Systematic review and meta-analysis of arsenic concentration in drinking water sources of Iran.

Recent studies have found arsenic contamination of drinking water in some parts of Iran, as in many other countries. Thus, a comprehensive systematic review is necessary to assess the distribution and concentration of arsenic in drinking water sources. For this purpose, articles published from the first identification until December 2023, were retrieved from various national and international databases. Of all the studies examined (11,726), 137 articles were selected for review based on their conceptual relationship to this survey. A review of the extracted studies presented that ICP methods (ICP-MS, ICP-OES, 56%) and atomic absorption spectrophotometry (AAS, 34.1%) were the two most commonly used techniques for the analysis of arsenic in water samples. The order of arsenic content in the defined study areas is descending, as follows: northwest ˃ southeast ˃ southwest ˃ northeast. A review of studies performed in Iran depicted that provinces such as Kurdistan, Azerbaijan, and Kerman have the highest arsenic concentrations in water resources. Accordingly, the maximum concentration of arsenic was reported in Rayen, Kerman, and ranged from < 0.5-25,000 µg/L. The primary cause of elevated arsenic levels in water resources appears to be geologic structure, including volcanic activity, biogeochemical processes, sulfur-bearing volcanic rocks, Jurassic shale, the spatial coincidence of arsenic anomalies in tube wells and springs, and, to some extent, mining activities. The findings of the presented survey indicate that it is essential to take serious measures at the national level to minimize the health risks of arsenic contamination from drinking water consumption.

Understanding paleo-earthquakes in the Kuril Trench based on Late-Holocene tsunami deposits in the distal region from wave sources, northern Hidaka, Hokkaido, Japan.

Geological evidence, such as tsunami deposits, is crucial for studying the largest rupture zone of the Kuril Trench in Hokkaido, Japan, due to its poor historical record. Although 17th-century tsunami deposits are widely distributed across Hokkaido, the presence of multiple wave sources during that period, including the collapse of Mt. Komagatake, complicates the correlation with their wave sources. Understanding the regional distribution of these tsunami deposits can provide valuable data to estimate the magnitude of megathrust earthquakes in the Kuril Trench. The northern part of Hidaka, Hokkaido, where tsunamis from multiple wave sources are expected to overlap, is distant from the Kuril Trench. To clarify the depositional history of tsunami deposits in such distal areas, evaluating the influence of the depositional environments on the event layer preservation becomes even more critical. We conducted field surveys in Kabari, located in the northern Hidaka region, identifying three sand layers from the 10th to the 17th century and two layers dating beyond 2.3 thousand years ago. The depositional ages of most sand layers potentially correlate with tsunami deposits resulting from the Kuril Trench earthquakes. Utilizing reconstructed paleo-sea level data, we estimated that most sand layers reached approximately 2 m in height. However, it is noteworthy that the latest sand layer from the 17th century exhibited an unusual distribution, more than 3 m in height. This suggests a different wave source as the Mt. Komagatake collapse. The discovery of multiple sand layers and their distributions is crucial to constraining the maximum magnitude of giant earthquakes in the Kuril Trench and understanding the volcanic tsunami events related to Mt. Komagatake.

Chapter 11: Astrobiology Education, Engagement, and Resources.

Although astrobiology is a relatively new field of science, the questions it seeks to answer (e.g., "What is life?" "What does life require?") have been investigated for millennia. In recent decades, formal programs dedicated specifically to the science of astrobiology have been organized at academic, governmental, and institutional scales. Constructing educational programs around this emerging science relies on input from broad expertise and backgrounds. Because of the interdisciplinary nature of this field, career pathways in astrobiology often begin in more specific fields such as astronomy, geology, or biology, and unlike many other sciences, typically involve substantial training outside one's primary discipline. The recent origin of astrobiology as a field of science has led to strong collaborations with education research in the development of astrobiology courses and offers a unique instructional laboratory for further pedagogical studies. This chapter is intended to support students, educators, and early career scientists by connecting them to materials and opportunities that the authors and colleagues have found advantageous. Annotated lists of relevant programs and resources are included as a series of appendices in the supplementary material.

An ensemble framework-based approach for modeling stability of expansive soil slopes: fusion of machine learning algorithms and protection structure disease data.

Slope failures lead to catastrophic consequences in numerous countries, so accurate slope stability evaluation is critical in geological disaster prevention and control. In this study, the type and characteristics of slope protection structure disease were determined through the field investigation of an expansive soil area, and this information is incorporated into the numerical simulations and works to develop prediction models of slope stability. Four base machine learning (ML) methods are used to capture the relationship between protection structure diseases and factor of safety (FOS). Further, with the help of stacked generalization (SG), four ML models are combined, and the final SG model is used to predict the FOS. The results show that ML methods can effectively utilize this information and achieve excellent prediction results. The proposed SG model exhibits superior accuracy and robustness in predicting FOS compared to other ML methods. With FOS as the regression variable, the main feature contributions are slope height (37.05%) > slip distance of retaining wall (25.43%) > expansive force (18.03%) > slope gradient (12.00%); the coupling relationship among features is also captured by the proposed model. It is concluded that the SG method is particularly suitable for slope stability modeling under small sample conditions. Besides, the SG-based model effectively captures the impact of protection structure diseases on slope stability, enhances the interpretability of the ML model, and provides a reference for the maintenance and repair of the protection structure.

Slope stability assessment in the seismically and landslide-prone road segment of Gerese to Belta, Rift Valley, Ethiopia.

Slope instability on several sections of the Gerese-Belta route in Southern Ethiopia poses a major risk to infrastructure and safety. This research was aimed at evaluating certain areas of the road susceptible to slope instability. Through intensive fieldwork including geological analysis, surveys, and testing, three crucial slope portions were determined. Both limit equilibrium and finite element calculations demonstrated that these sections are problematic under different circumstances. The slope modification analysis shows that the safety factor increases as bench widths and the number of benches increase. In the slope section D1S3, this factor reached 1.222 when two benches measuring 5 meters in width were used on slide 2D. This initially showed an unstable safety factor of 0.26. Three benches of the same width were used under slide 2D. This resulted in a safety factor of 1.219. At the slope section (D1S2), flattening of the slope angle from initial 45° to 35°, 28°, 25° and 18° increases the factor of safety of the slope from initial 0.284 to 0.77, 0.89, 1.022, and 1.151 respectively under slide 2D analysis. At the slope section (D2S1), flattening the slope angle from initial 46° to 35°, 25°, 23°, and 20° increases the safety factor from initial 0.412 to 0.684, 0.920, 1.02, and 1.315 respectively. Based on the analysis of the study results, it can be concluded that the identified slope sections are susceptible to failure under actual field scenarios, depending on the conditions under which they are predicted to occur. According to this study, the Benching method is an economical method for mitigating soil slopes, as a result of which it was recommended to be used.

Calculation and program realization of coal pillar setting parameters in Huainan mining area.

Coal pillar retention plays a crucial role in ensuring safety and minimizing ground deformation in coal mining operations. However, accurately and efficiently determining the optimal size of protective pillars, reducing coal pillar pressure, and addressing challenges such as limited access to retention parameters, lengthy observation times, and high labor costs are challenges that must be addressed. In this paper, we presented a methodology using Huainan mine as a case study to address these challenges. The solution involves deriving the formula for coal pillar retention parameters based on the Three Regulations definition and requirements. The total least squares algorithm was integrated with surface observation station data and the MATLAB software platform to automate the coal pillar retention solution. Furthermore, a linear regression model of coal pillar retention-related parameters was established using the geological mining condition data. The proposed ELM neural network model was optimized using a genetic algorithm and combined with the linear regression model to establish a predictive model. The results demonstrated that the proposed machine learning algorithm attains the requisite level of accuracy for industrial production.

Using radial basis artificial neural networks to predict radiation hazard indices in geological materials.

The estimation of exposures to humans from the various sources of radiation is important. Radiation hazard indices are computed using procedures described in the literature for evaluating the combined effects of the activity concentrations of primordial radionuclides, namely, 238U, 232Th, and 40 K. The computed indices are then compared to the allowed limits defined by International Radiation Protection Organizations to determine any radiation hazard associated with the geological materials. In this paper, four distinct radial basis function artificial neural network (RBF-ANN) models were developed to predict radiation hazard indices, namely, external gamma dose rates, annual effective dose, radium equivalent activity, and external hazard index. To make RBF-ANN models, 348 different geological materials' gamma spectrometry data were acquired from the literature. Radiation hazards indices predicted from each RBF-ANN model were compared to the radiation hazards calculated using gamma spectrum analysis. The predicted hazard indices values of each RBF-ANN model were found to precisely align with the calculated values. To validate the accuracy and the adaptability of each RBF-ANN model, statistical tests (determination coefficient (R2), relative absolute error (RAE), root mean square error (RMSE), Nash-Sutcliffe Efficiency (NSE)), and significance tests (F-test and Student's t-test) were performed to analyze the relationship between calculated and predicted hazard indices. Low RAE and RMSE values as well as high R2, NSE, and p-values greater than 0.95, 0.71, and 0.05, respectively, were found for RBF-ANN models. The statistical tests' results show that all RBF-ANN models created exhibit precise performance, indicating their applicability and efficiency in forecasting the radiation hazard indices of geological materials. All the RBF-ANN models can be used to predict radiation hazard indices of geological materials quite efficiently, according to the performance level attained.

Analysis of the spatial distribution of the landslides triggered by the 1923 Great Kanto Earthquake, Japan.

The Great Kanto Earthquake that occurred in the southern part of Kanto district, Japan, on September 1, 1923, was reported to have triggered numerous landslides (over 89,080 slope failures over an area of 86.32 km2). This study investigated the relationship between the landslide occurrence caused by this earthquake and geomorphology, geology, soil, seismic ground motion, and coseismic deformation. We found that a higher landslide density was mainly related to a larger absolute curvature and a higher slope angle, as well as to several geological units (Neogene plutonic rock, accretionary prism, and metamorphic rocks). Moreover, we performed decision tree analyses, which showed that slope angle, geology, and coseismic deformation were correlated to landslide density in that order. However, no clear correlation was found between landslide density and seismic ground motion. These results suggest that landslide density was greater in areas of large slope angle or fragile geology in the area with strong shaking enough to trigger landslides.

Influence of buffer distance on environmental geological hazard susceptibility assessment.

Previous researches seldom studied the selection of buffer distance between geological hazards (positive samples) and non-geological hazards (negative samples), and its reasonable selection plays a very important role in improving the accuracy of susceptibility zoning, protecting the environment and reducing the cost of hazard management. Based on GIS technology and random forest (RF) and frequency-ratio random forest (FR-RF) models, this study innovatively explored the influence of randomly selected non-geological hazard samples outside different buffer distances on the susceptibility evaluation results, with buffer distances of 100 m, 500 m, 1000 m and 2000 m in sequence. The results show that through the confusion matrix and ROC curve test, the accuracy of the model increases first and then decreases with the increase of buffer distance. Both RF and FR-RF models have the highest accuracy when the buffer distance is 1000 m, and the accuracy of the RF model is generally higher than that of the FR-RF model under the same buffer distance. Similar attribute values of positive samples and randomly selected negative samples or "extreme" attribute values of negative samples are the main reasons for the differences in evaluation results of different buffer distances. According to the weight analysis of causative factors, the distance from road, the distance from river and the normalized vegetation index (NDVI) are the main factors affecting the occurrence of hazards. The high and very high susceptibility areas in the study area are mainly distributed on both sides of roads and water systems, which are the key areas for hazard prevention and reduction. The HMC of RF-1000m decreased by 3.55% on average compared with other models. The results of this study improve the accuracy of geological hazard susceptibility assessment, maintain the safety of ecological environment, and provide a scientific basis for the selection of buffer distance index in local and surrounding areas in the future.

Research on landslide susceptibility prediction model based on LSTM-RF-MDBN.

The occurrence of landslide disasters causes huge economic losses and casualties. Although many achievements have been made in predicting the probability of landslide disasters, various factors such as the scale and spatial location of landslide geological disasters should still be fully considered. Further research on how to quantitatively characterize the susceptibility of landslide geological disasters is necessarily important. To this end, taking the Wenchuan earthquake as the research area and extracting eight influencing factors, including terrain information entropy (Ht), lithology, distance from rivers, distance from faults, vegetation coverage (NDVI), distance from roads, peak ground motion acceleration (PGA), and annual rainfall, a landslide susceptibility prediction model was hereby established based on LSTM-RF-MDBN, a landslide susceptibility prediction map was drawn, and the spatial distribution characteristics of landslide disasters were analyzed. The results showed that (1) LSTM had good prediction results for the eight influencing factors, with an average prediction accuracy of 85%; (2) compared with models such as DNN and LR for predicting landslide disaster points, the AUC value of RF for predicting landslide point positions reached 0.88, presenting a higher accuracy compared to other models; (3) the AUC value of the landslide susceptibility prediction model based on LSTM-RF-MDBN reached 0.965, which had a high accuracy in predicting landslide susceptibility. Overall, the research results can provide a scientific basis for selecting the best strategy for landslide disaster warning, prevention, and mitigation.

Towards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother.

Categorical parameter distributions consisting of geologic facies with distinct properties, for example, high-permeability channels embedded in a low-permeability matrix, are common at contaminated sites. At these sites, low-permeability facies store solute mass, acting as secondary sources to higher-permeability facies, sustaining concentrations for decades while increasing risk and cleanup costs. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. This paper presents a novel approach based on Traveling Pilot Points (TRIPS) and an iterative ensemble smoother (IES) to solve the categorical inverse problem. Groundwater flow and solute transport in a hypothetical aquifer with a categorical parameter distribution are simulated using MODFLOW 6. Heads and concentrations are recorded at multiple monitoring locations. IES is used to generate posterior ensembles assuming a TRIPS prior and an approximate multi-Gaussian prior. The ensembles are used to predict solute concentrations and mass into the future. The evaluation also includes an assessment of how the number of measurements and the choice of the geological prior determine the characteristics of the posterior ensemble and the resulting predictions. The results indicate that IES was able to efficiently sample the posterior distribution and showed that even with an approximate geological prior, a high degree of parameterization and history matching could lead to parameter ensembles that can be useful for making certain types of predictions (heads, concentrations). However, the approximate geological prior was insufficient for predicting mass. The analysis demonstrates how decision-makers can quantify uncertainty and make informed decisions with an ensemble-based approach.

Continental Scale Hydrostratigraphy: Basin-Scale Testing of Alternative Data-Driven Approaches.

Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data-driven approaches for estimating hydraulic conductivity and subsurface properties at the continental-scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large-scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large-and continental-scale hydrologic modeling.

Assessing three soil removal methods for environmental DNA analysis of mock forensic geology evidence.

Soil is useful in criminal investigations as it is highly variable and readily transferred. Forensic geologists use several different techniques to removal soil from evidence prior to the analysis of inorganic components. There has been recent interest from the forensic science community to analyze environmental deoxyribonucleic acid (eDNA) associated with soil to augment existing forensic analyses. Notably however, limited research has been conducted to compare commonly used soil removal methods for downstream eDNA analysis. In this study, three soil removal methods were assessed: picking/scraping, sonication, and swabbing. Three mock evidence types (t-shirts, boot soles, and trowels) were sampled in triplicate with each removal method (n = 27). Soil samples underwent DNA isolation, quantification, and amplification of four genomic barcode regions: 16S for bacteria, ITS1 for fungi, ITS2 for plants, and COI for arthropods. Amplicons were prepared into libraries for DNA sequencing on an Illumina® MiniSeq. DNA concentrations were highest in picked/scraped samples and were statistically significant compared with swabbed and sonicated samples. Amplicon sequence variants (ASVs) were identified, and removal methods had no impact on the recovery of the total number of target ASVs. Additionally, when assessing each sample in multidimensional space, picked/scraped samples tended to cluster separately from swabbed and sonicated samples. The soil core used a reference in this study also clustered with the picked/scraped samples, indicating that these samples may be more reflective of the communities collected from soil cores. Based on these data, we identified that picking/scraping is an acceptable soil removal method for eDNA analysis.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP).

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The results of this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

The Cuatro Ciénegas Basin.

In the state of Coahuila, Mexico, there is a very special place, just 290 km from the border with Texas: the oasis in the Cuatro Ciénegas Basin. Souza et al. describe how the geology of the basin has given rise to a unique chemistry and a community of organisms that have survived for eons and are found nowhere else on Earth.

Metal toxin threat in wildland fires determined by geology and fire severity.

Accentuated by climate change, catastrophic wildfires are a growing, distributed global public health risk from inhalation of smoke and dust. Underrecognized, however, are the health threats arising from fire-altered toxic metals natural to soils and plants. Here, we demonstrate that high temperatures during California wildfires catalyzed widespread transformation of chromium to its carcinogenic form in soil and ash, as hexavalent chromium, particularly in areas with metal-rich geologies (e.g., serpentinite). In wildfire ash, we observed dangerous levels (327-13,100 µg kg-1) of reactive hexavalent chromium in wind-dispersible particulates. Relatively dry post-fire weather contributed to the persistence of elevated hexavalent chromium in surficial soil layers for up to ten months post-fire. The geographic distribution of metal-rich soils and fire incidents illustrate the broad global threat of wildfire smoke- and dust-born metals to populations. Our findings provide new insights into why wildfire smoke exposure appears to be more hazardous to humans than pollution from other sources.