Using Cs-137 to help trace the transport and fate of PCBs in the Hudson River

Part II:

1. Overview of the important distributions. Plot the raw concentration data for PCBs, Cs137 and % Carbon vs. River mile (be sure to separate the 4 tributary cores from those in the main River in these graphs). Although this combines all sediment levels together, it should illustrate the regions in the River where these substances have accumulated more than others. Note these regions. Does the Cs distribution reflect the point source inputs (see lecture notes from 11/13)? How do the distributions compare among these parameters?
2. Integrating cores to estimate regional inventories. Each of you should integrate the PCBs, Cs-137 and Carbon content of 2-3 cores. We will compile these data into a combined data set for further analysis. There are several steps involved in integrating the parameters with depth.
• Each of the concentration units for the 3 parameters must be converted to total mass (or total Curies for Cs) in each depth stratum. This is done by multiplying concentration by the corrected weight of each layer.
• The mass or radioactivity in each depth stratum is summed to give the total inventory for that core.
• The area of the core is extrapolated to yield an estimate for the amount deposited per km² (note that all the cores except HR-009 are 5.7 cm in diameter).

 Part III:

 (NOTE: this part uses the compiled data from the integrated cores, get Table )

1. Sediment inventories. Plot the PCB inventories vs. mile point. Is the overall trend any different than that for the raw concentration data that you plotted earlier? If there are low values in the upper River, identify them and discuss why they are lower than other values in this region. Plot the Cs-137 inventories vs. mile post. On this graph, denote the average atmospheric fallout value (~83 mCi/Km²) with a horizontal line to make the point sources stand out. If the Indian Point nuclear power plant is at mile point ~42, why are there elevated Cs-137 levels immediately upstream from this point?
2. Relationships among inventories. Plot the carbon inventory vs. the Cs-l 37 inventory in a scatter diagram and insert a linear regression trendline for the relationship. Be sure to add the coefficients and r² value to the plot. Also perform a significance test using the EXCEL REGRESSION function as discussed in class. In general, does the result support the assumption that Cs-137 is behaving similar to organic matter in the environment? Identify some of the outliers on the graph and discuss why they are scattered more than others from the calculated regression line. Next, plot the PCB inventories vs. Cs-137 inventories as a scattergram (with the coefficients and r² value, also perform a significance test). Do the Cs inventories explain more or less of the variance of the PCB values than they did the variance of the carbon values? What does this tell you about the environmental behavior of Cs relative to PCBs? Finally, plot the ratio of PCB to Cs-137 vs. river mile for use in the last section of this lab.

Part IV:

(NOTE: this part uses the provided summary table, get Table)

1. PCB budget of the Hudson River. On the table distributed to you the River has been broken into 7 regions and the area of each region has been estimated. Also on the table is the estimate of the total amount of Cs in that part of the River. This Cs budget is based on 240 cores and thus allowed for the calculation of the standard deviations and errors for each section. Estimate the PCB to Cs ratio for each region based on the PCB to Cs distribution you plotted in Part III-2 above, calculate an average PCB/Cs ratio for each region. Based on that ratio and the Cs budget, estimate the PCB budget for each region as well as for the whole River. General Electric's permit allowed it to discharge 30 pounds of PCBs per day for a period of 19.2 years. How does your PCB budget compare to GE's purported total discharge?
2. If time permits, estimate the standard error of your PCB/Cs estimates as well as the propagated error of your overall PCB budget. Based on these statistics, how confident can you be that there is a difference between the budget and GE's mandated discharge limit?