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Effective methods for prediction and visualization of contaminated soil volumes in 3D with GIS

Sjögren, Sofia LU (2016) In Student thesis series INES NGEM01 20161
Dept of Physical Geography and Ecosystem Science
Abstract
Geographical Information Systems (GISs) have shown to be of great help in the work at contaminated sites. Today there is an increase in the development of 3D modeling in many fields. However, to combine the advantage working with spatial data in a GIS and 3D modeling for subsurface soil data is limited. In this study, the possibilities of interpolating volumes of soil contamination in 3D were investigated. The study is focused on the ability of integrating 3D modeling with the GIS-environment in projects for contaminated land. Three different interpolation techniques were evaluated; Kriging-, Inverse Distance Weighting
(IDW)-, and Nearest Neighbor interpolation. The data used in the study originates from a contamination project at a... (More)
Geographical Information Systems (GISs) have shown to be of great help in the work at contaminated sites. Today there is an increase in the development of 3D modeling in many fields. However, to combine the advantage working with spatial data in a GIS and 3D modeling for subsurface soil data is limited. In this study, the possibilities of interpolating volumes of soil contamination in 3D were investigated. The study is focused on the ability of integrating 3D modeling with the GIS-environment in projects for contaminated land. Three different interpolation techniques were evaluated; Kriging-, Inverse Distance Weighting
(IDW)-, and Nearest Neighbor interpolation. The data used in the study originates from a contamination project at a former gasworks site in Norrköping, Sweden. The data was sampled by the consultancy company Sweco. Three major contaminants of different characteristics were evaluated for potential of volume interpolation in 3D (lead, benzene, and polycyclic aromatic hydrocarbons (PAHs)). The study also aimed at determining if the interpolation method with greatest potential differs in relation to contaminant type. Prior the 3D interpolation possible GIS software and other methods for 3D interpolation were identified. Geostatistical analyses were performed where the optimized parameters for the interpolations were determined. In the geostatistical analyses a spatial dependence at short
distances was found for all contaminants in the vertical direction (1-2 m) but not in the horizontal plane. The lack of spatial dependence in the horizontal plane indicates an effect of the coarse sampling density (about 10 m compared to 0.5 m for the vertical direction). The distribution patterns of the three contaminants are expected. Lead and PAH are both distributed differently depending on the soil material. Benzene seems to be distributed equally
in all material and was interpolated with the same parameters in all soil types. All contaminants show greatest potential for volume interpolation with Kriging and secondly IDW. The least accurate method was Nearest Neighbor. The optimized parameters for interpolation are similar for lead and PAH but differed for benzene. That reflects their difference in grade of mobility. Benzene shows the most accurate 3D volume interpolation while PAH the least. It is suggested that an effective volume interpolation of pollutants in 3D must combine regular GISs with software handling 3D volumes, or a development of the GISsoftware
is necessary. (Less)
Popular Abstract (Swedish)
Geografiska Informationssystem (GIS) har visat sig vara bra hjälpmedel i projekt för förorenad mark. Idag utvecklas 3D-modellering inom många områden mer och mer. Att kombinera 3D-modellering under markytan med fördelarna med att använda GIS för rumslig data är däremot begränsad. I den här studien har möjligheterna till interpolering av förorenade jordvolymer i 3D undersökts. Studien är fokuserad på integrering av 3D-modellering och GIS i projekt för förorenad mark. De tre olika interpoleringsmetoderna Kriging, avståndsviktad medelvärdesinterpolation (IDW) och närmaste-granne interpolering är undersökta. Det data som används i studien kommer från ett markföroreningsprojekt vid ett gammalt gasverk i Norrköping. Det är konsultbolaget Sweco... (More)
Geografiska Informationssystem (GIS) har visat sig vara bra hjälpmedel i projekt för förorenad mark. Idag utvecklas 3D-modellering inom många områden mer och mer. Att kombinera 3D-modellering under markytan med fördelarna med att använda GIS för rumslig data är däremot begränsad. I den här studien har möjligheterna till interpolering av förorenade jordvolymer i 3D undersökts. Studien är fokuserad på integrering av 3D-modellering och GIS i projekt för förorenad mark. De tre olika interpoleringsmetoderna Kriging, avståndsviktad medelvärdesinterpolation (IDW) och närmaste-granne interpolering är undersökta. Det data som används i studien kommer från ett markföroreningsprojekt vid ett gammalt gasverk i Norrköping. Det är konsultbolaget Sweco som har tagit prover på jorden. Tre olika markföroreningar med olika karaktär och som är viktiga i området undersöks för att utvärdera möjligheterna till interpolering av volymer i 3D (bly, bensen och polycykliska aromatiska kolväten (PAH)). I studien undersöks också om det är någon skillnad i bästa interpoleringsmetoden beroende på typ av förorening. Innan 3D-interpoleringen utfördes identifierades vilka möjliga metoder och GIS-program som är tillgängliga. Geostatistiska analyser utfördes för att hitta de optimala parametrarna för interpoleringen. Ett kort rumsligt samband (1-2 m) i vertikal riktning men inte i horisontell riktning hittades i de geostatistiska analyserna för alla föroreningar. Avsaknaden av ett rumsligt samband indikerar en effekt av
den låga tätheten i provtagningen (10 m jämfört med 0,5 m i vertikal riktning).
Spridningsmönstret hos de tre föroreningarna är förväntat. Både bly och PAH har spridits på olika sätt beroende på jordtypen. Bensen verkar ha samma spridningsmönster oavsett material och interpolerades med samma parametrar i alla jordtyper. Resultatet från alla föroreningar visar på att Kriging ger den bästa volyminterpoleringen och IDW den näst bästa. Resultatet av närmaste granne interpoleringen var minst korrekt. De optimala parametrarna för interpolering är liknande för bly och PAH men annorlunda för bensen. Det reflekterar deras
olikheter i mobilitet. Bensen är den förorening med noggrannast resultat vid interpolering av volymer medan PAH minst korrekt. Studien visar på att en effektiv 3D-interpolering av föroreningar måste kombinera GIS med andra program som hanterar 3D-volymer, alternativt krävs en utveckling av GIS-programvarorna. (Less)
Please use this url to cite or link to this publication:
author
Sjögren, Sofia LU
supervisor
organization
course
NGEM01 20161
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Kriging, geostatistics, GIS, 3D interpolation, 3D volumes, remediation, soil contamination, Geomatics, Physical Geography and Ecosystem Science
publication/series
Student thesis series INES
report number
400
language
English
additional info
External supervisor: Patrik Andersson,Sweco, Linköping
id
8893271
date added to LUP
2016-10-13 13:01:23
date last changed
2016-10-13 13:01:23
@misc{8893271,
  abstract     = {Geographical Information Systems (GISs) have shown to be of great help in the work at contaminated sites. Today there is an increase in the development of 3D modeling in many fields. However, to combine the advantage working with spatial data in a GIS and 3D modeling for subsurface soil data is limited. In this study, the possibilities of interpolating volumes of soil contamination in 3D were investigated. The study is focused on the ability of integrating 3D modeling with the GIS-environment in projects for contaminated land. Three different interpolation techniques were evaluated; Kriging-, Inverse Distance Weighting
(IDW)-, and Nearest Neighbor interpolation. The data used in the study originates from a contamination project at a former gasworks site in Norrköping, Sweden. The data was sampled by the consultancy company Sweco. Three major contaminants of different characteristics were evaluated for potential of volume interpolation in 3D (lead, benzene, and polycyclic aromatic hydrocarbons (PAHs)). The study also aimed at determining if the interpolation method with greatest potential differs in relation to contaminant type. Prior the 3D interpolation possible GIS software and other methods for 3D interpolation were identified. Geostatistical analyses were performed where the optimized parameters for the interpolations were determined. In the geostatistical analyses a spatial dependence at short
distances was found for all contaminants in the vertical direction (1-2 m) but not in the horizontal plane. The lack of spatial dependence in the horizontal plane indicates an effect of the coarse sampling density (about 10 m compared to 0.5 m for the vertical direction). The distribution patterns of the three contaminants are expected. Lead and PAH are both distributed differently depending on the soil material. Benzene seems to be distributed equally
in all material and was interpolated with the same parameters in all soil types. All contaminants show greatest potential for volume interpolation with Kriging and secondly IDW. The least accurate method was Nearest Neighbor. The optimized parameters for interpolation are similar for lead and PAH but differed for benzene. That reflects their difference in grade of mobility. Benzene shows the most accurate 3D volume interpolation while PAH the least. It is suggested that an effective volume interpolation of pollutants in 3D must combine regular GISs with software handling 3D volumes, or a development of the GISsoftware
is necessary.},
  author       = {Sjögren, Sofia},
  keyword      = {Kriging,geostatistics,GIS,3D interpolation,3D volumes,remediation,soil contamination,Geomatics,Physical Geography and Ecosystem Science},
  language     = {eng},
  note         = {Student Paper},
  series       = {Student thesis series INES},
  title        = {Effective methods for prediction and visualization of contaminated soil volumes in 3D with GIS},
  year         = {2016},
}