Sludge Composition

This page describes the data source used to estimate the N content of wastewater treatment plants sludge in our study, based on N:P ratio (we know the P content of sludge). The composition values listed here are also used to assess the coherence of our results.

Code

knitr::opts_chunk$set(warning=F, message=F, results=F, dev='svg')

library(tidyverse) #loads multiple packages (see https://tidyverse.tidyverse.org/)

#core tidyverse packages loaded:
# ggplot2, for data visualisation. https://ggplot2.tidyverse.org/
# dplyr, for data manipulation. https://dplyr.tidyverse.org/
# tidyr, for data tidying. https://tidyr.tidyverse.org/
# readr, for data import. https://readr.tidyverse.org/
# purrr, for functional programming. https://purrr.tidyverse.org/
# tibble, for tibbles, a modern re-imagining of data frames. https://tibble.tidyverse.org/
# stringr, for strings. https://stringr.tidyverse.org/
# forcats, for factors. https://forcats.tidyverse.org/
# lubridate, for date/times. https://lubridate.tidyverse.org/

#also loads the following packages (less frequently used):
# Working with specific types of vectors:
#     hms, for times. https://hms.tidyverse.org/
# Importing other types of data:
#     feather, for sharing with Python and other languages. https://github.com/wesm/feather
#     haven, for SPSS, SAS and Stata files. https://haven.tidyverse.org/
#     httr, for web apis. https://httr.r-lib.org/
#     jsonlite for JSON. https://arxiv.org/abs/1403.2805
#     readxl, for .xls and .xlsx files. https://readxl.tidyverse.org/
#     rvest, for web scraping. https://rvest.tidyverse.org/
#     xml2, for XML. https://xml2.r-lib.org/
# Modelling
#     modelr, for modelling within a pipeline. https://modelr.tidyverse.org/
#     broom, for turning models into tidy data. https://broom.tidymodels.org/

# Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors


#setting graphs theme
theme_set(
  theme_minimal() +
    theme(plot.title = element_text(face="bold"))
  )

#setting viridis theme for colors
scale_colour_continuous <- scale_colour_viridis_c
scale_colour_discrete   <- scale_colour_viridis_d
scale_colour_binned     <- scale_colour_viridis_b
#setting viridis theme for fill
scale_fill_continuous <- scale_fill_viridis_c
scale_fill_discrete   <- scale_fill_viridis_d
scale_fill_binned     <- scale_fill_viridis_b

Source <- "Sources: Water Agencies\nComputation Thomas Starck"

# Load the function file
source("functions.R")

Esco MAFOR

We use a French review (collective scientific expertise) on organic fertilizers, called Esco MAFOR. Link to the review (values used page 413).

Review
Mean values used for our study

Below are the P content, N content and N:P ratio in the reported studies, for composted and not composted urban sludge. Mohanty et al. (2006) seems to be an outlier due to very low P content and high N:P ratio. We will exclude it in the following.

Code

#path of the data
path_source <- "source_data/0_sludge_composition/"

#load data on sludge composition
review_sludge <- read_csv(paste0(path_source, "sludge_composition_ESCO_MAFOR.csv")) 
review_sludge$compost <- factor(
  review_sludge$compost,
  c("not composted", "composted")
)

#caption of the graphs
Source <- "Source: French Collective Scientific Expertise on\nOrganic Fertilizers, 2014"

Code

#function to plot sludge composition
g_composition <- function(dataset, select_variable){
  
  #order studies by increasing value
  dataset$Study <- reorder(dataset$Study, dataset %>% pull(!!as.symbol(select_variable)))
  
  #remove empty values
  dataset <- dataset %>% filter(is.na(!!as.symbol(select_variable))==F)
  
  #plot
  ggplot(dataset) +
    geom_col(aes(Study, !!as.symbol(select_variable), fill=compost)) +
    geom_text(
      aes(Study, !!as.symbol(select_variable), label=!!as.symbol(select_variable)),
      hjust=0, fontface="italic"
      ) +
    coord_flip() +
    theme(
      legend.position = "none"
      ) +
    labs(
      x="", y="", 
      fill = "", 
      caption = Source
    ) +
    facet_wrap(vars(compost))
}

Code

#we use rounded values for the graph
rounded_values <- review_sludge %>%
  mutate(
    P = round(P, 0), 
    N= round(N, 0),
    N_P_ratio = round(N_P_ratio, 1)
  )

Code

g_composition(rounded_values, "P") +
  labs(
    title = "Review of P content in urban sludge",
    subtitle = "g of P per kg of Dry Matter"
  ) +
  ylim(0, 40)

Code

g_composition(rounded_values, "N") +
  labs(
    title = "Review of N content in urban sludge",
    subtitle = "g of N per kg of Dry Matter"
  ) +
  ylim(0, 50)

Code

g_composition(rounded_values, "N_P_ratio") +
  labs(
    title = "Review of N:P ratio in urban sludge",
    subtitle = ""
  ) +
  ylim(0, 8)

Code

ggsave(#svg
  "graphs/sludge_ratio_review.svg",
  dpi=500, width=6, height=4, bg="white", create.dir = T
  )
ggsave(#pdf
  "graphs/sludge_ratio_review.pdf",
  dpi=500, width=6, height=4, bg="white", create.dir = T
  )
ggsave(#png
  "graphs/sludge_ratio_review.png",
  dpi=500, width=6, height=4, bg="white", create.dir = T
  )

For P and N:P values, we remove the Mohanty et al. (2006) value which seems to be an outlier. Composted values are indicative, we will not use them because there are only 2 very old studies.

We compute 2 different mean values for the N:P ratio.

The mean of the ratios, based on the 6 values
The ratio of the N and P average contents, based on 13 P values and 7 N values

For first method gives a ratio of 2.2, the second 1.5. We will use a ratio of 2 for our study.

Code

#mean P content no composted
temp <- review_sludge %>% filter(compost=="not composted", Study!= "Mohanty et al., 2006")
mean_P_no_compost <- round(mean(temp$P, na.rm=T), 0)

#mean P content composted
temp <- review_sludge %>% filter(Study!= "Mohanty et al., 2006" & compost=="composted")
mean_P_compost <- round(mean(temp$P, na.rm=T), 0)

#mean N content not composted
temp <- review_sludge %>% filter(compost=="not composted")
mean_N_no_compost <- round(mean(temp$N, na.rm=T), 0)

#mean N content composted
temp <- review_sludge %>% filter(compost=="composted")
mean_N_compost <- round(mean(temp$N, na.rm=T), 0)

#mean N:P ratio not composted
temp <- review_sludge %>% filter(compost=="not composted", Study!= "Mohanty et al., 2006")
mean_ratio_NP_no_compost <- round(mean(temp$N_P_ratio, na.rm=T), 1)

#mean N:P ratio composted
temp <- review_sludge %>% filter(compost=="composted")
mean_ratio_NP_compost <- round(mean(temp$N_P_ratio, na.rm=T), 1)

#ratio of means N and P no compost
ratio_means_NP_no_compost <- round(mean_N_no_compost/mean_P_no_compost, 1)

#ratio of means N and P compost
ratio_means_NP_compost <- round(mean_N_compost/mean_P_compost, 1)

#create dataset of computed values
mean_values <- data.frame(
  compost = c("not composted", "composted"),
  P = c(mean_P_no_compost, mean_P_compost),
  N = c(mean_N_no_compost, mean_N_compost),
  N_P_mean_of_ratios = c(mean_ratio_NP_no_compost, mean_ratio_NP_compost),
  N_P_ratio_of_means = c(ratio_means_NP_no_compost, ratio_means_NP_compost),
  N_P_ratio = c(2, 0.9)
)
mean_values$compost <- factor(
  mean_values$compost,
  c("not composted", "composted")
)

#save computed values
f_save_csv_files(
  mean_values, 
  "output_data/sludge_composition/",
  "N_P_sludge_review.csv"
)

Code

g_composition(rounded_values %>% filter(Study!= "Mohanty et al., 2006"), "P") +
  labs(
    title = "Review of P content in urban sludge",
    subtitle = "g of P per kg of Dry Matter"
  ) +
  ylim(0, 40) +
  geom_hline(data = mean_values, aes(yintercept=P), linetype="dashed") +
  geom_text(
    data = mean_values, hjust=-0.2, family = "Times New Roman", fontface = "italic",
    aes(x="Traore et al., 1999", y=P, label=paste("mean:", P))
    )

Code

g_composition(rounded_values, "N") +
  labs(
    title = "Review of N content in urban sludge",
    subtitle = "g of N per kg of Dry Matter"
  ) +
  ylim(0, 50) +
  geom_hline(data = mean_values, aes(yintercept=N), linetype="dashed") +
  geom_text(
    data = mean_values, hjust=-0.2, family = "Times New Roman", fontface = "italic",
    aes(x="Mohanty et al., 2006", y=N, label=paste("mean:", N))
    )

Code

g_composition(rounded_values %>% filter(Study!= "Mohanty et al., 2006"), "N_P_ratio") +
  labs(
    title = "Review of N:P ratio in urban sludge",
    subtitle = "we will use a N:P ratio of 2 for uncomposted sludge"
  ) +
  ylim(0, 4) +
  #mean of ratios
  geom_hline(data = mean_values, aes(yintercept=N_P_mean_of_ratios, linetype="mean of ratios")) +
  geom_text(
    data = mean_values, hjust=-0.2, family = "Times New Roman", fontface = "italic",
    aes(x=c("Sikora et al., 1982", "Traore et al., 1999"), y=N_P_mean_of_ratios, label= N_P_mean_of_ratios)
    ) +
  #ratio of means
  geom_hline(data = mean_values, aes(yintercept=N_P_ratio_of_means, linetype="ratio of means")) +
  geom_text(
    data = mean_values, hjust=1.2, family = "Times New Roman", fontface = "italic",
    aes(x=c("Sikora et al., 1982", "Traore et al., 1999"), y=N_P_ratio_of_means, label= N_P_ratio_of_means)
    ) +
  theme(legend.position = "top") +
  labs(linetype="") + 
  guides(fill = "none") #not showing composted/not composted legend

Code

rm(list = ls())