Modeling Heavy Metals in Soil Using Spatial Regression Analysis
| dc.contributor.author | DeschĂȘnes, Steeve | |
| dc.contributor.supervisor | Setton, Eleanor May | |
| dc.contributor.supervisor | Keller, C. Peter | |
| dc.date.accessioned | 2013-04-30T22:08:53Z | |
| dc.date.available | 2013-04-30T22:08:53Z | |
| dc.date.copyright | 2013 | en_US |
| dc.date.issued | 2013-04-30 | |
| dc.degree.department | Department of Geography | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | High levels of toxic heavy metals in the environment are a major concern and our knowledge about their adverse impacts and distribution patterns is improving. To mitigate human exposure for large regions, understanding the spatial distribution of metals in soil is key. Several types of models are available to predict the concentration levels, but they are often complex and data-intensive. The objective of this research is to explore the application of a simple method that produces geographically referenced predictions of surface soil concentrations of heavy metals. The approach uses publicly-available Canadian soil sample data, Geographic Information Science, statistical correlation and regression analyses. Geographically Weighted Regression (GWR) was used to investigate the spatial variability of the relationship between surface and the subsurface soil metal concentrations. Correlation analysis (Pearsonâs) between the log of concentration levels of the two layers shows relationships of 0.51 for arsenic (As), and 0.23 for lead (Pb). Although the correlation results showed levels in the two layers are related, GWR analysis illustrates that the degree of this relation varies geographically. This study suggests that factors (natural and anthropogenic) other than the subsurface concentration itself are contributing to the concentration surface levels for all of the studied metals in this dataset. Based on the above findings, two linear regression models were developed to predict As and Pb levels in surface soil. Independent variables in the models were developed using geographic data on factors hypothesized to influence surface levels, an approach that has been extensively used for modelling air pollution and known as Land Use Regression (LUR). For the LUR analysis, the results show that industrial activities account for more than 70% of the variation of Pb concentrations in surface soil. Interestingly, the LUR model for As suggests that the bedrock geology and the total length of road at a location are the main factors. Both variables account for more than 40% of the variations of the As levels in surface soil in BC. The LUR results suggest that regional scale modeling of As and Pb surface soil concentrations can provide information about their spatial patterns that may be useful for understanding potential human exposure and the conduct of environmental epidemiological studies. | en_US |
| dc.description.proquestcode | 768 | en_US |
| dc.description.proquestcode | 573 | en_US |
| dc.description.proquestcode | 481 | en_US |
| dc.description.proquestemail | steeved@uvic.ca | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/4577 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights.temp | Available to the World Wide Web | en_US |
| dc.subject | Metals | en_US |
| dc.subject | Soil | en_US |
| dc.subject | Human exposure | en_US |
| dc.subject | Spatial Regression | en_US |
| dc.title | Modeling Heavy Metals in Soil Using Spatial Regression Analysis | en_US |
| dc.type | Thesis | en_US |