Field-scale modelling of carbon and nitrogen dynamics in soils amended with urban waste composts

Composting has emerged as a valuable route for the disposal of urban waste, with the prospect of applying composts on arable fields as organic amendments. Proper management of urban waste composts (UWC) requires a capacity to predict their impacts on carbon and nitrogen dynamics in the field, an issue in which simulation models are expected to play a prominent role.Here, we used a deterministic soil-crop model to simulate C–N dynamics in an arable field amended with three types of UWC (green waste and sludge, biodegradable waste, and solid waste), and a reference amendment (farmyard manure). The model is a version of CERES in which the soil C–N module was substituted with the NCSOIL model, whose microbiological parameters were determined from either laboratory incubation data or biochemical fractionation in a previous paper. CERES was tested against data from a field trial set up in 1998 in the Paris area, and managed as a maize (Zea mays L.)–wheat (Triticum aestivum L.) rotation. Comparison of observed and simulated data over the first 4 years of the field trial showed that CERES predicted the soil moisture and inorganic N dynamics reasonably well, as well as the variations in soil organic C. In particular, the parameterization of UWC organic matter from biochemical fractions achieved a similar fit as the parameterization based on incubation data. Wheat yields were also correctly predicted, but a systematic under-estimation of maize yields pointed at an under-estimation of spring and summer mineralization of N by CERES.Simulated N fluxes showed that the organic amendments induced an additional leaching ranging from 1 to 8 kg N ha⁻¹ yr⁻¹, which can be related to the initial mineral N content of the amendments. After 4 years, the composts had mineralized 3–8% of their initial organic N content, depending on their stability. Composts with slower N release had higher N availability for the crops. CERES could thus be used to aid in selecting the timing of compost application, in relation to its stability, based on both environmental and agronomical criteria.     

Using multi-objective classification to model communities of soil microarthropods

In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.     

Diffusion of tritiated water and Na through non-degraded hardened cement pastes

Diffusion experiments through hardened cement pastes (HCP) using tritiated water (HTO) and ²²Na⁺, considered to be conservative tracers, have been carried out in triplicates in a glove box under a controlled nitrogen atmosphere. Each experiment consisted of a through-diffusion test followed by an out-diffusion test.The experimental data were inversely modelled applying an automated Marquardt–Levenberg procedure. The analysis of the through-diffusion data allowed the extraction of values for the effective diffusion coefficients, D_e, and the rock capacity factor, α. Good agreement between measured and calculated tracer breakthrough curves was achieved using both a simple diffusion model without sorption and a diffusion/linear sorption model. The best-fit K_d-values were found to be consistent with R_d-values measured in previous batch-sorption experiments.The best-fit values from the through-diffusion tests were then used to predict the results of subsequent out-diffusion experiments. Good agreement between experimental data and predictions was achieved only for the case of linear sorption.Isotopic exchange can only partially account for both the amount of tracer taken up in the batch-sorption tests and the measured retardation in the diffusion experiments and, hence, additional mechanisms have to be invoked to explain the data.     

Modelling the fate of organic chemicals in the soil plant environment: Model study of root uptake of pesticides

The fate of organic chemicals in the soil-plant-atmosphere environment and the governing processes were studied with a coupled dynamic soil transport and plant compartment model. Scenarios with applications of pesticides on sand and loam soils with chemical uptake in barley and wheat were used in the model calculations. Root uptake and concentrations in the plant compartments stem, leave and fruit were calculated for the pesticides terbuthylazine, isoproturon and carbofuran.

The effectivity of uptake from soils with different soil sorption coefficients had been shown for sand and loam soils. The processes degradation in plant and volatilization from leaves to atmosphere are especially effective for carbofuran and terbuthylazine. Although the concentrations in corn at harvest are lower than the maximum allowed concentrations, the peak concentrations in the course of the vegetation period are significantly higher (factor ≤ 200).     

Modelling Ecological Presence–absence Data along an Environmental Gradient: Threshold Levels of the Environment

A methodology for estimating environmental thresholds of binary presence–absence data is presented where the level of the threshold is parameterised. Presence–absence data is fitted to three complementary different models: an independent null-model, a monotonically increasing or decreasing model, and an optimum model. The range of the three models is strictly between zero and one and the models are therefore well suited for modelling presence probabilities. The results of the three models may be combined by using Bayesian model selection methodologies. The proposed methodology is exemplified on observed binary presence–absence data of Bauera rubioides along an elevation gradient. Received: May 2005 / Revised: July 2005 An erratum to this article is available at.     

Biodegradation modelling of a dissolved gasoline plume applying independent laboratory and field parameters

Biodegradation of organic contaminants in groundwater is a microscale process which is often observed on scales of 100s of metres or larger. Unfortunately, there are no known equivalent parameters for characterizing the biodegradation process at the macroscale as there are, for example, in the case of hydrodynamic dispersion. Zero- and first-order degradation rates estimated at the laboratory scale by model fitting generally overpredict the rate of biodegradation when applied to the field scale because limited electron acceptor availability and microbial growth are not considered. On the other hand, field-estimated zero- and first-order rates are often not suitable for predicting plume development because they may oversimplify or neglect several key field scale processes, phenomena and characteristics. This study uses the numerical model BIO3D to link the laboratory and field scales by applying laboratory-derived Monod kinetic degradation parameters to simulate a dissolved gasoline field experiment at the Canadian Forces Base (CFB) Borden. All input parameters were derived from independent laboratory and field measurements or taken from the literature a priori to the simulations. The simulated results match the experimental results reasonably well without model calibration. A sensitivity analysis on the most uncertain input parameters showed only a minor influence on the simulation results. Furthermore, it is shown that the flow field, the amount of electron acceptor (oxygen) available, and the Monod kinetic parameters have a significant influence on the simulated results. It is concluded that laboratory-derived Monod kinetic parameters can adequately describe field scale degradation, provided all controlling factors are incorporated in the field scale model. These factors include advective–dispersive transport of multiple contaminants and electron acceptors and large-scale spatial heterogeneities.     

Modelling photochemical oxidant formation, transport, deposition and exposure of terrestrial ecosystems

The chemical processes responsible for production of photochemical oxidants within the troposphere have been the subject of laboratory and field study throughout the last three decades. During the same period, models to simulate the atmospheric chemistry, transport and deposition of ozone (O(3)) from individual urban sources and from regions have been developed. The models differ greatly in the complexity of chemical schemes, in the underlying meteorology and in spatial and temporal resolution. Input information from land use, spatial and temporally disaggregated emission inventories and meteorology have all improved considerably in recent years and are not fully implemented in current models. The development of control strategies in both North America and Europe to close the gaps between current exceedances of environmental limits, guide values, critical levels or loads and full compliance with these limits provides the focus for policy makers and the support agencies for the research. The models represent the only method of testing a range of control options in advance of implementation. This paper describes currently applied models of photochemical oxidant production and transport at global and regional scales and their ability to simulate individual episodes as well as photochemical oxidant climatology. The success of current models in quantifying the exposure of terrestrial surfaces and the population to potentially damaging O(3) concentrations (and dose) is examined. The analysis shows the degree to which the underlying processes and their application within the models limit the quality of the model products.     

Individual-based modelling of fishermen search behaviour with neural networks and reinforcement learning

A model to mimic the search behaviour of fishermen is built with two neural networks to cope with two separate decision-making processes in fishing activities. One neural network deals with decisions to stay or move to new fishing grounds and the other is constructed for the purpose of finding prey within the fishing areas. Some similarities with the behaviour of real fishermen are found: concentrated local search once a prey has been located to increase the probability of remaining near a prey patch and the straightforward movement to other fishing grounds. The artificial fisherman prefers areas near the port when conditions in different fishing grounds are similar or when there is high uncertainty in its world. In the latter case a reluctance to navigate to other areas is observed. The artificial fisherman selects areas with higher concentration of prey, even if they are far from the port of departure, unless a high uncertainty is related to the fishing ground. Connected areas are preferred and followed in orderly fashion if a higher catch is expected. The observed behaviour of the artificial fisherman in uncertain scenarios can be described as a risk-averse attitude. The approach seems appropriate for an individual-based modelling of fishery systems, focusing on the learning and adaptive characteristics of fishermen and on interactions that take place at a fine scale.     

Uncertainty,complexity and controversy in dolphin threat management: A role for post-normal science?

Managing fishing threats to populations of endemic, threatened Hector’s and Māui dolphins around New Zealand is a complex and controversial issue, underpinned by uncertain scientific knowledge. As such, it can be argued that it falls into the realm of post-normal science, which advocates transparency about uncertainties and stakeholder peer review of knowledge feeding into decision-making. This paper focuses on selected examples of modelling and risk assessment research relating to Hector’s and Māui dolphin threat management. It explores how knowledge is developed, shared and utilised by decision-makers, finding that uncertain scientific knowledge may be shared in ways that make it appear more certain, with some of the subjectivities involved in knowledge production hidden from view. Interviews with stakeholders illustrate how some stakeholders are aware of the subjectivities involved when uncertain knowledge underpins decision-making, so a lack of transparency may be leading to erosion of social trust in decisions made. This in turn can lead to a lack of support for dolphin conservation measures from key stakeholders such as the commercial fishing industry. The paper concludes that while moves towards increasing transparency and stakeholder involvement are apparent, a deeper embrace of post-normal science approaches to knowledge production and dissemination would contribute to effective dolphin threat management in New Zealand.     

Sedimentary radioactive tracers and diffusive models

This paper examines the underlying assumptions and consequences of applying a steady-state equation to sediment profiles of radioactive tracers in order to deconvolute sedimentation from bioturbation processes modelled as a diffusive type process.Several factors follow immediately from this investigation:

(i)

if the observed radioactive concentration increases with depth over any finite depth range then the proposed steady-state, constant flux equation is not applicable. Any increase in radioactive concentration with depth implies a negative mixing coefficient which is a physical impossibility;

(ii)

when the radioactive concentration systematically decreases with increasing sedimentary depth then solutions to the steady-state conservation equation exist only when either the constant solid state flux to the sediment surface is small enough so that a positive mixing coefficient results or when the mixing coefficient is small enough so that a positive flux results.

If the radioactive concentration, porosity and/or density of the solid phase are such that the proposed equation is inappropriate (because no physically acceptable solution exists) then one must abandon the proposed steady-state equation.Further: if the flux of solid sediment to the sediment surface varies with time then, of course, a steady-state conservation equation is also inappropriate.Simple examples illustrate that the assumption of steady-state restricts the applicability of this modelling approach to a relatively small sub-set of expected situations in the real world.