Showing papers in "Environmental Earth Sciences in 2015"

Building a new and sustainable “Silk Road economic belt”

Abstract: The building of the Silk Road economic belt is an exciting prospect that may bring immense economic benefits to Eurasian countries. However, intensive human activities to be induced by it may double the water crisis in central Asia, deteriorate the vulnerable environment, and accelerate energy consumption in this area. To build a new and sustainable Silk Road economic belt, advancing scientific research, reinforcing international collaboration and enhancing education are necessary steps. With careful planning, sound research, good data and the support from governments and the people, the Silk Road economic belt can be developed in an environmentally sustainable manner that is a credit to all involved.

Hydrochemical characterization of drinking groundwater with special reference to fluoride in an arid area of China and the control of aquifer leakage on its concentrations

Abstract: The objective of the study is to determine the distribution and origin of fluoride in two confined aquifers within the depth of 80–240 m (mid-deep confined aquifers) in Shizuishan City, an arid area in Northwest China. Based on geological and hydrogeological conditions, the distribution patterns of fluoride in the two confined aquifers were studied. Correlation analysis was used to reveal the mechanisms and reactions influencing the groundwater chemistry. The control of aquifer leakage induced by intense groundwater abstraction on fluoride concentrations was also discussed. The results show that the concentration of F− in some locations of the study area is beyond the limit of the Standards for Drinking Water Quality of China. Correlation analysis shows that the major reactions occurring in the confined aquifers are dissolution of gypsum, fluorite and halite, and precipitation of calcite and dolomite. Groundwater leakage from shallow aquifers is also responsible for the elevation of fluoride in mid-deep confined aquifers. The research is a scientific basis for the rational development and utilization of groundwater in this area, and it may be referenced by international scholars for similar research in other parts of the world.

Evaluation of the groundwater quality with WQI (Water Quality Index) and multivariate analysis: a case study of the Tefenni plain (Burdur/Turkey)

Abstract: Groundwater is a vital source of water for domestic and agricultural activities in the Tefenni plain. Therefore, groundwater quality, seasonal variations and its suitability for drinking, irrigation and industrial usage were evaluated. In this study, 56 water samples were collected from springs, wells, and lake in dry and wet seasons. Ca–Mg–HCO3, Mg–Ca–HCO3, Na–CO3–Cl, and Na–HCO3–Cl water types are the dominant water types in the investigation area. Parameters, which are controlled to chemical variations of groundwater, were analyzed with R-mode factor and correlation analysis. According to R-mode factor analysis, total dissolved solids, Na, Cl, HCO3, and NH3 are the most important parameters. In addition, Water Quality Index (WQI) was applied to suitability for drinking purpose and to investigation of groundwater quality. Quality of groundwaters are suitable for drinkable both dry and wet season in study area. In terms of the irrigation and industrial usage, generally groundwater is suitable in dry season but is not suitable in wet season. Groundwater chemistry is affected with water–rock interaction and densely agricultural activities.

Effect of biochar on heavy metal immobilization and uptake by lettuce ( Lactuca sativa L.) in agricultural soil

Abstract: Many remediation options have been applied to the heavy metal-contaminated agricultural soils nearby abandoned mining sites mainly due to hazard effects of heavy metals to human through agricultural crop dietary. Hence, the current study was carried to examine the heavy metal immobilizing effect of biochar produced from rice hull and subsequent heavy metal uptake by lettuce. Rice hull biochar was incorporated into a heavy metal-contaminated upland soil at six application rates (0, 0.5, 1, 2, 5, and 10 % (v/v)) and soil biochar mixtures were examined using both incubation and pot trials for cultivation of lettuce. Incubation studies showed that biochar incorporation induced significant declines (>80 %) in the phytoavailable metal pool as assessed via 1 M NH4NO3 extraction, possibly due to increased heavy metal adsorption onto the applied biochar and increases in soil pH. Similar results were also observed in pot trials, where the uptake of heavy metals by lettuce was significantly reduced as biochar application rate increased. Despite the significant decline in soil phytoavailable metal pools, lettuce growth still declined as biochar application rate increased. This was attributed to the adsorption of available nitrogen on to the biochar resulting in nitrogen deficiency. Therefore, when the biochar is used for metal immobilization in agricultural soils, maintaining soil nutrient status should be also considered to ensure optimum growth of the crop plants besides metal immobilization rate.

Preliminary assessment of heavy metal contamination in surface sediments from a river in Bangladesh

Abstract: Contamination of heavy metals in sediment is regarded as a major crisis globally, with a large SHARE in developing countries, such as Bangladesh. The objective of this study is to assess the contamination level and seasonal variation of heavy metals and their ecological risk in sediments. Heavy metals (Cr, Ni, Cu, As, Cd, and Pb) in sediments were investigated from eight different sites of Paira River situated at the southern part of Bangladesh and metals were measured by using inductively coupled plasma mass spectrometer. The mean concentration of Cr, Ni, Cu, As, Cd, and Pb in sediments was 45, 34, 30, 12, 0.72 and 25 mg/kg dw, respectively. Metals in sediment during winter were higher than summer season. The concentrations of metals in sediment were compared with both background and toxicological reference values. The comparative results suggested that the selected metals created an adverse effect on the aquatic ecosystems of the studied river. The pollution load index for three sites were higher than baseline values indicating progressive deterioration of sediments by heavy metal. Potential ecological risks of metals in sediment indicated moderate to considerable risk. This study suggested that more attention should be directed to the comprehensive risk assessment of heavy metals of this riverine aquatic environment.

A review of assessment methods for river hydromorphology

Abstract: Numerous hydromorphological assessment methods have been developed in different countries during recent decades, with notable differences in their aims, scales, and approaches. Although these methods are increasingly applied to support river management, the strengths and limitations have been insufficiently investigated. This review of 121 methods analyses hydromorphological assessment methods dating from 1983 to 2013, identifying their main strengths, limitations, gaps, the potential to integrate different approaches, and the need for further improvements. For this purpose methods have been grouped into four categories: (1) physical habitat assessment; (2) riparian habitat assessment; (3) morphological assessment; (4) assessment of hydrological regime alteration. Seventeen categories of information covering general characteristics, recorded features and river processes encompassing over 90 features were recorded for each method reviewed, allowing a comparative analysis of the four assessment categories. The main gap in most methods is insufficient consideration of physical processes. Thus, an integrated hydromorphological analysis is recommended, where the morphological and hydrological components are the key parts to classify hydromorphological conditions. Additional physical and riparian habitat methods strengthen the link with ecological conditions.

Surface subsidence control theory and application to backfill coal mining technology

Abstract: Solid backfill technology, which can achieve precise control of surface subsidence, has become the primary method used to extract “under three” coal resources (under railways, buildings, and water bodies), especially under buildings. This paper proposes a probability integration model for surface subsidence prediction based on the equivalent mining height (EMH) theory and describes the basic control principle for surface subsidence, i.e., guaranteeing a maximum security standard for surface buildings, based on the maximum EMH, by controlling the backfill body’s compression ratio (BBCR). Based on this control principle, an engineering design process for solid backfill mining under buildings was established, and an engineering design method that employs the BBCR as the critical control indicator and a method for determining the key parameters in subsidence prediction are proposed. In applications at the Huayuan coal mine in China, the measured subsidence values were less than predicted; the measured BBCR was controlled at a level higher than 90 %, which was greater than in the theoretical design; the surface subsidence of buildings was controlled at mining level I. The results of application of the methods proposed in this paper show that the basic principles of controlling the BBCR and maximum EMH provide clear guidance for surface subsidence control in solid backfill mining engineering practice.

Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach

Abstract: Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting.

Water resources and their management in central Asia in the early twenty first century: status, challenges and future prospects

Abstract: Large parts of Central Asia are characterized by a semiarid to arid climate. Therefore, areas close to shallow groundwater, rivers and lakes are characterized by unique water-dependent ecosystems and human societies which have developed over millennia in close interaction with the naturally limited water resources. In the early 21st century, global climate change, population growth, river damming, large-scale water abstractions and rising levels of pollution exert multiple pressures on the region’s water resources, aquatic and terrestrial ecosystems at historically high levels. Water scarcity threatens the livelihood of populations locally and in transboundary settings by a growing competition over a limited resource. This context is of particular importance since all major rivers of the region cross at least one international border. The complexity and character of water-related challenges in the region mean that management approaches need to be integrative, taking into account the natural resource basis, environmental limits and the socio-cultural and geopolitical dimension. This paper frames the thematic issue of Environmental Earth Sciences and provides a comprehensive overview about the current state of knowledge about water resources and their management in Central Asia. There is a focus on case studies looking at the Selenga–Baikal–Angara Basin, the Lake Aral Basin including the Syr Darya and Amu Darya river systems, the Tarim and the Illi River Basins. Aiming to be an up-to-date interdisciplinary scientific reference on the region’s water-related challenges, this thematic issue gives theoretical and practical insights into solutions and best practice examples of water management.

Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia

Abstract: A comprehensive landslide susceptibility mapping (LSM) should be produced to reduce damages to individuals and infrastructures. In the international landslide literature, various statistical methods such as logistic regression (LR) and frequency ratio (FR) have been widely used individually for LSM. However, these methods have some weaknesses. This study aims to propose an ensemble method of FR and LR to overcome their weak points. This ensemble method was applied on Fayfa area which is located in the southwest of Saudi Arabia (SA) in Jazan region. The study area covers an area of ~260 km2, and is located between latitudes 17°11′46″N to 17°21′16′′N and longitudes 43°0′30′′E to 43°11′38′′E. For LSM, an inventory map with 86 landslide occurrences was extracted from various sources. Then, the landslide inventory was randomly divided into two datasets: 70 % for training the models and 30 % for validation. The landslide conditioning factors used in the LSL include altitude, curvature, distance from wadis, distance from road, distance from fault, stream power index, topographic wetness index, soil type, geology, slope, and aspect. The produced susceptibility maps were validated by an area under the curve technique. The success rate curves of the models measure the goodness of fit of the training data, while the prediction rate curves provide the validation of the susceptibility maps, measuring the prediction capability. The validation results showed 65, 69, and 79 % success rates for FR, LR, and ensemble models, respectively. Furthermore, the prediction rates were 58, 77, and 82 % for FR, LR, and ensemble models, respectively. The proposed ensemble method can be replicated in other natural hazard studies because it can produce accurate assessment for disaster management and decision making.

Landfill site selection using fuzzy AHP and fuzzy TOPSIS: a case study for Istanbul

Abstract: Landfill site selection is a multi-attribute decision problem, through which factors like available land area, soil conditions, climatological conditions, and economic considerations are investigated in detail. Frequently, it is a challenge to come up with “the one” solution while tackling such complex systems. Therefore, use of tools such as fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) should be preferred in order to emphasize pros and cons for each of the studied options. In this study, three possible landfill sites for the city of Istanbul are evaluated through expert opinion and by facilitating fuzzy AHP and fuzzy TOPSIS. Initially, the landfill site selection problem is presented in the framework of a model and then the model is mathematically solved by calculating the individual criterion weights. In conclusion, considering the rapid rate of urbanization for the city of Istanbul, the possible landfill sites convey similar overall results, but differ in specific criteria.

Influence of added hydrogen on underground gas storage: a review of key issues

Abstract: The existing infrastructure of the natural gas transportation pipeline network and underground gas storage (UGS) facilities in Germany provides an opportunity and huge capacity to feed, transport and store hydrogen and synthetic fuel gases containing hydrogen, produced from renewable sources. At low hydrogen concentrations, only minor changes to gas transportation equipment will be required. In contrast, the UGS designed in converted gas fields and aquifers are particularly susceptible to the effect of hydrogen. Due to a lack of adequate knowledge about the hydrogen concentration in natural gas, which can be tolerated by the downhole equipment, reservoir and caprocks, the injection of natural gas containing hydrogen in the existing porous UGS is strongly limited. Key issues addressed in this paper are the change in capacity and efficiency of UGS associated with the blending of hydrogen in the stored natural gas, the geological integrity of the reservoir and caprocks, the technical integrity of gas storage wells, durability of the materials used for well completions, corrosion and environmental risks associated with the products of microbial metabolism.

Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran

Abstract: A remote sensing and geographic information system-based study has been carried out to map areas susceptible to landslides using three statistical models, frequency ratio (FR), logistic regression (LR), and fuzzy logic at the central Zab basin in the mountainsides in the southwest West Azerbaijan province in Iran. Ten factors such as slope, aspect, elevation, lithology, normalized difference vegetation index (NDVI), land cover, precipitation, distance to fault, distance to drainage, and distance to road were considered. Landsat ETM+ images were used for NDVI and land cover maps. A landslide inventory map of the study area was identified by a SPOT 5 satellite after which fuzzy algebraic operators were applied to the fuzzy membership values of landslide susceptibility mapping. In addition, FR and LR models were applied to determine the landslide susceptibility. The three models are validated using the receiver operating characteristic and the area under which curve values were calculated. The validation results showed that the LR model (accuracy is 96 %) has better prediction than fuzzy logic (accuracy is 95 %) and FR (accuracy is 94 %) models. Also, among the fuzzy operators, the gamma operator (λ = 0.975) showed the best accuracy (94.64 %) while the fuzzy OR operator when applied showed the worst accuracy (85.11 %).

Thermal damage pattern and thresholds of granite

Abstract: High temperature may lead to the development of new microcracks or growth of pre-existing microcracks within granite, varying its physical and mechanical properties. Experiments were conducted to study the evolution of the physical and mechanical properties of granite specimens from room temperature to 800 °C. The specimens were heated in heating furnace and uniaxial compression tests were done using MTS servo-controlled testing machine. The results indicate five phases in the variation of physical and mechanical properties with temperature: from room temperature to 100, 100–300, 300–400, 400–600, and 600–800 °C. The first phase corresponds to the vaporization-escaping interval of adhered water, bound water, and structural water. Larger changes of physical and mechanical parameters in the temperature range of 300–600 °C, mostly 400–600 °C, are probably caused by the transition from the brittle state to plasticity (or ductility) of granite, and 400 °C may be a critical threshold of its thermal damage. These results confirm the important link among physical and mechanical properties in response to thermal treatment.

Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting

Abstract: One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R2) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R2, RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy.

Spatial assessment of soil erosion risk using RUSLE and GIS techniques

Abstract: Soil erosion by water is considered a major cause for land degradation in Jordan, where 0.14 cm of productive top soil is eroded annually. This investigation is intended to estimate the annual soil loss in Wadi Kerak watershed, and to examine the spatial patterns of soil loss and intensity, as an essential procedure for proper planning of conservation measures. To achieve these objectives, the revised universal soil loss equation (RUSLE) model has been applied in a geographical information system framework. After computing the RUSLE parameters (R, K, LS, C and P) soil erosion risk and intensity maps were generated, then integrated with physical factors (terrain units, elevation, slope, and land uses/cover) to explore the influence of these factors on the spatial patterns of soil erosion loss. The estimated potential annual average soil loss is 64 ton ha−1year−1, and the potential erosion rates from calculated class ranges from 0.0 to 790 ton ha−1year−1. Soil erosion risk assessment indicates that 54.5 % of the catchment is prone to high to extreme soil losses higher than 25 ton ha−1year−1. The lower and middle parts of the catchment suffer from high, severe, to extreme soil erosion. While 45.5 % of the basin still undergoes slight and moderate levels of soil loss of less than 25 ton ha−1year−1, yet 76.91 % of soil erosion occurred on four different terrain units, and 72.29 % of soil erosion occurred in zones less than 600 m in elevation, with 88 % present on areas of 0°–6°, 5°–15°, and 15°–25° slope categories. 32.6, 30.3, and 33.1 % of soil erosion occurred on rainfed mixed farming and irrigated areas, barren, and rangeland, respectively. The present results provide a vital database necessary to control soil erosion in order to ensure sustainable agriculture in the highland region of Jordan.

A modified-DRASTIC model (DRASTICA) for assessment of groundwater vulnerability to pollution in an urbanized environment in Lucknow, India

Abstract: Groundwater contamination and vulnerability in urbanized areas are of major concern and need proper attention. Several models including the DRASTIC model are used to evaluate groundwater vulnerability. In the present study, a modified DRASTIC model named as DRASTICA was used, by including anthropogenic influence as a model parameter. The study included an innovative methodology to characterize the anthropogenic influence by using satellite observations of night-lights from human settlements as a proxy and land-use/land-cover surrounding the urbanized area in Lucknow, the capital city of the most populous State of Uttar Pradesh in India. Geographical information system was used for spatial integration of different parameter maps. The groundwater vulnerability to pollution indicated that about 0.7 % area is covered under very high vulnerable zone, 24.5 % area under high vulnerable zone, 66.6 % area under moderately vulnerable zone and 8.2 % area under low vulnerable zone. The results were validated using nitrate concentration in ground water. It was shown that the proposed DRASTICA model performed better than conventional DRASTIC model in an urbanized environment. Sensitivity analysis indicated that anthropogenic impact and depth to water table largely influenced the groundwater vulnerability to pollution, thereby signifying that anthropogenic influence has to be addressed precisely in such studies. The modified-DRASTIC/DRASTICA model proposed in this study will help in better categorization of groundwater vulnerable zones to pollution where anthropogenic contamination is high, particularly in and around urban centers.

Spatio-temporal variation of sediment transport in the Selenga River Basin, Mongolia and Russia

Abstract: Many Asian rivers have been intensively used to boost economic growth, resulting in sudden and drastic changes in sediment transport patterns. However, a few rivers are still undisturbed. The present paper considers the unregulated Selenga River and its basin, located in Russia and Mongolia. The river contributes to 50 % of the total inflow to Lake Baikal. Pending scientific challenges include the quantification of sediment loads and erosion–deposition patterns along the Selenga River system, the understanding of suspended particulate matter composition and the importance of peak flow events for total sediment discharge and heavy metal transport. Field data and hydraulic modeling converge on showing that peak flow events during spring and summer contribute to the main part (70–80 %) of the annual sediment and pollution loads in upstream parts of the basin. The Selenga River carries mostly silt and sand. The average particle size differs by a factor of four between summer floods and base flow periods. The low amount of particulate organic matter (ranging between 1 and 16 % in the studied rivers) is consistent with the significant role of sediments originating from mining areas and in-channel sources. The bed load transport in the downstream part of the river basin is high (up to 50 % of the total transport), and channel storage plays an important role in the total sediment transport to Lake Baikal. Reported statistically significant multi-decadal declines in sediment fluxes in the downstream Selenga River can be attributed to the abandonment of cultivated lands and (most likely) to changing hydroclimatic factors.

An improved Maxwell creep model for rock based on variable-order fractional derivatives

Abstract: For describing the time-dependent mechanical property of rock during the creep, a new method of building creep model based on variable-order fractional derivatives is proposed. The order of the fractional derivative is allowed to be a function of the independent variable (time), rather than a constant of arbitrary order. Through the segmentation treatment, according to different creep stages of the experimental results, it is found that the improved creep model based on variable-order fractional derivatives agrees well with the experimental data. In addition, the fact is verified that variable order of fractional derivatives can be regarded as a step function, which is reasonable and reliable. In addition, through further piecewise fitting, the parameters in the model are determined on the basis of existing experimental results. All estimated results show that the theoretical model proposed in the paper properly depicts the creep properties, providing an excellent agreement with the experimental data.

Anticipating and managing engineering problems in the complex karst environment

Abstract: Karst environments are characterized by distinctive landforms and a peculiar hydrologic behavior dominated by subsurface drainage. Karst systems can be extremely complex, heterogeneous, and unpredictable due to the wide range of geological and hydrological controlling factors. The great variability results in serious problems for engineers, and in difficulties to characterize the karstified rock masses, and in designing the engineering works to be performed. The design and development of engineering projects in karst environments require specific approaches aimed at minimizing the detrimental effects of hazardous processes and environmental problems. Further, karst aquifers (that provide approximately 20–25 % of the world’s drinking water) are extremely vulnerable to pollution, due to the direct connection between the surface and the subsurface drainage, the rapidity of the water flow in conduit networks, and the very low depuration capability. Sinkholes are the main source of engineering problems in karst environments, and may cause severe damage in any human structure. The strategies and solutions that may be applied to mitigate sinkhole problems are highly variable and largely depend on the kind of engineering structure, the karst setting, and the typology and size of the sinkholes. A sound geological model, properly considering the peculiarities of karst and its interactions with the human environment, is essential for the design of cost-effective and successful risk reduction programs. Due to the unique direct interaction between surface and subsurface environments, and the frequent ground instability problems related to underground karstification, management of karst environments is a very delicate matter. Disregarding such circumstances in land-use planning and development inevitably results in severe problems with high economic impacts. Karst environments require specific investigation methods in order to properly manage and safeguard the sensitive geo-ecosystems and natural resources associated with them.

Antibiotic contamination in animal manure, soil, and sewage sludge in Shenyang, northeast China

Abstract: The occurrence and pollution characteristics of common antibiotics in manure, soil, and sewage sludge in Shenyang, the biggest city in northeastern China, were investigated. Commonly used antibiotics tetracyclines (TCs), including tetracycline, chlortetracycline, oxytetracycline, and deoxytetracycline; and sulfonamides (SAs), including sulfadiazine, sulfamerazine, sulfadimidine, and sulfamethoxazole, were measured by high-performance liquid chromatography–tandem mass spectrometry via solid-phase extraction. Multiple antibiotics could be simultaneously measured in a single manure sample. The highest concentration of antibiotics in manure was 143.97 mg kg−1 (chlortetracycline). There were no significant differences in concentrations of antibiotics between large-scale farms and individual household farms. The concentrations of TCs were significantly higher than those of SAs in manure and soils. However, the concentrations and detection frequencies of antibiotics in soils were significantly lower than in manure. The concentrations of antibiotics varied from 2.56 μg kg−1 of sulfadimidine to 1590.16 μg kg−1 of chlortetracycline, and the detection frequencies were 27.14–64.29 % for TCs and 5.71–28.57 % for SAs in soils. The concentrations of antibiotics in the vegetable field fertilized with manure were higher than those in the corn field not fertilized with manure. Various kinds of antibiotics were detected in sewage sludge, although concentrations were not high. Concentrations of antibiotics were higher in winter than in summer, The concentrations of antibiotics observed in this study were similar to those observed in other regions of China, but, in general, higher than those in other countries.

Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran

Abstract: Forests are important natural resources having the role of supporting economic activity which plays a significant role in regulating the climate and the carbon cycle. Forest ecosystems are increasingly threatened by fires which caused by a range of natural and anthropogenic factors. Hence, spatial assessment of fire risk is very important to reduce the impacts of wild land fires. In current research, evaluation of forest fire susceptibility is performed using remote sensing and geographic information system data of Minudasht forests, Golestan province, Iran. Factors affecting the fire occurrence, such as normalized difference vegetation index (NDVI) and land use were extracted from classified Landsat-7 ETM+ imagery. Slope degree, slope aspect, topographic wetness index, topographic position index, and plan curvature were computed using topographical database. Other factors affecting on the forest fires are distance to roads, distance to rivers, distance to villages, soil texture, wind effect, annual temperature, and annual rain. To delineate forest fire susceptibility mapping in the study area, the Shannon’s entropy (SE) and frequency ratio (FR) models has been applied. Forest fire locations were specified in the study area from MODIS data and extensive field surveys. 106 (≈70 %) locations, out of 151 forest fire identified, were used for the forest fire susceptibility maps, while the remaining 45 (≈30 %) cases were used for the model validation. The findings revealed that the most important conditioning factors were the NDVI, land use, soil and annual temperature. Therefore, preventive measures need to be applied in the ecological conditions. Ultimately, the receiver operating characteristic curve for forest fire susceptibility maps was depicted and the area under the curve was computed. The validation results showed that the area under the curve for SE and FR is equal of 83.16 and 79.85 % with standard errors of 0.044 and 0.047, respectively. The mentioned results can be applied to early warning, fire suppression resources planning and allocation works.

Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets

Abstract: The potential of using maximum entropy modeling for landslide susceptibility mapping is investigated in this paper. Although the maximum entropy model has been applied widely to species distribution modeling in ecology, its applicability to other kinds of predictive modeling such as landslide susceptibility mapping has not yet been investigated fully. In the present case study of Boeun in Korea, multiple environmental factors including continuous and categorical data were used as inputs for maximum entropy modeling. From the optimal setting test based on cross-validation, the effective feature type for continuous data representation was found to be a hinge feature and its combination with categorical data showed the best predictive performance. Factor contribution analysis indicated that distances from lineaments and slope layers were the most influential factors. From interpretations on a response curve, steeply sloping and weathered areas that consisted of excessively drained granite residuum soils were very susceptible to landslides. Predictive performance of maximum entropy modeling was slightly better than that of a logistic regression model which has been used widely to assess landslide susceptibility. Therefore, maximum entropy modeling is shown to be an effective prediction model for landslide susceptibility mapping.

A multi-scale assessment of human vulnerability to climate change in the Aral Sea basin

Abstract: Vulnerability to climate change impacts is defined by three dimensions of human–environmental systems, such as exposure, sensitivity, and adaptive capacity. Climate change affects various aspects of human–environmental interactions, such as water stress, food security, human health, and well-being at multiple spatial and temporal scales. However, the existing protocols of vulnerability assessment fail to incorporate the multitude of scales associated with climate change processes. Changing trends in the Aral Sea basin are driven by multiple interconnected factors, such as changes in the global atmospheric circulation associated with the GHG-enhanced warming, regional hydrological and hydrometeorological changes caused by mountain-glacial melting and massive irrigation, land-use and land-cover changes, as well as hydrological, biogeochemical, and meso- and microclimatic changes in the remains of the Aral Sea and its exposed dry bottom. This review examines the role of scale in the assessment human vulnerability to climate change and offers a multi-scale approach to vulnerability assessment. In addition to the global climate change impacts, it takes into account regional and local land-use and land-cover changes, social, cultural, political, and institutional factors.

Heavy metal contamination of Yellow River alluvial sediments, northwest China

Abstract: This study concerns the distribution and potential sources of elevated heavy metal concentrations (Cr, Ni, Pb, Zn) in alluvial sediments of the Yellow River close to an industrial area in northwest China. Sediment samples were collected from 25 locations and analyzed for common ions (Na+, K+, Ca2+, Mg2+, Cl−, SO42−, HCO3−), total salts, and heavy metals (Cr, Ni, Pb, Zn). Concentrations of Cr, Ni, Pb, and Zn were found to exceed background values observed at up-gradient sites and in surrounding non-industrialized areas. The surficial sediments can be classified as “slightly polluted” and seem to present a low, albeit significant potential ecological risk in the center of the study area where elevated Cr, Ni, Pb, and Zn appear to be associated with the presence of several industrial plants and a wastewater drainage ditch. Correlation and multivariate analyses confirm that the heavy metal pollution is anthropogenic in origin. Urgent action is required at both administrative and technical levels to avoid further degradation of the sediments by industrial waste. Necessary measures include the establishment of soil, sediment and water monitoring programs, a strengthening of compliance standards and greater enforcement of regulations.

Adsorption of Cd(II), Cu(II), Ni(II) and Pb(II) onto natural bentonite: study in mono- and multi-metal systems

Abstract: In the present study, removal of Cd2+, Cu2+, Ni2+ and Pb2+ from mono- and multi-metal ion aqueous solutions using natural Ca-bentonite was studied. Removal efficiency from synthetic aqueous solutions and real wastewater was investigated under various conditions such as variable concentration of metal ion, amount of clay, pH, and contact time. Increasing pH favors the removal of metal ions while the removal is rapid with maximum adsorption being observed within 20 min. Langmuir isotherm fits the data better in both single and multi-component systems. The monolayer adsorption capacities ( $$q_{m,i}^{o}$$ ) of bentonite for Cd2+, Cu2+, Ni2+ and Pb2+ in single metal systems were found as 31.25, 32.26, 26.32 and 85.47 mg g−1, respectively. The competitive adsorption equilibrium on a multi-component system (Cd2+/Cu2+/Ni2+/Pb2+) with different initial metal ion concentrations was also determined. For all studied metal ions, the adsorption capacities in the case of multi-component system ( $$q_{m,i}^{\text{mix}}$$ ) are lower than those obtained for the particular single metal system ( $$q_{m,i}^{o}$$ ). It was observed that the equilibrium uptake amounts of each metal Cd2+, Cu2+, Ni2+ and Pb2+ in the multi-component system onto bentonite decreased considerably with increasing concentrations of the other metals due to their antagonistic effect. Bentonite displays a high selectivity toward one metal in a multi-component system with an affinity order of Cu2+ > Ni2+ > Pb2+ > Cd2+ which is related to the first hydrolysis equilibrium constant. The values of $${{q_{m,i}^{\text{mix}} } \mathord{\left/ {\vphantom {{q_{m,i}^{\text{mix}} } {q_{m,i}^{o} < 1}}} \right. \kern-0pt} {q_{m,i}^{o} < 1}}$$ demonstrate the mutual competitive effect between heavy metals in multi-component systems.

Groundwater-derived land subsidence in the North China Plain

Abstract: Land subsidence usually bears strong relationship to abstraction of underground fluid. Land subsidence occurs commonly in the North China Plain (NCP) and has become a major environmental factor hindering regional sustainable development. This paper focuses on issues associated with mechanism of land subsidence in the NCP. The analysis shows that multi-layer aquifer systems with deep confined aquifers and the thick normally consolidated or unconsolidated compressible clay layers are the key of geological and hydrogeological conditions favorable for the development of land subsidence in the NCP. Groundwater withdrawal results in an increase in the distribution of effective stress within the strata and the compression of the aquifers and the confining layers, and then triggers land subsidence. In the middle-east plain of the NCP, the land subsidence volume approximately represents the amount of water released from compression of deep aquifers and aquitards in that land subsidence is primarily caused by excessive groundwater withdrawal in deep aquifer system. The percentage of water released from compression of aquifers and aquitards in deep groundwater abstraction is significant but distinct in Cangzhou City and in the whole middle-east plain. This is due to the difference in local lithological structure and recharge and discharge conditions of deep groundwater system. The hysteresis of land subsidence is also discussed in typical areas and the results reveal that the time for completing the primary consolidation ranges from less than one year to tens of years, and that the rate of secondary compression tends to increase with the moisture content.

Rainfall thresholds for landslide activity in Portugal: a state of the art

Abstract: Rainfall is the most important physical process responsible for the landslide triggering in Portugal. Results obtained worldwide have shown that control of rainfall on landslides differs substantially depending upon landslide depth and kinematics and the affected material. Therefore, the critical rainfall conditions for failure are not the same for different types of landslides, and may be strongly influenced by regional geologic and geomorphologic conditions. Rapid debris flows are typically triggered by very intense showers concentrated in just a few hours, and shallow translational soil slips are usually triggered by intense precipitation falling within the 1–15 days long range. On the contrary, activity of deep-seated landslides of rotational, translational and complex types is related to periods of nearly constant rainfall, lasting from several weeks to several months. The different rainfall intensity–duration conditions are associated with different hydrologic mechanisms for slope failure. The generation of surface run-off and high peak discharges in first-order mountain catchments is a critical triggering mechanism for debris flows. The intense rainfall allows the rapid growth of pore water pressure and the drop of capillarity forces that sustain the apparent cohesion of thin soils. As a consequence, shallow soil slips occur within the soil material or at the contact with the underlying less permeable bedrock. Long lasting rainfall episodes enable the steady rise of the groundwater table and the development of positive pore water pressures into the soil. Consequently, deep-seated failures occur in relation to the reduction of shear strength of affected materials. In this work, we present the state of the art concerning the proposition of empirical rainfall thresholds in Portugal for different types of landslides observed in different zones of the country: the Lisbon region, the Douro Valley and the NW Mountains, and the Povoacao Municipality in Sao Miguel Island (Azores). The empirical thresholds applied in Portugal are based on the identification of past landslide events and include (i) the computation of antecedent rainfall threshold defined by linear regression, (ii) the normalization of rainfall by the mean annual precipitation, (iii) the definition of lower limit and upper limit rainfall thresholds and (iv) the definition of combined rainfall thresholds, which integrates the rainfall event and the antecedent rainfall for different time periods.

A GIS based identification of groundwater recharge potential zones using RS and IF technique: a case study in Ottapidaram taluk, Tuticorin district, Tamil Nadu

Abstract: This study is an approach to better estimate the groundwater recharge potential zones using geographical information system (GIS), influence factor and RS techniques. This concept has been applied in Ottapidaram taluk in Tuticorin district to determine the overall groundwater recharge potentiality. Survey of India toposheets and Indian Remote Sensing-1C satellite imageries are used to prepare various thematic layers such as: lithology, slope, land use, lineament, drainage, soil, and rainfall. These layers were then transformed into raster data using the feature to raster converter tool in ArcGIS 9.3 software. Subjective weights are assigned to the respective thematic layers and overlaid in GIS platform for the identification of potential groundwater zones within the study area. These potential zones were categorized as ‘high’, ‘moderate’, and ‘low’ zones with respect to the assigned weightage of different thematic layers. The results reveal that the areas of good groundwater potential are estimated to cover 260.25 km2 (35 % of the study area), moderate potential 297.43 km2 (40 %), and low potential 185 km2 (25 %). Moreover, only 34 % of the total average annual precipitated water (680 mm) percolates into subsurface and ultimately contributes to recharge the groundwater. These results suggest that the high potential zones will have a key role in future expansion of drinking water and irrigation development in the study area.

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

Abstract: Landslides occur frequently in the Three Gorges in China, posing threats to human life and the normal operation of the Three Gorges Dam. A number of preexisting landslides have been reactivated since the initial impoundment of the Three Gorges Reservoir in June 2003. An effective and accurate method of predicting landslide displacement is necessary to mitigate the effects of these disastrous landslides. This study carries out a landslide displacement prediction for the Shuping landslide using 7 years of monitoring data, wavelet analysis, and a particle swarm-optimized support vector machine (PSO-SVM) model. The landslide’s displacement is strongly influenced by periodic precipitation and reservoir level fluctuations, and the cumulative displacement curve versus time indicates a step-like character. Based on the deformation characteristics of this landslide, the total displacement is divided into its trend and periodic components by means of the wavelet analysis. An S-curve estimation is used to predict the trend displacement via the curve fitting of the historical displacement versus time. Five primary factors are used as the input variables for a PSO-SVM model to predict periodic displacement. These factors include cumulative precipitation over the previous month, cumulative precipitation during a two-month period, maximum continuous decrement in the reservoir level during the current month, and cumulative increments and decrements in the reservoir level during the current month. The mean squared error, squared correlation coefficient, and Akaike’s information criterion of the wavelet-PSO-SVM model at GPS monitoring points ZG85 and ZG87 are 2.45, 0.945, and 20.80 and 10.46, 80.981, and 36.38, respectively. This method can be applied to the prediction of displacement in colluvial landslides in the Three Gorges. This study may provide useful information to engineers and planners involved in landslide prevention and reduction.