Decadal Changes in Water Quality and Net Productivity of a Shallow Danish Estuary Following Significant Nutrient Reductions

We utilized an extensive data set (1977–2013) from a water quality monitoring program to investigate the recovery of a Danish estuary following large reductions in total phosphorus (TP) and total nitrogen (TN) loading. Monthly rates of net transport and biogeochemical transformation of dissolved inorganic nitrogen (DIN) and phosphorus (DIP) were computed in two basins of the estuary using a box model approach, and oxygen-based rates of net ecosystem production (NEP) were determined. Since 1990, nutrient loading was reduced by 58 % for nitrogen and 80 % for phosphorus, causing significant decreases in DIN (60 %) and DIP (85 %) concentrations. Reductions in nutrient loadings and concentrations reduced annual chlorophyll levels by 50 % in the inner estuary and improved Secchi depth by approximately 1 m during the same period, particularly in the summer period. In the outer, deeper region of the estuary trends in water quality was less evident. Improvements in the inner estuary were strongly coupled to declines in DIN. Thresholds of DIN and DIP concentrations limiting phytoplankton growth indicated that both regions of the estuary were nitrogen limited. NEP rates indicated the development of more net autotrophic conditions over time that were likely associated with higher benthic primary production stimulated by improved light conditions. Box model computations revealed a modest reduction in summer net production of DIP over time, despite the persistence of elevated fluxes for several years after external loads were reduced. Since the mid-1990s, nutrient loading and transformation were stable while nutrient concentrations continued to decline and water quality improved in the inner estuary. The oligotrophication trajectory involved an initial fast transformation and modest retention of nutrients followed by a gradual decline in the rate of improvement towards a new stable condition.     

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.     

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.     

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

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.     

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.     

Effects of degree of saturation on shallow landslides triggered by rainfall

The empirical rainfall threshold concept and the physical-based model are two commonly used approaches for the assessment of shallow landslides triggered by rainfall. To investigate in detail the rainfall-triggered shallow landslides, many physical-based models coupling the infinite slope stability analysis with the rainfall infiltration modeling in variably saturated soil were developed. However, in those physical-based shallow landslide models, the unit weight and the unsaturated shear strength were assumed constant rather than depending on the degree of saturation. In this study, the effects of the unit weight and the unsaturated shear strength as function of degree of saturation on rainfall-triggered shallow landslides are examined. Several designed scenarios and a real case scenario are used to conduct the examinations. The results show that not only the occurrence of shallow landslides but also the failure depth and the time to failure could be misassessed if the influences of degree of saturation on the unit weight and the unsaturated shear strength are neglected.     

The Response of Estuarine Macrobenthic Communities to Natural- and Human-Induced Changes: Dynamics and Ecological Quality

Anthropogenic activities are a disturbance factor of coastal systems and can be widely recognized as a major threat to the health of coastal systems. However, natural events cannot be disregarded from management issues because of their significant influence on the communities living in these areas. Based on long-term subtidal data from the Mondego Estuary (Portugal), the effects of natural events (e.g., floods and droughts) on macrobenthic communities were compared with the anthropogenic events. Sampling stations were grouped into characteristic zones (mouth, north arm, south arm) so the community dynamics of each of these estuarine areas could be followed over time. Environmental assessment was performed for stations using the Benthic Assessment Tool (BAT), and compared with the existing pressures. Human impacts persist over a number of years and gradually reduce ecosystem health, as discussed in the European Water Framework Directive. Paradoxically, natural events cause stronger impacts but are of a shorter duration, which allows for a faster recovery of macrobenthic communities. The study showed that caution should be taken when developing and implementing water policies so as not to disregard the importance of the different events (natural and human-caused) on the ecosystem health (e.g., community degradation and water quality and ecological quality status assessment).