The HgIDAR package can be use to calculate Hg concentrations and Hg methylation and demathylation ratios using isotopic dilution approaches (Monperrus et al., 2005; Rodriguez-Gonzalez et al., 2013).
This package is available in github repository and installation can be done with the following command:
- First, you need to install the devtools package. You can do this from CRAN. Invoke R and then type
install.packages("devtools")
- Load the devtools package.
library(devtools)
- In most cases, you just use install_github("author/package"). For example, with my R/broman package, which exists at github.com/micro-hg-cpg/HgIDAR", you’d type
install_github("micro-hg-cpg/HgIDAR")
library(HgIDAR)
Three examples are provided:
- Example 1 correspondes to water samples in which isotope 199 enriched inorganic mercury (IHg) and isotope 201 enriched monomethylmercury (MMHg) were used of incubation and isotope 198 enriched IHg and 202 MMHg were used for quantification.
- Example 2 corresponds to sediment samples in which isotope 204 enriched inorganic mercury (IHg) and isotope 200 enriched monomethylmercury (MMHg) were used of incubation and isotope 199 enriched IHg and 201 MMHg were used for quantification.
- Example 3 corresponds also to sediment samples in which isotope 199 enriched inorganic mercury (IHg) and isotope 201 enriched monomethylmercury (MMHg) were used of incubation and isotope 198 enriched IHg and 202 MMHg were used for quantification.
To explore the functions available in this package, you can use any of the mock-up scenarios provided in the examples. We have choose to use example 1 in this explanation. This mock up experiment is a sea water that has been double spiked, with isotope 199 enriched inorganic mercury (IHg) and isotope 201 enriched monomethylmercury (MMHg) and then incubated. These incubation spikes will allow us to measure the transformations; by measuring the concentration of 199 MMHg, we can calculate how much MMHg has form during the incubation and thus the potential methylation rate constant. Likewise by measuring the concentration of 201 IHg, we can calculate how much MMHg has undergone demethylation and thus calculate the potencial demethylation rate constant.
In order to measure the concentrations of each isotope, we need to add an quantification spike (isotope enriched 198 IHg and 202 MMHg) to act as an internal reference.
We need several files in order to make the calculations:
- The integrated peak areas of Hg isotopes. The results from the ICP-MS-GC need to be processed before hand and only the peaks areas will be used for this calculations.
head(peaks_1)
- The masses of enriched stable isotopes used for quantification and the mass of water sample. In the case of sediments or other solids, mass of sediment sample and acid used during the digestions and the mass of extract used for derivatization.
head(masses_1)
- The working abundance matrix. Relative abundance of Hg isotopes of the isotopic modified spikes used in the incubation and quantification plus at least one isotope with the natural abundance. The abundance matrix needs to have 5 columns.
abundances_1
We provided the necessary files to run this packages and we recommend to use them as templates for your own samples (this will facilitate the running of the package)
The variability of your peak integrations should be checked before starting the IDA calculations. We have implemented two steps in the quality check.
- Initially, the mean, sd and rsd of the peak's area are calculated. We can directly see the variation among the injections. The package provides two different commands to obtain this information, rsd_peaks() will provide a table with all information and plot_rsd_peaks() will provide plots with the rsd (y-axis) and sample number (x-axis).
step1_quality_verification_table <- rsd_peaks(peaks_1, form = 'all', cutoff = 2)
step1_quality_verification_plot <- plot_rsd_peaks(peaks_1, form = 'all', cutoff = 2)
- Secondly and crucially, we calculate the ratio between the isotopes of interest: the quantification and incubation isotopic modified spikes as well as the isotope selected for natural abundance. Once the ratios are calculated for each injection (analytic replicates), this package can calculate the relative standard deviation (rsd). The package provides two different commands to obtain this information, rsd_ratios() will provide a table with all ratios and their rsd and plot_rsd_ratios() will provide plots with the rsd incub/quant (y-axis) and sample number (x-axis).
step2_quality_verification_table <- rsd_ratios(peaks_1, form = 'MMHg', sample = 24, incub_IHg = 199, incub_MMHg = 201, quant_IHg = 198, quant_MMHg = 202, nat = 200)
step2_quality_verification_plot <- plot_rsd_ratios(peaks_1, form = 'MMHg', sample = 24, incub_IHg = 199, incub_MMHg = 201, quant_IHg = 198, quant_MMHg = 202, nat = 200)
When the relative standard deviation among injections is too high, you need to go back to your integrations and recheck them before moving on. The default cutoff for the rsd is set to 10%, however this can be modified.
This package offers the possibility of calculating the concentrations of both IHg and MMHg either by injection or by sample.
IHg and mean_IHg return a final table in which you have the final inorganic mercury concentrations for all the isotopes in each analytical replicate/injection or in each sample respectively. The same for MMHg and mean_MMHg; you will obtain the concentration of monomethylmercury in each of the isotopes.
#Either calculating the individual values of each injection
IHg_concentrations_byinjection <- IHg(peaks_1, masses_1, abundances_1, 24, 3, type = 'liquid', 198, 5)
MMHg_concentrations_byinjection <- MMHg(peaks_1, masses_1, abundances_1, 24, 3, type = 'liquid', 202, 5)
#or calculating the mean between the injections
mean_IHg_concentration <- mean_IHg(peaks_1, masses_1, abundances_1, 24, 3, type = 'liquid', 198, 5)
mean_MMHg_concentration <- mean_MMHg(peaks_1, masses_1, abundances_1, 24, 3, type = 'liquid', 202, 0.5)
The functions km and mean_km can be used to calculate directly the potential methylation yield by injection or by sample.
#Either calculating the individual values of each injection
km_byinjection <- km(peaks_1, masses_1, abundances_1, samples = 24, injections = 3, type = 'liquid', incub_IHg=199, quant_MMHg = 202, quant_IHg= 198, spike_concentration_MMHg=0.5, spike_concentration_IHg=5)
#or the mean between the injections
mean_km <- mean_km(peaks_1,masses_1,abundances_1,samples = 24 , injections = 3, type = 'liquid', incub_IHg=199, quant_MMHg = 202, quant_IHg= 198, spike_concentration_MMHg=0.5, spike_concentration_IHg=5)
Similarly to the functioning of km and mean_km, the functions kd and mean_kd can be used to obtain the demethylation yield.
#Either calculating the individual values of each injection
kd_byinjection <- kd(peaks_1, masses_1, abundances_1, samples = 24, injections = 3, type = 'liquid', incub_IHg=199, quant_MMHg = 202, quant_IHg= 198, spike_concentration_MMHg=0.5, spike_concentration_IHg=5)
#or the mean between the injections
mean_kd <- mean_kd(peaks_1,masses_1,abundances_1,samples = 24 , injections = 3, type = 'liquid', incub_IHg=199, quant_MMHg = 202, quant_IHg= 198, spike_concentration_MMHg=0.5, spike_concentration_IHg=5)
This package supports also calculations when only one spike was added during the incubation. The same functions can be use to calculate the concentrations as well as the methylation yields. The only difference is that incub_MMHg should be missing from the options.
When calculating the concentrations or de/methylation yields, the initial abundance file should contain only the isotopes that will be use for the analysis. For example, if IHg 199 is added during incubation and IHg 198 and MMHg 201 are added during quantification, you can choose your natural isotope between 200 and 202 (we recommend 202 because its natural abundance is larger and it will be easier to measure). Thus, the final abundance file should contain only those four isotopes: 198, 199, 201 and 202.
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Monperrus, M., Tessier, E., Veschambre, S., Amouroux, D., & Donard, O. (2005). Simultaneous speciation of mercury and butyltin compounds in natural waters and snow by propylation and species-specific isotope dilution mass spectrometry analysis. Analytical and Bioanalytical Chemistry, 381(4), 854–862. https://doi.org/10.1007/s00216-004-2973-7
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Rodriguez-Gonzalez, P., Bouchet, S., Monperrus, M., Tessier, E., & Amouroux, D. (2013). In situ experiments for element species-specific environmental reactivity of tin and mercury compounds using isotopic tracers and multiple linear regression. Environmental Science and Pollution Research, 20(3), 1269-1280. https://doi.org/10.1007/s11356-012-1019-5