Bioaccumulation of total mercury in the earthworm Eisenia andrei

Earthworms are a major part of the total biomass of soil fauna and play a vital role in soil maintenance. They process large amounts of plant and soil material and can accumulate many pollutants that may be present in the soil. Earthworms have been explored as bioaccumulators for many heavy metal species such as Pb, Cu and Zn but limited information is available for mercury uptake and bioaccumulation in earth- worms and very few report on the factors that influence the kinetics of Hg uptake by earthworms. It is known however that the uptake of Hg is strongly influenced by the presence of organic matter, hence the influence of ligands are a major factor contribut - ing to the kinetics of mercury uptake in biosystems. In this work we have focused on the uptake of mercury by earthworms ( Eisenia andrei ) in the presence of humic acid (HA) under varying physical conditions of pH and temperature, done to assess the role of humic acid in the bioaccumulation of mercury by earthworms from soils. The study was conducted over a 5-day uptake period and all earthworm samples were analysed by direct mercury analysis. Mercury distribution profiles as a function of time, bioac- cumulation factors (BAFs), first order rate constants and body burden constants for mercury uptake under selected conditions of temperature, pH as well as via the dermal and gut route were evaluated in one comprehensive approach. The results showed that the uptake of Hg was influenced by pH, temperature and the presence of HA. Uptake of Hg 2 + was improved at low pH and temperature when the earthworms in soil were in contact with a saturating aqueous phase. The total amount of Hg 2 + uptake decreased from 75 to 48 % as a function of pH. For earthworms in dry soil, the uptake was strongly influenced by the presence of the ligand. Calculated BAF values ranged from 0.1 to 0.8. Mercury uptake typically followed first order kinetics with rate constants determined as 0.2 to 1 h ? 1 .Scopus 201

Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data

West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management.Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162days as well as a—hopefully—limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near real-time GRACE forecast over the regions that exhibit strong teleconnections

Biodiversity and structure of spider communities along a metal pollution gradient

The objective of the study was to determine whether long-term metal pollution affects communities of epigeal spiders (Aranea), studied at three taxonomic levels: species, genera, and families. Biodiversity was defined by three indices: the Hierarchical Richness Index (HRI), Margalef index (DM) and Pielou evenness index (J). In different ways the indices describe taxa richness and the distribution of individuals among taxa. The dominance pattern of the communities was described with four measures: number of dominant species at a site, percentage of dominant species at a site, average dominant species abundance at a site, and the share of the most numerous species (Alopecosa cuneata) at a site. Spiders were collected along a metal pollution gradient in southern Poland, extending ca. 33 km from zinc and lead smelter to an uncontaminated area. The zinc concentration in soil was used as the pollution index.The study revealed a significant effect of metal pollution on spider biodiversity as described by HRI for species (p = 0.039), genera (p = 0.0041) and families (p = 0.0147), and by DM for genera (p = 0.0259) and families (p = 0.0028). HRI correlated negatively with pollution level, while DM correlated positively. This means that although broadly described HRI diversity decreased with increasing pollution level, species richness increased with increasing contamination. Mesophilic meadows were generally richer. Pielou (J) did not show any significant correlations. There were a few evidences for the intermediate disturbance hypothesis: certain indices reached their highest values at moderate pollution levels rather than at the cleanest or most polluted sites

Markov Chain Monte Carlo and the Application to Geodetic Time Series Analysis

The time evolution of geophysical phenomena can be characterised by stochastic time series. The stochastic nature of the signal stems from the geophysical phenomena involved and any noise, which may be due to, e.g., un-modelled effects or measurement errors. Until the 1990's, it was usually assumed that white noise could fully characterise this noise. However, this was demonstrated to be not the case and it was proven that this assumption leads to underestimated uncertainties of the geophysical parameters inferred from the geodetic time series. Therefore, in order to fully quantify all the uncertainties as robustly as possible, it is imperative to estimate not only the deterministic but also the stochastic parameters of the time series. In this regard, the Markov Chain Monte Carlo (MCMC) method can provide a sample of the distribution function of all parameters, including those regarding the noise, e.g., spectral index and amplitudes. After presenting the MCMC method and its implementation in our MCMC software we apply it to synthetic and real time series and perform a cross-evaluation using Maximum Likelihood Estimation (MLE) as implemented in the CATS software. Several examples as to how the MCMC method performs as a parameter estimation method for geodetic time series are given in this chapter. These include the applications to GPS position time series, superconducting gravity time series and monthly mean sea level (MSL) records, which all show very different stochastic properties. The impact of the estimated parameter uncertainties on sub-sequentially derived products is briefly demonstrated for the case of plate motion models. Finally, the MCMC results for weekly downsampled versions of the benchmark synthetic GNSS time series as provided in Chapter 2 are presented separately in an appendix

Ecotoxicological effect of altered TiO2 nanocomposite on the earthworm, Eisenia fetida

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Land Water Storage Changes from Ground and Space Geodesy: First Results from the GHYRAF (Gravity and Hydrology in Africa) Experiment

This paper is devoted to the first results from the GHYRAF (Gravity and Hydrology in Africa) experiment conducted since 2008 in West Africa and is aimed at investigating the changes in water storage in different regions sampling a strong rainfall gradient from the Sahara to the monsoon zone. The analysis of GPS vertical displacement in Niamey (Niger) and Djougou (Benin) shows that there is a clear annual signature of the hydrological load in agreement with global hydrology models like GLDAS. The comparison of GRACE solutions in West Africa, and more specifically in the Niger and Lake Chad basins, reveals a good agreement for the large scale annual water storage changes between global hydrology models and space gravity observations. Ground gravity observations done with an FG5 absolute gravimeter also show signals which can be well related to measured changes in soil and ground water. We present the first results for two sites in the Sahelian band (Wankama and Diffa in Niger) and one (Djougou in Benin) in the Sudanian monsoon region related to the recharge–discharge processes due to the monsoonal event in summer 2008 and the following dry season. It is confirmed that ground gravimetry is a useful tool to constrain local water storage changes when associated to hydrological and subsurface geophysical in situ measurements