A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment.

Prolonged exposure to excessive levels of nitrate through drinking water is a potential risk for human health. The current research reports the analytical results and associated health risk for water quality in term of nitrate in 39 groundwater samples during January 2018 in rural areas of Gonabad and Bajestan, Iran. Nitrate concentrations ranged from 1.8 to 82.2 and from 5.5 to 84.3 mg/L for Gonabad and Bajestan, respectively. In this work, the potential risk to human health was determined using the hazard quotient (HQ) for three age groups including adults, children and infants. Comparison of HQs among the 39 sampling sites showed that the rural areas in Bajestan had higher HQs than Gonabad. Among the studied groups, infants exposed to a higher risk than children and adults. The results also indicated that the health of individuals from nitrate exposure in most of the groundwater studied was not acceptable and most of the consumers were in danger from current nitrate concentrations. Therefore, there is an urgent need for enforcing effective plans to improve groundwater quality and to better manage and control probable nitrate contaminated sources.

Health risk assessment of nitrate exposure in groundwater of rural areas of Gonabad and Bajestan, Iran

A comprehensive review of warm mix asphalt mixtures-laboratory to field

This study aims to modify the SINTACS and DRASTIC models with a land-use (LU) layer and compares the modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment (GVA) in the southern Tehran aquifer, Iran. Single parameter sensitivity analysis (SPSA) served to determine the most significant parameters for the modified-DRASTIC, modified-SINTACS and SI approaches, and to revise model weights from "theoretical" to "effective." The inherent implementation of LU in the SI model may explain its better performance compared to unenhanced versions of DRASTIC and SINTACS models. Validation of all models, using nitrate concentrations from 20 wells within the study area, showed the modified-SINTACS model to outperform other models. The SPSA showed that the vadose zone and LU strongly influenced the modified-DRASTIC and modified-SINTACS models, while SI was strongly influenced by aquifer media and LU. To improve performance, models were implemented using "effective" instead of "theoretical" weights. Model robustness was assessed using nitrate concentrations in the aquifer and the outcomes confirmed the positive impact of using "effective" versus "theoretical" weights in the models. Modified-SINTACS showed the strongest correlation between nitrate and the vulnerability index (coefficient of determination = 0.75). Application of the modified-SINTACS while using "effective" weights, led to the conclusion that 19.6%, 55.2%, 23.4%, and 1.6% of the study area housed very high, high, moderate and low vulnerability zones, respectively.

Modified-DRASTIC, modified-SINTACS and SI methods for groundwater vulnerability assessment in the southern Tehran aquifer

Rutting and fatigue performance of asphalt mixtures containing amorphous carbon as filler and binder modifier

Groundwater vulnerability assessment of urban areas is a challenging task in the fast trend of urbanization around the globe. This study introduces a new approach for modifying well-known parameters of common vulnerability indexes to adjust them for urban areas. The approach is independent of a specific weighting system. The aquifer of Mashhad city, contaminated by domestic wastewater, is selected as a case in this study. In order to evaluate the aquifer vulnerability due to anthropogenic activities, at first, parameters of depth to groundwater, recharge, land use, and soil are modified based on their basic concepts and their influences on contamination attenuation. Then, the modified parameters are used simultaneously in several index methods to investigate the capability of the modified parameters to increase correlation coefficient of all employed index methods with the measured nitrate concentration. Accuracy of the modified methods is evaluated by Spearman nonparametric correlation. It is shown that considering the wastewater discharge into recharge parameter leads to an increase of 20% in correlation coefficient. Also, level difference technique shows that more than 70% of the vulnerable areas are predicted correctly in all utilized methods. The accurate prediction in all employed methods indicates that these modifications are independent of the type of index method. Moreover, sensitivity analysis reveals that the recharge and the land use are both the most significant parameters for evaluating the vulnerability.

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Vulnerability assessment of urban groundwater resources to nitrate: the case study of Mashhad, Iran

The aim of this study is to develop a new fuzzy optimization model to find the optimal factor weights of modified DRASTIC index for groundwater vulnerability mapping an urban aquifer to nitrate contamination. Eight factors including water table depth, recharge, aquifer media, soil media, topography, impact of vadose zone, hydraulic conductivity, and land use are considered and rated. A fuzzy linear regression is formulated between the values of eight factors and corresponding nitrate concentration in groundwater. An optimization model based on real code genetic algorithm with objective of minimizing the sum of the fuzzy spread of the regression coefficients is implemented. Aquifer of Mashhad metropolis (northeast of Iran) is chosen to evaluate the proposed model. The results show the proposed model is a promising tool for weighting the factors with avoiding the subjectivity and also ambiguities accompanied by parameters to produce an accurate specific vulnerability mapping of an urban aquifer.

Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination

One of the methods newly introduced in pavement technology to address environmental concerns and reduce total gas emission is warm mix asphalt (WMA) technology. In this study, polyethylene wax is used to produce WMA mixtures. Although this technology has several positive features, moisture and rutting resistance of WMA mixtures are always questionable. To evaluate moisture and rutting resistance of polyethylene-modified mixtures, indirect tensile strength and dynamic creep tests were used, respectively. Results indicated that polyethylene has positive effect at low compaction temperature on moisture resistance, and also has negative effect on rutting resistance.

Rutting and moisture resistance evaluation of polyethylene wax–modified asphalt mixtures

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

Parisa Asadi

Papers

Vulnerability assessment of urban groundwater resources to nitrate: the case study of Mashhad, Iran

Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination

Rutting and moisture resistance evaluation of polyethylene wax–modified asphalt mixtures

Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Estimating leaf wetness duration with machine learning and climate reanalysis data