scholarly journals A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile)

2022 ◽  
Author(s):  
Antonio Aruta ◽  
Stefano Albanese ◽  
Linda Daniele ◽  
Claudia Cannatelli ◽  
Jamie T. Buscher ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Spatial Variation ◽  
Probabilistic Approach ◽  
Urban Traffic ◽  
Capital City ◽  
Geochemical Data ◽  
Compositional Data Analysis ◽  
Contamination Sources ◽  
Binary Partition ◽  
Log Ratio

AbstractIn 2017, a geochemical survey was carried out across the Commune of Santiago, a local administrative unit located at the center of the namesake capital city of Chile, and the concentration of a number of major and trace elements (53 in total) was determined on 121 topsoil samples. Multifractal IDW (MIDW) interpolation method was applied to raw data to generate geochemical baseline maps of 15 potential toxic elements (PTEs); the concentration–area (C-A) plot was applied to MIDW grids to highlight the fractal distribution of geochemical data. Data of PTEs were elaborated to statistically determine local geochemical baselines and to assess the spatial variation of the degree of soil contamination by means of a new method taking into account both the severity of contamination and its complexity. Afterwards, to discriminate the sources of PTEs in soils, a robust Principal Component Analysis (PCA) was applied to data expressed in isometric log-ratio (ilr) coordinates. Based on PCA results, a Sequential Binary Partition (SBP) was also defined and balances were determined to generate contrasts among those elements considered as proxies of specific contamination sources (Urban traffic, productive settlements, etc.). A risk assessment was finally completed to potentially relate contamination sources to their potential effect on public health in the long term. A probabilistic approach, based on Monte Carlo method, was deemed more appropriate to include uncertainty due to spatial variation of geochemical data across the study area. Results showed how the integrated use of multivariate statistics and compositional data analysis gave the authors the chance to both discriminate between main contamination processes characterizing the soil of Santiago and to observe the existence of secondary phenomena that are normally difficult to constrain. Furthermore, it was demonstrated how a probabilistic approach in risk assessment could offer a more reliable view of the complexity of the process considering uncertainty as an integral part of the results.

Compositional data analysis of sedimentological, mineralogical and geochemical data for the evaluation of Austrian loess and loess loam deposits

2020 ◽  
Author(s):  
Heinz Reitner ◽  
Christian Benold ◽  
Peter Filzmoser ◽  
Maria Heinrich ◽  
Gerhard Hobiger ◽  
...  
Keyword(s):  
Information System ◽  
Statistical Analysis ◽  
Raw Materials ◽  
Analysis Data ◽  
Grain Size Analysis ◽  
Geochemical Data ◽  
Size Analysis ◽  
Compositional Data Analysis ◽  
Rock Composition ◽  
Log Ratio

<p>Austrian loess and loess loam deposits represent an important source of raw materials for the heavy clay industry for centuries. Building material quality of loess and loess loam deposits and their suitability for different applications is significantly influenced by their heterogeneous properties. These depend on the geology of the source area, climatic conditions, geomorphological location, stratigraphic position, intensity of weathering and redeposition potential. The description of occurrences, properties and availability of these raw materials is therefore an important prerequisite to meet the industrial quality requirements. A large number of different sub-datasets exist at the Geological Survey of Austria, which comprise grain-size analysis, bulk rock composition, clay mineralogy, and geochemistry data of loess and loess loam. Within our project, these individual data sets underwent a thorough examination and have been merged into a coherent database to enable the joint regional and statistical analysis of the data. By applying a log-ratio approach the compositional nature of the analysis data has been taken into account for multivariate statistical methods. <br>Within our study we focused on the classic Austrian loess regions in the Northern Alpine foreland areas of Upper and Lower Austria and in the Vienna Basin. By transferring the results of the statistical analysis to a Geographic Information System (GIS) these served as the fundamental basis for our categorization of the loess and loess loam occurrences. Taking into account previously published approaches based on soil profile classifications as well as trends and patterns derived from the analysis data, we finally were able to delineate different districts of brick raw materials deposits. These will be made publically accessible to the industry and interested parties as part of the web application of the Austrian Interactive Raw Material Information System IRIS-Online.</p>

Mobility, spatial variation and human health risk assessment of mercury in soil from an informal e-waste recycling site, Lagos, Nigeria

2021 ◽  
Vol 193 (7) ◽  
Author(s):  
Oluwaseun H. Anselm ◽  
Olga Cavoura ◽  
Christine M. Davidson ◽  
Temilola O. Oluseyi ◽  
Aderonke O. Oyeyiola ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Health Risk ◽  
Spatial Variation ◽  
Human Health ◽  
Health Risk Assessment ◽  
Human Health Risk ◽  
Waste Recycling ◽  
Human Health Risk Assessment

Risk assessment of white phosphorus in military training areas, a probabilistic approach

2006 ◽  
Vol 164 ◽  
pp. S139-S140 ◽  
Author(s):  
Øyvind Albert Voie ◽  
Kjetil S. Longva ◽  
Arnljot E. Strømseng ◽  
Arnt Johnsen
Keyword(s):  
Risk Assessment ◽  
Military Training ◽  
Probabilistic Approach ◽  
White Phosphorus ◽  
Military Training Areas

Mineral exploration in the glaciated terrain using upper soil horizon geochemistry and compositional statistical data analysis

2021 ◽  
Author(s):  
Pertti Sarala ◽  
Solveig Pospiech ◽  
Maarit Middleton ◽  
Anne Taivalkoski ◽  
Helena Hulkki ◽  
...  
Keyword(s):  
Data Analysis ◽  
Statistical Data ◽  
Mineral Exploration ◽  
Environmentally Friendly ◽  
Soil Horizon ◽  
Geochemical Data ◽  
Elemental Distribution ◽  
Statistical Data Analysis ◽  
Aqua Regia ◽  
Log Ratio

<p>Vulnerable nature in northernmost Europe requires development of new, environmentally friendly sampling and analyses techniques for mineral exploration. Those areas are typically covered by transported glaciogenic sediments where the glacial till is most dominant. To offer an alternative for conventional basal till and bedrock sampling with heavy machines, the use of different surface geochemical sampling media and techniques which are quick and cost-effective have been actively applied during the last decade. Particularly, the development of selective and weak leach techniques for the upper soil (Ah and B) horizons’ geochemistry has been intensive, but the reliability needs to be improved and testing is required in different glaciogenic environments.</p><p>In this research, carried out under the project New Exploration Technologies (NEXT), funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776804, we used stratified random sampling strategy for choosing sampling locations and developed novel compositional statistical data analysis for the interpretation of geochemical data obtained by surface geochemical techniques. The test area is located in the Rajapalot area, Ylitornio, northern Finland, where an active project is carried out by Mawson Oy for Au-Co exploration. The thickness of till cover varies from some metres to 5 m and the glacial morphology is composed of the ribbed moraine ridges with peatlands in between. A sampling network for the Ah and B horizon samples was comprised of 89 routine samples and 10 field replicates acquired of mineral Podsol-type soils. The chemical analyses methods used were Ultratrace 1:1:1 Aqua Regia leach and 0.1 M sodium pyrophosphate leach for the Ah horizon samples, and Ionic leach and Super Trace Aqua Regia leach methods for the B horizon samples. The laboratory analyses were supported by the portable X-Ray Fluorescence (pXRF) analyses done directly in the field. The statistical analysis was based on log-ratio transformations of the geochemical compositions to avoid spurious results. In addition, the response ratios were calculated to measure the degree of enrichment in each element per sample.</p><p>The preliminary results of the soil geochemistry show a significant response to many elements (e.g. Au, Co, Cu, Mo, Sc, Te and W) with known mineralized bedrock targets observed in the drill core data. Elemental distribution is also reflecting the lithological variations of the rock units in the bedrock. Based on the results, it is obvious that a) there is good or moderate correlation for several elements between the surface geochemical data and underlying bedrock, and b) soil analysis method using certain soil sampling procedure and selective extraction is an effective, environmentally friendly geochemical exploration technique in the glaciated terrains.</p>

Sentinel-1 InSAR survey to constrain subsidence-induced surface faulting and quantify its induced risk in major cities of central Mexico

2021 ◽  
Author(s):  
Francesca Cigna ◽  
Deodato Tapete
Keyword(s):  
Risk Assessment ◽  
Mexico City ◽  
Land Subsidence ◽  
Groundwater Resources ◽  
Urban Infrastructure ◽  
Capital City ◽  
Transport Infrastructure ◽  
Central Mexico ◽  
Groundwater Exploitation ◽  
Persistent Scatterers

<p>Several major cities in central Mexico suffer from aquifer depletion and land subsidence driven by overexploitation of groundwater resources to address increasing water demands for domestic, industrial and agricultural use. Ground settlement often combines with surface faulting, fracturing and cracking, causing damage to urban infrastructure, including private properties and public buildings, as well as transport infrastructure and utility networks. These impacts are very common and induce significant economic loss, thus representing a key topic of concern for inhabitants, authorities and stakeholders. This work provides an Interferometric Synthetic Aperture Radar (InSAR) 2014-2020 survey based on parallel processing of Sentinel-1 IW big data stacks within ESA’s Geohazards Exploitation Platform (GEP), using hosted on-demand services based on multi-temporal InSAR methods including Small BAseline Subset (SBAS) and Persistent Scatterers Interferometry (PSI). Surface faulting hazard is constrained based on differential settlement observations and the estimation of angular distortions that are produced on urban structures. The assessment of the E-W deformation field and computation of horizontal strain also allows the identification of hogging (tensile strain or extension) and sagging (compression) zones, where building cracks are more likely to develop at the highest and lowest elevations, respectively. Sentinel-1 observations agree with in-situ observations, static GPS surveying and continuous GNSS monitoring data. The distribution of field surveyed faults and fissures compared with maps of angular distortions and strain also enables the identification of areas with potentially yet-unmapped and incipient ground discontinuities. A methodology to embed such information into the process of surface faulting risk assessment for urban infrastructure is proposed and demonstrated for the Metropolitan Area of Mexico City [1], one of the fastest sinking cities globally (up to 40 cm/year subsidence rates), and the state of Aguascalientes [2], where a structurally-controlled fast subsidence process (over 10 cm/year rates) affects the namesake valley and capital city. The value of this research lies in the demonstration that InSAR data and their derived parameters are not only essential to constrain the deformation processes, but can also serve as a direct input into risk assessment to quantify (at least, as a lower bound) the percentage of properties and population at risk, and monitor how this percentage may change as land subsidence evolves.</p><p>[1] Cigna F., Tapete D. 2021. Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR. <em>Remote Sens. Environ.</em> 253, 1-19, doi:10.1016/j.rse.2020.112161</p><p>[2] Cigna F., Tapete D. 2021. Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico. <em>Remote Sens. Environ.</em> 254, 1-23, doi:10.1016/j.rse.2020.112254</p>

scholarly journals Development of a Bayesian network for probabilistic risk assessment of pesticides

2021 ◽  
Author(s):  
Sophie Mentzel ◽  
Merete Grung ◽  
Knut Erik Tollefsen ◽  
Marianne Stenrod ◽  
Karina Petersen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Environmental Risk ◽  
Environmental Risk Assessment ◽  
Probability Distributions ◽  
Probabilistic Approach ◽  
Probabilistic Risk Assessment ◽  
Risk Calculation ◽  
Link Information ◽  
Probabilistic Risk

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency, by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network (BN) modelling is explored as an alternative to traditional risk calculation. BNs can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a BN has been developed and parameterised for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterisation using azoxystrobin as an example. We also demonstrate seasonal risk calculation for the three pesticides.

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