architecture of the ANN is optimized in terms of the number of neurons in each layer to enhance its generalization capability. Our methodology has been demonstrated using a custom-designed portable monitoring platform and reference data acquired from public stations in Gdansk. The results are indicative of excellent calibration reliability. The achieved correlation coefficients w.r.t. the reference readings are 0.86, 0.88, and 0.72 for PM1, PM10, and PM2.5, respectively, whereas RMSE values are only 3.0, 3.9, and 5.4 µg/m³.

Unintended Consequences of Technostress Mitigation: An Employee Perspective on the Effectiveness of Mitigation Measures

The continuous use of IT, even beyond regular office hours, is considered a cause of technostress, which impairs the health and performance of employees. To mitigate technostress, European countries have established the right to disconnect, and many organizations are struggling to identify and implement other effective measures. Based on a qualitative study (N=30) with 23 IT workers, five managers, and two CIOs, this study identifies eight technological, social, and cultural measures to mitigate common techno-stressors. By focusing on the employees' perspective, the results reveal the extent to which the measures actually work, showing that well-intended countermeasures, such as email restrictions, might have unintended negative and even harmful side effects. Our analysis shows that mitigation measures seldom work in isolation and without spillover effects. We conclude that although technostress mitigation is complex and mitigation measures adopted in isolation can fail and sometimes cause additional harm, employees still appreciate the effort.

Characterization and sources of childhood PAEs exposure from residential airborne dust in China cities

To explore the multicenter characteristics of endocrine-like phthalate esters (PAEs) in household dust and propose effective control strategies for global indoor public health. An on-site observational investigation was conducted in nine Chinese cities from 2018 to 2019. A total of 246 household dust samples were collected and analyzed for ten PAE congeners using Gas Chromatography-Mass Spectrometry (GC-MS). Questionnaires were used to gather information on building conditions, indoor behaviors, and ventilation habits. In residential dust from the nine cities, the total concentrations of the ten PAE congeners (∑PAEs) ranged from 0.921 to 29097.297 µg/g. Dicyclohexyl phthalate (DCHP) and di (2-ethylhexyl) phthalate (DEHP) were the dominant congeners in ∑PAEs. Childhood exposure to PAEs through dust ingestion was four orders of magnitude higher than through inhalation, with a carcinogenic risk of 5.47 × 10−6 for DEHP exposure in household dust. Higher ∑PAEs concentrations were associated with higher temperature, double glazing, wall paint usage, television and computer use, and indoor plant growth. This multicenter on-site investigation confirmed PAE pollution characteristics and uncovered the inacceptable risk of daily DEHP exposure in household dust under real living conditions. Effective mitigation measures based on household-related information, residential characteristics, decoration materials, and lifestyle should be taken to build a healthy household environment.

Modeling water flow and volumetric water content in a degraded peat comparing unimodal with bimodal porosity and flux with pressure head boundary condition

AbstractDegraded peatlands release large amounts of greenhouse gases. The development of effective mitigation and management measures requires an understanding of relevant site‐specific biogeochemical and hydraulic processes. However, the simulation of water fluxes and vadose zone state variables of degrading peatlands relies on proper process description, parameterization of hydraulic functions, and representation of the boundary conditions. The objective of this study was to analyze the effects of unimodal versus bimodal soil hydraulic functions and pressure head versus flux‐type lower boundary conditions (LBCs) on the calculated hydraulic characteristics of a degraded peat profile. HYDRUS‐1D was used to study the hydraulic flow dynamics parameterized with data from a weighable groundwater lysimeter for the period from May 1 to December 31, 2019. Simulations comparing uni‐ and bimodal hydraulic functions showed only minor differences. Simulations of soil water pressure at a depth of 30 cm using a flux‐type LBC (RMSE: 27 cm, where RMSE is root mean square error) performed better than simulations using a pressure head LBC (RMSE: 48 cm). The pressure head LBC performed better at simulating volumetric water contents in 30‐cm depth than the flux LBC variant (RMSE: 0.05 vs. 0.09 cm3 cm−3). For specific site conditions with a shallow, fluctuating groundwater table and temporary air entrapment, the choice of LBC was important for a more accurate simulation of soil water fluxes and volumetric water content.

Wildlife fencing at German highways and federal roads – requirements and management implications

In Germany, the high risk of wildlife–vehicle collisions (WVC) is further increasing due to increasing traffic volumes and road densities as well as the growing population densities of common ungulate species. As a result, threats to human health and property as well as wildlife mortality of widely spread and rare species increase. Currently, three basic types of wildlife fences are used in Germany: type A, a conventional galvanized‐high tensile deer fence (made of knot braid); type B, a chain wire (mesh wire) fence; type C, a rigid welded wire mesh fence panels. Since fencing needs to restrict access of many different species with different behaviours (e.g. jumping, climbing, digging), fencing needs to be multifaceted to be effectively. Furthermore, the occurrence of wildlife species with habits such as jumping, climbing or digging determines the optimal (functional) fence design per location. We surveyed road managers in combination with a personal assessment of road sections in Germany and derived the following recommendations for optimal fencing to reduce WVC: 1) To deter digging, use plates made of recycled synthetic materials or a concrete foundation, instead of barbed wire. 2) Fencing should be made of chain wire (mesh wire, type B) fence or rigid welded wire mesh fence panels (type C) instead of knot braid mesh (type A). 3) To restrict climbing, use angled chain wire fence or rigid welded wire mesh fence panels at the top of the fences. 4) Maintain fences so that they are kept free from ingrown vegetation/woods. Maintenance conditions should be considered while planning and building the fence, especially to ensure sufficient space on both sides for accessibility. 5) Fencing should be established preferably near the roadside and moved away from property lines. In conclusion, wildlife fencing could be a very effective mitigation measure to prevent WVC with common as well as protected species, when proper designed and recommendations of the survey will be consequently considered. Currently many wildlife fences lack functionality due to an inappropriate design and maintenance issues in Germany.

Advances in Genetic Enhancement of Nutritional Quality of Tropical Maize in West and Central Africa

Micronutrient deficiencies are pervasive in the diets of millions of people in developing countries, calling for effective mitigation measures. The development of biofortified cultivars through breeding holds promise for sustainable and affordable solutions to combat micronutrient deficiencies. Breeding efforts in the past decade have resulted in dozens of biofortified open-pollinated varieties and hybrids adapted to diverse agroecological zones. Advances in genomics and molecular tools enabled rapid identification of maize cultivars enriched with essential micronutrients such as pro vitamin A (PVA), iron (Fe), and zinc (Zn). Leveraging Multi-omics-driven discovery of the genetic factors underlying the vast array of nutritional traits is paramount to mainstreaming breeding for quality traits in the product profile. Molecular breeding schemes, and integrating emerging Omics tools at every stage of the breeding pipeline, are vital to enhancing genetic gain. The recent momentum in elucidating the metabolism of micronutrients should be expanded to novel breeding targets as well as to the simultaneous enhancement of nutritional qualities while curtailing anti-nutritional factors in staple food crops. Harnessing new technologies to establish comprehensive and integrated breeding approaches involving nutrigenomics, genome editing, and agronomic biofortification is crucial in tackling nutritional insecurity. This review highlights the prospect of integrating modern tools in hastening the genetic improvement of nutritionally enriched maize.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

On the need to assess and mitigate the risk from uncontrolled re-entries of artificial space objects in view of the current and future developments in space activities

From 1 January 2010 to 24 August 2023, 566 orbital stages and 511 spacecraft with a radar cross section >1 m2 have re-entered without control the Earth's atmosphere. The total returned mass was 1650 metric tons, corresponding to a mean of 115 metric tons per year. 77% of the mass belonged to orbital stages, 23% to spacecraft. The uncontrolled re-entries of orbital stages are currently dominated by China, accounting for more than half of the decaying mass, while for spacecraft 2/3 of the mass belongs to American satellites. 60% of the re-entries occurred within 2 years of the launch. The ground casualty expectancy due to orbital stages was always predominant over that from spacecraft, by an average factor of nearly three. From 2010 to 2018, the total casualty probability remained substantially stable, with a mean annual value just over 1%. Since 2019, instead, the annual casualty probability of both spacecraft and orbital stages progressively increased, reaching a total value of around 3% in 2022 and 2023 (extrapolated). Even assuming a stable launch activity, in the coming years, when many of the recently launched spacecraft will start to re-enter, the casualty expectancy of orbital stages will remain basically the same, while that of spacecraft might progressively increase by a factor of 20. This would lead to an annual casualty probability of about 20%, even more in case of a further growth in launch activity, very likely based on current forecasts. The quick implementation of widespread and effective mitigation measures, like controlled de-orbiting and design for demise, is therefore necessary, to prevent the situation from deteriorating too much.

Submicron aerosol pollution in Greater Cairo (Egypt): A new type of urban haze?

Greater Cairo, the largest megacity of the Middle East North Africa (MENA) region, is currently suffering from major aerosol pollution, posing a significant threat to public health. However, the main sources of pollution remain insufficiently characterized due to limited atmospheric observations. To bridge this knowledge gap, we conducted a continuous 2-month field study during the winter of 2019–2020 at an urban background site, documenting for the first time the chemical and physical properties of submicron (PM1) aerosols. Crustal material from both desert dust and road traffic dust resuspension contributed as much as 24 % of the total PM1 mass (rising to 66 % during desert dust events), a figure not commonly observed in urban environments. Our observations showed significant decreases in black carbon concentrations and ammonium sulfate compared to data from 15 years ago, indicating an important reduction in both local and regional emissions as a result of effective mitigation measures. The diurnal variability of carbonaceous aerosols was attributed to emissions emanating from local traffic at rush hours and nighttime open biomass burning. Surprisingly, semi-volatile ammonium chloride (NH4Cl) originating from local open biomass and waste burning was found to be the main chemical species in PM1 over Cairo. Its nighttime formation contributed to aerosol water uptake during morning hours, thereby playing a major role in the build-up of urban haze. While our results confirm the persistence of a significant dust reservoir over Cairo, they also unveil an additional source of highly hygroscopic (semi-volatile) inorganic salts, leading to a unique type of urban haze. This haze, with dominant contributors present in both submicron (primarily as NH4Cl) and supermicron (largely as dust) modes, underscores the potential implications of heterogeneous chemical transformation of air pollutants in urban environments.

Big data-driven risk decision-making and safety management in agricultural supply chains

In the era of digitization, the integration of big data technologies has become instrumental in advancing agri-cultural supply chain management and bolstering risk decision-making processes. Agricultural supply chains, critical to ensuring food security and bolstering rural economies, face vulnerabilities stemming from a myriad of internal and external elements, including natural disasters and market dynamics. Consequently, the urgency to adopt effective risk management strategies is paramount. Contemporary studies have explored the utilization of big data in decision-making processes specific to agricultural supply chain risks, predominantly concentrat-ing on preliminary risk prediction and characterization. Nonetheless, there exists a shortfall in comprehensively analyzing the intricate interplay among risk factors and establishing a holistic risk management decision-making framework based on such analyses. This research addresses these deficiencies through two principal investigative components. First, this research explores the analysis of risk factors and their interrelationships in the agricultural supply chain based on a decision tree algorithm with a transition structure. This algorithm enhances decision-makers’ understanding of risk factors and their interrelationships, and guide the implementation of effective risk mitigation measures and the formulation of contingency plans. Subsequently, the research constructs a corresponding data-driven multi-criteria decision-making method, assisting managers in balancing different risk management strategies in a volatile supply chain environment, considering costs, benefits, and feasibility to formulate the optimal strategy. The innovation of this research lies in the development of a novel risk analysis tool based on the transition decision tree algorithm. This is the first time that such advanced algorithms are applied to agricultural supply chain risk management, filling a gap in the current research. The outcomes of this study not only contribute to enhancing risk management practices within agricultural supply chains but also offer novel insights and methodological tools that are applicable in research and practices across related domains.

Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations

Subway systems are a crucial component of urban public transportation, especially in terms of safety during seismic events. Soil liquefaction triggered by earthquakes is one of the key factors that can lead to underground structural damage. This study investigates the impact of deep soil liquefaction on the response of subway station structures during seismic activity, aiming to provide evidence and suggestions for earthquake-resistant measures in underground constructions. The advanced finite element software PLAXIS was utilized for dynamic numerical simulations. Non-linear dynamic analysis methods were employed to construct models of subway stations and the surrounding soil layers, including soil–structure interactions. The UBC3D-PLM liquefaction constitutive model was applied to describe the liquefaction behavior of soil layers, while the HS constitutive model was used to depict the dynamic characteristics of non-liquefied soil layers. The study examined the influence of deep soil liquefaction on the dynamic response of subway station structures under different seismic waves. The findings indicate that deep soil liquefaction significantly increases the vertical displacement and acceleration responses of subway stations compared to non-liquefied conditions. The liquefaction behavior of deep soil layers leads to increased horizontal effective stress on both sides of the structure, thereby increasing the horizontal deformation of the structure and posing a potential threat to the safety and functionality of subway stations. This research employed detailed numerical simulation methods, incorporating the non-linear characteristics of deep soil layer liquefaction, providing an analytical framework based on regulatory standards for evaluating the impact of deep soil liquefaction on the seismic responses of subway stations. Compared to traditional studies, this paper significantly enhances simulation precision and practical applicability. Results from this research indicate that deep soil layer liquefaction poses a non-negligible risk to the structural safety of subway stations during earthquakes. Therefore, the issue of deep soil liquefaction should receive increased attention in engineering design and construction, with effective prevention and mitigation measures being implemented.

Multi-temporal analysis of landslide susceptibility in the Greater Bogor Area and its relation to land use change and rainfall variation

Landslide susceptibility in the Greater Bogor Area is a significant concern due to its hilly topography, soil types, and high rainfall. This research examines the geographical and temporal variations in landslide susceptibility and the relationship between land use change, rainfall, and landslide occurrence. Landsat 7 ETM+, CHIRPS, and DEM satellite data were used to simulate landslide susceptibility. The study used 2000, 2010, and 2020 data to examine land use and precipitation changes. The weighted overlay method created a landslide susceptibility model that considered precipitation, land use, slope, rock, and slope. The association between land use change, rainfall, and landslide susceptibility was examined using correlation analysis. According to the findings, the Greater Bogor Area is highly prone to landslides, with a growing tendency in landslide events. 22.48% of the area has a very high susceptibility classification to landslide; meanwhile, 66.46% has a high susceptibility classification. Moreover, this study also found that the land use factor correlates more positively with landslide susceptibility than the rainfall factor. Overall, this study underlines the need to take land use dynamics and rainfall patterns when assessing landslide susceptibility and the need for effective mitigation measures in the area.

Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.