Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

Gender,Place, and Social Contacts: Understanding Awareness of Vulnerability to Terrorism

Abstract

Creating resiliency, accomplished in part by individuals preparing for disaster, is the primary strategy outside of law enforcement for responding to the threat of urban terrorism. Individuals prepare when they perceive a need to do so, yet little is known about what shapes a person's awareness of vulnerability to terrorism. Because evidence indicates that social contacts act as conduits of information and affect perception of risk to natural hazards, it is possible that such contacts also affect terrorism vulnerability awareness. Because social contacts are also known to be systematically segmented by gender and location, we hypothesized that conversations about terrorism vary by gender and place, specifically the home and work place. Drawing on data from 93 interviews with householders in Boston, the study demonstrates that: (1) family networks generated discussions of home preparedness, whereas workplace networks engaged a wider variety of topics; (2) women discussed terrorism more frequently and in greater depth than did men; and (3) women heard more preparedness messages for the home than did men, whereas men undertook preparedness activities external to the home. The findings bridge geography and terrorism studies by theorizing emplaced and subjective human experiences that prompt conversations about terrorism. These conversations, in turn, help urban emergency managers and risk-hazards geographers promote rational dialogue and action vis-à-vis terrorism. Hazards researchers have shown that the more people discuss terrorism, the less they seek extreme and unwarranted responses.     

Wavelet triangulated irregular networks

GIS applications have recently begun to emerge on the Internet. The management of three-dimensional geographic datasets in this distributed environment poses a particularly challenging problem, which highlights the need for a good data representation. This paper presents a new multiresolution data representation: the Wavelet Triangulated Irregular Network (WTIN). Compared to the traditional cell-based Digital Elevation Model (DEM) format and the Triangulated Irregular Network (TIN) format, it is more compact and suitable for scalable distributed GIS services. This format is based on the second-generation wavelet theory and is specially designed for geographical height field data. The modified Butterfly scheme is used for constructing the wavelet transform. For every point in the geographic surface, only a single wavelet coefficient is used, which makes the final data representation very efficient and easy to compress. Because the transform used in the data representation is a linear filter operation, the computational efficiency is better than other multiresolution data formats for terrain surfaces. Results from numerical experiments on real data are given to demonstrate that the proposed data representation can be efficiently implemented. The results show that the proposed WTIN data format can provide multiresolution data sets, which achieve significant compression while preserving geographical features. The quality is found to be quite acceptable for geographical terrain representation.     

Incorporating the LCIA concept into fuzzy risk assessment as a tool for environmental impact assessment

Environmental impact assessment (EIA) is a procedural tool for environmental management that identifies, predicts, evaluates and mitigates the environmental impact of development proposals. In the process of EIA, EIA reports, prepared by developers, are expected to delineate the environmental impact, but in practice they usually determine whether the amounts or concentrations of pollutants comply with the relevant standards. Actually, many analytical tools can improve the analysis of environmental impact in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA that takes account of the causal relationships between environmental hazards and damage. Incorporating the concept of LCIA into an ERA as an integrated tool for the preparation of EIA reports extends the focus of the reports from the regulatory compliance of the environmental impact, to determine the significance of the environmental impact. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations, because of a lack of sufficient information; therefore, so ERA should be generalized to a fuzzy risk assessment (FRA). Therefore, this paper proposes the integration of a LCIA and a FRA as an assessment tool for the preparation of EIA reports, whereby the LCIA clearly identifies the causal linkage for hazard–pathway–receptor–damage and then better explain the significance of the impact; furthermore, a FRA copes with fuzzy and probabilistic situations in the assessment of pollution severity and the estimation of exposure probability. Finally, the use of the proposed methodology is demonstrated in a case study of the expansion plan for the world’s largest plastics processing factory.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

Influence of El Niño–Southern Oscillation on the long-term record of floods over Bangladesh

Theoretical and Applied Climatology - The present study analyzed a long-term record of major floods over Bangladesh under the influence of El Niño–Southern Oscillation (ENSO). Bangladesh...     

Hazard management and risk design by optimal statistical analysis

Elicitation methods are used in decision making with respect to risk hazards to allow a researcher to infer the subjective utilities of outcomes from the observed preferences of an individual. A questionnaire method is presented, in this study, which takes into account the inevitable distortion of preferences by random errors and minimizes the effect of such errors. Under mild assumptions, the method for eliciting the utilities of many outcomes is a three-stage procedure. First, the questionnaire is utilized to elicit responses from which a subjective score is defined. Second, individual risk factors are discussed. Finally, the regression model presents individual risk preferences given the overall organizational risk culture, risk management policy, risk identification, and risk analysis. This paper addresses how company managers face risk and their tolerance of risk with respect to risk management.     

Present-day trends of vertical ground motion along the coast lines

Vertical ground motion (VGM) rates stand as crucial information, either for predicting the impact of the actual sea level rise along low-lying coasts or refining geodynamic problems. Because present day VGM rates have a magnitude smaller than 10 mm/yr, they remain challenging to quantify and often elusive. We focus on the quantification of global-scale VGM rates in order to identify global or regional trends. We computed VGM rates by combining tide gauges records and local satellite altimetry, which yield a new dataset of 634 VGM rates. We further compare this database to previous studies that use geodetic techniques and tide gauges records in order to evaluate the consistency of both our results and previous ones. The magnitudes differ by less than 5 mm/yr, and similar subsidence and uplift general tendencies appear. Even if the asset of our database stands in the greater number of sites, the combination of all studies, each with different pros and cons, yields a hybrid dataset that makes our attempt to extract VGM trends more robust than any other, independent study. Fennoscandia, the West coast of North America, and the eastern coast of Australia are uplifting, while the eastern coast of North America, the British Isles and Western Europe, the eastern Mediterranean Sea, Japan, and the western coast of Australia are subsiding. Glacial Isostatic Adjustment (GIA) is expected to provide a major contribution to the present-day signal. Aside from Fennoscandia, observed VGM often depart from the GIA model predictions of Peltier (2004). This either results from an underestimate of the model predictions or from the influence of other processes: indeed, the influence of the geodynamic setting appears in particular along the coasts of western North America or Japan, where the alternation of transform faults and subduction zones makes it possible to assign contrasted behaviours to the local geodynamic context. Local mechanisms like anthropogenic processes or sediment compaction, also contribute to VGM. This remains true for the critical cases of Venice, the Gulf of Mexico, the Ganges delta, and the Maldives, which are particularly exposed to the current sea level rise.     

Seasonal and Spatial Effects of Wastewater Effluent on Growth,Survival, and Accumulation of Microbial Contaminants by Oysters in Mobile Bay,Alabama

We measured seasonal effects of wastewater treatment plant (WTP) effluent on growth, survival, and accumulation of microbes in oysters near a major WTP in Mobile Bay, AL. Despite higher nutrients near the WTP, seasonal conditions rather than distance affected chlorophyll a concentration and oyster growth. In summer and fall, when oyster growth was higher, δ¹⁵N‰ in oysters near the WTP changed through time to reflect δ¹⁵N‰ in effluent (approx. −4‰). Microbial indicators (male-specific coliphage, fecal coliforms) were highest in oysters near the WTP in all seasons and correlated with δ¹⁵N‰ in fall and summer. Increased riverine discharge and slower acquisition of δ¹⁵N‰ likely confounded correlations in winter/spring. Although we did not detect gross ecological effects of wastewater exposure for oysters, data indicated wastewater-derived particles entered the local food web and accumulated in oysters. These data highlight the importance of using multiple indicators of wastewater exposure and considering both seasonal and spatial effects when defining wastewater influence on a system or species.