Review of water and soil contamination in and around Salihli geothermal field (Manisa,Turkey)

The town of Salihli is situated in Gediz Graben in the western Anatolia. This region is important in terms of industry, mining, geothermal energy, water sources, and agricultural production. Geothermal flow and anthropogenic activities in Salihli threaten the surrounding environment due to the contamination of cold groundwater, surface water, and soil. The goal of the present study is to determine the environmental effects of the geothermal and anthropogenic activities in Salihli on soil, stream sediments, and water. Stream sediments and farm soil have been contaminated by substances derived from geothermal and industrial effluents. To this end, the quality review of the water was completed and the heavy metal levels in stream sediment samples were measured to determine the extent of contamination. The elements As, B, Br, Fe, and Ni are the major contaminants present in surface water and groundwater in the study area. The concentrations of these elements excess tolerance limits of international water standards. Gibbsite, K-mica, kaolinite, sepiolite, halite, sulfur, willemite, and Pb(OH)₂ might be precipitated as scales at low temperatures on the soil; this could be interpreted as a resultant from soil contamination. The concentrations of 17 elements (As, Ba, B, Cd, Co, Cr, Cu, Fe, Hg, Li, Mo, Mn, Ni, Pb, Sb, Sr, and Zn) were measured in samples from stream sediments and surface soils. In the study area, especially geothermal and anthropogenic activities give rise to environmental pollution.     

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain,China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.     

Basin hydrogeological characterization using remote sensing,hydrogeochemical and isotope methods (the case of Baro-Akobo,Eastern Nile,Ethiopia)

Hydrogeochemical and isotopic signatures of the waters of the Baro-Akobo River Basin show deviation from signatures in other Ethiopian river basins. In this study, hydrogeochemical and isotope methods were employed to determine regional and local hydrogeology and characteristics of the basin. Optical, thermal and radar remote sensing products were used to update geological and structural maps of the basin and determine sampling points using the judgment sampling method. A total of 363 samples from wells, springs, rivers, lakes, swamps and rain were collected for this study, and an additional 270 water quality data sets were added from previous studies. These data were analyzed for their hydrogeochemical characteristics and isotope signatures. Analysis of the oxygen, deuterium and tritium isotopes shows the groundwater of the basin is modern water. Among all basins in Ethiopia, the Baro-Akobo Basin shows the highest enrichment. This indicates the proximity of the rainfall sources, which presumably are the Sudd and other wetlands in South Sudan. The hydrochemical properties of the waters show evapotranspiration is the dominant hydrologic process in the basin and explains the large amount of water that is lost in the lowland plain. Analysis of radon-222 shows no significant groundwater flux over the wetlands, which are part of Machar Marshes. This shows evaporation to be dominant hydrologic process in this zone. Results from all analyses help explain the limited holding capacity of the aquifers in the recharge zone and their vulnerability to anthropogenic impacts and climate variability. There is a trend of decreasing surface flow and rainfall and increasing water soil erosion.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.     

Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest

Biophysical and biochemical plant foliage parameters play a key role in assessing vegetation health. Those plant parameters determine the spectral reflectance and transmittance properties of vegetation; therefore, hyperspectral remote sensing, particularly imaging spectroscopy, can provide estimates of leaf and canopy chemical properties. Based on the relationship between spectral response and biochemical/biophysical properties of the leaves and canopies, the PROSPECT radiative transfer model simulates the interaction of light with leaves. In this study, more than 1100 leaf samples from the Amazon forest of Ecuador were collected at several study sites, some of which are affected by petroleum pollution, and across the vertical profile of the forest. For every sample, field spectroscopy at leaf level was conducted with a spectroradiometer. The goal of this study was to assess leaf optical properties of polluted and unpolluted rainforest canopies across the vertical profile and identify vegetation stress expressed in changes of biophysical and biochemical properties of vegetation. An ANOVA followed by Holme’s multiple comparisons of means and a principal component analysis showed that photosynthetic pigments, chlorophyll and carotenoids have significantly lower levels across the vertical profile of the forest, particularly in sites affected by petroleum pollution. On the other hand, foliar water content showed significantly higher levels in the polluted site. Those findings are symptoms of vegetation stress caused by reduced photosynthetic activity and consequently decreased transpiration and water-use efficiency of the plants. Cross-comparison between SPAD-502 chlorophyll content meter index and chlorophyll content showed strong positive correlation coefficients (r = 0.71 and r ² = 0.51) which suggests that using the SPAD-502 chlorophyll index itself is sensitive enough to detect vegetation stress in a multispecies tropical forest. Therefore, the SPAD-502 can be used to assess chlorophyll content of vegetation across polluted and non-polluted sites at different canopy layers. The results presented in this paper contribute to the very limited literature on field spectroscopy and radiative transfer models applied to the vertical profile of the Amazon forest.     

Regional modeling of dust storm of February 8, 2015 in the southwest of Iran

According to World Meteorological Organization report in 2015, the southwest of Iran has become one of the dust sources in the region. And the objective of this research is to study the dust storms originating in this region. For this purpose, based on the weather data of 14 stations, the dust storms of the region were investigated, and the dust storm of February 7, 2015, was selected due to its very high concentration of dust particles (66 times the normal values). For the analysis of source areas and storm paths, the FNL data was used. The regional models of Navy Aerosol Analysis and Prediction System (NAAPS), Barcelona Supercomputing Centre-Dust Regional Atmospheric Model 8b (DREAM 8b), Non-hydrostatic Multiscale Model Barcelona Supercomputing Center (NMMB/BSC), and hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) were used to study and analyze the selected storm. The results showed that the dust event in February 8, 2016, has been the result of the polar front jet stream (PJF) caused by western immigrant system that had been over the Sahara in Africa, the deserts of Iraq, Syria, Saudi Arabia, and finally southwest of Iran in making the extreme dust event. According to the Moderate Resolution Imaging Spectroradiometer data and point models of NAAPS, optical depth was very high. The DREAM 8b and NMMB/BSC models confirmed the impact of the local factors and closeness to the dust source regions. The backward tracking of the model with the HYSPLIT model showed three tracks transporting the dust particles to the region. This software also showed that the dust particles occupied an atmospheric tunnel of 1.5 km in diameter.     

丽水凹陷基岩岩性分布预测

东海陆架盆地丽水凹陷基岩潜山十分发育,钻井已证实其含油气性,勘探潜力巨大。综合现有基岩岩芯、铸体薄片、测井、测年、三维地震资料,在构建基岩岩相-地震相模板及岩石物理分析的基础上,对丽水凹陷基岩岩性开展定性和半定量预测,进而预测岩性平面分布特征。丽水凹陷基岩主要由片麻岩、花岗片麻岩、花岗岩组成,片麻岩为古元古界变质岩陆核,花岗岩来自中生代岩浆,沿深大断裂上涌侵入。地震剖面上片麻岩表现为中—高频,中—强振幅,同相轴连续性、成层性好;花岗岩表现为中—低频,弱—中振幅,呈杂乱反射;花岗片麻岩地震相介于前二者之间。岩石物理分析结果显示,密度参数对岩性区分效果最好,叠前密度反演能较好地半定量预测基岩分布特征。研究表明,丽水A洼主体基岩纵向呈片麻岩上覆花岗岩的二元结构特征;平面上不同构造单元的基岩顶面岩性各异,凸起区花岗岩剥蚀程度高,基岩为片麻岩或花岗片麻岩,洼槽区缺乏深大断裂,为原始区域片麻岩基底,其他区域为花岗岩。     

地下水化学组成的时空聚类分析与多级嵌套水流系统识别

水文地球化学是识别地下水流系统的重要方法,然而区域尺度上多级嵌套地下水流系统的复杂性使地下水化学组成的分析和解释难度增加.以鄂尔多斯北部盆地湖泊集中区典型的胡同察汗淖地下水流系统为例,基于丰水期和枯水期3个期次不同深度地下水样品的物理化学数据,应用时空聚类与主成分分析方法,揭示地下水化学组成的空间分布特征、变化规律及其...     

云南镇雄县滑坡主要致灾因子筛选及其回归模型的建立

助地理信息系统空间分析技术和CF法,对该县滑坡各致灾因子进行敏感性分析,获得各因子的敏感性区段。分析表明,岩性、坡度、坡向、起伏度、与河流距离和与主要道路距离是影响该县滑坡发育的主要因子。根据各致灾因子的CF值建立的回归模型进行研究,结果表明所建模型可解释百分之八十的已发滑坡。     

Climate impact of high northern vegetation: Late Miocene and present

The Late Miocene belongs to the late phase of the Cenozoic. Climate at that time was still warmer and more humid as compared to today, especially in the high latitudes. Corresponding to the climate situation, palaeobotanical evidences support that vegetation in the high northern latitudes changed significantly from the Late Miocene until today. To quantify the climate impact of this vegetation change, we analyse how vegetation in the high northern latitudes contribute to climate evolution. For that, we perform climate modelling sensitivity experiments for the present and for the Late Miocene (Tortonian, 11–7 Ma). For our present-day sensitivity experiment, we introduce the Tortonian vegetation in the high northern latitudes. For our Tortonian sensitivity experiment, we introduce the modern vegetation on the same grid cells. In the Tortonian and in the present, the modern vegetation leads to a strong cooling of the northern extratropics (up to −4°C). Nevertheless, the meridional heat transports remain nearly unchanged in both cases. In general, the vegetation impact on climate is similar in the Tortonian and in the present. However, some exceptions occur. Due to the Tethys Ocean in the Tortonian, temperatures decline only weakly in eastern Europe and western Asia. In the Tortonian climate, temperatures on the Sahara realm rise (up to +1.5°C), while the temperatures do not change remarkably in the present-day climate. This different behaviour is caused by a stronger and more sensitive hydrological cycle on the Sahara region during the Tortonian.