A comparison of emission calculations using different modeled indicators with 1-year online measurements

The overall measurement of farm level greenhouse gas (GHG) emissions in dairy production is not feasible, from either an engineering or administrative point of view. Instead, computational model systems are used to generate emission inventories, demanding a validation by measurement data. This paper tests the GHG calculation of the dairy farm-level optimization model DAIRYDYN, including methane (CH₄) from enteric fermentation and managed manure. The model involves four emission calculation procedures (indicators), differing in the aggregation level of relevant input variables. The corresponding emission factors used by the indicators range from default per cow (activity level) emissions up to emission factors based on feed intake, manure amount, and milk production intensity. For validation of the CH₄ accounting of the model, 1-year CH₄ measurements of an experimental free-stall dairy farm in Germany are compared to model simulation results. An advantage of this interdisciplinary study is given by the correspondence of the model parameterization and simulation horizon with the experimental farm’s characteristics and measurement period. The results clarify that modeled emission inventories (2,898, 4,637, 4,247, and 3,600 kg CO₂-eq. cow^?1 year^?1) lead to more or less good approximations of online measurements (average 3,845 kg CO₂-eq. cow^?1 year^?1 (±275 owing to manure management)) depending on the indicator utilized. The more farm-specific characteristics are used by the GHG indicator; the lower is the bias of the modeled emissions. Results underline that an accurate emission calculation procedure should capture differences in energy intake, owing to milk production intensity as well as manure storage time. Despite the differences between indicator estimates, the deviation of modeled GHGs using detailed indicators in DAIRYDYN from on-farm measurements is relatively low (between ?6.4 % and 10.5 %), compared with findings from the literature.     

Distributions and sources of polycyclic aromatic hydrocarbons in surface sediments from the Cross River estuary,S.E. Niger Delta,Nigeria

Polycyclic aromatic hydrocarbon (PAH) analyses of surface sediments from the Cross River estuary by gas chromatography–mass spectrometry indicated natural diagenetically derived PAHs in the upper estuary, with minor and variable amounts of petrogenic and combustion-derived PAHs from human activities (lower estuary). The occurrence of significant amounts of perylene (average 23% of all PAHs) with the diagenetic natural PAHs in the middle estuary bordered by mangrove forests supports its origin from terrestrial organic matter. The natural PAHs represent the highest percentage (average 76%) of the total PAHs in this tropical environment. The traditional geochemical parameters, including the petrogenic PAHs, confirmed trace petroleum contamination in the estuary. Specific PAH ratios such as Fl/Py and Fl/(Fl+Py) also support this source contribution.     

Metals in biomass

Background, Aim and Scope

Metal ions generally share the ability/tendency of interacting with biological material by forming complexes, except possibly for the heavy alkali metals K, Rb and Cs. This is unrelated to the metals being either essential for sustaining life and its reproduction, apparently insignificant for biology, although perhaps undergoing bioconcentration or even being outright toxic, even at low admission levels. Yet, those different kinds of metal-biomass interactions should in some way depend on properties describing coordination chemistries of these very metals. Nevertheless, both ubiquitously essential metals and others sometimes used in biology should share these properties in numeric terms, since it can be anticipated that they will be distinguished from nonessential and/or toxic ones. These features noted above include bioconcentration, the involvement of metal ions such as Zn, Mg, Cu, Fe, etc. in biocatalysis as crucial components of metalloenzymes and the introduction of a certain set of essential metals common to (almost) all living beings (K, Mg, Mo, Mn, Fe, Cu and Zn), which occurred probably very early in biological evolution by ‘natural selection of the chemical elements’ (more exactly speaking, of the metallomes).

Materials and Methods

The approach is semiempirical and consists of three consecutive steps: 1) derivation of a regression equation which links complex stability data of different complexes containing the same metal ion to electrochemical data pertinent to the (replaced) ligands, thus describing properties of metal ions in complexes, 2) a graphical representation of the properties-two typical numbers c and x for each metal ion-in some map across the c/x-space, which additionally contains information about biological functions of these metal ions, i.e. whether they are essential in general (e.g. Mg, Mn, Zn) or, for a few organisms of various kinds (e.g. Cd, V), not essential (e.g. rare earth element ions) or even generally highly toxic (Hg, U). It is hypothesized that, if coordination properties of metals control their biological ‘feasibility’ in some way, this should show up in the mappings (one each for mono and bidentate-bonding ligands). 3) eventually, the regression equation produced in step 1) is inverted to calculate complex stabilities pertinent to biological systems: 3a) complex stabilities are mapped for ligands delivered to soil (-water) by green plants (e.g. citrate, malate) and fungi and, compared to their unlike selectivities and demands of metal use (photosynthesis taking place or not), 3b) the evolution of the metallome during late chemical evolution is reconstructed.

Results

These maps show some ‘window of essentiality’, a small, contrived range/area of c and x parameters in which essential metal ions gather almost exclusively. c and x thus control the possibility of a metal ion becoming essential by their influencing details of metal-substrate or (in cases of catalytic activities) metal-product interactions. Exceptions are not known to be involved in biocatalysis anyhow.

Discussion

Effects of ligands secreted, e.g. from tree roots or agaric mycelia to the soil on the respective modes (selectivities) of metal bioconcentration can be calculated by the equation giving complex stability constants, with obvious ramifications for a thorough, systematic interpretation of biomonitoring data. Eventually, alterations of C, N and P-compounds during chemical evolution are investigated — which converted CH₄ or CO₂, N₂ and other non-ligands to amino acids, etc., for example, with the latter behaving as efficient chelating ligands: Did they cause metal ions to accumulate in what was going to become biological matter and was there a selectivity, a positive bias in favour of nowessential metals (see above) in this process? Though there was no complete selectivity of this kind, neither a RNA world in which early ribozymes effected most of biocatalysis, nor a paleoatmosphere containing substantial amounts of CO could have paved the way to the present biochemistry and metallomes.

Conclusions

This way of reasoning provides a causal account for abundance distributions described earlier in the Biological System of Elements (BSE; Markert 1994, Fränzle &; Markert 2000, 2002). There is a pronounced change from chemical evolution, where but few transformations depended on metal ion catalysis to biology.

Recommendations and Perspectives

The application of this numerical approach can be used for modified, weighted evaluation of biomonitoring analytical data, likewise for the prediction of bioconcentration hazards due to a manifold of metal ions, including organometallic ones. This is relevant in ecotoxicology and biomonitoring. In combining apoproteins or peptides synthesized from scratch for purposes of catalysing certain transformations, the map and numerical approaches might prove useful for the selection of central ions which are even more efficient than the ‘natural’ ones, like for Co²⁺ in many Zn enzymes.

     

Dendroremediation of trinitrotoluene (TNT) Part 2: Fate of radio-labelled TNT in trees

BACKGROUND, AIM AND SCOPE: Problems of long-term existence of the environmental contaminant 2,4,6-trinitrotoluene (TNT) and necessities for the use of trees ('dendroremediation') in sustainable phytoremediation strategies for TNT are described in the first part of this paper. Aims of the second part are estimation of [14C]-TNT uptake, localisation of TNT-derived radioactivity in mature tree tissues, and the determination of the degree of TNT-degradation during dendroremediation processes. METHODS: Four-year-old trees of hybrid willow (Salix spec., clone EW-20) and of Norway spruce (Picea abies) were cultivated in sand or ammunition plant soil (AP-soil) in wick supplied growth vessels. Trees were exposed to a single pulse application with water solved [U-14C]-TNT reaching a calculated initial concentration of 5.2 mg TNT per kg dry soil. Two months after application overall radioactivity and extractability of 14C were determined in sand/soil, roots, stem-wood, stem-bark, branches, leaves, needles, and Picea May sprouts. Root extracts were analysed by radio TLC. RESULTS: 60 days after [14C]-TNT application, recovered 14C is accumulated in roots (70% for sand variants, 34% for AP-soil variant). 15-28% of 14C remained in sand and 61% in AP-soil. 3.3 to 14.4% of 14C were located in aboveground tree portions. Above-ground distribution of 14C differed considerably between the angiosperm Salix and the gymnosperm Picea. In Salix, nearly half of above-ground-14C was detected in bark-free wood, whereas in Picea older needles contained most of the above-ground-14C (54-69%). TNT was readily transformed in tree tissue. Approximately 80% of 14C was non-extractably bound in roots, stems, wood, and leaves or needles. Only quantitatively less important stem-bark of Salix and Picea and May shoots of Picea showed higher extraction yields (up to 56%). DISCUSSION: Pulse application of [14C]-TNT provided evidence for the first time that after TNT-exposure, in tree root extracts, no TNT and none of the known metabolites, mono-amino-dinitrotoluenes (ADNT), diaminonitrotoluenes (DANT), trinitrobenzene (TNB) and no dinitrotoluenes (DNTs) were present. Extractable portions of 14C were small and contained at least three unknown metabolites (or groups) for Salix. In Picea, four extractable metabolites (or groups) were detected, where only one metabolite (or group) seemed to be identical for Salix and Picea. All unknown extractables were of a very polar nature. CONCLUSIONS: Results of complete TNT-transformation in trees explain some of our previous findings with 'cold analytics', where no TNT and no ADNT-metabolites could be found in tissues of TNT-exposed Salix and Populus clones. It is concluded that 'cold' tissue analysis of tree organs is not suited for quantitative success control of phytoremediation in situ. RECOMMENDATIONS AND OUTLOOK: Both short rotation Salicaceae trees and conifer forests possess a dendroremediation potential for TNT polluted soils. The degradation capacity and the large biomass of adult forest trees with their woody compartments of roots and stems may be utilized for detoxification of soil xenobiotics.     

Moosmonitoring als Spiegel der Landnutzung?

Goal, Scope and Background

The study was conducted to test the hypothesis that the regional variability of nitrogen (N) and metal accumulations in terrestrial ecosystems are due to historical and recent ways of land use. To this end, in two regions of Central Europe the metal and N accumulations in both regions should be examined by comparative moss analysis. The regions should be of quantitatively specified representativity for selected ecological characteristics of Europe. Within both regions these characteristics should be covered by the sites where the moss samples were collected. The number of samples should allow for geostatistical estimation of the measured nitrogen and metal loads.

Methods

The two regions of investigation were selected according to an ecological land classification of Europe which was computed by classification trees. Within each of both research areas the sampling points were localized according to the areas occupied by the ecologically defined land classes. The sampling and chemical analysis of mosses was conducted in accordance with an appropriate UNECE guideline by means of ICP-MS (metals) and combustion analysis (N). The quality of measurements was assured using certified reference materials. The differences of deposition loads were tested for statistical significance with regard to time and space. Variogram analysis was used to examine and model the spatial autocorrelation function of the measurements. Ordinary kriging was then applied for surface estimations.

Results

By use of the ecological regionalisation of Europe the Weser-Ems Region (WER) and the Euro Region Nissa (ERN) were selected for investigation. The sampling sites represent quite well the natural landscapes and the land use categories of both regions. The measurement values corroborate the decline of metal accumulation observed since the beginning of the European Mosses Monitoring Survey in 1990. The metal loads of the mosses in the ERN exceed those in the WER significantly. The opposite holds true for the N concentrations: those in the WER are significantly higher than those in the ERN.

Discussion

The decrease of heavy metal emissions is correlated with lowered deposition and accumulation rates in terrestrial ecosystems. The accumulation of nitrogen in the biosphere is not following this trend.

Conclusions

The technique of moss analysis is adequate for spatially valid biomonitoring of spatial and temporal trends of metals and nitrogen in terrestrial ecosystems. By this, it enables to prove the efficiency of environmental policies.

Recommendations and Perspectives

The accumulation of N in ecosystems is still a serious environmental problem. Related ecological impacts are the eutrophication of aquatic ecosystems like ground waters, lakes, rivers and oceans as well as the biocoenotic changes in terrestrial ecosystems. Thus, a statistically valid exposure analysis must encompass both, accumulation of metals and N bioaccumulation. Further, the bioaccumulation of persistent organic pollutants should be monitored. Finally, environmental biomonitoring should be conducted in much closer contact with human health aspects.     

A contribution to the distribution of some chemical elements inMolinia caerulea andEriophorum vaginatum during reinstatement of the Leegmoor,Emsland, FRG

The concentrations of Ag, Al, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, Sr, Ti, V and Zn were determined by AES/ICP inMolinia caerulea andEriophorum vaginatum grown in the Leegmoor, which is in reinstatement. In the soil substrate analytical detection limits were obtained for Ag, Co, Li, Mo and Rb and additionally for Al, Be, Cd, Cr, La, Li, Ni, Pb, Ti and V in plants. Compared with mineral soils, the concentration of elements in peat is low. The elements K and P were enriched byMolinia caerulea andEriophorum vaginatum, and in addition Mn and Zn byEriophorum vaginatum. Altogether the ability of accumulation essential elements seem to be more efficient byEriophorum vaginatumthan byMolinia caerulea, which can be an ecophysiological advantage on peats, whereMolinia caerulea is normally not distributed.     

Variations in the elemental compositions of plants,and computer‐aided sampling in ecosystems∗

To obtain comparable results of multi‐element analysis of plant materials by different laboratories, a harmonized sampling procedure for terrestrial and marine ecosystems is essential. The heterogeneous distribution of chemical elements in living organisms is influenced by different biological parameters. These parameters are mainly characterized by genetic predetermination, seasonal changes, edaphic and climatic conditions, and delocalization processes of chemical substances by metabolic activities.

The biological variations of the element content in plants were divided into 5 systematic levels, which are: 1. the plant species; 2. the population; 3. the stand (within an ecosystem); 4. the individual; and 5. the plant compartment. Each of these systematic levels can be related to: 1. genetic variabilities; 2. different climatic, edaphic and anthropogenic influences; 3. microclimatic or microedaphic conditions; 4. age of plants (stage of development), exposure to environmental influences (light, wind, pollution etc.), seasonal changes; and 5. transport and deposition of substances within the different plant compartments (organs, tissues, cells, organelles).

An expert system for random and systematic sampling for multi‐element analysis of environmental materials, such as plants, soils and precipitation is presented. After statistical division of the research area, the program provides advice for contamination‐free collection of environmental samples.