Effect of collection system on mineral content of biowaste

The EU is committed to encourage biological treatments of organic waste as an alternative to landfill and also to enhance organic matter recycling. When these wastes are composted, the composition of the initial raw materials is very important in order to obtain a good quality product. In this article, the mineral composition of the organic fraction obtained from source-sorted collection (SC) and the organic fraction mechanically separated (MS) from mass-collected municipal solid waste was evaluated. Also, the compositions of these 2 raw materials that are used in the current Spanish municipal solid waste biological treatment facilities were compared. The mineral elements analyzed were the total content of the heavy metals Zn, Cu, Ni, Cr, Pb and Cd, and the plant nutrients P, K, Na, Ca, Mg, Fe and Mn. The results obtained were expressed on dry matter basis and on mineral matter basis. Important differences were detected between SC and MS samples, on both dry and mineral matter basis. In general, nutrient contents are higher in SC than in MS, and heavy metal contents are significantly lower in SC. Our results also support the idea that the heavy metal migration from the non-compostable materials to the decomposable matrix takes place from the beginning of the process while both types of materials are in contact.     

Seasonal characterization of municipal solid waste (MSW) in the city of Chihuahua,Mexico

Management of municipal solid waste (MSW) has become a significant environmental problem, especially in fast-growing cities. The amount of waste generated increases each year and this makes it difficult to create solutions which due to the increase in waste generation year after year and having to identify a solution that will have minimum impact on the environment. To determine the most sustainable waste management strategy for Chihuahua, it is first necessary to identify the nature and composition of the city’s urban waste. The MSW composition varied considerably depending on many factors, the time of year is one of them. Therefore, as part of our attempt to implement an integral waste management system in the city of Chihuahua, we conducted a study of the characteristics of MSW composition for the different seasons. This paper analyzes and compares the findings of the study of the characterization and the generation of solid waste from households at three different socio-economic levels in the city over three periods (April and August, 2006 and January, 2007).The average weight of waste generated in Chihuahua, taking into account all three seasons, was 0.592 kg capita^?1 day^?1. Our results show that the lowest income groups generated the least amount of waste. We also found that less waste was generated during the winter season. The breakdown for the composition of the waste shows that organic waste accounts for the largest proportion (45%), followed by paper (17%) and others (16%).     

Freshwaters: which NOM matters?

One of the difficulties encountered in the study of natural organic matter (NOM) in aquatic environments concerns terminology: a large number of different names and acronyms are used. In fact, this diversity merely reflects the variety of approaches applied to the study of NOM, leading to the definition and measurement of different operationally-defined fractions. As a result, the choice of the ‘best’ method to use and of the ‘best’ fraction to work on in a particular case is far from straightforward. This has as a consequence that often very simple methods for NOM characterisation are used in applied environmental studies and surveys. This study includes, first, a review of the most widely applied terminology, as described in over 500 articles, and, second, a first attempt at assessing how the panoply of methods available is really used in applied environmental studies.     

A design of two simple models to predict PCDD/F concentrations in vegetation and soils

The vegetation and soil levels of the 17 polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/F) toxic congeners were calculated by means of a vegetation and a soil model, respectively. Both models predicted the levels of the 17 PCDD/F congeners in quite good agreement with the observed results although the soil model was more accurate than the vegetation model. Four different pathways of contribution to the vegetation concentrations were taken into account: vapour-phase absorption, dry particle deposition, wet particle deposition and uptake by root. The most important pathway was the vapour-phase absorption and the less was the uptake by root. In the soils model four pathways were considered: background soil concentration, dry particle deposition, wet particle deposition and uptake by root. After the background concentration, the most important pathway was the wet deposition.     

Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing.

Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.     

The effect of the presence of trace metals on the oxidation of Sb(III) by hydrogen peroxide in aqueous solution

Despite its importance for understanding the behaviour of antimony in the environment, the oxidation kinetics of Sb(III) with natural oxidants is still not well understood. We have studied the oxidation of Sb(III) by hydrogen peroxide on a time scale of hours in the presence of some trace metals, Cu(II), Mn(II), Zn(II) and Pb(II), under pH and concentration conditions close to natural ones. The effects that these trace metals have on Sb(iii) oxidation by hydrogen peroxide vary. Zn(II) had no catalytic effect at all, but Cu(II), Mn(II) and Pb(II) did, though their effects were not uniform. Cu(II) significantly accelerated the reaction, which remained first-order with respect to Sb(III) at any Cu(II) concentration tested. Pb(II) and Mn(II) also enhanced the reaction rates, but the apparent order of the reaction with respect to Sb(III) changed to two. The trace metal effect observed was concentration dependent for Pb(II). The addition of the hydroxyl radical scavenger 2-propanol suggests that the trace metal catalytic effect observed involves the action of hydroxyl radicals, but that they are not responsible for the oxidation of Sb(III) by H2O2 in the absence of trace metals. The fact that Sb(III) can be oxidized by hydroxyl radicals present in water, even if it is not capable of producing them, has important environmental implications because hydroxyl radicals are known to be abundant in many natural waters such as seawater, humic-rich surface waters or rainwater.     

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.