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Microbially induced carbonate precipitation (MICP) stands as a potent technique for remediating soils contaminated with heavy metals. However, the lack of efficient methods to detect the efficacy of MICP necessitates the use of electrical resistivity as an indicator. Consequently, an empirical model was devised to assess the strength and lead curing rate of the remediated soil. Through resistivity tests and microscopic experiments, it became evident that water content and lead contamination concentration exerted an influence on the electrical resistivity of the soil. Remarkably, the MICP technology led to a significant increase in the electrical resistivity of the remediated soil. This phenomenon can be attributed to the immobilization of lead ions within the contaminated soil, which consequently alters the soil's pore structure, thereby resulting in noticeable modifications in electrical resistivity. The empirical model further revealed a linear correlation between the strength of the remediated soil and its electrical resistivity. As the electrical resistivity increased from 1.09 to 8.71 Ω m, the strength of the soil improved from 175 to 1070 kPa. Additionally, a multifactor linear framework elucidated the interrelation between the lead curing rate and water content, primary lead contamination concentration, and electrical resistivity. The rate of lead solidification showed a positive correlation with water content but exhibited a negative correlation with both the initial concentration of heavy metal pollutants and the electrical resistivity. Notably, the highest rate of lead curing rate, reaching 90.89%, was observed at a water content of 16.1%, a pollutant concentration of 100 mg/kg, and an electrical resistivity of 1.54 Ω m. These findings firmly establish electrical resistivity as an effective means of evaluating the remediation effect of MICP, thereby providing a theoretical foundation for assessing the impact of MICP technology in the field. [ABSTRACT FROM AUTHOR]

Wind erosion is one of the most severe environmental problems in arid, semiarid, and dry sub-humid regions of the planet. This paper aimed to identify areas sensitive to wind erosion in Northeastern Algeria (Wilaya of Tebessa) based on empirical model using analytic hierarchy process, fuzzy analytic hierarchy process approaches, and geomatics-based techniques. Sixteen causative factors were used incorporating meteorological, soil erodibility, physical environment, and anthropogenic impacts as main available inputs in this approach. Weighted linear combination algorithm was adopted to combine all standardized raster layers. Area under curve value equal to 0.96 indicates an excellent accuracy for the proposed approach. Globally, wind erosion risk increases gradually from the North to South of the whole area. Besides, it was found that areas with slight, moderate, high, and very high risk covered 9.65%, 25.83%, 24.30%, and 40.22% of the total area, respectively. Our results highlighted the potential of additive linear model and free available medium resolution multi-source remote sensing data in studying natural hazards and disasters mainly under data-scarce or areas of difficult access in developing countries. In addition, restoration and re-vegetation activities of sensitive areas at high risk of wind erosion represent a challenge for researchers and decision-makers. [ABSTRACT FROM AUTHOR]

A dry debris avalanche will produce different volumes of colluviums or depositions (loose materials), which can have a significant impact on mountainous rivers or gullies. The loose material supply process caused by a debris avalanche is an important issue for understanding secondary disasters that form via the coupling of water flow and loose materials. Two flumes were designed for laboratory tests of the loose materials supply process to rivers/gullies, and the related impact factors were analyzed. Experimental results show that the supply of loose materials is a continuous process that directly relates to the avalanche's mass movement processes. The sliding masses with smaller particle sizes are more sensitive to the flume slope and exhibited a longer supply time. The time-consuming for the debris avalanche travel in the flume decreased with the increasing particle size (such as flume B, time-consuming is decreased 0.2 s when the particle size increased from <1.0 to 20-60 mm), landslide volume and flume slope (flume A, consuming 1.6-2.1 s when flume slope is 29° decreased to consuming 1.3-1.5 s when flume slope is 41°), which means the increasing mobility of loose materials. The total supply time increased with the increasing landslide volume or decreasing particle size and flume slope. An empirical model for the process is presented based on numerous laboratory tests and numerical simulations, which can successfully describe the supply process for loose materials to a river/gully. The supply process of loose materials to mountainous gully from a dry debris avalanche is controlled by the material compositions of sliding masses, topographical conditions, landslide volume and bed friction, where large-volume debris avalanches that occur in mountainous river regions are more likely to obstruct the river flow and form a landslide-dammed lake. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose three predictive models namely empirical, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of ground vibration produced by blasting operations conducted in Gol-E-Gohar Iron mine, Iran. In this way, 115 operations were precisely monitored and related parameters of blasting were measured. Furthermore, maximum charge per delay and the distance from the blast-face were set and applied to construct the ground vibration predictive models. By assigning all data sets into training and testing, many ANFIS and ANN models were constructed. The results revealed that the proposed ANFIS model can estimate ground vibrations more accurately than other developed models. Root-mean-square error value of 4.644, for testing data set, shows superiority of the ANFIS predictive system in predicting ground vibration while they were achieved as 7.522 and 10.689 for ANN and empirical models, respectively. [ABSTRACT FROM AUTHOR]

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R2), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site. [ABSTRACT FROM AUTHOR]