- 1 Contamination risk assessment (RA) – a decision support tool
- 2 SOURCE: Contamination mobility – field & lab leaching tests
- 3 PATHWAY: Contamination transport – transport & reaction modelling
- 4 RECEPTORS: Contamination deposition – human & ecosystems
- 4.1 Human receptors: DRINKING WATER
- 4.2 Human receptors: URBAN ENVIRONMENT
- 4.3 Ecosystem receptors: CATCHMENT GEOCHEMISTRY
- 4.4 Ecosystem receptors: FLOODPLAIN GEOCHEMISTRY
- 4.5 Ecosystem & human receptors: GEOCHEMICAL MAPPING
- 4.5.1 Recsk Mining Area, Hungary – high-resolution catchment-based stream sediment mapping
- 4.5.2 Ajka Mining Area, Hungary – catchment-based stream sediment geochemical mapping
- 4.5.3 Baiut Mining Area, Romania – catchment-based stream water & sediment geochemical mapping
- 4.5.4 Floodplain Project – 3D floodplain contamination mapping in Baiut Mining Area, Romania
- 4.5.5 GEMAS Project – agricultural and grazing land soils mapping in Europe
- 4.5.6 UN Danube Basin (ICPDR) Project – web-based geochemical maps for the DanubeBasin
- 4.5.7 URGE Project – urban geochemistry in Europe
- 4.5.8 BOTLED WATER Project – groundwater quality geochemical mapping in Europe
- 5 LANDSCAPE GEOCHEMISTRY – an integrated modelling method
- 6 TIME SERIES ANALYSIS – environmental dynamics & prediction
- 7 DIGITAL TERRAIN ANALYSIS – numerical methods: surface & transport modelling
- 8 3D GEOLOGICAL MODELLING – spatial models for geology & environment
- 8.1 3D geological model of Bukkalja, Hungary, for the tectonic investigation of sedimentary basins.
- 8.2 3D reconstruction of pre-quarter and pre-tertiary paleo-surfaces in the Bataapati Nuclear Waste Repository Site, Hungary
- 8.3 3D digital geological model of the Pannonian Basin, Hungary. Knowledge-based model construction
- 9 MOBILE GIS TECH – development and application
Contamination risk assessment (RA) – a decision support tool
Current research results include the development of the EU guidance document on the risk-based pre-selection protocol for the inventory of mining waste sites in Europe:
Stanley G., Jordan G., Hamor T., Sponar, M. 2011. Guidance Document for A Risk-Based Selection Protocol for the Inventory of Closed Waste Facilities as required by Article 20 of Directive 2006/21/EC, Brussels, and it also considers the European Commission PECOMINES Project results published in the book:
Jordan G., D’Alessandro M. (eds) 2004. Mining, mining waste and related environmental issues: problems and solutions in the Central and Eastern European candidate countries. Joint Research Centre of the European Commission, Ispra. LB-NA-20868-EN-C, 13-34.
Current research focuses on the comparison, testing and development of various risk assessment methods to the risk-based inventory of mine sites under the EU Mine Waste Directive, using case studies in Hungary. A specific challenge is how to make use of geological knowledge for efficient risk-assessment of mine sites. The other research focus is the testing of the semi-quantitative risk assessment methods against detailed geochemical reaction and transport models in mine catchments.
This project is developed by a PhD student at the Szeged University.
Risk assessment (RA), defined in its broadest sense, deals with the probability of any adverse effects. Various types of risk to be considered at the mine project life cycle include regulatory risk, engineering risk, facility risk, financial risk, human health risk and ecological risk. Risks posed by regular or accidental contamination emissions to human beings (human health risk assessment, HHRA) or to ecosystems (ecological risk assessment, ERA) are studied by mine RA.
While human risk assessment studies the probability of impact on a single individual, ecological risk assessment studies the impact on populations. A difficulty in ERA is the choice of receptors such as fish species in stream water that are indicators of total risk to the ecosystem. Although risk assessment is not directly related to one economic activity, RAs are concerned with the risk involved at a specific site, at a specific time, and due to specific causes. Contamination risk is the combined effect of the probability of contamination and the significance of toxic impacts. This is studied through the pathway from (1) hazard description, through (2) dose/response (toxicity) analysis, (3) contaminant transport, (4) exposure assessment, to (5) risk characterization, and (6) risk management.
RA of AMD is not different from RA used for any other waste. RA is not designed to study risks of indirect impacts of pollution. For example in the frame of ERA, acidification of waters can have direct toxic effect on aquatic biota. However, acidification can lead to the secondary release of heavy metals from sediments thus becoming available for human metal toxicity. Also, hazard of AMD release might be reduced by remediation of waste dumps, for example, but secondary sources of metals remain in lands around the site that were polluted during active mining. This requires a separate RA of contaminated sites. At the exposure assessment part of RA, temporal aspects and stability are also important. While heavy metals in AMD can be efficiently retained in nearby organic-rich wetland sediments for example, climatic change or anthropogenic activity can lead to a drop in groundwater levels that in turn leads to erosion and oxidation of reduced sediments thus exposing metals to human intake. Pre-mining natural contamination can already have local or regional adverse effects on human health for example. Effects of mining can be measured only relative to existing impacts. This makes the study of differences between natural and mine-induced contamination pathways essential both in EIA baseline studies and risk assessment for mining.
An example for risk assessment methods is provided by the PRAMS (Preliminary Risk Assessment Method for Soil Contamination) risk assessment method developed by EEA contracted institutes and working group to support the EU Soil Thematic Strategy. PRAMS is based on the possible widest literature review, and it follows a tiered approach (Tier 0, Tier 1 and Tier 2) providing semi-quantitative risk ranking of contaminated sites for both human health and ecological risk assessment. PRAMS modified for the needs of mine site assessment can be an option for the risk-based inventory of mine sites in the EU. Another method is the M2 method proposed by ICPDR as a risk assessment method for the ranking of contaminated sites on floodplains in the DanubeRiver Basin. M2 follows a tiered approach and it is based on the checklist methodology. M2 is in fact a hazard assessment tool since it addresses contamination sources.
Other relevant environmental decision support tools include (1) environmental impact assessment, (2) material flow analysis, (3) life cycle assessment, in addition to (4) landscape ecology, (5) industrial ecology, (6) landscape geochemistry, and (7) USGS geo-environmental models. See the book ’Jordan, G. and M. D’Alessandro (eds), 2004. Mining, Mining Waste and Related Environmental Issues: Problems and Solutions in the Central and Eastern European Candidate Countries. Joint Research Centre of the European Commission, Ispra. LB-NA-20868-EN-C’ for more detail.
SOURCE: Contamination mobility – field & lab leaching tests
The Mine Waste Directive requires the assessment of toxic element mobility by leaching tests. Two projects are on leaching tests:
- Ajka/Kolontar RED MUD waste
- ORE MINES waste (Recsk Mining Area, Hungary & Baiut Mining Area, Romania).
RED MUD leaching tests
Leaching with different solvents (mobility tests) for heavy metals:
- Leaching with deionized water (pH at the end was about 10)
- Leaching with ammonium acetate (pH at the end was about 8)
- Leaching with acetic acid (pH=8; 5 and 3)
- Aqua regia (heavy metals)
- Total decomposition by mixed acids (HCl, HNO3, HF) in microwave unit (heavy metals)
- Decomposition by LiBO2 in platinum crucible (major components, REE)
Leaching with deionized water according to MSZ 21978-9:1998 Hungarian Standard method
50 ml of deionized water was added to 5 g sample. Shaking, filtering than analysing by ICP-OES method
The concentration of the solution was 100 g/l. The pH at the end was about 10.
Leaching with ammonium acetate buffer solution according to MSZ 21978-9:1998 Hungarian Standard method
100 ml of 0,2 M ammonium-acetate puffer (pH=4,5) was added to 10 g of sample. Shaking, centrifuging, filtering than analysing by ICP-OES method The concentration of the solution was 100 g/l. The pH at the end was about 8.
Leaching with acetic acid on three different pH values (3; 5; 8) according to US EPA Extraction Procedure Toxicity (Method 1310)
A modified version of the US EPA Extraction Procedure Toxicity (Method 1310) test was performed during the leaching procedure. 50 ml of deionized water was added to 5 g sample then pH was set to 5±0,2 with 0,5 M/l acetic acid. Meanwhile the 24 hours shaking the pH was often tested and set to 5±0,2. After shaking the solution was centrifuged then filtered through 0,45 µm membrane filter. The solution was analysed by ICP-OES method. The test was repeated on pH =3 and pH=8 as well. The concentration of the solution was 100 g/l.
Leaching with aqua regia in open vessel according to MÁFI 9.5/2 own standard method
0,25 g sample was weighed into a glass baker, then 3,8 ml of cc HCl and 1,2 ml of cc HNO3 was added. The solution is evaporated up to almost dry on a steambath then the residue is dissolved with 3 ml of 1:1 HCl. The solution is filled up to 50 ml in a volumetric flask. The solution was analysed by ICP-OES method. The concentration of the stock solution was 5 g/l.
Total decomposition using HF in microwave decomposition unit according to MÁFI 11.8:2004 and 9.6: 2004 own standard method
0,25 g sample was weighed into the vessel of a microwave decomposition unit, then 1 ml of cc HCl, 1 ml of cc HNO3 and 1 ml of cc HF was added. After the decomposition program the vessels were cooled down, opened and 10 ml of saturated boric acid was added to catch the excess of fluoride and a decomposition program was run again. After the procedure the solution was filled up to 50 ml in a volumetric flask. The solution was analysed by ICP-OES method. The concentration of the stock solution was 5 g/l.
- pH dependent mobility of heavy metals: As and Mo are relatively mobile even with deionized water at high pH (10-12)
- Treatment at home of red mud by acetic acid (recommended in the beginning in the media) is dangerous. At pH=3 (overneutralization) about 10% of total heavy metal content is mobile
- Most of the heavy metal content exceed the tolerated soil limit. Deposition of redmud as sludge: dangerous. As, Co and Ni content exceed the sewage sludge limit for agricultural utilization. Probably if it is mixed with uncontaminated soil (in 1:1-1:2 ratio), then they can be ploughed
- Because of As and Mo content the samples are in dangerous category (C) for waste, they can be deposited according to special rules only.
ORE MINE WASTE toxic element assessment
This project tests and compares various lab and field leaching tests for the assessment of toxic element mobility in mine waste dumps.
Test have been run for Recsk (Lahoca) near-surface epithermal hydrothermal Au-Cu deposit waste rock dump, the Paradsasvar Pb-Zn polymetallic deposit waste rock dump (high buffer capacity due to calcite in veins), and the Baiut Pb-Zn polymetallic deposit in Romania are the following:
PATHWAY: Contamination transport – transport & reaction modelling
Catchment Erosion and Sediment Transport Modelling: Quantifying Particle-bound Contamination Fate
Current research focuses on the use of various soil erosion and sediment transport models (WATEM/SEDEM, AGNPS, ANSWERS, etc.) in order to derive quantitative estimates of soil erosion, sediment transport and deposition within the catchment, and loss of material from the catchment. Emphasis is on model calibration, verification and error assessment. Both the quantity and spatial distribution of processes are investigated. For example, the location of model-predicted depositional areas are used for follow-up field sampling or the location of predicted contamination discharge into the receiving stream is used for stream sediment sampling and thus for model verification. A special research aim is to study the impact of land use/land cover changes on soil erosion and sediment transport. The final goal is to link these transport models with (1) formal risk assessment methods and with (2) geochemical reaction models in mining areas.
Some contaminants, including heavy metals, bound strongly to soil and sediment solid particles such as original rock minerals, clays, oxides or organic matter. These contaminants are transported in solid phase rather than in solution. Therefore, soil erosion and sediment transport models can be used to model their fate from source to pathway along flow lines and to deposition at the receiving environment such as streams, lakes or floodplains.
Geochemical reaction modelling
Current research focuses on reaction modelling with PHREEQC in ground water and surface water for heavy metal contamination fate at mining areas. Test sites include the Recsk Mining Area, Hungary for groundwater modelling and the Baiut Mining Area, near Baia Mare, Romania. Reaction modelling uses (1) statistical (stochastic) modelling and (2) thermodynamic (deterministic) modelling.
Current research is limited to mining areas and associated acid mine drainage and heavy metal contamination. On-going research tries to model geochemical process at the contamination sources of mine waste dumps, along pathways in surface waters such as metal speciation, precipitation and adsorption, and at the receptor environment with special focus on stream sediments and overbank (floodplain) sediments. Samples collected on waste rock dumps in the Recsk Mining Area, Hungary are analysed for mineralogical composition in order to verify thermodynamic modelling of pore water evaporation and precipitation/dissolution of secondary soluble salt (mostly sulphates) and iron, manganese, aluminium oxi-hydroxides. Contamination pathway modelling involves the calculation of possible precipitating fractions from stream water and mixing calculations at confluences of background and contaminated stream waters. Both source and pathway geochemical modelling uses statistical methods as well. Receptors of stream, lake and overbank sediments are modelled by statistical methods at present. Thermodynamic geochemical modelling uses USGS PHREEQC software, while statistical modelling uses STATGRAPHICS software.
In general, two types of numerical modelling methods are used in geochemistry: stochastic (statistical) models and deterministic (thermodynamic) models. Stochastic models assume that observed geochemical processes are results of stochastic process i.e. there outcome is driven essentially by random processes. Statistical methods are mostly used in spatial analysis such as geochemical mapping, or in revealing associations among measured parameters such as dissolved heavy metals and water pH by means of regression analysis and multivariate methods including cluster analysis, principal components and factor analyses and multiple-regression analysis. Statistical modelling can use either classical statistics (hypothesis testing, parametric methods) or exploratory data analysis methods (data mining, robust methods, non-parametric methods). Deterministic thermodynamic modelling has two approaches, in general. In forward modelling the guiding physical-chemical equations are known and predictions are made for the effects, then these predictions are compared against measured data values. In this case, boundary and initial conditions, model parameters and relations linking variables are defined in advance for modelling. In inverse modelling, the effects are know, i.e. a set of collected data is given and then possible driving processes leading to the observations are tried to be identified. Forward modelling is always well-posed, i.e. it provides unique and continuous solutions. Inverse modelling is however always ill-posed because a selection has to be made from all of the possible processes that could lead to the observation. In applied environmental geochemistry, forward models are called reaction path models because there is a predefined group of chemical reactions used to describe the processes leading from the initial geochemical composition to the model results, such as chemical speciation and saturation calculations for solutes. Inverse models are called mass-balance models because often there are concentration measurements available along a stream course, for example, and driving processes prevailing in the given site such as the dissolution of minerals in contact with the studied water have to be identified and described. In this case, for the selection of the most probable driving processes, the only a priory knowledge or constrain is the law of mass conservation, i.e. the initial and resultant masses must be equal.
For PHREEQC data reading software see Downloads.
RECEPTORS: Contamination deposition – human & ecosystems
Five sub-components are implemented for contamination receptor analysis.
Human & Ecosystem health: MEDICAL GEOLOGY
This is a new research project focusing on human health and food safety, and ecosystem health. Literature review on methods and approaches is completed. This project has carried out field campaign for collecting mushrooms to study the transfer of toxic elements from abiotic historic mining to the ecosystem biotic cycles. Plant (mushroom) samples have been collected in the Recsk Mining Area, Hungary, for toxic element content.
Human receptors: DRINKING WATER
Bottled water chemistry has been analysed for the geochemical mapping of groundwater quality in Europe.
See Bottled water chemistry Project – geochemical mapping of groundwater quality in Europe.
Human receptors: URBAN ENVIRONMENT
Urban soil for direct contact contamination intake and attic dust for airborn contamination intake has been analysed in Ajka, Hungary.
See URGE Project – urban geochemistry in Europe, sub-components: Attic dust geochemical survey, and Urban soil geochemical survey.
Ecosystem receptors: CATCHMENT GEOCHEMISTRY
There are 3 on-going catchment contamination and geochemistry projects:
- Recsk Mining Area, Hungary – high-resolution catchment-based sediment mapping
- Ajka Mining Area, Hungary – catchment-based stream sediment geochemical mapping
- Baiut Mining Area, Romania – catchment-based stream water & sediment geochemical mapping.
See Ecosystem & human receptors: GEOCHEMICAL MAPPING section for details.
Ecosystem receptors: FLOODPLAIN GEOCHEMISTRY
This project investigates the 3D spatial distribution of toxic elements in the floodplains that act as important food production and groundwater resource areas. More than 15 2-3m deep cores have been taken along 3 transects in the Varatic Creek floodplain and were analysed for metal content, grain size, XRD and OSL age determination (selected samples only) at 50 cm intervals. The objective is to identify historic records of upstream mining contamination, geochemical background and metal distribution in the 3D soil space.
Ecosystem & human receptors: GEOCHEMICAL MAPPING
Multi-media (surface and ground water, soil, stream sediment and floodplain sediment) geochemical mapping is the means to reveal spatial distribution of contamination and its relation to natural (geological) and anthropogenic sources. See the following geochemical mapping projects:
Recsk Mining Area, Hungary – high-resolution catchment-based stream sediment mapping
More than 100 stream sediment samples have been collected in the ca. 10x10km Recsk Mining Area catchement to be analysed for toxic content, grain size, XRD mineral composition, and heavy minerals. The objective of the project is to (1) map and separate natural elevated metal contents (geochemical background) and anthropogenic (mining) contamination, (2) test methods to identify source areas of sediments, and (3) calibrate numerical transport models.
Ajka Mining Area, Hungary – catchment-based stream sediment geochemical mapping
Sampling included the collection of 60 steam sediments in the Ajka bauxite and coal mining catchment, heving the Ajka Alumina Industry plant. 68 samples floodplain sediment samples (upper 25cm) were also collected. In areas covered by the acidental red mud spill samples were collected in transects perpendicular to the contaminated Torna Creek to represent stream sediments, flood plain soils on both sides, and areas outside of the spill contamination (geochemical background). Also, radioactive radiation measurements were taken at 60 sites (field and lab). The broken dam material was also sampled and analysed for chemical and mineralogical composition. Laboratory analysis includes:
1. Mineralogical and sample size
- Particle size
2. Chemical composition
- Decomposition with different solvents (mobility tests: DW: pH=12, acetic acid: pH=10,
- 5, 3, ammonium acetate extraction)
- Total decomposition by aqua regia leaching-heavy metals
- Total decomposition by mixed acids (HCl, HNO3, HF) in microwave unit-heavy metals
- Total decomposition by LiBO2 in platinum crucible-major components and REE content
- Mercury determination-AMA 254 direct AAS mercury analyser
3. Measurement of radioactive radiation (ELTE – MÁFI)
- Laboratory and site measurements
- Laboratory measurement of radon
Baiut Mining Area, Romania – catchment-based stream water & sediment geochemical mapping
The objective is to investigate the heavy metal contamination along the stream water courses of the Varatyik Creek in the Erzsébetbánya (Baiut) polymetallic ore mining area, near Baia Mare in Romania. Water and stream sediment samples were collected in summer in 2009 and 2010 and were analysed for dissolved and sediment-bound heavy metal content. Toxic elements, including Pb, Cu and Zn concentrations were found above national and international environmental standards. Based on the collected samples it was possible to characterize natural geochemical background and pollution due to historic mining.
Floodplain Project – 3D floodplain contamination mapping in Baiut Mining Area, Romania
See RECEPTORS: CONTAMINATION DEPOSITION – human & ecosystems, Ecosystem receptors: FLOODPLAIN GEOCHEMISTRY section for details.
GEMAS Project – agricultural and grazing land soils mapping in Europe
UN Danube Basin (ICPDR) Project – web-based geochemical maps for the DanubeBasin
URGE Project – urban geochemistry in Europe
BOTLED WATER Project – groundwater quality geochemical mapping in Europe
See International Co-operation Projects section for details:
- GEMAS Project – geochemical mapping of soils in Europe
- UN Danube Basin (ICPDR) Project – web-based geochemical maps for the DanubeBasin
- Bottled water chemistry – geochemical mapping of groundwater quality in Europe
- URGE Project – urban geochemical mapping in Europe
LANDSCAPE GEOCHEMISTRY – an integrated modelling method
Current research includes the further development of landscape geochemistry methodology, use of GIS technology for the implementation of landscape geochemical spatial analyses such as geochemical barrier identification, combination of landscape geochemistry with catchment hydrological and sediment transport models. Current activities are limited to the catchment scale (geochemical landscapes) and on mining related contamination problems.
Landscape Geochemistry provides methodological framework and practical techniques for linking environmental geochemical process and landscape scale structures in order to assist land management and spatial planning. Geochemical landscape analysis studies the flow of chemical entities in the supergene zone and how these flows interact with landscapes and ecosystems based on the principle that geochemical flow is important in landscape evolution as being the principle link between biotic and abiotic components. Geochemical landscape analysis studies processes at all scales but focus is on the landscape mosaic (soil patch) because this is the primary scale of human activity and decision making. Elementary landscape, the smallest landscape geochemical unit is defined by the soil type and ‘geochemical landscape’ denotes the association of the elementary landscapes connected by material flow. The principles of landscape geochemistry are the concepts of geochemical abundance, geochemical gradients, element migration, geochemical flow, geochemical barriers, geochemical landscape classification and historical geochemistry, together with the principles of hierarchy, similar to landscape ecology. Perelman (1986) developed the powerful tool of landscape geochemical barriers and Glazovskaya (1963) developed the basics of geochemical landscape classification. Traditional landscape geochemistry is limited by the lack of application of geochemical transport and reaction models, lack of use of GIS technology and lack of link with landscape ecology. Geochemical landscape analysis answers questions such as where the contamination in the landscape is and in what form and phase, how stable it is, which are the contamination transport processes, what is the relationship of contamination to other chemical element cycles and to other landscapes and ecosystems, how land use change affects geochemical processes, and where and what kind of geochemical reaction and transport control elements such as barriers and corridors are present in the landscape that can be used for contamination risk management.
TIME SERIES ANALYSIS – environmental dynamics & prediction
Current research investigates the efficient use of classical statistical and EDA methods, together with signal processing methods with the following running sub-projects:
Hydrological and sediment dynamics of River Danube floodplain – Donau Auen National Park, Austria
This research describes the dynamics of suspended sediments on the Donau-Aaen floodplain in Austria in relation to flood events.
Water quality dynamics in the Pecsely catchment
Another activity models time series features of more than 25 streamwater hydrochemical parameters measured in the lat 30 years in the Pecsely Basin at Lake Balaton, Hungary. The objective of the research is to relate the identified time series features (e.g. increasing trend in nitrate and contamination events) land use and land management changes and try to describe the change in hydrogeochemical processes induced by seasonal variation and climate change.
Radon time series in a karstic cave
An LRG ELTE University PhD student project targets hourly Radon measurements in a karstic cave in Hungary.
Radon time series in soils
An LRG ELTE University PhD student project targets 15 minutes Radon measurements in sub-soil for a one year period.
Time series analysis of hydrogeochemical parameters such as pH, major and trace elements measured at groundwater and surface water monitoring stations provides and opportunity to understand and model the dynamic changes of water composition due to external forces such as change in the hydrological system, land use and land management changes, and climate change. Mathematical statistical time series analysis (TSA) involves the breaking down the time series into its (additive) components: cycle (modelled by moving averages), trend (modelled by polynomials), periodicity or seasonality (modelled by Fourier series or seasonal subseries), autoregressive component (modelled by autocorrelations, variograms, or by ARMA models), and the random residuals (modelled by statistical distribution fitting). This traditional modelling scheme is however very sensitive to non-stacionarity (when the mean and variance depend on location), and to transients such as outliers and breakpoints representing events in the time series. While stacionarity can often be achieved by traditional methods (differencing, for example), transient signal components have to be removed although these features often carry the most important geochemical information such as contamination events. Under these circumstances Exploratory Data Analysis (EDA) techniques shall be used including resistant smoothing (median or xRSSH smoothes) and graphical methods such as steam-and-leaf histograms. A useful signal processing method is wavelet analysis indeed designed to handle non-stacionarity and localised transients.
DIGITAL TERRAIN ANALYSIS – numerical methods: surface & transport modelling
Current research The objective of this research is to develop various methods for digital terrain analysis (DTA). Here two recent interesting surface modelling projects are shown.
Morphotectonic analysis and field verification of thrust-faulted zone using DEMs. A case study for the Villány Hills, Hungary.
Based on the field measured striae and other kinematic indicators, different structural phases were determined from Cretaceous to date in the Villanyi Mts., Hungary. Shaded relief model is used to identify morphotectonic lineaments in order to compare them to the known tectonic lines. Various numerical differential geometry methods are used to calculate slope and aspect maps in order to identify areas of uniform steep slopes and aspect that delineate morphological boundaries potentially indicative of tectonic influence. Lines of abrupt slope-breaks represented by the profile curvature map are located by the identification of the subpopulation group with the highest curvature values in the histogram. In order to reduce the noise in the second-order derivatives calculation the DEM was smoothed prior to curvature calculations. More than 200 digital morphologic cross sections were made which show the different elevation distribution in the direction parallel and transverse to the morphological features along the known tectonic lines. Linear valleys and ridge lines were defined as drainage and watershed lines by means of digital drainage network analysis. Results show that the orientations of the measured structural field data correlate to the orientations of the various observed morphological lineaments such as drainage elements.
This project is developed by a PhD student at the ELTEUniversity.
Syn-eruptive morphometric variability of monogenetic scoria cones in the Tenerife, Canary Islands
This study examines the morphometric variability of nine young (a few ka old) monogenetic scoria cones located along rift zones in Tenerife, Canary Islands, using high resolution Digital Elevation Models (DEMs) in order to assess their slope angle variability. Because of the young age and poorly developed erosional valleys, their morphometric variability can be interpreted as the result of syn-eruptive processes including: (1) pre-eruptive surface inclination, (2) vent migration and lava outflow and associated crater breaching, and (3) diversity of pyroclastic rocks accumulated in the flanks of these volcanic edifices. Results show that slope angle differences among the studied edifices formedian in the same time period, measured on individual flank sectors of single volcanoes, could be as high as 12°, well above the 2° range assumedian by the Wood model. This suggests that the straightforward interpretation of morphometric data in terms of morphometry-based relative age requires careful consideration, especially in field scale morphometric investigations.
This project is developed by a PhD student at the Massey University in New Zealand.
Research focus is on terrain pattern description, feature recognition and characterisation. Use of differential geometry and signal processing methods is emphasised. Applications include morphometry, tectonic geomorphology, terrain modelling for hydrological, soil erosion and sediment transport modelling. Studied DEM (Digital Elevation Model) formats include various scale contour maps, photogrammetric data (TIN based on irregular control points), radar altimetry data (SRTM global DEM) and high-resolution airborn laser scan DEMs (LIDAR). In this research various numerical terrain analysis methods have been developed such as a novel method for digital drainage line extraction, adaptive terrain smoothing, a systematic procedure for digital tectonic geomorphology and methods for aspect analysis. Special emphasis is put on error assessment and uncertainty analysis. Softwares used includes ILWIS raster GIS, IDRISI raster GIS, SURFER, RockWorks, TOPAZ.
Digital terrain models (DTM) are ordered arrays of numbers that represent the spatial distribution of terrain attributes. A digital elevation model (DEM) is defined as an ordered array of numbers that represent the spatial distribution of elevations above some arbitrary datum in a landscape. DEMs are the most basic type of DTMs. Digital terrain analysis (DTA) is implemented on digital elevation models in order to derive digital terrain models of various terrain attributes. Topographic attributes, such as slope and aspect can be derived from contour, TIN and grid DEMs, however, the most efficient DEM structure for the estimation and analysis of topographic attributes is generally the grid-based method. Regular grid data structure is needed also for spatial data manipulation by GIS. In order to make maximum advantage of well-established methods of GIS technology, digital image processing and DTA, the present discussion on terrain analysis is confined to the most widely-used grid-based DEMs. Topographic attributes can be classified into primary and secondary (or compound) attributes according to their complexity. Primary attributes are directly calculated from a DEM and include (1) point attributes of elevation, slope, aspect and curvatures, and (2) area properties such as integrals (e.g. area or surface integrals such as catchment area, or line integrals such as flow path length), and statistical properties of elevation over an area (e.g. mean and standard deviation of elevation or slope).
Fractal dimension of terrain can also be regarded as a primary attribute. Compound attributes involve combination of primary attributes and can be used to characterise the spatial variability of specific processes occurring in the landscape, such as surface water saturation (wetness index) and sheet erosion. This classification originates from geomorphology. Topographic attributes can also be classified according to their spatial character. Point attributes result from spatial (also called local) operations that modify each pixel value based on neighbouring pixel values. Examples are gradients calculated for each pixel in a moving kernel. Spatial attributes result from point (also called global) operations that modify pixel values independently from neighbouring pixels. Line and area integrals, and overall or local statistical calculations yield spatial attributes. This classification originates from digital image processing.
3D GEOLOGICAL MODELLING – spatial models for geology & environment
Current research has three sub-components as follows. Research aims at extensive 3D spatial volume and surface calculations therefore voxel models are used exclusively.
3D geological model of Bukkalja, Hungary, for the tectonic investigation of sedimentary basins.
This project is developed by a PhD student at the ELTE University is currently at the database development and conceptual model construction phase.
3D reconstruction of pre-quarter and pre-tertiary paleo-surfaces in the Bataapati Nuclear Waste Repository Site, Hungary
This project is developed by a GIS research colleagues at a private cartographic company.
3D digital geological model of the Pannonian Basin, Hungary. Knowledge-based model construction
Stratigraphic models have been created for various parts of Hungary, including the complete 3D geological model of the country (PANNONIAN BASIN) in the support of the EU Water Framework Directive implementation. Further applications include the 3D model of thermal Lake Heviz surroundings to support groundwater modelling, the 3D model of the Zala Basin for hydrocarbon reservoir modelling, and the 3D model of Cretaceous formations in western Hungary for groundwater resources research. The focus of research on developing methods for the best numerical/digital representation of geological knowledge and data with special emphasis on validation against borehole data, error assessment and uncertainty analysis. 3D stratigraphic modelling is considered the 3D expansion of 2D digital terrain modelling with the additional 3D spatial geological constrains. 3D modelling thus heavily builds on results obtained in the digital terrain modelling research and results. Earlier modelling used the 3DView software and recent studies use Rockware´s RockWorks software.
Three dimensional geological models have two basic types, similar to 2D digital maps: vector and voxel (raster) data models. Vector surfaces are most often represented by TIN (triangular irregular network) or grid models. Vector models are superior in representation of surfaces (e.g. bedrock top), especially when these surfaces are complex such as overturning folds. The vector model has advantage in topological (neighbourhood) problems, spatial problems such as surface intersection calculations (e.g. fault-fault, fault-bedrock top, groundwater surface-bedrock), rock body representation and surface integral calculations. Vector models cannot represent the space between the surfaces (voids) and cannot represent gradually changing spatial parameters such as ore content of mineralised rock. The voxel model is composed of elementary cubes called voxels (VOlume piXEL) that fill the whole studied space leaving no voids. Voxel models can be either stratigraphic models or block models.
Voxel models can be either stratigraphic models or block models. Stratigraphic models represent rock bodies bound by top and bottom surfaces with voxels of uniform attribute value in between. Block models represent gradually changing spatial parameters such as ore content of mineralised rock or porosity of an aquifer. In this case voxels have variable attribute values in the studied space. Voxel models have advantage in volume integrals but they have limitations in surface representation (since a single surface has to be represented by single voxel-thick layer). Often, a single surface such as a bedrock top can be extracted as a pixel map from voxel models. Voxel models are also limited by large demand for computer resources.
MOBILE GIS TECH – development and application
Current research aims at the development of state-of-the-art mobile GIS technology for efficient geochemical field work. We use ALGIZ-7 model linked to satellite information transfer through mobile phone web connection based on ArcPAD10 and BEEGIS software facilities. Field testing is on-going.