Kinetics of microbial landfill methane oxidation in biofilters

A methane oxidizing biofilter system fitted to the passive venting system of a harbor sludge landfill in Germany was characterized with respect to the the methanotrophic population, methane oxidizing capacity, and reaction kinetics. Methanotrophic cell counts stabilized on a high level with 1.3 x 10(8) to 7.1 x 10(9) cells g dw(-1) about one year after first biofilter operation, and a maximum of 1.2 x 10(11) cells g dw(-1). Potential methane oxidizing activity varied between 5.3 and 10.7 microg h(-1) g dw(-1). Cell numbers correlated well with methane oxidation activities. Extrapolation of potential activities gave methane removal rates between 35 and 109 g CH4 h(-1) m(-3), calculated for 30 degrees C. Optimum temperature was 38 degrees C for freshly sampled biofilter material and 22 degrees C for a methanotrophic enrichment culture grown at 10 degrees C incubation temperature. Substrate kinetics revealed the presence of a low-affinity methane oxidizing community with a high Vmax of 1.78 micromol CH4 h(-1) g ww(-1) and a high K(M) of 15.1 microM. K(MO2) for methane oxidation was 58 microM. No substantial methane oxidizing activity was detected below 1.7-2.6 vol.-% O2 in the gaseous phase. Methane deprivation led to a decrease in methane oxidation activity within 5-9 weeks but could still be detected after 25 weeks of substrate deprivation and was fully restored within 3 weeks of continuous methane supply. Very high salt loads are leached from the novel biofilter material, expanded clay, yielding electric conductivity values of up to 15 mS cm(-1) in the leachate. Values > 6 mS cm(-1) were shown to depress methane consumption. Water retention characteristics of the material proved to be favourable for methane oxidizing systems with a gas permeable volume of 78% of bulk volume at field capacity water content. Correspondingly, no influence of water content on methane oxidation activity could be detected at water contents between 2.5 and 20 vol.-%.     

A model of the emergence of infectious pancreatic necrosis virus in Scottish salmon farms 1996–2003

Infectious pancreatic necrosis virus (IPNV) is an increasingly common pathogen in farmed Scottish Atlantic salmon; over 80% of marine sites are now infected, although most do not suffer clinical disease. The increasing prevalence of this pathogen in farms over the period 1996–2003 is modelled using a simple susceptible–infected (SI) epidemic model. Because salmon production approximately doubled over this period, population-dependent and -independent transmission models are compared. The model generates mean R₀ (increase ratio) of 1.41 in fresh and 1.45 in marine water farms at the national level; higher values apply with time under population-dependent transmission (1.58, freshwater, 1.80 marine by 2003). Regional differences in R₀ are mostly moderate, indicating similar regional processes in spite of substantial difference in prevalence. Prevalence of IPNV for marine sites was further increased by the use of smolts (young salmon) from multiple freshwater sources. The model suggests that prevalence is entering dynamic equilibrium and will stabilise or only increase slowly as population increases. Cutting freshwater transmission is the most effective single strategy at reducing IPNV prevalence, but a combination of strategies (including reducing the number of sources of smolts) is better and indeed is required for eradication. Eradication would require cuts in transmission of at least 30–45% and this is unlikely to be practicable.     

Application of XAFS and XRD methods for describing the copper and zinc adsorption characteristics in hydromorphic soils

Environmental Geochemistry and Health - Modeling metal sorption in soils is of great importance to predict the fate of heavy metals and to assess the actual risk driven from pollution. The present...     

Polycentric agglomeration and haze pollution: evidence from China

Polycentric agglomeration has gradually become a salient feature of rapid growth in urbanization in China. Using province-level balanced panel data over the period 2000–18, we examine the impact of polycentric agglomeration on haze pollution and its mechanism of action. The results show that the impact of polycentric agglomeration on haze pollution exhibits a significant inverted U-shaped feature. Nevertheless, except for a few provinces where polycentric agglomeration exceeds the turning point, the degree of polycentric concentration in most provinces lies to the left of the turning point. Further, a mediating effect model illustrates that industrial structure rationalization and technological progress are effective paths through which polycentric agglomeration affects haze pollution. Finally, we demonstrate that the effect of polycentric agglomeration on haze pollution is influenced by transportation and communication infrastructure; improved transportation and communication infrastructure contributes to the haze control effect of polycentric agglomeration.

     

Butanol extraction to predict bioavailability of PAHs in soil

The feasibility of a mild-solvent extraction procedure to predict the bioavailability of individual polycyclic aromatic hydrocarbons (PAHs) in soil was assessed. The quantities that were degraded during the course of biodegradation of phenanthrene and pyrene in soil with or without plants correlated with the amounts extracted by n-butanol, with R2 values of 0.971 and 0.994, respectively. Six consecutive groups of earthworms removed ca. 70% of the pyrene remaining after extensive biodegradation, a value similar to the quantity extracted by n-butanol. The amount of chrysene aged in sterilized soil that was extracted by n-butanol was not statistically different from the quantities assimilated by earth-worms (Eisenia fetida) introduced into the soil. Such a mild extraction procedure may be useful as a means of predicting PAH bioavailability.     

Mediation of area and edge effects in forest fragments by adjacent land use

Habitat loss, fragmentation, and degradation have pervasive detrimental effects on tropical forest biodiversity, but the role of the surrounding land use (i.e., matrix) in determining the severity of these impacts remains poorly understood. We surveyed bird species across an interior-edge-matrix gradient to assess the effects of matrix type on biodiversity at 49 different sites with varying levels of landscape fragmentation in the Brazilian Atlantic Forest—a highly threatened biodiversity hotspot. Both area and edge effects were more pronounced in forest patches bordering pasture matrix, whereas patches bordering Eucalyptus plantation maintained compositionally similar bird communities between the edge and the interior and exhibited reduced effects of patch size. These results suggest the type of matrix in which forest fragments are situated can explain a substantial amount of the widely reported variability in biodiversity responses to forest loss and fragmentation.     

Recognizing animal personhood in compassionate conservation

Compassionate conservation is based on the ethical position that actions taken to protect biodiversity should be guided by compassion for all sentient beings. Critics argue that there are 3 core reasons harming animals is acceptable in conservation programs: the primary purpose of conservation is biodiversity protection; conservation is already compassionate to animals; and conservation should prioritize compassion to humans. We used argument analysis to clarify the values and logics underlying the debate around compassionate conservation. We found that objections to compassionate conservation are expressions of human exceptionalism, the view that humans are of a categorically separate and higher moral status than all other species. In contrast, compassionate conservationists believe that conservation should expand its moral community by recognizing all sentient beings as persons. Personhood, in an ethical sense, implies the individual is owed respect and should not be treated merely as a means to other ends. On scientific and ethical grounds, there are good reasons to extend personhood to sentient animals, particularly in conservation. The moral exclusion or subordination of members of other species legitimates the ongoing manipulation and exploitation of the living worlds, the very reason conservation was needed in the first place. Embracing compassion can help dismantle human exceptionalism, recognize nonhuman personhood, and navigate a more expansive moral space.     

Information processing in models for density-dependent emigration: A comparison

Density-dependent emigration has been recognized as a fitness enhancing strategy. Yet, especially in the modelling literature there is no consensus about how density-dependent emigration should quantitatively be incorporated into metapopulation models. In this paper we compare the performance of five different dispersal strategies (defined by the functional link between density and emigration probability). Four of these strategies are based on published functional relationships between local population density and emigration probability, one assumes density-independent dispersal. We use individual-based simulations of time-discrete metapopulation dynamics and conduct evolution experiments for a broad range of values for dispersal mortality and environmental stochasticity. For each set of these conditions we analyze the evolution of emigration rates in ‘monoculture experiments’ (with only one type of dispersal strategy used by all individuals in the metapopulation) as well as in selection experiments that allow a pair-wise comparison of the performance of each functional type. We find that a single-parameter ‘asymptotic threshold’ strategy - derived from the marginal value theorem - with a decelerating increase of emigration rate with increasing population density, out-competes any other strategy, i.e. density-independent emigration, a ‘linear threshold’ strategy and a flexible three-parameter strategy. Only when environmental conditions select for extremely high emigration probabilities (close to one), strategies may perform approximately equally. A simple threshold strategy derived for the case of continuous population growth performs even worse than the density-independent strategy. As the functional type of the dispersal function implemented in metapopulation models may severely affect predictions concerning the survival of populations, range expansion, or community changes we clearly recommend to carefully select adequate functions to model density-dependent dispersal.     

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.