Risk Zones Research Articles

Landslide susceptibility modelling in the Doda Kishtwar Ramban (DKR) region of Jammu and Kashmir using Remote Sensing and Geographic Information System

The present study represents a significant understanding, employing cutting-edge Remote Sensing (RS) and Geographic Information System (GIS) technologies by conducting a comprehensive landslide susceptibility modelling in the Doda, Kishtwar, and Ramban (DKR) districts of the Union Territory of the Jammu and Kashmir in the NW Himalaya. The primary objective of this study is to determine the areas that are at high risk of landslides and accordingly classified these risk zones into five distinct categories, ranging from very high (VH) to very low (VL) susceptibility. To achieve this, a multifaceted approach, involving the utilization of various terrain thematic layers in the GIS environment was applied. The preferred framework employed in this study is the Weighted Overlay (WO) analysis. The final outcome of the study is the final landslide susceptibility map which is a significant and valuable resource for a wide range of stakeholders, including decision-makers, land managers, and local communities. It equips these stakeholders with the information and insights required to adopt proactive strategies in response to the identified landslide susceptibility zones.

The landslide traces inventory in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau: a case study of Jianzha County, China

The upper reaches of the Yellow River in China, influenced by erosion of the Yellow River and tectonic activities, are prone to landslides. Therefore, it is necessary to investigate the existing landslide traces. Based on visual interpretation on high-resolution satellite images and terrain data, supplemented and validated by existing landslide records, this paper prepared the most complete and detailed landslide traces inventory in Jianzha County, Huangnan Tibetan Autonomous Prefecture, Qinghai Province, to date. The results indicate that within the study area of 1714 km2, there are at least 713 landslide traces, ranging in scale from 3,556 m2 to 11.13 km2, with a total area of 134.46 km2. The total landslide area excluding the overlap area is 126.30 km2. The overall landslide point density and area density in the study area are 0.42 km-2 and 7.37% respectively. The maximum point density and maximum area density of landslide traces in the area are as high as 5.69 km-2 and 98.0% respectively. The landslides are primarily distributed in the relatively low-elevation northeastern part of Jianzha County, characterized mainly by large-scale loess landslides, with 14 landslides exceeding 1×106 m2. This inventory not only supplements the landslide trace data in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau, but also provides an important basis for subsequent landslide risk zoning, response to climate change, and landscape evolution. Additionally, it holds significant reference value for compiling landslide inventories in similar geological environments.

Sense of Place and Perceived Impacts in the Rural Industrialized Nexus: Insights for Sustainability Pathways

As representative of the water-energy-food nexus, fossil fuel development and industrial agriculture are rural industries that continue to expand and increasingly occur in the same areas. Being a top agricultural export county and the fossil fuel capital of California while ranking among the worst in the US for industrial pollution, Kern County is a poster child of rural nexus development and, thus, an essential place for initiating sustainability transitions. Such transitions rely on policy support and the adoption of methods by individuals and communities who may disagree with such changes. While sense of place and impact perceptions are recognized as playing critical roles in sustainability management, they have yet to be utilized in nexus research. A survey (N = 256) of the perceived impacts of nexus industries with place meaning and place attachment as possible drivers for perceptions was conducted in nexus industry pollution exposure risk zones. Factor analysis and bivariate correlations showed that place meaning and place attachment are drivers for perceptions while also being drivers for concern for changes in nexus industries. While perceptions of impacts indicated contested place meanings, participants strongly perceive the economy and environment as being in decline. To build support for sustainability policy, directing funds from Kern County’s renewable energy industry to local sectors of society, implementation of regenerative agriculture, cooperative management, and nurturing place meaning as aligned with nature’s restorative quality are important paths forward. These nexus management foci could strengthen place attachment, build trust in government, and repair environmental alienation.

The challenges of the COVID-19 pandemic in geographic science

The COVID-19 pandemic has impacted geographers and specialists of other areas, driving them to generate knowledge aimed to explain and find solutions to the health crisis that emerged in March 2020. Within the field of geography, quantitative methods, and geotechnologies have been employed to collect measurable data which prove useful explanation and the logical relationship between variables, verifying hypotheses related to COVID-19 contagion and mortality cases. Health geography, as a disciplinary branch, has investigated the spatial-temporal distribution and dynamics of diseases, seeking to understand the processes explaining the spatial structure of them during a pandemic. In this context, a case of study, Mexico City, seeks to address questions from a health geography perspective, such as: What were the causes behind the high levels of pandemic contagion? Which environmental, social, and health factors in time and space relate and contribute to a greater impact of the pandemic? How do these factors interact with each other, and how have they influenced the increase or decrease in contagion and mortality cases? What are the short, medium, and long-term scenarios of COVID-19? To address these inquiries, spatial analysis methods and geotechnological techniques, approached holistically and have efficiently supported the identification of COVID-19 contagion risk zones and their specific characteristics. These insights prove invaluable information for spatial decision-making in comprehensive planning and territorial management.

Risk Assessment and Categorization of Flood Risk Zones Using Geospatial Data and Multi-Criteria Decision Model in a Low-Lying Deltaic Region, Kuttanad, India

Risk Assessment and Categorization of Flood Risk Zones Using Geospatial Data and Multi-Criteria Decision Model in a Low-Lying Deltaic Region, Kuttanad, India

ANALISIS MITIGASI BENCANA BANJIR DI KOTA TANGERANG SELATAN

Safe and disaster-resistant residential areas are an urgent need to reduce disaster risk. The role of the government, especially the South Tangerang City Regional Disaster Management Agency (BPBD), is very important in increasing community preparedness in a comprehensive and targeted manner. In this research, we used a qualitative method by utilizing the study literature scheme as a secondary and primary data source obtained from Scopus, Google Scholar, books, performance reports and annual final reports. Our research results show that South Tangerang City has adopted various measures to reduce disaster risk, especially through disaster risk reduction information. The government has implemented mitigation efforts, such as disaster simulations and disseminating mitigation messages through mass media and social media. Apart from that, the government is also making other persuasive efforts to change people's mindsets in realizing a culture of safety, including through habituation and increasing preparedness in disaster prevention. In this context, residential areas that dominate the spatial layout and have different facilities are the main focus. Risk zoning can be done to divide residential areas into zones with different levels of disaster risk, thereby enabling appropriate spatial planning. Early warning infrastructure also needs to be installed to provide residents with information about impending disasters and allow sufficient time to take necessary actions. Clear and well-identified evacuation routes should be provided, while green open spaces can act as "protection zones" and gathering places during evacuation. Apart from that, disaster-resistant buildings need to be considered in planning this residential area. An effective drainage system is also important to reduce the risk of flooding. Public education and awareness about disaster risks as well as regular preparedness training need to be carried out. The government needs to monitor and evaluate the mitigation measures implemented to ensure their effectiveness. Overall, this research underlines the important role of the government and BPBD South Tangerang City in reducing disaster risk in residential areas. By combining comprehensive mitigation measures and involving active community participation, it is hoped that a safety culture can be created that can increase preparedness in facing disasters

A Cadaveric Study on Radial Nerve Risk Zone in the Distal Part of the Arm

Introduction: Injury to the radial nerve in the arm is very frequent following fractures of the shaft of the humerus and may also occur during surgery on the arm. These iatrogenic injuries can be prevented by predicting the point at which the nerve penetrates the lateral intermuscular septum during its course from the posterior-to-anterior compartment of the arm. Aims and Objectives: Our study aims to find out the point of penetration of the radial nerve in the lateral intermuscular septum (radial nerve risk zone [RNRZ]). Materials and Methods: The study was conducted on 30 upper limbs of adult formalin-embalmed cadavers during MBBS undergraduate dissection at the department of anatomy. The posterior aspect of the arm was dissected to expose the radial nerve, and measurements of parameters were recorded. Results: The mean humeral length was 29.91 ± 2.66 cm. The mean distance from the lateral epicondyle of the humerus to the point on the lateral intermuscular septum where radial nerve pierces during its course from posterior-to-anterior compartment was 11.42 ± 1.042 cm, r = 0.368, and P < 0.05 which was statistically significant. The mean (standard deviation) humeral length of both left and right side difference was statistically significant with P = 0.009 (P < 0.05). Conclusion: We believe that our study will help orthopedic surgeons to estimate the point of penetration of radial nerve into the lateral intermuscular septum which is the RNRZ. This minimizes the risk of radial nerve damage during the fixation of the fractured shaft of the humerus.

Solar Alpha Particle Radiation Increases Human Mortality – An Example from the Ischemic Heart Diseases mortality in the European Union

A dangerous phenomenon for humanity is described. In the joint analysis of data from satellites in orbit around the Earth and from the health statistics source EUROSTAT, it became clear that, by some mechanism, flows of positively charged particles with high energy entering the Earth's orbit increase mortality on the planet's surface. The increase in mortality is in a zone of maximum risk in the Northern Hemisphere, parallel to the Equator and bounded by the parallels of 30° and 50° north latitude. Examples are given for the European Union mortality from Ischemic heart diseases confirming the described phenomenon. A hypothetical mechanism based on observational evidence has been proposed, according to which this dangerous phenomenon is due to solar alpha particles of high energy sufficient to overcome the atmosphere's resistance and reach the Earth's surface in a limited area of maximum death impact.

Numerical simulation of the distal stent graft-induced new entry after TEVAR.

The study aimed to investigate the mechanical factors for distal stent graft-induced new entry (dSINE) in aortic dissection patients and discussed these factors in conjunction with aortic morphology. Two patients (one dSINE and one non-dSINE), with the same age, gender, and type of implanted stent, were selected, then aortic morphological parameters were calculated. In addition, the stent material parameters used by the patients were also fitted. Simulations were performed based on the patient's aortic model and the stent graft used. The true lumen segment at the distal stent graft was designated as the "dSINE risk zone," and mechanical parameters (maximum principal strain, maximum principal stress) were computed. When approaching the area with higher mechanical parameters in the dSINE risk zone, dSINE patient exhibited higher values and growth rates in mechanical parameters compared to non-dSINE patient. Furthermore, dSINE patient also presented larger aortic taper ratio, stent oversizing ratio, and expansion mismatch ratio of the distal true lumen (EMRDTR). The larger mechanical parameters and growth rates in dSINE patient corresponded to a greater aortic taper ratio, stent oversizing ratio, and EMRDTR. The failure of dSINE prediction by the stent tortuosity index indicated that mechanical parameters were the fundamental reasons for dSINE development.

Investigation of Underlying Association between Anthropometric and Cardiorespiratory Fitness Markers among Overweight and Obese Adolescents in Canada.

Adolescents who experience overweight or obesity commonly persist in these conditions into adulthood, thereby elevating their vulnerability to health issues. The focus of this study is on health risk markers such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body surface area (BSA), and cardiorespiratory fitness (CRF). The objectives include updating normative values for BMI, WC, WHtR, and BSA in Canadian adolescents, establishing cardiometabolic risk zones, and developing a composite score considering both anthropometric and CRF markers. Involving 1864 adolescents, the study used the LMS method to generate percentile norms, stratified by age and sex. Cardiometabolic risk zones were established for each marker based on Z-scores, and a composite score was created. An increase in WC of 5.8 and 7.4 cm for boys and girls, respectively, was observed since 1981. Forward multiple regression analyses were conducted to assess the robustness and validity of the proposed model. The results indicated that the model explained nearly 90% (R2 = 0.890) of the common variance between the composite score and the retained independent variables. Moreover, the model demonstrated a mean absolute error (MAE) of approximately 6 percentiles, confirming its high precision. Furthermore, these analyses yielded key thresholds for identifying adolescents at risk: the 70th percentile for high cardiometabolic risk and the 85th percentile for very-high risk. Individually, WC or WHtR seem to be better markers for evaluating cardiometabolic risk than BMI during adolescence. However, CRF showed comparable importance to anthropometric markers in determining cardiometabolic risk. The simultaneous inclusion of anthropometric and CRF markers provides a better picture of the global cardiometabolic risk in adolescents.

Research on Remote-Sensing Identification Method of Typical Disaster-Bearing Body Based on Deep Learning and Spatial Constraint Strategy

The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method’s applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields.

Exploring Trauma Patterns and Contributing Factors With Slim Straight Electrode Array After Cochlear Implantation.

To assess trauma patterns associated with the insertion of lateral wall electrode arrays. The study focused on 3 categories-scala tympani (ST), intermediate, and scala vestibuli (SV)-to identify traumatic patterns and contributing factors. Retrospective study. Data from 106 cochlear implant recipients at a tertiary otologic center. Demographic and surgical data were collected from recipients who underwent cochlear implantation manually and with RobOtol®. Measurements included cochlear dimensions, angular depth of insertion, and position of the first electrode. Three-dimensional reconstructions were used to analyze the electrode array location relative to the basilar membrane, categorized into ST, intermediate, and SV electrodes. Nontraumatic insertion was defined as all electrodes in the ST, while traumatic insertions had 1 or more electrodes in intermediate or SV locations. Out of 106 cases, 44% had nontraumatic and 56% had traumatic insertions. Demographic and surgical characteristics showed no association with traumatic insertions. A deeper position of the first electrode, relative to the round window, was associated with traumatic insertions (P = .03). Three trauma patterns were observed: distal (facing the apical electrodes), proximal (facing the middle electrodes around 180°), and distal/proximal. This study considers the intermediate position which could be associated with basilar membrane lesions. Risk zones for intracochlear trauma with lateral wall arrays were identified distally and proximally. Traumatic insertions were independently linked to deeper array placement. Future studies should explore whether gentler insertion, without insisting on further electrode array insertion depth, could reduce the trauma during cochlear implantation.

Quantifying High-Temperature-Induced Injury in Nanfeng Tangerine Plants: Insights from Photosynthetic and Biochemical Mechanisms

High temperatures significantly injure the flowering, pollination, fruit growth, and quality of plants. Photosynthesis, the fundamental process supporting plant life, is crucial. Nevertheless, the quantitative evaluation of the physiological activity of the photosynthetic system of Nanfeng tangerine (NT) plants under high-temperature conditions remains a challenge. This research utilized NT plants, a distinctive citrus variety in Jiangxi Province, as the experimental subject. The study investigated the effects of varying degrees of high-temperature stress and duration on 16 photosynthetic physiological parameters of NT plants. The study examined the impact of four varying high-temperature treatment levels (32/22 °C, 35/25 °C, 38/28 °C, and 41/31 °C) for durations of 2, 4, 6, and 8 days, respectively. Principal component analysis was utilized to identify the key indicators of photosynthetic physiological activity in NT plants, with Fv/Fm, Pmax, LCP, H2O2, MDA, and POD being selected as key parameters. The high-temperature stress index model previously constructed was used to calculate the high-temperature stress index value of the NT plants exposed to varying degrees and durations of high temperature, in order to provide a comprehensive assessment of the photosynthetic system of NT plants under high-temperature stress. Subsequently, the high-temperature stress levels were categorized into five levels based on the calculated values: Level 0 for 0 < HSI ≤ 2, Level 1 for 2 < HSI ≤ 4, Level 2 for 4 < HSI ≤ 6, Level 3 for 6 < HSI ≤ 8, and Level 4 for HSI > 8. The research results provide valuable data for agricultural meteorological departments to carry out disaster risk zoning and risk assessment in the future.

Advancements in mapping landslide susceptibility in Bafoussam and its surroundings area using multi-criteria decision analysis, statistical methods, and machine learning models

Landslides pose a significant threat to lives and socio-economic stability globally. In this study, we conducted a comprehensive landslide susceptibility mapping (LSM) in the western region of Cameroon, focusing on Bafoussam and its surroundings. The integration of multi-criteria decision analysis models (AHP), statistical methods (Information Value IV, Shannon Entropy SE, Frequency Ratio FR), and machine learning algorithms (Naïve Bayes and Logistic Regression) provided a robust assessment of landslide risk. Our analysis, based on 54 recorded landslides, carefully selected Landslide Conditioning Factors (LCF), and influential parameters such as lithology, slope, altitude, and precipitation, resulted in susceptibility maps categorizing the area into five risk zones. The Spatial distribution shows the centre and northwestern regions as high-risk areas. Model sensitivity differences underscore the need for tailored LSM selection. Validation using the Area Under Curve/Receiver Operating Characteristics (AUC/ROC) method indicates the LR and NB methods have the highest accuracy (82.7% and 84.1%, respectively). Comparative analysis of landslide events in Gouaché, Sichuan, Souk Ahras, and Kekem reveals correlations between heavy rainfall and geological conditions. The study supplies valuable insights for decision-makers in landslide-prone areas, emphasizing the importance of integrating multiple methodologies for comprehensive risk assessment and management.

Unveiling and modelling the flood risk and multidimensional poverty determinants using geospatial multi-criteria approach: Evidence from Jigawa, Nigeria

Floods are recurring natural disasters that pose major challenges to communities worldwide, especially in regions where poverty is widespread. This research focuses on analysing the relationship between flood risk and poverty factors in Jigawa. It uses geospatial data, historical flood records and remote sensing to locate flood-prone areas. The integration of these data sets creates a comprehensive model for measuring flood-prone zones and their impact on multidimensional poverty. The flood risk zone included 3% and 13% as extremely high and high. In addition, 40% of the population is particularly vulnerable to flood disasters, leading to multidimensional poverty in the region. At the same time, poverty determinants such as agricultural activities, income level, education, access to health care, housing conditions, and road networks were analysed using statistical methods of principal component analysis (PCA). Findings from this research will go a long way toward understanding the complex interactions between natural disasters (flood) and poverty and will shed light on the mechanisms that perpetuate vulnerability in impoverished regions. By identifying the specific determinants of poverty that exacerbate the impact of flooding, this study provides evidence-based insights into possible avenues for targeted interventions and risk-reduction strategies. In addition, this study underscores the importance of using geospatial technology for disaster risk management and poverty alleviation. These findings will help policymakers and local authorities implement more efficient and adaptive disaster preparedness and response policies, thereby building the resilience of communities facing the intersecting challenges of poverty and flood risk.

Nitrate contamination in groundwater and its evaluation of non-carcinogenic health hazards from Arjunanadi River basin, south India

Nitrate (NO3-) contamination of groundwater is increasingly getting common in many places across India. Here, we present new data about nitrate pollution and the associated health consequences for infant, children, and adult population by evaluating 94 groundwater samples from the Madurai and Virudhunagar regions within the Arjunanadi River basin, situated at the southern region of India. Nitrate (9–221 mg/L) remained surpassed the WHO's recommended limit (>45 mg/L) in 34% samples. The piper diagram results specified that 75% of the samples fall in Mixed CaMgCl type. The correlation of nitrate results showed that groundwater of the region degraded by agricultural and industrial activities. The Nitrate Pollution Index (NPI) results of samples revealed significant pollution class, n = 18 (19%) and very significant pollution class, n = 26 (28%) in the study region. Its spatial distribution covered an area of 456.3 km2 in the risk zone with more than permissible nitrate. Among the shallow aquifers, about 16% of whole samples were in the high risk and 9% in very high-risk. Similarly, about 6% samples from deep aquifers were also in the category of high risk. The Water Quality Index (WQI) of groundwater samples indicateed 39% as good, 48% as poor and 9% as very poor to unsuitable. The health risk assessment from intake (HQintake) and skin contact (HQdermal) suggested 64%, 45% and 32% of samples with THI >1 pose hazard for infant, children, and adult population, respectively. The health risks and assessing groundwater quality indicated the necessity of regular groundwater quality monitoring for better long-term health management in this region.

Integrated GIS and analytic hierarchy process for flood risk assessment in the Dades Wadi watershed (Central High Atlas, Morocco)

Flood risk assessment is crucial for delineating flood hazard zones and formulating effective mitigation strategies. Employing a multi-criteria decision support system, this study focused on assessing a Flood Risk Index (FHI) at the Dades Wadi watershed scale. Seven main flood-causing criteria were broadly selected, namely flow accumulation, distance from hydrographic network, drainage network density, land use, slope, rainfall, and permeability. The relative importance of each criterion prioritized as per their contribution toward flood risk, which were employed a blend of the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS)/Remote Sensing (RS) techniques. The significance of each criterion was determined based on their contribution to flood hazard, and their relative importance was established through an AHP pair-wise comparison matrix. The efficacy of the AHP model was performed with a consistency ratio of 0.08, which indicated that each flood criterion weight is confirmed. Among the flood-causing criteria, the hydrologic flow accumulation factor was identified as the most influential (weight: 3.11), while permeability exhibited the least prominence (weight: 0.58). Approximately 40.36% of the total area, equivalent to around 1319, 89 km2, was concentrated within very high flood-risk zones situated near rivers. In contrast, an area of approximately 399,943 km2 (56.33%) exhibited a low to very low flood risk zone. The validation of the FHI map encompassed the application of the Receiver Operating Characteristic Curve (ROC) technique, revealing an Area Under the Curve (AUC) of 85%.

Exploring the Impact of Climate Variables on Livestock Anthrax Outbreaks: A Machine Learning Approach

Anthrax, a globally significant disease, poses substantial threats to both livestock and human populations. Timely identification of anthrax outbreaks is paramount to mitigate its impact on animal health, human health, and public safety. This study aims to construct a predictive model for livestock anthrax disease occurrence. By leveraging the potential of advanced Machine-Learning techniques, we projected the likelihood of anthrax outbreaks across India, through incorporating a diverse set of meteorological, and remote sensing parameters. The ultimate goal is to establish a spatial risk map that can serve as an early warning system, aiding in the anticipation and management of future anthrax outbreaks in India's livestock population. Our analysis revealed elevated risk zones for anthrax outbreaks in the southern and north-eastern regions of India, contrasting with medium to low-risk areas in the central parts. Notably, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), rainfall, soil moisture, and wind speed emerged as pivotal variables driving the model's predictive accuracy. Among the employed models, the random forest, adaptive boosting, and classification tree analysis approaches showcased superior performance in livestock anthrax risk assessment. The risk map was generated using significant variables by exploiting best fitted models. These findings hold profound implications for policymakers, guiding the targeted deployment of control strategies against anthrax outbreaks. The dynamic risk maps generated through this study enhance public awareness, equipping decision-makers with vital insights for informed action. By spotlighting risk management endeavours, these maps further enhance governance and risk mitigation efforts.

Multi-model seismic susceptibility assessment of the 1950 great Assam earthquake in the Eastern Himalayan front

The seismic susceptibility and mitigation management is paramount concern in tectonically active area like Northeastern India. This area has been devastated innumerably during the 1950 Assam great earthquake. The present study area falls in the foreland basin (Brahmaputra Valley) of Eastern Himalaya. This region is seismically vulnerable due to the tectonic complexity caused by the convergence of the Eurasian, Indian, and Burmese plates. In such, an area optimal disaster management and preparedness is necessary to define the non-linear character of seismic susceptibility, where population and unscientific urbanization have increased manifold. Therefore, for the present study, various multi-criteria decision making (MCDM) methods such as analytical hierarchy process (AHP), fuzzy-AHP (FAHP), and maximum entropy technique (MaxEnt) have been used for determining the seismic susceptibility, by assigning weightage to nine controlling factors such as: predominant frequency (f0), geology (G), vulnerability index (K), peak amplification (A0), liquefaction potential (LP), groundwater condition (WT), shear wave velocity (Vs30), peak ground acceleration (PGA), and land use/land cover (LU). The MaxEnt model exhibits the highest accuracy (87.5%) when the performance of the models was compared using the receiver operating characteristic curve (ROC) and area under the curve (AUC) value. Further, overlay analysis of best seismic susceptibility model using MaxEnt and PGV-based Japan Meteorological Agency (JMA) intensity shows that 40% the study area is in the very high and high seismic risk zone. In tectonically active areas, this kind of integration work is essential to improves the mitigation strategy and aids urban planners in designing earthquake-resistant buildings.

Risk Assessment Of Ambient Air Pollution PM2.5 Exposure To Communities In The Cement Industrial Area, Pangkep Regency, Indonesia

Introduction: PM2.5 particles are significant problem and the most significant environmental health risk causing premature death. The study aims to assess the health risk analysis of PM2.5 exposure in the residential areas around the cement industry. Material and Methods: the study used a cross sectional study with an environmental health risk analysis approach. The population of the study were 827 households. The study applied inclusion criteria involving the selecting only one respondent from the same family, so there were 297 respondents from population target. The sample selection was calculated by Lemeshow formula and resulted 98 samples. The measurement PM2.5 located at intervals of 0-5 km from the source of primary pollution areas. The measurement of PM2.5 used HazDust Epam. Guideline standards from the Environmental Protection Agency were used to assess the risk of exposure to pollutants. Results: the average concentration of the southern zone is 60 µg/m3 , the northern zone is 40 µg/m3 , the eastern zone is 21.67 µg/m3 , and the western zone is 46.67 µg/m3 . The Risk Level (RQ) of Real-time PM2.5 intake is relatively safe with RQ<1; and intake lifetime risk is RQ>1; except for the eastern zone RQ<1. Conclusion: the result indicates that PM2.5 exceed the environmental quality standard according to WHO, which is > 10 µg/m3 from the pollution source. Risk Management recommends a safe concentration in the risk zone, proper environmental management and control efforts to reduce health risks in the affected resident.