Erprobung und Erfolgskontrolle eines Phytoremediations-verfahrens zur Sanierung Sprengstoff-kontaminierter Böden

Results of a field experiment (please refer to Warrelmann et al. 2000a) document TNT-degradation and regeneration after the establishment of a phytoremediation. Various approaches are included in a complex monitoring system, which are conducted in the field (nitroaromatics in soil, seepage water, and plants; soil fauna; soil fungi; decomposition) as well as in the lab (a battery of 6 biotests). The data document a high heterogeneity of the TNT-contamination, a rapid initial decrease and a mediocre displacement of nitroaromatics in plants and seepage water. The initial rapid transformation of TNT to aminodinitrotoluenes ceases significantly after 3 months. Results of the biotest battery allow a sophisticated assessment of soil toxicity; the results from the soil ecological field investigations make the long-term effects of TNT-contamination likely. In a third part of the series of articles, a synopsis and judgement of the project will be delivered.     

Quantifying the sensitivity of ephemeral streams to land disturbance activities in arid ecosystems at the watershed scale

Large areas of public lands administered by the Bureau of Land Management and located in arid regions of the southwestern United States are being considered for the development of utility-scale solar energy facilities. Land-disturbing activities in these desert, alluvium-filled valleys have the potential to adversely affect the hydrologic and ecologic functions of ephemeral streams. Regulation and management of ephemeral streams typically falls under a spectrum of federal, state, and local programs, but scientifically based guidelines for protecting ephemeral streams with respect to land-development activities are largely nonexistent. This study developed an assessment approach for quantifying the sensitivity to land disturbance of ephemeral stream reaches located in proposed solar energy zones (SEZs). The ephemeral stream assessment approach used publicly-available geospatial data on hydrology, topography, surficial geology, and soil characteristics, as well as high-resolution aerial imagery. These datasets were used to inform a professional judgment-based score index of potential land disturbance impacts on selected critical functions of ephemeral streams, including flow and sediment conveyance, ecological habitat value, and groundwater recharge. The total sensitivity scores (sum of scores for the critical stream functions of flow and sediment conveyance, ecological habitats, and groundwater recharge) were used to identify highly sensitive stream reaches to inform decisions on developable areas in SEZs. Total sensitivity scores typically reflected the scores of the individual stream functions; some exceptions pertain to groundwater recharge and ecological habitats. The primary limitations of this assessment approach were the lack of high-resolution identification of ephemeral stream channels in the existing National Hydrography Dataset, and the lack of mechanistic processes describing potential impacts on ephemeral stream functions at the watershed scale. The primary strength of this assessment approach is that it allows watershed-scale planning for low-impact development in arid ecosystems; the qualitative scoring of potential impacts can also be adjusted to accommodate new geospatial data, and to allow for expert and stakeholder input into decisions regarding the identification and potential avoidance of highly sensitive stream reaches.     

Evidence-Based Knowledge Versus Negotiated Indicators for Assessment of Ecological Sustainability: The Swedish Forest Stewardship Council Standard as a Case Study

Assessing ecological sustainability involves monitoring of indicators and comparison of their states with performance targets that are deemed sustainable. First, a normative model was developed centered on evidence-based knowledge about (a) forest composition, structure, and function at multiple scales, and (b) performance targets derived by quantifying the habitat amount in naturally dynamic forests, and as required for presence of populations of specialized focal species. Second, we compared the Forest Stewardship Council (FSC) certification standards’ ecological indicators from 1998 and 2010 in Sweden to the normative model using a Specific, Measurable, Accurate, Realistic, and Timebound (SMART) indicator approach. Indicator variables and targets for riparian and aquatic ecosystems were clearly under-represented compared to terrestrial ones. FSC’s ecological indicators expanded over time from composition and structure towards function, and from finer to coarser spatial scales. However, SMART indicators were few. Moreover, they poorly reflected quantitative evidence-based knowledge, a consequence of the fact that forest certification mirrors the outcome of a complex social negotiation process.     

Optimal spatial resolution for collection of ground data and multi-sensor image mapping of a soil erosion cover factor

Military training activities disturb ground and vegetation cover of landscapes and increases potential soil erosion. To monitor the dynamics of soil erosion, there is an important need for an optimal sampling design in which determining the optimal spatial resolutions in terms of size of sample plots used for the collection of ground data and the size of pixels for mapping. Given a sample size, an optimal spatial resolution should be cost-efficient in both sampling costs and map accuracy. This study presents a spatial variability-based method for that purpose and compared it with the traditional methods in a study area in which a soil erosion cover factor was sampled and mapped with multiple plot sizes and multi-sensor images. The results showed that the optimal spatial resolutions obtained using the spatial variability-based method were 12 and 20m for years 1999 and 2000, respectively, and were consistent with those using the traditional methods. Moreover, the most appropriate spatial resolutions using the high-resolution images were also consistent with those using ground sample data, which provides a potential to use the high-resolution images instead of ground data to determine the optimal spatial resolutions before sampling. The most appropriate spatial resolutions above were then verified in terms of cost-efficiency which was defined as the product of sampling cost and map error using ordinary kriging without images and sequential Gaussian co-simulation with images to generate maps.     

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem

Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty.For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.     

Correlation between the seasonal distribution of harbour porpoises and their prey in the Sound,Baltic Sea

Low densities of harbour porpoises in winter (November–March) and high densities in summer (April–October) were found in the Sound, connecting the Baltic Sea and Kattegat. Due to their high energy requirements, it is hypothesized that the density of harbour porpoises is related to local prey abundance. This was tested by examining the stomach content of 53 harbour porpoises collected between 1987 and 2010 in the Sound (high season, 34 porpoises; low season, 19 porpoises). A total of 1,442 individual fish specimens from thirteen species were identified. Twelve of these were present in the high–porpoise density season and seven in the low-density season. The distribution of occurrence and the distribution of number of fish species were different between seasons, indicating a shift in prey intake between seasons. Furthermore, during the high-density season, the mean and total prey weight per stomach as well as the prey species diversity was higher. However, no difference was found in the number of prey species between the two seasons, indicating a higher quality of prey in the high-density season. Atlantic cod was found to be the main prey species in terms of weight in the high-density season while Atlantic herring and Atlantic cod were equally important during the low-density season. Prey availability and predictability are suggested as the main drivers for harbour porpoise distribution, and this could be caused by the formation of frontal zones in spring in the northern part of the Sound, leading to prey concentrations in predictable areas.     

Application of capillary electrophoresis combined with a modified BCR sequential extraction for estimating of distribution of selected trace metals in PM2.5 fractions of urban airborne particulate matter

The capillary electrophoresis (CE) method combined with a sequential extraction was applied to determine the distribution of Fe, Zn, Cu, Mn and Cd in urban ambient air PM2.5 samples. PM2.5 was collected on Teflon filters with dichotomous sampler, and the modified extraction procedure following the BCR leaching procedure was used to chemically fractionate metals into "easily exchangeable" with water, "acid extractable" with 0.11 mol/l acetic acid, "reducible" with 0.1 mol/l hydroxylamine hydrochloride acidified to pH 2.0 with nitric acid, and "oxidisable" with oxidation by 8.8 mol/l hydrogen peroxide (H2O2) followed by extraction with 1.0 mol/l ammonium acetate. Based on the obtained results it was concluded that the application of the studied methodology provides chemical fractionation data that reflect the general sources and potential health hazards of the studied metals.     

Changes in soil respiration across a chronosequence of tallgrass prairie reconstructions

Close relationships among climatic factors and soil respiration (R_s) are commonly reported. However, variation in R_s across the landscape is compounded by site-specific differences that impede the development of spatially explicit models. Among factors that influence R_s, the effect of ecosystem age is poorly documented. We hypothesized that R_s increases with grassland age and tested this hypothesis in a chronosequence of tallgrass prairie reconstructions in central Iowa, U.S.A. We also assessed changes in root biomass, root ingrowth, aboveground net primary productivity (ANPP), and the strength of soil temperature and moisture in predicting R_s. We found a significant increase in total growing season R_s with prairie age (R² = 0.79), ranging from 714 g C m^?2 in the youngest reconstruction (age 4) to 939 g C m^?2 in the oldest prairie (age 12). Soil temperature was a strong predictor of intra-seasonal R_s among prairies (R² = 0.78–0.87) but mean growing season soil temperature and moisture did not relate to total R_s. The increase in R_s with age was positively correlated with root biomass (r = 0.80) and ANPP (r = 0.87) but not with root ingrowth. Our findings suggest that growing season R_s increases with tallgrass prairie age, root biomass, and ANPP during young grassland development.