Sequential extraction of copper from soils and relationships with copper in maize

We studied copper uptake by maize grown on soils that have been contaminated with CuSO₄. In soil the total copper level ranged from 24 to 135 mg kg^–1. The copper distribution in soil fractions was assessed by sequential extraction, showing that anthropogenic copper is mainly concentrated in oxides fractions. The copper concentration of maize at the maturity stage reached values from 36.3 to 65.9 mg kg^–1 compared to copper levels usually found in non-contaminated crops (5–30 mg kg^–1). Here we demonstrate that copper can be accumulated by maize and that copper concentration in maize can be predicted by equations including copper concentration of soil fractions.     

Turbulence-Based Models for Gas Transfer Analysis with Channel Shape Factor Influence

Gas transfer through surface water of streams is an effective process for the environmental quality of the aquatic ecosystem. Several theoretical approaches have been proposed to estimate gas transfer rate. This paper is devoted to present a turbulence-based model and to compare it with other 3 turbulence-based modeling frameworks that provide an estimation of gas-transfer coefficient K_L at the air-water interface. These models were derived for the reaeration process. In this paper, they have been verified both for reaeration and volatilization using experimental data collected in a laboratory rectangular flume and in a circular sewer reach. These data refer to oxygen absorption and cyclohexane volatilization, respectively. Comparison of results for oxygen shows that the tested models exhibit an average absolute difference between their results and the experimental data ranging from 12.5% and 25.6%. Also, the scaling analysis of the experimental data support both small-eddy based models and the model proposed by the authors. Moreover, volatilization results show that the process is also affected by a channel shape factor, which was, finally, quantified.     

The Modelling of Turbulence from Traffic in Urban Dispersion Models — Part II: Evaluation Against Laboratory and Full-Scale Concentration Measurements in Street Canyons

The paper addresses the problem of the parameterisation of traffic induced turbulent motion in urban dispersion models. Results from a variety of full-scale and wind-tunnel studies are analysed and interpreted within a modelling framework based on scaling considerations. The combined effects of traffic and wind induced dispersive motions are quantified for different traffic situations (variable traffic densities, vehicle velocities and vehicle types) and incorporated into the developed parameterisations. A new dispersive velocity scale is formulated and recommendations regarding its application in urban dispersion models are given. The necessity of accounting for traffic induced air motions in predictions of street-canyon pollution levels is demonstrated. Further research is needed to verify the empirical constants in the proposed parameterisations and to generalize the developed approach for a broader range of urban building configurations, meteorological conditions, and traffic situations.     

Hierarchical modeling of count data with application to nuclear fall-out

We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R². We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998).     

Using land as a control variable in density-dependent bioeconomic models

The bioeconomic analysis of endangered species without consumptive values can be problematic when analysed with density-dependent models that assume a fixed environment size. Most bioeconomic models use harvest as a control variable, yet when modelling non-harvestable species, frequently the only variable under control of conservationists is the quantity of habitat to be made available. The authors explore the implications of this in a model developed to analyse the potential population recovery of New Zealand’s yellow-eyed penguin. The penguin faces severe competition with man for the terrestrial resources required for breeding and has declined in population to perilously low levels. The model was developed to estimate the land use required for recovery and preservation of the species and to compare the results to current tourism-driven conservation efforts. It is demonstrated that land may serve as a useful control variable in bioeconomic models and that such a model may be useful for determining whether sufficient incentives exist to preserve a species. However, the model may generate less useful results for providing a specific estimate of the optimal allocation of land to such a species.     

Single-species versus multiple-species models: the economic implications

Ecologists frequently note the importance of modelling entire ecosystems rather than individual species, but most bioeconomic models in the current literature focus on a single species. While the mathematical difficulty of modelling multiple species may be substantial, it is important to recognise the implications of the single-species assumption to a model’s results. In this paper, the authors address the economic significance of this assumption through the development of an analytical multiple-species model and demonstrate the importance of ecological interrelationships and economic values to the survival of endangered species.     

Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model

Abstract:  Organisms respond to their surroundings at multiple spatial scales, and different organisms respond differently to the same environment. Existing landscape models, such as the "fragmentation model" (or patch-matrix-corridor model) and the "variegation model," can be limited in their ability to explain complex patterns for different species and across multiple scales. An alternative approach is to conceptualize landscapes as overlaid species-specific habitat contour maps. Key characteristics of this approach are that different species may respond differently to the same environmental conditions and at different spatial scales. Although similar approaches are being used in ecological modeling, there is much room for habitat contours as a useful conceptual tool. By providing an alternative view of landscapes, a contour model may stimulate more field investigations stratified on the basis of ecological variables other than human-defined patches and patch boundaries. A conceptual model of habitat contours may also help to communicate ecological complexity to land managers. Finally, by incorporating additional ecological complexity, a conceptual model based on habitat contours may help to bridge the perceived gap between pattern and process in landscape ecology. Habitat contours do not preclude the use of existing landscape models and should be seen as a complementary approach most suited to heterogeneous human-modified landscapes.     

MODEL DEVELOPMENT FOR CONJUNCTIVE USE STUDY OF THE SAN JACINTO BASIN,CALIFORNIA1

ABSTRACT: A two-layered confined-unconfined numerical model for flow and mass transport is developed for the San Jacinto Basin. The model structure is determined by the geological structure of the Basin and model parameters are calibrated using 20 years of historical records. The total number of historical head observations used for the flow model calibration is 1,117 and the total number of the estimated parameters is 91. The two-layered transport model is also calibrated using historical water quality records. Sensitivity analysis of the flow model shows that only 68 parameters (out of a total of 91) are relatively sensitive and reliable. However, the unreliable parameters (23 of them) are found to be insensitive and thus not significant to the prediction and management of conjunctive use of surface water and ground water. The developed flow model has been used to study the two proposed artificial recharge scenarios for the San Jacinto Basin. We have found that during a relatively dry condition, an artificial recharge rate of 80 acre-ft/day can be achieved during the recharge period October through January. However, for a relatively wet condition, only 80 percent of the proposed rate can be effectively stored in the Basin during these months.     

THE RATIONAL METHOD FOR PEAK FLOW RATE ESTIMATION1

ABSTRACT: The Rational Method continues to be the most widely used approach for estimating P-year return frequency peak flow rates for small catchments of about one square mile or less in area. The Balanced Design Storm unit hydrograph method is perhaps the second most widely used technique for estimating peak flow rates (and is the most widely used method for developing runoff hydrographs) but is generally considered to be more accurate than the Rational Method. In this paper, both of these T-year return frequency peak flow rate estimators are shown to be mathematically comparable. The close similarity between these two approximators may help explain why the Rational Method continues to be widely used even though other more computationally sophisticated techniques are readily available due to widespread computer software.