How Do River Nitrate Concentrations Respond to Changes in Land-use? A Modelling Case Study of Headwaters in the River Derwent Catchment,North Yorkshire,UK

A combined semi-distributed hydrological model (CASCADE/QUESTOR) is used to evaluate the steady-state that may be achieved after changes in land-use or management and to explore what additional factors need to be considered in representing catchment processes. Two rural headwater catchments of the River Derwent (North Yorkshire, UK) were studied where significant change in land-use occurred in the 1990s and the early 2000s. Much larger increases in mean nitrate concentration (55%) were observed in the catchment with significant groundwater influence (Pickering Beck) compared with the surface water-dominated catchment (13% increase). The increases in Pickering Beck were considerably greater than could be explained by the model in terms of land-use change. Consequently, the study serves to focus attention on the long-term increases in nitrate concentration reported in major UK aquifers and the ongoing and chronic impact this trend is likely to be having on surface water concentrations. For river environments, where groundwater is a source, such trends will mask the impact of measures proposed to reduce the risk of nitrate leaching from agricultural land. Model estimates of within-channel losses account for 15–40% of nitrate entering rivers.     

Selecting Variables for Habitat Suitability of <Emphasis Type="Italic">Asellus</Emphasis> (Crustacea,Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the ‘partial derivatives’ method; (2) the ‘weights’ method; (3) the ‘perturb’ method; (4) the ‘profile’ method; (5) the ‘classical stepwise’ method; (6) the ‘improved stepwise’ method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000–2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.     

A Spatially Explicit Model for Estimating Annual Average Loads of Nonpoint Source Nutrient at the Watershed Scale

The overloaded nonpoint source (NPS) nutrients in upper streams always result in the nutrient enrichment at lakes and estuaries downstream. As NPS pollution has become a serious environmental concern in watershed management, the information about nutrient output distribution across a watershed has been critical in the designing of regional development policies. But existing watershed evaluation models often encounter difficulties in application because of their complicated structures and strict requirements for the input data. In this paper, a spatially explicit and process-based model, Integrated Grid’s Exporting and Delivery model, was introduced to estimate annual in-stream nutrient levels. Each grid cell in this model was regarded as having potentials of both exporting new nutrients and trapping nutrients passing by. The combined nutrient dynamics of a grid is mainly determined by the grid’s features in land use/land cover, soil drainage, and geomorphology. This simple-concept model was tested at some basins in north Georgia in the USA. Stations in one basin were used to calibrate the model. Then an external validation was employed by applying the calibrated model to stations in the other neighbor basins. Model evaluation statistics implied the model’s validity and good performance in estimating the annual NPS nutrients’ fluxes at the watershed scale. This study also provides a promising prospect that in-stream annual nutrient loads can be accurately estimated from a few public available datasets.     

Development of Dissolved Oxygen Model for a Highly Variable Flow River: A Case Study of Ravi River in Pakistan

A framework for dissolved oxygen (DO) modeling of the Ravi River has been developed based on a combination of laboratory measurements and field and monitoring data. Both the classical Streeter-Phelps (CSP) and the modified Streeter-Phelps (MSP) models are used for DO simulations. The MSP model considers the carbonaceous biochemical oxygen demand (CBOD) and nitrogenous biochemical oxygen demand (NBOD) separately, whereas the CSP model is evaluated considering only the CBOD and NBOD is incorporated in the overall BOD utilization rate. CBOD, NBOD and BOD rates have been determined through long-term BOD analysis of five main wastewater outfalls and two surface drains discharging into the Ravi River over a 98 km stretch. Analysis by Thomas Method manifests strong coefficient of determination “R²” between 0.72 and 0.98 for all the three types of BOD rates. Sensitivity analyses have also been carried out to find out a suitable reaeration rate formula for highly variable flows in the Ravi River. The CSP model results based on classical approach of considering only CBOD show significant difference between the model predictions and field measurements suggesting that NBOD needs to be incorporated for the model development. The dissolved oxygen values calculated using the MSP model and the CSP model based on overall BOD rate are in close agreement with field measurements and are thus suitable to model DO levels in the Ravi River.     

Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor,Florida, USA

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.     

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.     

Fuzzy logic modeling of bioaccumulation pattern of metals in coastal biota of Ondo State,Nigeria

The accumulation patterns of ten metals in tissues of plant, Eichornia crassipes, and fishes, Hydrocynus forskahlii and Oreochromis mossambicus, were modeled with simple fuzzy classification (SFC) to assess toxic effects of anthropogenic activities on the coastal biota. The plant sample was separated into root, stem, and leaves and the fishes into bones, internal tissues, and muscles. They were analyzed for As, Cd, Cr, Cu, Ni, Pb, V, Fe, Mn, and Zn after wet oxidation of their dried samples. The results were converted into membership functions of five accumulation classes and aggregated with SFC. The classification results showed that there was no metal accumulation in the plant parts while the fishes were classified into low accumulation category. The internal tissues of the fishes had higher metal accumulation than the other parts. Generally, Fe and Mn had highest concentrations in the biota but are natural to the area and may not constitute significant risk. Cr had the highest transfer and accumulation from the coastal water into the aquatic lives and may be indicative of risk prone system being a toxic metal. Metal contaminations in the zone had not significantly accumulated in the biota making them less prone to risk associated with metal accumulation.     

Changing environment and urban identity following open-cast mining and thermic power plant in Turkey: case of Soma

This paper is a summary of a project changed into a book named by “Changing Environment, City and Identity in Soma with the Geographical Evaluations” issued on May 2005. In this research, Soma, which is one of the most remarkable districts in Manisa in the West Anatolia from the point of economical figures, is assessed with its physical environment potential, improving economical activities and changing socio-economical structure. Owing to the open coal basins in the northeast and southwest of the district where lignite is produced and the impact of the thermic power plant near the city centre, Soma has changed on a large scale. This change has introduced some environmental problems into the district such as the devastation of the forestry land; the infertility of farming land; and soil, water and air pollution. Even though the change under discussion has led to many problems to deal with, it has also influenced its socio-economical structure to a large extent and revealed new type of inhabitants having different life expectations and aims. In conclusion, in this article, changing environment and city structure after lignite processing and thermic station establishment in Soma are discussed through the effective geographical factors. The new city profile formed by the local dynamics in question is evaluated according to the data obtained by the studies made in the neighbourhood.     

Speciation of heavy metals in biosolids of wastewater treatment plants at Mysore,Karnataka, India

Urban wastewater treatment leads to the generation of large quantities of biosolids. Accumulation of biosolids is a problem of environmental relevance due to the existence of heavy metals in the biosolids. Determination of total metal in biosolid provides information relating pollution levels. Determination of their mobilization capacity and behaviour in the environment is an important task. An experimental approach commonly used for studying the mobility, transport and bioavailability of metal in biosolids is the use of selective sequential extraction procedure. In the present study an attempt has been made to study the heavy metal properties in biosolid samples collected from urban wastewater treatment plants located at Mysore, Karnataka. Few heavy metals selected for the present study are cadmium, chromium, copper, iron, nickel and zinc. The concentration of these metals in biosolids and their partition in different fractions are studied. The speciation of metals based on the sequential extraction scheme was carried out. The concentration of heavy metals is lower than that established by European legislation. The residual fraction has the maximum percentage of heavy metals whereas, only a small fraction of heavy metals (Fe, Zn and Cd) are extracted in the most soluble fractions, exchangeable and carbonate fractions.     

Heavy metal contamination in the Delhi segment of Yamuna basin

Concentration of heavy metals (Cd, Ni, Zn, Fe, Cu, Mn, Pb, Cr, Hg and As) in the waters of River Yamuna and in the soil of agricultural fields along its course in Delhi are reported from 13 sites, spread through the Delhi stretch of Yamuna, starting from the Wazirabad barrage till the Okhla barrage. Varying concentration of heavy metals was found. Peaks were observed in samples collected downstream of Wazirabad and Okhla barrage, indicating the anthropogenic nature of the contamination. The Wazirabad section of the river receives wastewater from Najafgarh and its supplementary drains, whereas the Shahdara drain releases its pollution load upstream of the Okhla barrage. Average heavy metal concentration at different locations in the river water varied in the order of Fe>Cr>Mn>Zn>Pb>Cu>Ni>Hg>As>Cd. The river basin soil shows higher level of contamination with lesser variation than the water samples among sampling locations, thereby suggesting deposition over long periods of time through the processes of adsorption and absorption. The average heavy metal concentration at different locations in soil varied in the order of Fe>Mn>Zn>Cr>Pb>Ni>Hg>Cu>As>Cd.