Modelling trace metal background to evaluate anthropogenic contamination in arable soils of south-western France

The trace metal (TM) content in arable soils has been monitored across a region of France characterised by a large proportion of calcareous soils. Within this particular geological context, the objectives were to first determine the natural levels of trace metals in the soils and secondly, to assess which sites were significantly contaminated. Because no universal contamination assessment method is currently available, four different methods were applied and compared in order to facilitate the best diagnosis of contamination. First, the TM geochemical background was determined by using basic descriptive statistics and linear regression models calculated with semi-conservative major elements as predictors. The natural concentrations of trace metals varied greatly due to the high soil heterogeneity encountered on the regional scale and were more-or-less accurately modelled according to the considered TM. Second, the basic descriptive statistics and the linear regression methods were then compared with the enrichment factor (EF) method and multivariate analysis (PCA), in order to evaluate whether the concentrations measured in soils were abnormally high or not. The advantages and disadvantages of each method were discussed and their results used to identify the most probable contamination cases, the influence of the soils characteristics, as well as the agricultural land cover. The basic descriptive method was good as a first and easy approach to describe the TM ambient concentrations, but may misinterpret the natural anomalies as contaminations. Based on geochemical associations, the linear regression method provided more realistic results even if the relationships between major and trace metals were not significant for the most mobile TM. The EF method was useful to identify high point source contaminations, but it was not suitable when considering a large dataset of low TM concentrations. Finally, the PCA method was a good preliminary tool for the description of the global TM concentrations in a studied area, but it could only give indication on the highest contaminated points. By comparing the results of the different methods in the studied region, we estimated that 24% of the arable soils were contaminated by at least one trace metal, mainly Cu in vineyards/orchards and Cd, Pb and/or Zn in grazing lands. In addition, the calcareous soils exhibited globally higher natural and anthropogenic TM concentrations than non-calcareous soils, probably because of the lower TM mobility at alkaline pH

Ecological Effects of Fear: How Spatiotemporal Heterogeneity in Predation Risk Influences Mule Deer Access to Forage in a Sky‐Island System

Forage availability and predation risk interact to affect habitat use of ungulates across many biomes. Within sky‐island habitats of the Mojave Desert, increased availability of diverse forage and cover may provide ungulates with unique opportunities to extend nutrient uptake and/or to mitigate predation risk. We addressed whether habitat use and foraging patterns of female mule deer (Odocoileus hemionus) responded to normalized difference vegetation index (NDVI), NDVI rate of change (green‐up), or the occurrence of cougars (Puma concolor). Female mule deer used available green‐up primarily in spring, although growing vegetation was available during other seasons. Mule deer and cougar shared similar habitat all year, and our models indicated cougars had a consistent, negative effect on mule deer access to growing vegetation, particularly in summer when cougar occurrence became concentrated at higher elevations. A seemingly late parturition date coincided with diminishing NDVI during the lactation period. Sky‐island populations, rarely studied, provide the opportunity to determine how mule deer respond to growing foliage along steep elevation and vegetation gradients when trapped with their predators and seasonally limited by aridity. Our findings indicate that fear of predation may restrict access to the forage resources found in sky islands

Mineral composition through soil-wine system of portuguese vineyards and its potential for wine traceability

The control of geographic origin is one of a highest priority issue regarding traceability and wine authenticity. The current study aimed to examine whether elemental composition can be used for the discrimination of wines according to geographical origin, taking into account the effects of soil, winemaking process, and year of production. The elemental composition of soils, grapes, musts, and wines from three DO (Designations of Origin) and for two vintage years was determined by using the ICP-MS semi-quantitative method, followed by multivariate statistical analysis. The elemental composition of soils varied according to geological formations, and for some elements, the variation due to soil provenance was also observed in musts and wines. Li, Mn, Sr and rare-earth elements (REE) allowed wine discrimination according to vineyard. Results evidenced the influence of winemaking processes and of vintage year on the wine’s elemental composition. The mineral composition pattern is transferred through the soil-wine system, and differences observed for soils are reflected in grape musts and wines, but not for all elements. Results suggest that winemaking processes and vintage year should be taken into account for the use of elemental composition as a tool for wine traceability. Therefore, understanding the evolution of mineral pattern composition from soil to wine, and how it is influenced by the climatic year, is indispensable for traceability purposesinfo:eu-repo/semantics/publishedVersio

Greenhouse Gas Mitigation in a Carbon Constrained World: The Role of Carbon Capture and Storage

In a carbon constrained world, at least four classes of greenhouse gas mitigation options are available: Energy efficiency, fuel switching, introduction of carbon dioxide capture and storage along with renewable generating technologies, and reductions in emissions of non-CO2 greenhouse gases. The role of energy technologies is considered crucial in climate change mitigation. In particular, carbon capture and storage (CCS) promises to allow for low-emissions fossil-fuel based power generation. The technology is under development; a number of technological, economic, environmental and safety issues remain to be solved. With regard to its sustainability impact, CCS raises a number of questions: On the one hand, CCS may prolong the prevailing coal-to-electricity regime and countervail efforts in other mitigation categories. On the other hand, given the indisputable need to continue using fossil fuels for some time, it may serve as a bridging technology towards a sustainable energy future. In this paper, we discuss the relevant issues for the case of Germany. We provide a survey of the current state of the art of CCS and activities, and perform an energy-environment-economic analysis using a general equilibrium model for Germany. The model analyzes the impact of introducing carbon constraints with respect to the deployment of CCS, to the resulting greenhouse gas emissions, to the energy and technology mix and with respect to interaction of different mitigation efforts. The results show the relative importance of the components in mitigating greenhouse gas emissions in Germany. For example, under the assumption of a CO2 policy, both energy efficiency and CCS will contribute to climate gas mitigation. A given climate target can be achieved at lower marginal costs when the option of CCS is included. We conclude that, given an appropriate legal and policy framework, CCS, energy efficiency and some other mitigation efforts are complementary measures and should form part of a broad mix of measures required for a successful CO2 mitigation strategy.

Assessment of Social Vulnerability to Floods in the Floodplain of Northern Italy

Practices for reducing the impacts of floods are becoming more and more advanced, centered on communities and reaching out to vulnerable populations. Vulnerable individuals are characterized by social and economic attributes and by societal dynamics rooted in each community. These indicators can magnify the negative impacts of disasters together with the capacity of each individual to cope with these events. The Social Vulnerability Index (SoVI) provides an empirical basis to compare social differences in various spatial scenarios and for specific environmental hazards. This research shows the application of the SoVI to the floodplain of northern Italy, based on the use of 15 census variables. The chosen study area is of particular interest for the high occurrence of flood events coupled with a high level of human activity, landscape transformations, and an elevated concentration of assets and people. The analysis identified a positive spatial autocorrelation across the floodplain that translates into the spatial detection of vulnerable groups, those that are likely to suffer the most from floods. In a second stage, the output of the index was superimposed on the flood hazard map of the study area to analyze the resulting risk. The Piemonte and Veneto regions contain the main areas prone to flood \u201csocial\u201d risk, highlighting the need for a cohesive management approach at all levels to recognize local capacities and increase communication, awareness, and preparedness to mitigate the undesirable effects of such events

Characterizing the Afghanistan aerosol environment using size- and time- resolved aerosol chemical composition measurements

Thesis (M.S.) University of Alaska Fairbanks, 2012The exposure to aerosols is one danger U.S. soldiers face in Afghanistan that may go unseen. Using the Davis Rotating-drum Universal-size-cut Monitoring (DRUM) cascade impactor, size- and time- resolved aerosol chemical concentrations from Bagram, Afghanistan were collected. These aerosol concentrations were combined with a meteorological analysis and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model meteorological backward trajectories to establish source sectors. These sectors, along with time of year, were then used as a predictive tool for the chemical composition and relative concentration of aerosols in Afghanistan. Principal components analysis (PCA) was used to determined potential source types. PM₁₀ and PM₂.₅ were compared to military exposure guidelines and U.S. national ambient air quality standards. Results reveal aerosol concentrations in Afghanistan were at levels for which adverse health effects could be anticipated.1. Introduction -- 1.1. Definition and formation of aerosols -- 1.2. Thesis goals -- 1.3. Climatology of the Afghanistan region -- 1.3.1. Wind patterns -- 1.3.2. Diurnal cycles -- 1.4. Elemental sources and uses -- 1.5. Aerosol chemistry and seasonality -- 1.5.1. Geological dust -- 1.5.2. Anthropogenic aerosols -- 1.5.2.1. Pakistan -- 1.5.2.2. Kazakhstan, Turkmenistan, and Uzbekistan -- 1.5.3. Biomass burning -- 1.5.4. Aerosols over seas and oceans -- 1.6. Health concerns and standards -- 2. Experimental methods -- 2.1. Wind roses -- 2.2. DRUM aerosol impactors -- 2.3. HYSPLIT and sector analysis -- 2.4. Principla components analysis -- 2.4.1. PCA procedure -- 2.4.2. Eigenvector loadings -- 2.4.3. PCA on aerosol samples -- 2.5. Chemical mass balance (CMB) model -- 3. Results and discussion -- 3.1. Wind roses -- 3.2. Elemental concentrations -- 3.2.1. Geological dust -- 3.2.2. Heavy metal events -- 3.3. PM₁₀ and PM₂.₅ concentrations and comparison to health safety standards -- 3.4. Sector analysis -- 3.5. PCA -- 3.6. CMB model -- 4. Conclusions -- 5. Future work -- References

Modeling And Prediction Of Ventilation Methane Emissions Of U. S. Longwall Mines Using Supervised Artificial Neural Networks

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production. Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines. Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates

Hydrogeochemical Characterization and Quality Assessment of Groundwater Based on Water Quality Index in Imo state, South Eastern Nigeria

Water Quality Index (WQI), Principal Component Analysis (PCA), Correlation matrix, Metal Pollution Index (MPI), Contamination Factor (CF), Pollution Load Index (PLI), Geoaccumulation Index (Igeo), Health Risk Assessment, and Hydrogeochemical facies were used to analyze statistical indexes and hydrogeochemical facies in groundwater resources in Imo state, Nigeria. All across the study area, twenty (20) groundwater samples were collected in a systematic manner. The samples were examined in accordance with the American Public Health Association standard (APHA) method. Findings from the study revealed that WQI, is of poor quality and should only be used for irrigation. Weathering and redox reactions are important in groundwater geochemistry, according to PCA results. TDS and Cl, HCO3 and  Zn, Cl; Mg and Ca, Ca and Na were all found to have a positive correlation in the correlation matrix while PH and K, HCO3 and Fe, Cl and SO4 are found to have a negative correlation in the correlation matrix. The findings show that the items have a weak correlation and that there is no relationship between the two variables. Further MPI, CF, and PLI findings revealed that groundwater is pure, the main source of pollution is geological and anthropogenic processes, and there is no pollution in sampled groundwater. Hydrogeochemical trend revealed that groundwater is Na++K+ > HCO3¯+CO3> Mg+ > SO4> Cl¯ > Ca+. Based on the finding, pre-use treatment of water resources is strongly advised

Monitoring For Underdetermined Underground Structures During Excavation Using Limited Sensor Data

A realistic field monitoring application to evaluate close proximity tunneling effects of a new tunnel on an existing tunnel is presented. A blind source separation (BSS)-based monitoring framework was developed using sensor data collected from the existing tunnel while the new tunnel was excavated. The developed monitoring framework is particularly useful to analyze underdetermined systems due to insufficient sensor data for explicit input force-output deformation relations. The analysis results show that the eigen-parameters obtained from the correlation matrix of raw sensor data can be used as excellent indicators to assess the tunnel structural behaviors during the excavation with powerful visualization capability of tunnel lining deformation. Since the presented methodology is data-driven and not limited to a specific sensor type, it can be employed in various proximity excavation monitoring applications