Showing papers in "Environmental Earth Sciences in 2017"

Shallow landslide susceptibility assessment using a novel hybrid intelligence approach

Abstract: We present a hybrid intelligent approach based on Naive Bayes trees (NBT) and random subspace (RS) ensemble for landslide susceptibility mapping at the Bijar region, Kurdistan province (Iran). According to current literature, both NB and RS are machine learning techniques that have been rarely used for modeling of landslides. NBT is a relatively new decision trees-based algorithm in conjunction with Bayesian theories in building trees for classification, whereas RS is a relatively new ensemble framework with ability to improve performance of prediction models. In the hybrid approach, RS is used to generate subsets from the training data each subset is then used to construct a based classifier using NBT. For this purpose, a geospatial database for the study area was constructed that consisted of 111 landslide locations and 17 conditioning factors (slope degree, slope aspect, elevation above sea, curvature, profile curvature, plan curvature, stream power index, topographic wetness index, length-angle of slope, lithology, land use, distance to road, distance to fault, distance to stream, fault density, stream density, and rainfall). The database was used to construct and verify the proposed model. Performance of the model was evaluated using the receiver operating characteristics curve and area under the curve (AUC). The results showed that the proposed model performed well in this study (AUC = 0.886), and it improved significantly the performance of the NBT base classifier (AUC = 0.811). Overall, RS–NBT is promising which can be utilized for landslide susceptibility assessment in other landslide-prone areas.

Investigation of microrubbers, microplastics and heavy metals in street dust: a study in Bushehr city, Iran

Abstract: This study aimed to (1) investigate microrubbers (MRs) for the first time and identify microplastics (MPs) in street dust, (2) determine the physicochemical and mineralogical characteristics and morphology of dust particles, (3) understand the concentration and the possible source(s) of heavy metals/metalloids, (4) identify the chemical speciation and mobility potential of trace metals in urban street dusts, and (5) determine adverse health effects of street dust on children and adults living in the city of Bushehr in southwestern Iran. Generally, twenty four street dust samples were collected and analyzed. Calculated enrichment factors indicate high levels of contamination. Statistical analysis reveals that the two main sources of trace elements include road traffic emissions (Cu, Zn, Sb, Hg, Pb, Mo) and re-suspended soil particles (Al, Mn, Ni, Ti, Cd, Co). BCR sequential extraction results indicated that As, Zn, Cu, and Pb mainly occur in the exchangeable fraction and hence are highly bioavailable. X-ray powder diffraction analysis revealed the presence of calcite, dolomite, quartz, and magnetite. The size distribution of dust particles was also investigated using a scanning electron microscope (SEM), while elemental distribution was analyzed using an attached energy dispersive X-ray spectrometer (SEM–EDS) unit. Dust particles from heavy traffic areas are much finer compared with other investigated areas. MPs and MRs, mostly fibers and fragments, were detected in all samples [ranging from 210 to 1658 (MPs) and 44 to 782 (MRs) items/10 g dust] using fluorescence microscopy. The hazard index for As is higher than 10−4 for children and adults indicative of high risk. According to the calculated potential ecological risk index, Hg indicated moderate ecological risk in the street dust of the study area.

Mechanism of fluoride enrichment in groundwater of hard rock aquifers in Medak, Telangana State, South India.

Abstract: A total of 194 groundwater samples were collected from wells in hard rock aquifers of the Medak district, South India, to assess the distribution of fluoride in groundwater and to determine whether this chemical constituent was likely to be causing adverse health effects on groundwater user in the region. The study revealed that the fluoride concentration in groundwater ranged between 0.2 and 7.4 mg/L with an average concentration of 2.7 mg/L. About 57% of groundwater tested has fluoride concentrations more than the maximum permissible limit of 1.5 mg/L. The highest concentrations of fluoride were measured in groundwater in the north-eastern part of the Medak region especially in the Siddipeta, Chinnakodur, Nanganoor and Dubhaka regions. The areas are underlain by granites which contain fluoride-bearing minerals like apatite and biotite. Due to water–rock interactions, the fluoride has become enriched in groundwater due to the weathering and leaching of fluoride-bearing minerals. The pH and bicarbonate concentrations of the groundwater are varied from 6.6 to 8.8 and 18 to 527 mg/L, respectively. High fluoride concentration in the groundwater of the study area is observed when pH and the bicarbonate concentration are high. Data plotted in Gibbs diagram show that all groundwater samples fall under rock weathering dominance group with a trend towards the evaporation dominance category. An assessment of the chemical composition of groundwater reveals that most of the groundwater samples have compositions of Ca2+–Mg2+–Cl− > Ca2+–Na+–HCO3 − > Ca2+–HCO3 − > Na+–HCO3 −. This suggests that the characteristics of the groundwater flow regime, long residence time and the extent of groundwater interaction with rocks are the major factors that influence the concentration of fluoride. It is advised not to utilize the groundwater for drinking purpose in the areas delineated, and they should depend on alternate safe source.

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China

Abstract: Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.

Spatiotemporal variation of groundwater quality in an arid area experiencing long-term paper wastewater irrigation, northwest China

Abstract: Groundwater is crucial for multiple uses over the world, especially in arid and semiarid regions. However, human activities significantly decreased groundwater quality. In this study, the spatiotemporal variation of groundwater quality was evaluated in an arid area where long-term paper wastewater irrigation has been implemented. For this study, seven wells were regularly monitored for physicochemical parameters over a period of 1 year. Statistical and graphical approaches were applied to interpret the spatiotemporal variation of groundwater quality parameters in the wastewater irrigation zone. Correlation analysis was also carried out to reveal the sources of some major ions. The results indicate that the groundwater type in the study area is dominated by the Cl–Na, followed by the HCO3–Na, the HCO3–Ca·Mg, and the SO4·Cl–Ca·Mg types. Groundwater in the area is significantly contaminated locally with fluoride, nitrite and ammonia, and the chemical oxygen demand levels were increased in some groundwater monitoring wells. Most contaminants showed an increasing trend from the Yellow River water irrigation zone toward the wastewater irrigation zone. Rock weathering, mineral dissolution, and cation exchange are important processes controlling groundwater quality, but human activities, such as wastewater irrigation, play an undeniable role in affecting groundwater quality in this area. The results of this study contribute to the understanding of the formation and circulation of groundwater under human activities and provide a scientific basis for regional water quality evaluation, water quality improvement, and protection.

Geochemical characteristics and controlling factors of chemical composition of groundwater in a part of Guntur district, Andhra Pradesh, India

Abstract: A survey on quality of groundwater was carried out for assessing the geochemical characteristics and controlling factors of chemical composition of groundwater in a part of Guntur district, Andhra Pradesh, India, where the area is underlain by Peninsular Gneissic Complex. The results of the groundwater chemistry show a variation in pH, EC, TDS, Ca2+, Mg2+, Na+, K+, HCO3 −, Cl−, SO4 2−, NO3 − and F−. The chemical composition of groundwater is mainly characterized by Na+−HCO3 − facies. Hydrogeochemical type transits from Na+–Cl−–HCO3 − to Na+–HCO3 −–Cl− along the flow path. Graphical and binary diagrams, correlation coefficients and saturation indices clearly explain that the chemical composition of groundwater is mainly controlled by geogenic processes (rock weathering, mineral dissolution, ion exchange and evaporation) and anthropogenic sources (irrigation return flow, wastewater, agrochemicals and constructional activities). The principal component (PC) analysis transforms the chemical variables into four PCs, which account for 87% of the total variance of the groundwater chemistry. The PC I has high positive loadings of pH, HCO3 −, NO3 −, K+, Mg2+ and F−, attributing to mineral weathering and dissolution, and agrochemicals (nitrogen, phosphate and potash fertilizers). The PC II loadings are highly positive for Na+, TDS, Cl− and F−, representing the rock weathering, mineral dissolution, ion exchange, evaporation, irrigation return flow and phosphate fertilizers. The PC III shows high loading of Ca2+, which is caused by mineral weathering and dissolution, and constructional activities. The PC IV has high positive loading of Mg2+ and SO4 2−, measuring the mineral weathering and dissolution, and soil amendments. The spatial distribution of PC scores explains that the geogenic processes are the primary contributors and man-made activities are the secondary factors responsible for modifications of groundwater chemistry. Further, geochemical modeling of groundwater also clearly confirms the water–rock interactions with respect to the phases of calcite, dolomite, fluorite, halite, gypsum, K-feldspar, albite and CO2, which are the prime factors controlling the chemistry of groundwater, while the rate of reaction and intensity are influenced by climate and anthropogenic activities. The study helps as baseline information to assess the sources of factors controlling the chemical composition of groundwater and also in enhancing the groundwater quality management.

Drought forecasting by ANN, ANFIS, and SVM and comparison of the models

Abstract: Drought is a natural disaster that causes significant impact on all parts of environment and cause to reduction of the agricultural products. Other natural phenomena, for instance climate change, earthquake, storm, flood, and landslide, are also commonplace. In recent years, various techniques of artificial intelligence are used for drought prediction. The presented paper describes drought forecasting, which makes use of and compares the artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM). The index that is used in this study is Standardized Precipitation Index (SPI). All of data from Bojnourd meteorological station (from January 1984 to December 2012) have been tested for 3-month time scales. The input parameters are as follows: temperature, humidity, and season precipitation, and the output parameter is SPI. This paper shows high accuracy of these models. The results indicated that the SVM model gives more accurate values for forecasting. On the other hand, we use the nonparametric inference to compare the proposal methods, and our results show that SVM model is more accurate than ANN and ANFIS.

Characteristics of seismic disasters and aseismic measures of tunnels in Wenchuan earthquake

Abstract: Over the past few years, accompanied by big and frequent earthquakes, more attention was paid to the tunnel earthquake resistance. To reduce tunnel seismic damage and explore the reasonable aseismic measures, the tunnel earthquake disaster investigation was employed to analyze and summarize the tunnel seismic damage on the basis of Wenchuan earthquake. Fifty-two tunnels near the epicenter of Sichuan Province were investigated: Only 7 tunnels did not show structure damage, 6 tunnels suffered the most serious damage, and the rest appeared damage to various extents. It indicates that most serious seismic damage happens to fault fracture zone, followed by entrance and common section of the tunnel. Additionally, the results display that the typical seismic damage of tunnels is lining cracking, collapsing, dislocation, construction joints cracking, and uplifting of invert, and usually lining cracking and collapsing account for a larger proportion. Therefore, the tunnel aseismic design should emphasize the fault fracture zone and tunnel entrance. Tunnel design should adopt the composite lining structure with shock absorber and whole chain alternative grouting to prevent the lining cracking and collapsing in the seismic fortification zone.

Finding harmony between the environment and humanity: an introduction to the thematic issue of the Silk Road

Abstract: The Silk Road initiative is both exciting and controversial, as it may bring environmental degradation and water resources concerns, and at the same time it promotes swift economic growth in poverty-stricken areas along the Silk Road. Finding a balance between environmental protection and economic growth is the motivation for publishing this thematic issue. The Guest Editors introduce the background of the Silk Road in this editorial and the papers included in this thematic issue. These studies represent only some preliminary efforts toward establishing the harmonious relationships required to address these issues and to encourage further investigations.

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

Abstract: This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naive Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.

“Sponge city” concept helps solve China’s urban water problems

Abstract: The scale of China’s urbanization in recent decades is almost unprecedented in human history. Urban water problems are always prevalent and have been intensifying during the rapid urbanization process. In response, the “sponge city” concept was put forward by Chinese government in 2013, which represents a new urban development mode that is intended to manage effectively urban rainwater. This concept gives priority to protection and remediation of natural environments in urban planning and construction to ensure their ecosystem service function of water conservation. “Sponge city” vividly describes an urban environment that is devoted to finding ecologically suitable alternatives to transform urban infrastructures into green infrastructures so these could capture, control and reuse precipitation in a useful, ecologically sound way. Moreover, the idea behind sponge city is to promote the renovation of drainage systems, the improvement of connectivity of water systems, the division of rainwater and sewage pipe networks, and other modern engineering measures to enhance the city’s ability to cope with water problems. By building sponge cities, the multi-objective integrated rainwater management of sponge cities that involves infiltration, stagnation, storage, purification, utilization, and discharge is expected to be achieved, so as to use the full potential of rainwater under the premise of not suffering urban flooding.

The Bode hydrological observatory: a platform for integrated, interdisciplinary hydro-ecological research within the TERENO Harz/Central German Lowland Observatory

Abstract: This article provides an overview about the Bode River catchment that was selected as the hydrological observatory and main region for hydro-ecological research within the TERrestrial ENvironmental Observatories Harz/Central German Lowland Observatory. It first provides information about the general characteristics of the catchment including climate, geology, soils, land use, water quality and aquatic ecology, followed by the description of the interdisciplinary research framework and the monitoring concept with the main components of the multi-scale and multi-temporal monitoring infrastructure. It also shows examples of interdisciplinary research projects aiming to advance the understanding of complex hydrological processes under natural and anthropogenic forcings and their interactions in a catchment context. The overview is complemented with research work conducted at a number of intensive research sites, each focusing on a particular functional zone or specific components and processes of the hydro-ecological system.

Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran

Abstract: The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.

Hydromechanical coupling tests for mechanical and permeability characteristics of fractured limestone in complete stress–strain process

Abstract: To clarify mechanical and permeability characteristics of fractured limestone in complete stress–strain process, the hydromechanical coupling tests with various differential water pressures and confining pressures were performed. The mechanical characteristics of fractured limestone specimens are sensitive to confining pressure, differential water pressure, and effective stress. The increasing differential water pressure weakens the rock strength and deformation modulus by activating the lateral deformation of fractured limestone, which is attributed to the decrease in the effective minimum principal stress. The experimental results verify the validity of Mohr–Coulomb yield criterion considering the effective stress effect under hydromechanical coupling condition. The permeability values display four stages of decrease–gradual increase–rapid increase–small drop in complete stress–strain process, which roughly correspond to volumetric compression stage, elastic deformation stage, yield, and post-peak stage, as well as residual strength stage, respectively. At a low differential water pressure in the range of 2–5 MPa, the corresponding relationship mentioned above is obvious. However, at high differential water pressures up to 8–14 MPa, there is a deviation from the correspondence above, i.e., permeability reduction stage is shorter than the stage of volumetric compression. A cubic polynomial is used to describe the relationship between permeability and volumetric strain at volumetric compression stage. However, it is difficult to describe the relationship between the permeability and volumetric strain by a uniform fitting equation at the dilatancy stage.

Groundwater quality ranking for drinking purposes, using the entropy method and the spatial autocorrelation index

Abstract: Groundwater quality for drinking purposes has been evaluated for 21 groundwater samples from the Azarshahr Plain in Iran using entropy theory, and its results have been compared with the spatial autocorrelation of effective parameters of water quality. In order to prevent the expert judgments of the parameters weight that occurs when the Water Quality Index (WQI) method is used, the entropy method was used. Entropy and its weight were calculated, and parameters spatial autocorrelation was then determined. The spatial autocorrelation assessment confirmed the entropy theory results. The maximum spatial autocorrelation, minimum entropy and therefore the highest effectiveness rate on groundwater quality of Azarshahr Plain were found to be associated with bicarbonate. Using the entropy weighted WQI, the groundwater quality was classified into five categories: excellent, good, moderate, poor and extremely poor. According to the entropy weighted WQI, the groundwater quality of study area can be classified into “good” to “poor” domains.

Modeling runoff–sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed

Abstract: Land use/land cover (LU/LC) change has significant influence on runoff and sediment characteristics of any catchment. LU/LC change studies are essential for policy planners to understand the problems and take course of action such as soil and water conservation measures for improvement. The present study is conducted for Beressa watershed using hydrological model integrated with GIS. Input data like LU/LC, weather and soil data features are required to undertake watershed simulation. The model has been calibrated and validated in SWAT-CUP. The data from 1980 to 1999 were used for calibration, while the data from 2000 to 2014 were used for validation. LU/LC analysis showed that agricultural and settlement areas have increased between 1984 and 2015, while barren, grazing land and forest area have decreased. However, the share of forest cover increased in between 1999 and 2015. SWAT model has successfully simulated and calibrated runoff and sediment yield. During calibration periods (1980–1999), the values of R 2, NSE, RSR and PBIAS were obtained as 0.72, 0.67, 0.52 and 3.9%, respectively, whereas during the validation periods (2000–2014) the values were 0.68, 0.64, 0.56 and 7.6%, respectively. Runoff and sediment yield has significantly increased. Thus, it is concluded that the change in LU/LC significantly influenced the runoff and sediment yield.

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Abstract: Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO4), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH4-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.

Shale gas fracturing using foam-based fracturing fluid: a review

Abstract: World energy resources are depleting at an alarming rate, and natural gas has been identified as an environmentally friendly energy resource, with shale gas being one option. However, the extremely low permeability of shale plays has caused them to fail to produce a commercially viable amount of gas. Therefore, appropriate production enhancement techniques, including hydro-fracturing, are required. This paper reviews the research on shale gas production enhancement using foam-based hydro-fracturing and focuses on research on shale deposit distribution around the world, the importance of shale gas recovery, major shale gas recovery enhancement techniques, the effectiveness of foam-based fracturing depending on the foam type used and the formation properties, advantages and limitations of foam-based fracturing compared to other fluids, and existing experimental and numerical studies and field studies. According to the available experimental and modelling studies on foam fracturing, N2-based foams are stronger than CO2-based foams. The effective viscosity that controls the foam rheology decreases with increasing temperature and decreasing pressure and foam quality, and fracture length reduces and fracture width increases with increasing foam quality. Although this technique has been tested in few shale plays worldwide, most studies have been performed in the USA and Canada. Therefore, the foam fracturing technique is still comparatively novel for other countries around the world.

Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions

Abstract: The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN.

Lake water quality assessment: a case study of Shahu Lake in the semiarid loess area of northwest China

Abstract: A comprehensive assessment on lake water quality was carried out in Shahu Lake, northwest China, to provide valuable information about present lake water quality for decision making. Major ions, general parameters, bacteriological parameters, organics and trace metals monitored monthly in 2014 were considered. Monitored parameters were compared with quality criteria for surface water of China, and overall water quality assessment was carried out using an entropy weighted water quality index (EWQI) based on 20 selected parameters. Lake water quality was also assessed for irrigation purpose. The results show that the lake water is of Cl·SO4–Na facies with high salinity and COD. The geochemistry of the lake water is regulated by intense evaporation and human activities. TP, TN and F− are major inorganic contaminants, with over 50% of the water samples polluted by them. Oil, mainly attributed by leaky motor tourist boats, is the major organic pollutants in the lake water, with 10 samples (37.04%) showing higher oil content than the permissible limit. The concentrations of other inorganic and organic contaminants as well as trace metals are well below the permissible limits. The present study indicates that inorganic contamination in the lake water is more severe than organic pollution. The overall lake water quality, assessed by EWQI, is poor and very poor with SO4 2−, TDS, TH and Cl− being the dominant contributing factors. The lake water is suitable for irrigation in terms of alkalinity, but is unsuitable for irrigation from the salinity point of view. Accelerating the circulation and replenishment of the lake water is an important way of reducing contaminant concentrations. This study is important in providing comprehensive information on lake water quality for decision makers and valuable reference for international lake water researchers.

Pharmaceutical residues in aquatic environment and water remediation by TiO2 heterogeneous photocatalysis: a review

Abstract: The occurrence of pharmaceutical compounds in the natural water sources has been reported as early as in the year 1980. Until now, the presence of pharmaceutical compounds in the aquatic environment has been frequently reported in the literature. Moreover, increasing evidence suggests that these contaminants have posed a threat to both humans and ecosystems. In this regard, the present review paper seeks to offer an overview of this environmental issue of pharmaceutical pollution where the subject matters to be reviewed include the effects, sources and mitigation strategies of pharmaceuticals in the aquatic environment. Besides, a review of the fundamentals and mechanisms of heterogeneous photocatalysis technology is also presented in this paper. Heterogeneous photocatalysis is a rapidly expanding technology which has been extensively investigated and applied in wastewater treatment for the remediation of persistent pollutants such as pharmaceutical compounds during the last decade. Furthermore, the ideal photocatalyst titanium dioxide (TiO2), which can collaborate and perform well in the photocatalysis treatment process, is also discussed. The advantages and limitations associated with the application of this treatment method are summarized and discussed in details. Finally, this review paper focuses on the future trend of the photocatalysis technology and identifies the barriers and lacking parts which need to be resolved in the near future.

Surface water pathways and fluxes of metals under changing environmental conditions and human interventions in the Selenga River system

Abstract: This paper presents the results of novel field campaigns in the extensive (447,000 km2) Selenga River basin, through which 304 samples of river water and 308 samples of suspended matter were collected during high and low water periods between 2011 and 2013. The Selenga River is the largest tributary (more than 50% of the inflow) to the Lake Baikal. Due to ongoing hydroclimatic change and human pressures under conditions of economic growth, the rivers of the area experience significant change in water quality. A key issue for improved understanding of regional impacts of the environmental change is to disentangle the influence of climate change from that of other pressures within the catchment. Our research aims to evaluate the pathways and mass flows of heavy metals and metalloids both in dissolved and suspended forms, taking a basin-scale perspective that previously has not been fully pursued in the Lake Baikal region. Results showed quality deterioration over short distances due to strong impact of hot spots from urban and industrial activities, including mining. The determined enrichment of dissolved metals in waters of Selenga River as well as the spatial and temporal variability of water and suspended sediment composition is further analyzed in the context of climatic, hydrological and land use drivers.

Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain, northwest of Iran

Abstract: The main aims of the present study are to identify the major factors affecting hydrogeochemistry of groundwater resources in the Marand plain, NW Iran and to evaluate the potential sources of major and trace elements using multivariate statistical analysis such as hierarchical clustering analysis (HCA) and factor analysis (FA). To achieve these goals, groundwater samples were collected in three sampling periods in September 2013, May 2014 and September 2014 and analyzed with regard to ions (e.g., Ca2+, Mg2+, Na+ and K+, HCO3 −, SO4 2−, Cl−, F− and NO3 −) and trace metals (e.g., Cr, Pb, Cd, Mn, Fe, Al and As). The piper diagrams show that the majority of samples belong to Na–Cl water type and are followed by Ca–HCO3 and mixed Ca–Na–HCO3. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, influence the hydrogeochemistry of the study area. The results of the FA demonstrate that 6 factors with 81.7% of total variance are effective in the overall hydrogeochemistry, which are attributed to geogenic and anthropogenic impacts. The HCA categorizes the samples into two clusters. Samples of cluster C1, which appear to have higher values of some trace metals like Pb and As, are spatially located at the eastern and central parts of the plain, while samples of cluster C2, which express the salinization of the groundwater, are situated mainly westward with few local exceptions.

Application of UAV photogrammetry for the multi-temporal estimation of surface extent and volumetric excavation in the Sa Pigada Bianca open-pit mine, Sardinia, Italy

Abstract: In open-pit mines, monitoring of topographic and volumetric changes through time is found to be of great importance to support excavation stages and to plan rehabilitation strategies. In this work, we describe a geomatic approach to assess changes in surface mine extent and to quantify excavated volume in the Sa Pigada open-pit mine, Sardinia, Italy. We performed two drone-based photogrammetric surveys in 2013 and 2015, and by means of the Structure from Motion (SfM) technique, we obtained related 3D dense point clouds and digital orthophotos. Images were georeferenced thanks to a series of ground control points surveyed with geodetic GPS. Distances between the two clouds were estimated with the recent Multiscale Model to Model Cloud Comparison (M3C2) plug-in included in the CloudCompare open-source software. Starting from cloud-to-cloud distances, we calculated the excavated volume of mineral resources between the two surveys. Results of the M3C2 comparison supported the analysis of the two orthophotos, through which accurate limits of the 2013 and 2015 active mine areas, rehabilitated area and temporary dumps were identified and drawn in a digital map. Results obtained in this study suggest that the applied geomatic techniques are suitable for performing accurate change detection analysis in open-pit environments and represent a valid support for scientists and technicians allowing to monitor with high spatial and temporal resolutions. This approach can be also considered a valid tool to reduce environmental impact from mining.

A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model

Abstract: Ground vibration is one of the common environmental effects of blasting operation in mining industry, and it may cause damage to the nearby structures and the surrounding residents. So, precise estimation of blast-produced ground vibration is necessary to identify blast-safety area and also to minimize environmental effects. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) was proposed to predict blast-produced ground vibration in Pengerang granite quarry, Malaysia. For this goal, 81 blasting were investigated, and the values of peak particle velocity, distance from the blast-face and maximum charge per delay were precisely measured. To demonstrate the performance of the hybrid PSO–ANFIS, ANFIS, and United States Bureau of Mines empirical models were also developed. Comparison of the predictive models was demonstrated that the PSO–ANFIS model [with root-mean-square error (RMSE) 0.48 and coefficient of determination (R 2) of 0.984] performed better than the ANFIS with RMSE of 1.61 and R 2 of 0.965. The mentioned results prove the superiority of the newly developed PSO–ANFIS model in estimating blast-produced ground vibrations.

Impacts of climate change on stream flow and hydro power generation in the Alpine region

Abstract: Electricity generated by hydro power is the most widely used form of renewable energy, and as such, its vulnerability to climate change is of great interest. The aim of this work is to estimate the change in river discharge characteristics in the Alpine region due to possible impacts of climate and the related changes in the power generation of run-of-river hydro power plants up to 2050. Four representative bias-corrected climate simulations from the ENSEMBLES project are chosen based on the SRES greenhouse gas emission scenario pathway A1B. Data of these simulations serve as input for a lumped-parameter rainfall-runoff model at a monthly time step, which is calibrated on discharge data of gauging stations along important rivers in the Alpine region. A power plant model fed with runoff data generated by the hydrological model is used to compute changes in the long-term average annual net electrical energy output of hydro power plants for the whole Alpine region; while the model for Austria is based on known technical parameters of the power plants, a more simplified approach is employed elsewhere. The general warming trend observed in all four climate scenarios causes to various degrees a seasonal shift towards earlier runoff. However, more diverse changes in precipitation for the different climate scenarios and time periods result in diverging hydrological projections. Although the annual runoff is found to decrease in some scenarios, the generally observed shift of runoff towards the winter season that typically shows higher energy consumption in the Alpine region suggests that the overall impact for the electricity sector tends to be positive rather than negative. Estimated changes in the average annual electricity generation of run-of-river plants are generally found to be within a single-digit percentage range but can be either positive or negative depending on the climate scenario. The estimated ranges reflect the diversity (uncertainty) of the climate models; the total bandwidth of possible changes in the water availability and hydro power generation in the Alpine region up to 2050 is assumed to be even higher, because of other uncertainties in the model chain that are not explicitly considered here. Nevertheless, as the general regional trends and bandwidth of changes in runoff and hydro power production strongly depend on the future changes in precipitation, the results of this work provide reasonable orders of magnitude of expected changes and are seen as a first step towards an improved understanding of climate impacts on hydro power production within the entire Alpine region.

Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India

Abstract: The River Ganges being the most sacred river and lifeline to millions of Indians in serving their water requirements is facing excessive threat of pollution. Under various river management and conservation strategies for its protection, the assessment of water quality of its main tributary Ramganga River is lacking. This study focuses on assessment of physicochemical and heavy metal pollution of the Ramganga River by application of multivariate statistical techniques. Sampling of Ramganga River at sixteen sampling sites was carried out in three seasons (summer, monsoon and winter) of 2014. The collected water samples were analyzed for physicochemical parameters and heavy metals. Results from cluster analysis (CA) of the data divided the whole stretch of the river into three clusters as elevation from 1304 to 259 m as less polluted, from 207 to 154 m as moderately polluted and from elevation 154 to 139 m as high-polluted stretches with anthropogenic as main sources of pollution in high-polluted stretch. Principal component analysis of the seasonal dataset resulted in three significant principal components (PC) in each season explaining 72–8% of total variance with strong loadings (>0.75) of PC1 on fluoride (F−), chloride (Cl−), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3 −), total dissolved solids and electrical conductivity. Temporal variation by one-way ANOVA (Analysis of Variance) showed significant seasonal variation was in the pH, chemical oxygen demand, biochemical oxygen demand, turbidity, HCO3 −, F−, Zn, cadmium (Cd) and Mn (p < 0.05). Turbidity showed approximately a twofold increase in monsoon season due to rainfall in the catchment area and subsequent flow of runoff into the river. Concentration of HCO3 −, F− and pH also showed similar increase in monsoon. The concentration of Zn, Cd and Mn showed an increasing trend in summers compared to monsoon and winter season due to dilution effect in the monsoon season and its lasting effect in winters.

A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China

Abstract: The determining of landslide-prone areas in mountainous terrain is essential for land planning and hazard mitigation. In this paper, a comparative study using three statistical models including weight of evidence model (WoE), logistic regression model (LR) and support vector machine method (SVM) was undertaken in the Zhouqu to Wudu segment in the Bailong River Basin, Southern Gansu, China. Six conditionally independent environmental factors, elevation, slope, aspect, distance from fault, lithology and settlement density, were selected as the explanatory variables that may contribute to landslide occurrence based on principal component analysis (PCA) and Chi-square test. The relation between landslide distributions and these variables was analyzed using the three models and the results then used to calculate the landslide susceptibility (LS). The performance of the models was then evaluated using both the highly accurate deformation signals produced by using the Small Baseline Subset Interferometric Synthetic Aperture Radar technique and Receiver Operating Characteristic (ROC) curve. Results show more deformation points in areas with high and very high LS levels, and also more stable points in areas with low and very low LS levels for the SVM model. In addition, the SVM has larger area under the ROC curve. It indicates that the SVM has better prediction accuracy and classified ability. For the interpretability, the WoE derives the class of factors that most contributed to landsliding in the study area, and the LR reveals that factors including elevation, settlement density and distance from fault played major roles in landslide occurrence and distribution, whereas the SVM cannot provide relative weights for the variables. The outperformed SVM could be employed to determine potential landslide zones in the study area. Outcome of this research would provide preliminary basis for general land planning such as choosing new urban areas and infrastructure construction in the future, as well as for landslide hazard mitigation in Bailong River Basin.

Energy storage in the geological subsurface: dimensioning, risk analysis and spatial planning: the ANGUS+ project

Abstract: New techniques and methods for energy storage are required for the transition to a renewable power supply, termed “Energiewende” in Germany. Energy storage in the geological subsurface provides large potential capacities to bridge temporal gaps between periods of production of solar or wind power and consumer demand and may also help to relieve the power grids. Storage options include storage of synthetic methane, hydrogen or compressed air in salt caverns or porous formations as well as heat storage in porous formations. In the ANGUS+ project, heat and gas storage in porous media and salt caverns and aspects of their use on subsurface spatial planning concepts are investigated. The optimal dimensioning of storage sites, the achievable charging and discharging rates and the effective storage capacity as well as the induced thermal, hydraulic, mechanical, geochemical and microbial effects are studied. The geological structures, the surface energy infrastructure and the governing processes are parameterized, using either literature data or own experimental studies. Numerical modeling tools are developed for the simulation of realistically defined synthetic storage scenarios. The feasible dimensioning of storage applications is assessed in site-specific numerical scenario analyses, and the related spatial extents and time scales of induced effects connected with the respective storage application are quantified. Additionally, geophysical monitoring methods, which allow for a better spatial resolution of the storage operation, induced effects or leakages, are evaluated based on these scenario simulations. Methods for the assessment of such subsurface geological storage sites are thus developed, which account for the spatial extension of the subsurface operation itself as well as its induced effects and the spatial requirements of adequate monitoring methods.

Quantification of the pore structures of Malan loess and the effects on loess permeability and environmental significance, Shaanxi Province, China: an experimental study

Abstract: The pore microstructures of loess control the porosity and permeability of the loess, affecting the patterns of groundwater flow and the transport of contaminants. In the present study, the pore microstructure of Malan loess was investigated quantitatively in samples from five different loess layers. Specimens were examined via SEM, and pore microstructure parameters were determined using the digital image analysis method. Pore structures (including pore area ratio (PAR), pore size distribution, pore shape and pore morphology), the effects of the pore structure on loess permeability and the environmental significance of these factors are discussed in this paper. The results indicate that the samples of five loess layers in this study are considered to be porous soil, according to both the PAR and porosity values. The number of micropores, small pores, mesopores and macropores decreased significantly. The differences in number were mainly due to the significant reduction in the number of round pores, indicating that round pores tend to be small. In terms of area, micropores, small pores, mesopores and macropores were dominated by elongated and irregular pores. The macro- and mesopore structural characteristics of loess determined the transfer characteristics of groundwater and pollutants in the loess. Additionally, the 62% area reduction in the area of elongated or irregular macro- and mesopores and the decreasing connectivity from top to bottom layers caused by the deformation and destruction of the pores due to the overlying soil reduced the permeability and the water/pollutant migration rate with depth.