Bioremediation of residual fertilizer nitrate: II. Soil redox potential and soluble iron as indicators of soil health during treatment

The prospect of using wastewater containing high loads of soluble organic matter (OM) for removing residual agricultural chemicals (fertilizer, pesticide, or herbicide) in farm soil, although promising, could have adverse effects on soil agricultural quality as a result of development of redoximorphic features in the soil profile. In this study, the effect of organic carbon supplement for bioremediation of residual fertilizer nitrate on soil properties, redox potential (Eh), pH, and metal ion mobilization was studied using sandy soils packed in columns. The study was included in a general project, described elsewhere (Ugwuegbu et al., 2000), undertaken to evaluate use of controlled water table management (WTM) systems to supply organic carbon for creating a reduced environment conducive to denitrification of residual fertilizer nitrate leaching from the farm to subsurface water. The columns were subjected to subirrigation with water containing soluble organic carbon in the form of glucose. The work was carried out in two experimental setups and the long-term effect of a range of glucose concentrations on the Eh, pH, and soluble levels of Fe and Mn was investigated. From the results obtained, it could be concluded that excessive organic carbon supplement to soil can have adverse effects on soil quality and that Eh and soluble Fe are the two most practical parameters for monitoring soil health during treatment of farm chemicals.     

Coregionalization analysis with a drift for multi-scale assessment of spatial relationships between ecological variables 1. Estimation of drift and random components

In two articles, we present ‘coregionalization analysis with a drift’ (CRAD), a method to assess the multi-scale variability of and relationships between ecological variables from a multivariate spatial data set. In phase I of CRAD (the first article), a deterministic drift component representing the large-scale pattern and a random component modeled as a second-order stationary process are estimated for each variable separately. In phase II (this article), a linear model of coregionalization (LMC) is fitted by estimated generalized least squares to the direct and cross experimental variograms of residuals (i.e., after the removal of estimated drifts). Structural correlations and coefficients of determination at smaller scales are then computed from the estimated coregionalization matrices, while the estimated drifts are used to calculate pseudo coefficients at large scale. The performance of five procedures in estimating correlations and coefficients of determination was compared using a Monte Carlo study. In four CRAD procedures, drift estimation was based on local polynomials of order 0, 1, 2 (L₀, L₁, L₂) or a global polynomial with forward selection of the basis functions; the fifth procedure was coregionalization analysis (CRA), in which large-scale patterns were modeled as a supplemental component in the LMC. In bivariate and multivariate analyses, the uncertainty in the estimation of correlations and coefficients of determination could be related to the interference between spatial components within a bounded sampling domain. In the bivariate case, most procedures provided acceptable estimates of correlations. In regionalized redundancy analysis, uncertainty was highest for CRA, while L₁ provided the best results overall. In a forest ecology example, the identification of scale-specific correlations between plant species diversity and soil and topographical variables illustrated the potential of CRAD to provide unique insight into the functioning of complex ecosystems.     

Marked spatio-temporal point patterns,periodicity analysis and earthquakes: an analytical extension including hypocenter depth

Environmental and Ecological Statistics - The longitude, latitude and depth of the hypocenter in 3-D space and the date and time of rupture makes an earthquake a “point” in a...