Application of lime and calcium hypochlorite in the dephenolisation and discolouration of olive mill wastewater

The application of hypochlorite for the removal of soluble COD, phenolic and polyphenolic like compounds, and other organic compounds responsible for the olive mill wastewater (OOWW) colour has been experimentally studied. After the OOWW filtration on a sand column, the effluent was subjected to a fast liming under optimal conditions. Lime application reduced polyphenols, COD and SS contents to half of their initial values but an important blackening of the treated OOWW was observed, especially when adding high concentrations of lime (10% (W/V) and 15% (W/V)).A second stage of treatment was applied using calcium hypochlorite. In this stage, removal of the studied compounds reached as much as 95% at higher concentrations, and particularly the colouring of OOWW which is generally difficult to eliminate was greatly reduced. The OOWW hypochloration acted through coagulation–flocculation and a rapid oxidation of the organic matter proceeded from the first 5 min. The kinetic study of the degradation of the waste polluting compounds from liming showed that Ca(ClO)₂ reacts similarly in the elimination of organic compounds, polyphenols, SS and colouration. The analysis of the organochloride compounds generated by the reaction between hypochlorite and the organic compounds showed that DDD, DDT and the heptachlor contents exceeded the values recommended by the International and European drinking water standards.     

From land cover change to land function dynamics: a major challenge to improve land characterization

Land cover change has always had a central role in land change science. This central role is largely the result of the possibilities to map and characterize land cover based on observations and remote sensing. This paper argues that more attention should be given to land use and land functions and linkages between these. Consideration of land functions that provide a wide range of goods and services makes more integrated assessments of land change possible. The increasing attention to multifunctional land use is another incentive to develop methods to assess changes in land functions. A number of methods to quantify and map the spatial extent of land use and land functions are discussed and the implications for modeling are identified based on recent model approaches in land change science. The mixed use of land cover, land use and land function in maps and models leads to inconsistencies in land change assessments. Explicit attention to the non-linear relations between land cover, land use and land function is essential to consistently address land change. New methods to map and quantify land function dynamics will enhance our ability to understand and model land system change and adequately inform policies and planning.     

Modeling the costs and benefits of dam construction from a multidisciplinary perspective

Although the benefits of dam construction are numerous, particularly in the context of climate change and growing global demand for electricity, recent experience has shown that many dams have serious negative environmental, human, and political consequences. Despite an extensive literature documenting the benefits and costs of dams from a single disciplinary perspective, few studies have simultaneously evaluated the distribution of biophysical, socio-economic, and geopolitical implications of dams. To meet the simultaneous demands for water, energy, and environmental protection well into the future, a broader view of dams is needed. We thus propose a new tool for evaluating the relative costs and benefits of dam construction based on multi-objective planning techniques. The Integrative Dam Assessment Modeling (IDAM) tool is designed to integrate biophysical, socio-economic, and geopolitical perspectives into a single cost/benefit analysis of dam construction. Each of 27 different impacts of dam construction is evaluated both objectively (e.g., flood protection, as measured by RYI years) and subjectively (i.e., the valuation of said flood protection) by a team of decision-makers. By providing a visual representation of the various costs and benefits associated with two or more dams, the IDAM tool allows decision-makers to evaluate alternatives and to articulate priorities associated with a dam project, making the decision process about dams more informed and more transparent. For all of these reasons, we believe that the IDAM tool represents an important evolutionary step in dam evaluation.     

Comparing the impacts of hiking, skiing and horse riding on trail and vegetation in different types of forest

Nature-based tourism in protected areas has increased and diversified dramatically during the last decades. Different recreational activities have a range of impacts on natural environments. This paper reports results from a comparison of the impacts of hiking, cross-country skiing and horse riding on trail characteristics and vegetation in northern Finland. Widths and depths of existing trails, and vegetation on trails and in the neighbouring forests were monitored in two research sites during 2001 and 2002. Trail characteristics and vegetation were clearly related to the recreational activity, research site and forest type. Horse trails were as deep as hiking trails, even though the annual number of users was 150-fold higher on the hiking trails. Simultaneously, cross-country skiing had the least effect on trails due to the protective snow cover during winter. Hiking trail plots had little or no vegetation cover, horse riding trail plots had lower vegetation cover than forest plots, while skiing had no impact on total vegetation cover. On the other hand, on horse riding trails there were more forbs and grasses, many of which did not grow naturally in the forest. These species that were limited to riding trails may change the structure of adjacent plant communities in the long run. Therefore, the type of activities undertaken and the sensitivity of habitats to these activities should be a major consideration in the planning and management of nature-based tourism. Establishment of artificial structures, such as stairs, duckboards and trail cover, or complete closure of the site, may be the only way to protect the most sensitive or deteriorated sites.     

A Multilevel Model of the Impact of Farm‐Level Best Management Practices on Phosphorus Runoff1

Abstract: Multilevel or hierarchical models have been applied for a number of years in the social sciences but only relatively recently in the environmental sciences. These models can be developed in either a frequentist or Bayesian context and have similarities to other methods such as empirical Bayes analysis and random coefficients regression. In essence, multilevel models take advantage of the hierarchical structure that exists in many multivariate datasets; for example, water quality measurements may be taken from individual lakes, lakes are located in various climatic zones, lakes may be natural or man‐made, and so on. The groups, or levels, may effectively yield different responses or behaviors (e.g., nutrient load response in lakes) that often make retaining group membership more effective when developing a predictive model than when working with either all of the data together or working separately with the individuals. Here, we develop a multilevel model of the impact of farm level best management practices (BMPs) on phosphorus runoff. The result of this research is a model with parameters which vary with key practice categories and thus may be used to evaluate the effectiveness of these practices on phosphorus runoff. For example, it was found that the effect of fertilizer application rate on farm‐scale phosphorus loss is a function of the application method, the hydrologic soil group, and the land use (crop type). Further, results indicate that the most effective method for controlling fertilizer loss is through soil injection. In summary, the resultant multilevel model can be used to estimate phosphorus loss from farms and hence serve as a useful tool for BMP selection.     

MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow1

Abstract: The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km² watershed in south‐central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight‐day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r² = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log‐transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base‐flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash‐Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log‐space) for the full 15‐month period, ?0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ?32%. The contribution of parameter uncertainties to model discrepancy was evaluated.     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.     

Assessment Tools for Urban Catchments: Defining Observable Biological Potential1

Abstract: Urbanization represents a strong and increasingly more prevalent impact on stream quality worldwide. One of the characteristic effects of increased urbanization is a consistent decline in biological stream condition. The characterization of this biological degradation with increasing urbanization presents a number of advantages for the study and management of urban streams and catchments. In this paper, the limitation of biological condition with urbanization, called observed biological potential, is characterized. Using an urban intensity index and a biological index developed specifically for urban systems in the Baltimore, Maryland; Cleveland, Ohio; and San Jose, California regions, two principal techniques were compared (quantile regression and bin regression) to define observed biological potential along urban gradients. Quantile regression was selected as the preferable tool for describing observed biological potential given the consistency with which it can be applied and its statistical efficiency, however, bin quantile regression performed similarly. Having identified a numeric approximation of observed biological potential, two methods for identifying factors related to distance from potential as a way of identifying critical environmental factors affecting biological condition in urban areas were explored. The results of this work can be used for identifying benchmarks for urban stream biological condition, identifying limiting catchment characteristics, and prioritizing urban stream management efforts.