Evaluation the feasibility of using clinoptilolite as a gravel pack in water wells for removal of lead from contaminated groundwater.

The ability of clinoptilolite zeolite as a filter in water wells to remove lead from polluted groundwater was tested in batch and fixed-bed column experiments. XRF, XRD, SEM, and BET were used to characterize the zeolite. Because of the pH variation in groundwater, batch experiments were performed at pH = 6, 7, and 8, with the highest removal efficiency (84.2%) at pH = 6 and 298 K within 90 min. The Freundlich model accurately predicted metal ion adsorption behavior and indicated a multilayer adsorption of Pb(II) molecules on the inhomogeneous surface of clinoptilolite. The best-fitting kinetic model for clinoptilolite is the pseudo-second order equation, highlighting that the rate of adsorption is dependent on absorbent capacity. Next, the effect of flow rate, bed depth, and grain size of clinoptilolite on lead removal was investigated in column experiments at an initial concentration of 450 mg pb/L. The highest removal efficiency was achieved in column experiments with a flow rate of 1 mL/min, a bed height of 10 cm, and a grain size of 0.6 to 0.8 mm. Breakthrough curves were predicted by the Thomas and Yoon-Nelson models, with excellent agreement with the corresponding experimental data. This research will be used to develop a new in situ remedial approach for removing lead from polluted groundwater.

Ce/Eu redox couple functionalized HKUST-1 MOF insight to sono-photodegradation of malathion.

The Ce/Eu redox pair-functionalized HKUST-1 MOF, as an innovative environmentally friendly and recyclable sono-photocatalyst, was hydrothermally mixed and fully characterized by XRD, PL, EIS, FE-SEM, EDS, Mott-Schottky, chronoamperometry, and DRS techniques. The obtained chemical and optical characteristics of the n-type Ce/Eu-HKUST-1 MOF showed that the transfer of additional 4f orbital electrons in the Ce/Eu redox pair improves the sono-photocatalytic activity. The performance of Ce/Eu-HKUST-1 MOF for the sono-photodegradation of Malathion (MA) was evaluated in the aqueous media in the simultaneous presence of blue light and ultrasonic irradiation. The optimization of the process was cross-examined using the response surface methodology as a function of the MA concentration (15-35 mg·L-1), Ce/Eu-HKUST-1 mass (10-30 mg), pH (4-12), and ultrasonic wave irradiation duration (10-30 min). The maximum sono-photocatalytic degradation capacity was found to be 99.99% under the optimum conditions set as 25 mg·L-1, 20 mg, 8, and 25 min for the concentration of Malathion, photocatalyst mass, pH, and irradiation duration, respectively. These findings were attributed to the suppression of electron-hole pair recombination, increased life-time of charge carriers, enhanced visible light absorption, and prominent proportion of hydroxyl and peroxide radicals formed.

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran.

In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.

Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS.

Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWRCFRF approach has a higher predictive accuracy 0.967 (96.7%) than the individual models of CF 0.763 (76.3%) and RF 0.776 (77.6%) and the CF-RF integrated model 0.897 (89.7%). Thus, the results of this research can be used by local managers and planners for environmental management.

GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches.

In arid and semi-arid areas, groundwater resource is one of the most important water sources by the humankind. Knowledge of groundwater distribution over space, associated flow and basic exploitation measures can play a significant role in planning sustainable development, especially in arid and semi-arid areas. Groundwater potential mapping (GWPM) fits in this context as the tool used to predict the spatial distribution of groundwater. In this research we tested four GIS-based models for GWPM, consisting of: i) random forest (RF); ii) weight of evidence (WoE); iii) binary logistic regression (BLR); and iv) technique for order preference by similarity to ideal solution (TOPSIS) multi-criteria. The Shahroud plain located in Iran, was selected to research the water scarcity and over-exploitation of groundwater resources over the past 20 years. In this research, using Iranian Department of Water Resources Management data, and extensive field surveys, 122 groundwater well data with high potential yield of ≥11 m3 h-1 were selected for GWPM. Specifically, we generated four different models selecting 70% (n = 85) of the wells and validated the resulting GWP maps upon the complementary 30% (n = 37).A total of fifteen ground water conditioning factors to explain the groundwater well distribution over the Shahroud plain were selected. From the Advanced Land Observing Satellite (ALOS), a DEM (30 m resolution) was extracted to calculate a set of morphometric properties which were combined with thematic ones such as land use/land cover (LU/LC) and Soil Type (ST). Results show that in RF (LU/LC), LR (ST), and AHP (Slope) are the most relevant contributors to groundwater occurrence. After that, using the natural break method, final maps were divided into five susceptibility classes of very low, low, moderate, high, and very high. The accuracy of models was ultimately tested using prediction rate (validation data), success rate (training data) and the seed cell area index (SCAI) indicators. Results of validation show that BLR with prediction rate of 0.905 (90.5%) and success rate of 0.918 (91.8%) had higher accuracy than WoE, RF and TOPSIS models with respective prediction rates of 0.885, 0.873 and 0.870 (88.5%, 87.3%, and 87%) and success rate of 0.900, 0.889, and 0.881 (90%, 88.9%, and 88.1%). SCAI results show that all models have acceptable classification accuracy although BLR outperformed the other models in terms of accuracy. Results show that the combination of remote sensing (RS) data and geographic information system (GIS) with new approaches can be used as a powerful tool in GWPM in arid and semi-arid areas. The results of this investigation introduced a potential novel methodology that could be used by decision-makers for the sustainable management of ground water resources.