Research Question
Are magnetic properties such as susceptibility related to heavy metal concentrations at the Formosa Mine Superfund Site? What sort of environmental factors contribute to the distribution of heavy metals in the affected area?
Data Set
The data consist of heavy metal concentrations, obtained with a portable X-Ray fluorescence gun (pXRF) as well as magnetic susceptibility at both low and high frequencies (0.4 and 4.7mT). Samples were taken semi-randomly from the Formosa Superfund Mine site and nearby streams, Middle Creek and Cow Creek. Samples were taken from areas that were accessible but also at random along trails and near site features such as the adit diversion system and identifiable seeps and drainage routes along roadsides at the site. Samples were divided into 4 fractions; bulk, >63µm, between 20-63 µm and <20 µm. These samples were then dried in an oven at 40C and then prepared for pXRF and magnetics measurements.
Hypothesis
Magnetic properties will show correlation with heavy metal concentrations because heavy metals tend to associate with magnetic minerals. Various environmental factors related to generation of acid rock drainage and hydrology will account for the distribution of certain metals in the affected area.
Past research has shown strong correlation between certain magnetic parameters and heavy metal concentrations. Magnetic techniques have been used to identify and delineate polluted areas in a number of applications from mapping atmospheric deposition of fly ashes to determining sources of contamination in urban environments (Lu and Bai, 2006). Furthermore, a Pollution Loading Index (PLI) can be calculated from the cumulative addition of all metals. PLIs are often well correlated with magnetic parameters. At this particular site, the main concern is acid rock drainage and the subsequent transport of heavy metals to streams nearby.

Approach
As this is the exploratory stage with respect to this data set, various methods were employed to categorize and organize the data. Much time was spent combining data from a number of sources including the susceptibility meter, pXRF and GPS used to store waypoints. Once in a usable format, waypoints and data were transferred into ArcGIS for hot spot analysis and general mapping needs.
Results
Hotspot Analysis
The hotspot analysis revealed that heavy metals are indeed found in greater concentrations at the Formosa Superfund Site. At this point, there is only one point that is not ‘hot’, and as such it represents a benchmark for comparison of other samples. Further sampling is required to properly delineate the zone of influence and to assess the degree and extent of contamination to nearby surface waters; Upper Middle Creek and South Fork Middle Creek.
Exploratory Regression
Once the magnetics data were properly normalized for mass and volume, they revealed some interesting correlations with heavy metals. In particular, Mn and V showed strong positive correlations with both low and high frequency susceptibility.
It is expected that additional data will smooth out some of the relationships. With so few points, it is difficult to assess variability and error in the data.
Pearson Correlation Coefficients
Correlation coefficients were calculated for all metals and magnetic susceptibility in both low and high field magnetization. The following pairs of variables were highly correlated (ie. had p-values less than 0.05): Ca and Zr, V and Ti, Fe and Cu, Fe and Zn, Fe and As, Cu and Zn, Cu and As, Zn and As and Zr and Ta. This suggests association of various metals with each other and in the case of Mn with high frequency susceptibility. Of particular interest are the metals correlated with Fe. Most magnetic minerals have Fe and hence these relationships are the most likely to be further elucidated by magnetic measurements. Additional data may yield stronger or weaker correlations between variables. This is yet to be determined.

Principal Component Analysis
PCA is meant to group components (factors) in a way that describes the maximum variance in a data set and hence each factor carries a weight associated with that variability. In this initial analysis, the majority of the variance is accounted for in the first 4 components (see Table 1). The weightings of factors for each component are listed in Table 2.
Table 1= Eigenvalues and percent variance covered by each principal component in analysis of sediments from Formosa Superfund Site

The first component is difficult to interpret. The weightings are low and tend to the negative, showing inverse relationships explain much of the variability in the data. In the second component there is evidence for explanation of variance based on magnetic parameters. The third component shows even stronger evidence of variance explained by high field susceptibility. This has implications towards understanding the mineralogy that drives the magnetic signal. Further correlations with heavy metals signifies important relationships between magnetic minerals and metal contaminants.

Table 2- Weighting of factors for first 4 components of PCA of sediments from Formosa Mine Superfund site

This third table associates the various components with their spatial location, identified by the labels in column 2. Further analysis would map these components to show the spatial aggregation of variables, giving further clues on environmental factors that drive them.
Table 3- Association of sample points with component drivers

Significance and Further Direction
The significance of this research lies in its capability to quickly assess the degree and extent of anthropogenic pollution. To date, magnetic techniques have been employed over a diverse range of applications and landscapes. Further expansion of applications is desirable from many perspectives. Having quick, easy ways of determining hot spots of contamination focuses reconnaissance on areas that are most affected and/or vulnerable and better affords important resources to be allocated towards clean up and mitigation.
At this point, it is impossible to make definitive conclusions about this analysis. More data are needed and there are some considerations to be made with respect to the methodology in analyzing the samples to begin with. Aggregation of particles and association of magnetic materials with organic matter are just two considerations that need to be addressed before further processing and analyses are done. Cross-reference with EPA and BLM data would be a useful endeavor as well. There are many opportunities for expansion and collaboration on this project which should be pursued at this time.

Learning Outcomes
-ArcGIS likes easy to read files- be careful with file names, pathways and column headings
-I learned Statgraphics—It’s an easy tool to use with no language barriers. It’s like a stand-in for quick analysis
– I made zero progress on Python, Modelbuilder or R (which I haven’t used in so long that I feel I need to start from basics again)

Reference
Lu SG, Bai SQ. 2006. Study on the correlation of magnetic properties and heavy metals content in urban soils of Hangzhou City, China. Journal of Applied Geophysics 60 : 1–12. DOI: 10.1016/j.jappgeo.2005.11.002

My goal in this course is to create a sort of ‘analytical recipe’ for dealing with the extensive data set I’ve accumulated over the past couple years. I am interested in using the magnetic properties of sediments as proxy for mapping heavy metal concentrations. Magnetic measurements are fast, non-destructive and require little to no preparation. As such, they present a possible replacement for expensive and time consuming geochemical analyses. Less money and less time on monitoring means more money and more time for remediation. Magnetic measurements also yield information that is not acquired in general geochemical approaches such as dominant mineralogy.
The data I will be analyzing consists of measurements for the following properties: magnetic susceptibility (high field, low field and frequency dependence), anhysteric remnant magnetization, isothermal remnant magnetization, various derived ratios and heavy metal concentrations taken using a portable x-ray fluorescence gun. Samples have been taken semi-randomly from the abandoned mine which spans an area of approximately 75km2 and also from nearby streams; Middle Creek and Cow Creek. There is no temporal dimension to the data…yet.
Here are a couple of maps showing sampling points and location of the mine.

Here are a couple pictures of the site showing the encapsulation mound and surrounding area. What a lovely view from such a degraded place.

It will be necessary to correlate various magnetic properties with heavy metal concentrations for different grain sizes. The end product will be statistical relationships that describe the correlation of specific properties with the concentration of various metals. It is highly likely that not all metals of concern can be inferred from magnetic measurements. It will be interesting to see which metals, if any, can be mapped using magnetic approaches. The end map could show concentrations of specific metals mapped as weighted circles for each point that was sampled. For statistically significant correlations, maps could be produced to show how the magnetic measurements relate to concentrations as a side-by-side comparison. Other suggestions are welcome for this.
It is expected that specific metals will associate with various magnetic properties and that the concentration of metals will be highest in areas that are closer to drainage ways and extraction sites.

UPDATE
I have been successful in mapping some of the metals data in ARCMap, however Hot Spot Analysis has been giving me errors. There are obvious hot spots for nickel which is to be expected as nickel is prevalent in this area. The map for copper shows no significant hotspots. There are likely ecological and physical explanations for this lack of copper. It was one of the metals mined at this site.
The first image shows hotspots for nickel and the second one shows copper.

I have included a screen shot of my error message. If anyone has any suggestions, please let me know.