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Transforms concentration from fractional to volumetric units

Source: R/flux_conc.R
flux_conc.Rd

This function converts gas concentrations from fractional units (e.g., ppm) to volumetric units (e.g., umol/liter) based on the ideal gas law and the specific conditions of the measurement.

Usage

flux_conc(
  conc_df,
  f_conc,
  temp_air_col,
  atm_pressure = 1,
  f_fluxid = f_fluxid,
  temp_air_unit = "celsius"
)

Arguments

conc_df

dataframe of gas concentration over time

f_conc

column with gas concentration. Supply as a bare (unquoted) column name (e.g. conc), not a string; this function uses tidy-evaluation with {{ }}.

temp_air_col

column containing the air temperature used to convert concentration. Supply as a bare (unquoted) column name (e.g. temp_air), not a string.

atm_pressure

atmospheric pressure in atm, can be a constant (numerical) or a variable (column name). Default is 1.

f_fluxid

column with ID of each flux. Supply as a bare (unquoted) column name (e.g. f_fluxid), not a string.

temp_air_unit

units in which air temperature was measured. Has to be either celsius (default), fahrenheit or kelvin.

Value

The same dataframe, with additional column f_conc_vol in volumetric concentration.

Details

Units

The units of the newly calculated volumetric concentration follow the units of the fractional concentration provided. For exemple, if the input is in ppm, the result is in umol/L; if the input is in ppb, the result is in nmol/L; if the input is in ppt, the result is in pmol/L.

Required temperature and pressure data

This function requires that each gas concentration data points to be paired with a corresponding air temperature and pressure measurement. If missing, data will be filled in the downup order. Pressure can also be provided as a constant for the entire dataset.

Examples

data(co2_conc)
flux_conc(co2_conc, conc, temp_air)
#> R constant set to 0.082057 L * atm * K^-1 * mol^-1
#> # A tibble: 1,251 × 14
#>    datetime            temp_air temp_soil  conc   PAR turfID       type 
#>    <dttm>                 <dbl>     <dbl> <dbl> <dbl> <fct>        <fct>
#>  1 2022-07-28 23:43:35    NA         NA    447. NA    156 AN2C 156 ER   
#>  2 2022-07-28 23:43:36     7.22      10.9  447.  1.68 156 AN2C 156 ER   
#>  3 2022-07-28 23:43:37    NA         NA    448. NA    156 AN2C 156 ER   
#>  4 2022-07-28 23:43:38    NA         NA    449. NA    156 AN2C 156 ER   
#>  5 2022-07-28 23:43:39    NA         NA    449. NA    156 AN2C 156 ER   
#>  6 2022-07-28 23:43:40    NA         NA    450. NA    156 AN2C 156 ER   
#>  7 2022-07-28 23:43:41    NA         NA    451. NA    156 AN2C 156 ER   
#>  8 2022-07-28 23:43:42    NA         NA    451. NA    156 AN2C 156 ER   
#>  9 2022-07-28 23:43:43    NA         NA    453. NA    156 AN2C 156 ER   
#> 10 2022-07-28 23:43:44    NA         NA    453. NA    156 AN2C 156 ER   
#> # ℹ 1,241 more rows
#> # ℹ 7 more variables: f_start <dttm>, f_end <dttm>, f_fluxid <fct>,
#> #   f_n_conc <dbl>, f_ratio <dbl>, f_flag_match <chr>, f_conc_vol <dbl>