| Title: | Clean Water Quality Data for NPDES Reasonable Potential Analyses |
| Version: | 0.1 |
| Date: | 2020-10-22 |
| Depends: | R (≥ 4.0), stats |
| Maintainer: | Matthew Reusswig <matt.reusswig@gmail.com> |
| BugReports: | https://github.com/mattreusswig/reasonabletools/issues |
| URL: | https://github.com/mattreusswig/reasonabletools |
| Description: | Functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991). |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2020-10-30 12:09:59 UTC; Lamp |
| Author: | Matthew Reusswig [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2020-11-05 09:40:02 UTC |
Compute AML
Description
Compute average monthly effluent limitation (AML) according to methods in EPA's Technical Support Document for Water Quality-based Toxics Control.
Usage
calc_AML(
WLAa,
WLAc,
WLAhh,
cv,
n_samples = 4,
prob_LTA = 0.99,
percentile_AML = 0.95
)
Arguments
WLAa |
Numeric. Acute wasteload allocation (WLA). All WLA units should be identical. |
WLAc |
Numeric. Chronic wasteload allocation (WLA). All WLA units should be identical. |
WLAhh |
Numeric. Human health wasteload allocation (WLA). All WLA units should be identical. |
cv |
Numeric. Coefficient of variation (CV) of effluent data. See cv_adj function. |
n_samples |
Numeric. Number of sample observations. |
prob_LTA |
Numeric (fraction). Allowable exceedance probability of the WLA used to estimate long-term average (LTA). |
percentile_AML |
Numeric (fraction). Lognormal distribution location for AML. |
Value
Numeric value in same units as the WLAs.
Examples
calc_AML(WLAa=4, WLAc=1, WLAhh=10, cv=0.6)
Compute MDL
Description
Compute Maximum Daily Effluent Limitation (MDL) according to methods in EPA's Technical Support Document for Water Quality-based Toxics Control.
Usage
calc_MDL(
WLAa,
WLAc,
WLAhh,
cv,
n_samples = 4,
prob_LTA = 0.99,
percentile_MDL = 0.99,
percentile_AML = 0.95
)
Arguments
WLAa |
Numeric. Acute wasteload allocation (WLA). All WLA units should be identical. |
WLAc |
Numeric. Chronic wasteload allocation (WLA). All WLA units should be identical. |
WLAhh |
Numeric. Human health wasteload allocation (WLA). All WLA units should be identical. |
cv |
Numeric. Coefficient of variation (CV) of effluent data. See cv_adj function. |
n_samples |
Numeric. Number of sample observations. |
prob_LTA |
Numeric (fraction). Allowable exceedance probability of the WLA used to estimate long-term average (LTA). |
percentile_MDL |
Numeric (fraction). Lognormal distribution location for MDL. |
percentile_AML |
Numeric (fraction). Lognormal distribution location for AML. |
Value
Numeric value in same units as the WLAs.
Examples
calc_MDL(WLAa=4, WLAc=1, WLAhh=10, cv=0.6)
Compute Wasteload Allocation
Description
Compute the wasteload allocation (WLA) used for effluent limit calculation.
Usage
calc_WLA(criteria, background, Qrsw, Qeff)
Arguments
criteria |
Limiting water quality criterion. Must be in same units as background argument. |
background |
Background pollutant concentration. Must be in same units as criteria argument. |
Qrsw |
Upstream limiting/design receiving water flowrate. Must be in same units as Qeff argument. Flow arguments may be entered in ratio form. |
Qeff |
Effluent limiting/design flowrate. Must be in same units as Qrsw argument. Flow arguments may be entered in ratio form. |
Value
WLA as numeric value in same units as criteria and background.
Examples
# WLA for pollutant with 2 ug/L acute criteria and upstream receiving water concentration
# of 0.1 ug/L. The critical flows are 3 MGD (1Q10) and 0.5 MGD (max daily flow).
calc_WLA(2, 0.1, 3, 0.5)
# When using dilution credits, put Qrsw and Qeff in terms of the dilution ratio (D).
D = 7 # Assume a jurisdiction that uses D = (Qrsw + Qeff) / Qeff
Qeff = 1 # Equal to 1 since its the denominator of the ratio, or you can use the critical flow
Qrsw = D - 1 # Same as the expression (D * Qeff) - Qeff
calc_WLA(2, 0.1, Qrsw = D - 1, Qeff = 1)
Sum detected and non-detect concentrations.
Description
Sum up congener sample concentrations to create a composite parameter value. Primarily intended as a helper function in fuse_samples.
Usage
cen_sum(qual, result, nd = c("<", "nd", "ND"))
Arguments
qual |
A character vector containing non-detect indicator strings, e.g., "<" or "ND". The strings used to indicate censored status can be edited in the "nd" argument. |
result |
A numeric vector of concentration measurements. |
nd |
A list indicating all the censoring flags included in the dataset. Defaults to "<", "nd", and "ND". |
Value
dataframe with two columns, qual (character) and result (numeric)
Find the adjusted coefficient of variation
Description
Calculates adjusted coefficient of variation (CV) according to methods described in EPA's Technical Support Document for Water Quality-based Toxics Control.
Usage
cv_adj(qual, result, nd = c("<", "nd", "ND"), nd_adjustment = 0.5)
Arguments
qual |
A character vector containing non-detect indicator strings, e.g., "<" or "ND". The strings used to indicate censored status can be edited in the "nd" argument. |
result |
A numeric vector of concentration measurements. |
nd |
A list indicating all the censoring flags included in the dataset. Defaults to "<", "nd", and "ND". |
nd_adjustment |
Adjustment factor for non-detect values. Non-detect values (as indicated in qual vector) are multiplied by nd_adjustment factor; i.e. result * nd_adjustment. Typically, method detection limits or reporting limits are used as result values for non-detects. |
Value
A numeric coefficient of variation (CV) value
Examples
# CV for all detected values
cen_result <- rep("", 10)
result <- c(1:10)
cv_adj(cen_result, result)
# CV for all non-detected values
cen_result <- rep("<", 10)
cv_adj(cen_result, result)
# CV for fewer than 10 measurements
cen_result <- rep("", 5)
result <- c(1:5)
cv_adj(cen_result, result)
# Change the default substitution value
cen_result <- c(rep("<", 5), rep("", 15))
result <- c(101:120)
cv_adj(cen_result, result) # Use default 0.5 multiplier
# Use 1.0 multiplier (equivalent to using MDL)
cv_adj(cen_result, result, nd_adjustment = 1.0)
# Use 0.0 multiplier (equivalent to zero substitution)
cv_adj(cen_result, result, nd_adjustment = 0)
Find the Maximum Observed Effluent Concentration (MEC)
Description
Find the MEC (no projection) from the observed dataset using methods described in EPA's Technical Support Document for Water Quality-based Toxics Control.
Usage
find_mec(qual, result, nd = c("<", "nd", "ND"), simple_output = FALSE)
Arguments
qual |
A character vector containing non-detect indicator strings, e.g., "<" or "ND". The strings used to indicate censored status can be edited in the "nd" argument. |
result |
A numeric vector of concentration measurements. |
nd |
A list indicating all the censoring flags included in the dataset. Defaults to "<", "nd", and "ND". |
simple_output |
Logical value. If TRUE, the output columns are concatenated into a single character string (e.g., "<0.2") which can be useful for constructing summary tables. |
Value
Dataframe with a qualifier column (character) and a MEC column (numeric).
Examples
# Find observed MEC
cen_result <- c(rep("", 10), rep("<", 10))
result <- 1:20
find_mec(cen_result, result)
cen_result <- rep("<", 20)
find_mec(cen_result, result)
cen_result <- rep("", 20)
find_mec(cen_result, result)
# Demonstrate simplified output
find_mec(cen_result, result, simple_output = TRUE)
# Define a set of custom non-detect flags
cen_result <- c(rep("non-detect", 5), rep("<", 10), rep("mdl", 5))
find_mec(cen_result, result, nd = c("non-detect", "<", "mdl"))
Combine values for a composite parameter
Description
Calculate composite parameter concentrations using congener concentrations grouped by a sampling date vector. An example would application would be summing PCB congeners collected on a specific sampling date to produce a total PCBs concentration.
Usage
fuse_samples(date_grp, qual, result, nd = c("<", "nd", "ND"))
Arguments
date_grp |
A date vector to group the dataset. |
qual |
A character vector containing non-detect indicator strings, e.g., "<" or "ND". The strings used to indicate censored status can be edited in the "nd" argument. |
result |
A numeric vector of concentration measurements. |
nd |
A list indicating all the censoring flags included in the dataset. Defaults to "<", "nd", and "ND". |
Value
A dataframe containing a column of sampling dates used to group data, a qualifier column (character), and a MEC result column (numeric).
Examples
df <- data.frame(dates = rep(seq.Date(from = as.Date("1982-03-10"),
to = as.Date("1982-03-15"),
by = 1), 5),
congeners = sort(rep(LETTERS[1:6], 5)),
qualifier = sample(c("<", ""), size = 30,
replace = TRUE, prob = c(0.8, 0.2)),
result = sample(seq(0.1, 0.5, 0.1), 30, replace = TRUE))
fuse_samples(df$dates, df$qualifier, df$result)
Merge together observed concentrations and detection limits
Description
Merge a results column with a detection limits column by overwriting the censored results values with the corresponding detection limit.
Usage
mrg_cen_cols(qual, result, limit, nd = c("<", "nd", "ND"))
Arguments
qual |
A character vector containing non-detect indicator strings, e.g., "<" or "ND". The strings used to indicate censored status can be edited in the "nd" argument. |
result |
A numeric vector of concentration measurements. |
limit |
A numeric vecotr of method detection limit/reporting limit values. |
nd |
A list indicating all the censoring flags included in the dataset. Defaults to "<", "nd", and "ND". |
Value
A numeric vector.
Examples
result <- runif(5, 1, 10)
cen_result <- sample(c("<", ""), length(result), replace = TRUE)
method_limit <- rep(0.1, length(result))
result <- mrg_cen_cols(cen_result, result, method_limit)
print(data.frame(cen_result, result, stringsAsFactors = FALSE))
Find the projected Maximum Observed Effluent Concentration (MEC)
Description
Find the MEC projected from a lognormal distribution using methods described in EPA's Technical Support Document for Water Quality-based Toxics Control.
Usage
project_mec(
qual,
result,
nd = c("<", "nd", "ND"),
percentile = 0.95,
conf_level = 0.99,
nd_adjustment = 0.5,
simple_output = FALSE
)
Arguments
qual |
A character vector containing non-detect indicator strings, e.g., "<" or "ND". The strings used to indicate censored status can be edited in the "nd" argument. |
result |
A numeric vector of concentration measurements. |
nd |
A list indicating all the censoring flags included in the dataset. Defaults to "<", "nd", and "ND". |
percentile |
Location on lognormal distribution to estimate the projected MEC. |
conf_level |
Confidence level of projected estimate. |
nd_adjustment |
Adjustment factor for non-dectect values. Non-detect values (as indicated in qual vector) are multiplied by nd_adjustment factor; i.e. result * nd_adjustment. Typically, method detection limits or reporting limits are used as result values for non-detects. |
simple_output |
Logical value. If TRUE, the output columns are concatenated into a single character string (e.g., "<0.2") which can be useful for constructing summary tables. |
Value
Dataframe with a qualifier column (character) and a MEC column (numeric).
Examples
# Find observed MEC
cen_result <- c(rep("", 10), rep("<", 10))
result <- 1:20
project_mec(cen_result, result)
# Demonstrate simplified output
cen_result <- rep("<", 20)
project_mec(cen_result, result, simple_output = TRUE)
# Define a set of custom non-detect flags
cen_result <- c(rep("non-detect", 5), rep("<", 10), rep("mdl", 5))
project_mec(cen_result, result, nd = c("non-detect", "<", "mdl"))
# Change the substitution multiplier used for non-detect values
cen_result <- c(rep("<", 5), rep("", 15))
result <- c(101:120)
project_mec(cen_result, result) # Use default 0.5 multiplier
# Use 1.0 multiplier (equivalent to using MDL)
project_mec(cen_result, result, nd_adjustment = 1.0)
# Use 0.0 multiplier (equivalent to zero substitution)
project_mec(cen_result, result, nd_adjustment = 0)