Precipitation, pH and metal load in AMD river basins: an application of fuzzy clustering algorithms to the process characterization

In the present work, Acid Mine Drainage (AMD) processes in the Chorrito Stream, which flows into the Cobica River (Iberian Pyrite Belt, Southwest Spain) are characterized by means of clustering techniques based on fuzzy logic. Also, pH behavior in contrast to precipitation is clearly explained, proving that the influence of rainfall inputs on the acidity and, as a result, on the metal load of a riverbed undergoing AMD processes highly depends on the moment when it occurs. In general, the riverbed dynamic behavior is the response to the sum of instant stimuli produced by isolated rainfall, the seasonal memory depending on the moment of the target hydrological year and, finally, the own inertia of the river basin, as a result of an accumulation process caused by age-long mining activity.     

Modeling rhizofiltration: heavy-metal uptake by plant roots

The discovery of phytoaccumulation potential of plant species has led to its application for remediation of heavy-metal-contaminated soil and wastewater, which is termed as phytoextraction/rhizofiltration. For prediction, analysis, planning and cost-effective design of such systems, mathematical models not only are used as a screening tool but also provide optimal parameters like harvesting time, irrigation schedule, etc. Several laboratory and field scale studies have been carried out in the past, and mathematical expressions have been developed by various researchers for different phenomena like metal adsorption in soil, plant root growth with time, moisture and metal uptake by plant root, moisture movement in unsaturated zone, soil moisture relationship, etc. The complete design of any such phytoremediation program would require the knowledge of behavior of heavy-metal movement in soil, water and plant root system. In this paper, a model for simulating heavy-metal dynamics in soil, water and plant root system is developed and discussed. The governing non-linear partial differential equation is solved numerically by implicit finite difference method using Picard's iterative technique, and the formulation has been illustrated using a characteristic example. The source code is written in MATLAB.     

A comparison of techniques to sample salamander assemblages along highland streams of Maryland

Amphibians may be useful indicators of biological condition in small streams so determining which sampling technique maximizes encounters at the least cost and at the optimal time of year is important. Area constrained surveys (ACS), used by the Maryland Biological Stream Survey, were tested against cover board surveys, drift fences with pitfall and funnel traps, quadrat leaf litter searches, and leaf litter bags. Sixteen, 100 m-long sites were established in headwater streams in the Savage River State Forest in Garrett County, Maryland. Each technique was randomly assigned to a 25 m stream section within each overall sampling site, and sites were sampled once each month from May to October (2005) with additional sampling in March and April (2006). Area constrained surveys yielded means of 2.7 taxa and 14.9 total individuals per sampling visit, which was significantly higher than the yield of all other methods in all months except October and March, when yields were low for all techniques. Area constrained surveys were also significantly more cost-effective per taxon and per individual compared to all other methods. September produced the most taxa and individuals, October and March produced the least, and yields for April through August were similar to September. We employed removal sampling at four sites in April 2006, but abundance could not be estimated because a significant linear decrease in the accumulated catch versus catch per unit effort did not occur for three of the sites.     

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.     

Responses of Physical, Chemical, and Biological Indicators of Water Quality to a Gradient of Agricultural Land Use in the Yakima River Basin, Washington

The condition of 25 stream sites in the Yakima River Basin, Washington, were assessed by the U.S. Geological Survey's National Water-Quality Assessment Program. Multimetric condition indices were developed and used to rank sites on the basis of physical, chemical, and biological characteristics. These indices showed that sites in the Cascades and Eastern Cascades ecoregions were largely unimpaired. In contrast, all but two sites in the Columbia Basin ecoregion were impaired, some severely. Agriculture (nutrients and pesticides) was the primary factor associated with impairment and all impaired sites were characterized by multiple indicators of impairment. All indices of biological condition (fish, invertebrates, and algae) declined as agricultural intensity increased. The response exhibited by invertebrates and algae suggested a threshold response with conditions declining precipitously at relatively low levels of agricultural intensity and little response at moderate to high levels of agricultural intensity. This pattern of response suggests that the success of mitigation will vary depending upon where on the response curve the mitigation is undertaken. Because the form of the community condition response is critical to effective water-quality management, the National Water-Quality Assessment Program is conducting studies to examine the response of biota to gradients of land-use intensity and the relevance of these responses to water-quality management. These land-use gradient pilot studies will be conducted in several urban areas starting in 1999.     

Genetic consequences of radioactive contamination by the Chernobyl fallout to agricultural crops

The genetic consequences of radioactive contamination by the fallout to agricultural crops after the accident at the Chernobyl NPP in 1986 have been studied. In the first, acute, period of this accident, when the absorbed dose was primarily due to external beta- and gamma-irradiation, the radiation injury of agricultural crops, according to the basic cytogenetic tests, resembled the effect produced by acute gamma-irradiation at comparable doses. The yield of cytogenetic damage in leaf meristem of plants grown in the 10-km zone of the ChNPP in 1987-1989 (the period of chronic, lower level radiation exposure) was shown to be enhanced and dependent on the level of radioactive contamination. The rate of decline with time in cytogenetic damage induced by chronic exposure lagged considerably behind that of the radiation exposure. Analysis of genetic variability in three sequential generations of rye and wheat revealed increased cytogenetic damage in plants exposed to chronic irradiation during the 2nd and 3rd years.     

Present and future climate resources for various types of tourism in the Bay of Palma,Spain

The Bay of Palma, in Mallorca, is a leading region for beach holidays in Europe. It is based on a mass tourism model strongly modulated by seasonality and with high environmental costs. Main tourism stakeholders are currently implementing complementary activities to mitigate seasonality, regardless of climate change. But climate is—and will remain—a key resource or even a limitation for many types of tourism. Assessing the present conditions and exploring the future evolution of climate potential for these activities have become a priority in this area. To this end, the climate index for tourism (CIT)—originally designed to rate the climate resource of beach tourism—is adapted to specifically appraise cycling, cultural tourism, football, golf, motor boating, sailing and hiking. Climate resources are derived by using observed and projected daily meteorological data. Projections have been obtained from a suite of Regional Climate Models run under the A1B emissions scenario. To properly derive CITs at such local scale, we apply a statistical adjustment. Present climate potentials ratify the appropriateness of the Bay of Palma for satisfactorily practicing all the examined activities. However, optimal conditions are projected to degrade during the peak visitation period while improving in spring and autumn. That is, climate change could further exacerbate the present imbalance between the seasonal distributions of ideal climate potentials and high attendance levels. With this information at hand, policy makers and regional tourism stakeholders can respond more effectively to the great challenge of local adaptation to climate change.     

DETERMINATION OF SEDIMENT YIELD BY TRANSFERRING RAINFALL DATA1

ABSTRACT: A methodology for obtaining the optimal design value to allow for sediment storage in a reservoir is presented for the situation where no data on sediment loads in the incoming streams are available. Information concerning the amount of sediment delivered to the reservoir over its life-time is obtained by a sediment yield model which uses data on rainfall amount and duration obtained from a nearby experimental watershed. Bayesian Decision Theory is used to obtain the optimal storage requirements in order to consider the natural variation of rainfall and the sampling error due to the short rainfall record available. The normally difficult calculations involved were made tractable by the use of simplifications and approximations valid in the context of the problem. Results show that sediment storage requirements can be calculated in this manner and that consideration of the uncertainties involved leads to a storage requirement substantially larger than that calculated without such consideration.     

Exoautometabolic regulation of transgenes in microorganisms

Data on the existence of a specific system regulating the growth, size, and structure of enterobacterial populations are first summarized in terms of the chemical ecology concept formulated by S.S. Shvarts. The role of this system in transgene functioning is described using the example of the lux operon integrated into Escherichia coli. A relatively simple test for estimating such interactions is proposed.Translated from Ekologiya, No. 2, 2005, pp. 91–96.Original Russian Text Copyright © 2005 by Pshenichnov, Maslennikova, Nikitina.     

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

The application of mathematical tools in initial steps of sediment quality assessment frameworks can be useful to provide an integrated interpretation of multiple measured variables. This study reveals that the Self-Organizing Map (SOM) artificial neural network can be an effective tool for the integration of multiple physical, chemical and ecotoxicological variables in order to classify different sites under study according to their similar sediment quality. Sediment samples from 40 sites of 3 estuaries of Cantabria (Spain) were classified with respect to 13 physical, chemical and toxicological variables using the SOM. Results obtained with the SOM, when compared to those of traditional multivariate statistical techniques commonly used in the field of sediment quality (principal component analysis (PCA) and hierarchical cluster analysis (HCA)), provided a more useful classification for further assessment steps. Especially, the powerful visualization tools of the SOM, which offer more information and in an easier way than HCA and PCA, facilitate the task of establishing an order of priority between the distinguished groups of sites depending on their need for further investigations or remediation actions in subsequent management steps.