Urea Emissions Estimation

This R Script uses a function that estimates CO2e emissions from urea fertilizer application across all 50 US states using methodology from the 2006 IPCC Guidelines.

This estimation returns gigagrams CO2e emissions for each state and then US totals for the years 1990-2020

UreaEmissionsUS<-
  function(input.filename="NAME", nyears=1, nstates=1, EF.mean=1, EF.min=1, EF.max=1, iseed=1234, nreps=10000)
    #Script Developed by: N Wiley
   # Originally Developed: August 24th, 2022
    # Last Update:
    # Script estimates CO2 emissions from urea fertilizer application across all 50 US states using Tier methodology from the 2006 IPCC Guidelines
    #Returns Gigagrams CO2 emissions for each state and then US totals for the years 1990-2020
    # Input.filename was originially in units of N fertilizer Metric Tonnes but was then converted to urea fertilizer in metric tonnes
    
    #-------Arguments--------
    #input.filename <---- Name of file with activity data, which is a comma delimited (CSV) file
                          #activity data is in metric tonnes of urea fertilizer
# EFmean <------- 0.2
# EFmin <----- 0.1
# EFmax <----- 0.2
# nyears <-----31
# nstates <------ 50

{
  #set the seed
  set.seed(iseed)
  
  # Load libraries
  library(triangle)
  
  # Import input file
 input.data<-read.csv(file="data/Ureafert.csv", header = TRUE, fill=FALSE)
  
  #---------Data Checks---------
  #
  # Check that urea amounts are numeric
  
  for (n in 1:(nyears*2)){
    input.data[,n+1] <- as.numeric(input.data[,n+1])
  }
  #
  #Check that SD's are numeric
  
  for(n in 1:(nyears*2)){
    input.data[,n+1]<-as.numeric(input.data[,n+1])
  }
  
  # Validity checks that urea.amount and SD's are within the expected min and max range
  for(y in (1:nyears)){
    for(s in (1:nstates)){
      check.urea.amount <- length(input.data[s,y+1]) > 0 
      if(!check.urea.amount){stop("Warning: Urea amount is not greater than 0")}
    }
        }
  #
#---------Deterministic Results--------
  
  Deterministic.CO2C.state<-matrix(0, nrow=nstates, ncol=nyears)
    for(y in (1:nyears)){
      for(s in (1:nstates)){
        Deterministic.CO2C.state[s,y]<- input.data[s,(y*2)]*EF.mean
      }
    }
  
# Sum the state CO2-C emissions for grand totals in each year
  Deterministic.CO2C.USA<-apply(Deterministic.CO2C.state, MAR=2, FUN="sum")

  
# Convert state and total CO2-C Emissions (in Metric Tonnes) to CO2e in Gigagrams
  ### 44/12 converts CO2-C to CO2e
  ### 10^6 converts metric tonnes to gigagrams
  Deterministic.CO2C.state<-(Deterministic.CO2C.state*(44/12))/10^6
  Deterministic.CO2C.USA<-(Deterministic.CO2C.USA*(44/12))/10^6
  
#--------Probabilistic Results----------
  
#simulate nreps for EFmean
  ### the EF shows a triangle distribution so there is a min, max, and mean EF
  
  EF.sim<-rtriangle(nreps, a=EF.min, b=EF.max, c=EF.mean)
  
# Simulate nreps for urea inputs
  urea.sim<-matrix(0, nrow=nstates*nyears, ncol=nreps)
  for(y in (1:nyears)){
    for(s in (1:nstates)){
      urea.sim[s+((y-1)*50),]<-rnorm(nreps, mean=input.data[s,(y*2)], sd=input.data[s,3+((y-1)*2)])
    }
  }
  
# Estimate probabilistic results (in CO2-C for individual state totals and then year totals)
  ### Remember that this is now a triangle distribution, so we need to account for the mode instead of the median
  
 ### Estimate the state total emissions for each year
  
   Probabilistic.CO2C.state<-matrix(0, nrow=nstates*nyears, ncol=nreps)
  for(y in (1:nyears)){
    for(s in (1:nstates)){
      Probabilistic.CO2C.state[s+((y-1)*nstates),]<- EF.sim*urea.sim[s+((y-1)*nstates),]
    }
  }

# Sum the US and state totals for all 31 years (remember to convert from metric tonnes to gigagrams 10^6 and convert CO2-C to CO2 44/12)
  
### Get the MODE for the states emissions, create mode function
  mode<-function(x,n=2){
    x<-round(x,n)
    u<-unique(x)
    u[which.max(tabulate(match(x,u)))]
 
}
  
### Sum the probabilistic emissions
  Probabilistic.CO2C.totalUS<-matrix(0, nrow=nyears, ncol=nreps)
  for(y in (1:nyears)){
    Probabilistic.CO2C.totalUS[y,]<-apply(Probabilistic.CO2C.state[(1+(50*(y-1))):(50+(50*(y-1))),], MAR=2, FUN="sum")}


  #------US Totals: Estimate mode and confidence intervals--------
  
  CO2C.USemission.results<-matrix(0, nrow=nyears, ncol=3)
  for (y in (1:nyears)){
    CO2C.USemission.results[y,1]<- mode(Probabilistic.CO2C.totalUS[y,])
    q<-quantile(Probabilistic.CO2C.totalUS[y,], probs = c(0.05, 1))
    CO2C.USemission.results[y,2]<- q[1]
    CO2C.USemission.results[y,3]<- q[2]
  }
  

    
### Convert US total CO2-C Emissions (in Metric Tonnes) to CO2e in Gigagrams
    ### 44/12 converts CO2-C to CO2e
    ### 10^6 converts metric tonnes to gigagrams
    CO2.USemission.results<-(CO2C.USemission.results*(44/12))/10^6
 
    
  #------Yearly state totals:Estimate mode and confidence intervals--------
    
  CO2C.Stateemission.results<-matrix(0, nrow=nstates*nyears, ncol=3)
    for(y in (1:(nyears*nstates))){
      CO2C.Stateemission.results[y,1]<- mode(Probabilistic.CO2C.state[y,])
      q<-quantile(Probabilistic.CO2C.state[y,], probs = c(0.05, 1))
      CO2C.Stateemission.results[y,2]<- q[1]
      CO2C.Stateemission.results[y,3]<- q[2]

    }
    ### Convert State  CO2-C Emissions (in Metric Tonnes) to CO2e in Gigagrams
    ### 44/12 converts CO2-C to CO2e
    ### 10^6 converts metric tonnes to gigagrams
    CO2.Stateemission.results<-(CO2C.Stateemission.results*(44/12))/10^6
    
    #
    
  #-----------Return Statement------------
    
    ### Total US Results
    CO2.USemission.results<-CO2.USemission.results
    colnames(CO2.USemission.results)<-c("mode.GgCO2", "5  Percentile", "100 Percentile")
    
    
    ### Results by state for each year
    CO2.Stateemission.results<-CO2.Stateemission.results
    colnames(State.emission.results)<-c("mode.GgCO2", "5  Percentile", "100 Percentile")
    
    
    
    #
    ###### End Script
  }

Final_UreaEmission <-UreaEmissionsUS(input.filename="data/Ureafert.csv")