.Rhistory

st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2])
st_read('shapefiles/Berths.shp') %>%
select(berth_name) %>%
st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2]) %>%
st_drop_geometry()
berths_v1 <- st_read('shapefiles/Berths.shp') %>%
select(berth_name) %>%
st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2]) %>%
st_drop_geometry()
berths_v1
class(berths_v1)
st_read('shapefiles/Berths.shp') %>%
select(berth_name) %>%
st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2]) %>%
st_drop_geometry() %>%
tibble()
berths_v2
names(berths_v2)
st_read('shapefiles/Berths.shp') %>%
select(berth_name) %>%
st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2]) %>%
st_drop_geometry()
read.csv('berths/POLARIS_Wharves.csv') %>%
rename('ï..Name' = 'berth_name' )
read.csv('berths/POLARIS_Wharves.csv') %>%
rename('berth_name' = 'ï..Name')
read.csv('berths/POLARIS_Wharves.csv') %>%
rename('berth_name' = 'ï..Name',
'x' = 'Longitude',
'y' = 'Latitude') %>% print(n=10)
read.csv('berths/POLARIS_Wharves.csv') %>%
rename('berth_name' = 'ï..Name',
'x' = 'Longitude',
'y' = 'Latitude') %>% print(10)
berths_v1 <- st_read('shapefiles/Berths.shp') %>%
select(berth_name) %>%
st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2]) %>%
st_drop_geometry()
berths_v2 <- read.csv('berths/POLARIS_Wharves.csv') %>%
rename('berth_name' = 'ï..Name',
'x' = 'Longitude',
'y' = 'Latitude')
head(berths_v1)
head(berths_v2)
library(sf)
library(tidyverse)
berths_v1 <- st_read('shapefiles/Berths.shp') %>%
select(berth_name) %>%
st_transform(4326) %>%
mutate(x = st_coordinates(geometry)[,1],
y = st_coordinates(geometry)[,2]) %>%
st_drop_geometry()
berths_v2 <- read.csv('berths/POLARIS_Wharves.csv') %>%
rename('berth_name' = 'ï..Name',
'x' = 'Longitude',
'y' = 'Latitude')
write_csv(berths_v1, 'berths_v1.csv')
write_csv(berths_v2, 'berths_v2.csv')
rm(list=ls(all=TRUE))
library(sf)
library(tidyverse)
library(scales)
library(snowfall)
library(fasterize)
library(raster)
library(RColorBrewer)
berths <- read_csv('berths/berths_v1.csv') %>%
st_as_sf(coords = c('x', 'y'))
berths
berths <- read_csv('berths/berths_v1.csv') %>%
st_as_sf(coords = c('x', 'y')) %>%
st_set_crs(4326)
plot(berths)
berths <- read_csv('berths/berths_v1.csv') %>%
st_as_sf(coords = c('x', 'y')) %>%
st_set_crs(4326) %>%
filter(berth_name != 'Coldharbour Jetty')
plot(berths)
rm(list=ls(all=TRUE))
library(sf)
library(tidyverse)
library(scales)
library(snowfall)
library(fasterize)
library(raster)
library(RColorBrewer)
latlong = "+init=epsg:4326"
ukgrid  = "+init=epsg:27700"
google  = "+init=epsg:3857"
the_thames <- st_read('https://raw.githubusercontent.com/KCL-ERG/useful_geography/master/thames.geojson')
plot(the_thames)
plot(st_geometry(the_thames))
vessel_class              <- read_csv('docs/vessel_classifications.csv')
vessel_class$code         <- as.character(vessel_class$code)
head(vessel_class)
# Get emissions by exact cut, substance and vessel type
emissions                 <- read_csv('emissions/inventory_export_2016.csv', col_types = cols())
emissions                 <- emissions[emissions$LAEIPLAExt == 'LAEI',]
emissions                 <- emissions[,c('VesselType', 'Substance', 'CellID', 'Sailing_kg', 'AtBerth_kg')]
emissions$CellID          <- as.numeric(emissions$CellID)
head(emissions)
names(emissions)          <- c('ship_type', 'pollutant', 'cellid', 'sailing', 'berth')
head(emissions)
pollutants_we_want        <- c('PM', 'PM2.5', 'NOx')
emissions                 <- emissions[emissions$pollutant %in% pollutants_we_want,]
rm(pollutants_we_want)
head(emissions)
head(vessel_class)
# Tidy up some of the vessel classifications in the emissions file to match the GPS ecssel types
emissions[emissions$ship_type == 'RoRo Cargo / Vehicle','ship_type'] <-'RoRo Cargo/Vehicle'
emissions[emissions$ship_type == 'Cruise ship','ship_type']          <-'Passenger (cruise)'
emissions[emissions$ship_type == 'Passenger', 'ship_type']           <-'Passenger (ferry)'
emissions                 <- left_join(emissions, unique(vessel_class[,c('aggregated_class', 'group')]),
by = c('ship_type' = 'aggregated_class'))
head(emissions)
# Now get the grid by exact cut
grid                      <- st_read('grids/LAEIGridExtensionV2.gpkg', quiet = T)
## Make a map showing the exaxt cut issue
temp                      <-  left_join(grid, emissions,  by = c('CellID' = 'cellid')) %>%
rename(large_grid_id = GRID_ID0, x = X_COORD, y = Y_COORD)
focus_area                <- st_as_sf(as(raster::extent(529735, 533671, 180049, 181052), 'SpatialPolygons')) %>%
st_set_crs(27700)
temp                      <- st_crop(temp, focus_area) %>%
filter(pollutant == 'NOx' & ship_type == 'Passenger (ferry)') %>%
arrange(sailing)
temp$sailing              <- factor(temp$sailing)
colourCount = length(unique(temp$sailing))
getPalette  = colorRampPalette(c('#ffffd4','#fed98e','#fe9929','#d95f0e','#993404'))
plot <- ggplot() +
geom_sf(data = temp, aes(fill = sailing)) +
scale_fill_manual(values = getPalette(colourCount), name = "Passenger ferry NOx \n emissions (kg/year)") +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size=5),
legend.title = element_text(size=6)) +
guides(fill=guide_legend(ncol=2),
color = guide_legend(override.aes = list(shape = 0.5))) +
geom_sf(data = st_crop(st_transform(the_thames,27700),temp), fill = NA, colour='blue')
plot
ggsave('large_grid_nox_passenger_sailing_before_merge.png', plot = plot, path = 'maps/', height = 5, width = 15, units='cm')
rm(plot, colourCount, getPalette, temp, focus_area)
temp                      <- left_join(emissions, grid, by = c('cellid' = 'CellID')) %>%
rename(large_grid_id = GRID_ID0, x = X_COORD, y = Y_COORD) %>%
dplyr::select(cellid, ship_type,large_grid_id, x, y, pollutant, group, sailing, berth) %>%
st_as_sf(coords = c("x", "y"), crs=27700) %>%
st_buffer(dist = 500, endCapStyle= "SQUARE")
nox_roro_sailing <- filter(temp, pollutant == 'NOx' & ship_type == 'RoRo Cargo/Vehicle' & !is.na(sailing) & sailing > 0) %>%
dplyr::select(sailing)
labels <-  list()
for (i in 1:5) {
labels[[i]]                   <- paste(round(quantile(nox_roro_sailing$sailing,seq(0,1,0.2))[i],0),
'-',
round(quantile(nox_roro_sailing$sailing,seq(0,1,0.2))[(i+1)],0))
}
labels                          <- unlist(labels)
nox_roro_sailing$emissions <- cut(nox_roro_sailing$sailing,
breaks=c(0,quantile(nox_roro_sailing$sailing,seq(0,1,0.2))[2:10]),
labels = labels)
colours <- c('#ffffd4','#fed98e','#fe9929','#d95f0e','#993404')
plot <- ggplot() +
geom_sf(data=temp) +
geom_sf(data = nox_roro_sailing, colour = 'black', aes(fill = emissions)) +
coord_sf() +
scale_fill_manual(values = colours, name = "NOx emissions (kg/year)") +
#scale_fill_discrete(name = expression(paste("NOx emissions (", mu, m^2, "/", m^3, ")", sep=""))) +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank())
plot
rm(plot, nox_roro_sailing, colours, i, labels)
nrow(emissions)
head(emissions)
# Now aggregte
emissions <- emissions %>%
dplyr::select(-ship_type) %>%
group_by(pollutant, cellid, group) %>%
summarise(sailing = sum(sailing, na.rm=T),
berth   = sum(berth, na.rm=T)) %>%
ungroup()
nrow(emissions)
head(emissions)
grid_emissions            <- left_join(emissions, grid, by = c('cellid' = 'CellID')) %>%
rename(large_grid_id = GRID_ID0, x = X_COORD, y = Y_COORD) %>%
dplyr::select(cellid, large_grid_id, x, y, pollutant, group, sailing, berth) %>%
as_tibble() %>%
group_by(pollutant, group, large_grid_id, x, y) %>%
summarise(sailing = sum(sailing), berth = sum(berth)) %>%
st_as_sf(coords = c("x", "y"), crs=27700) %>%
st_buffer(dist = 500, endCapStyle= "SQUARE")
head(grid_emissions)
rm(emissions, grid)
nox_group_one_sailing <- filter(grid_emissions, pollutant == 'NOx' & group == 1 & !is.na(sailing) & sailing > 0) %>%
dplyr::select(sailing)
labels <-  list()
for (i in 1:5) {
labels[[i]]                   <- paste(round(quantile(nox_group_one_sailing$sailing,seq(0,1,0.2))[i],0),
'-',
round(quantile(nox_group_one_sailing$sailing,seq(0,1,0.2))[(i+1)],0))
}
labels                          <- unlist(labels)
nox_group_one_sailing$emissions <- cut(nox_group_one_sailing$sailing,
breaks=c(0,quantile(nox_group_one_sailing$sailing,seq(0,1,0.2))[2:10]),
labels = labels)
colours <- c('#ffffd4','#fed98e','#fe9929','#d95f0e','#993404')
plot <- ggplot() +
geom_sf(data = temp) +
geom_sf(data = nox_group_one_sailing, colour = 'black', aes(fill = emissions)) +
coord_sf() +
scale_fill_manual(values = colours, name = "NOx emissions (kg/year)") +
#scale_fill_discrete(name = expression(paste("NOx emissions (", mu, m^2, "/", m^3, ")", sep=""))) +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank())
ggsave('large_grid_sailing_group_one_nox.png', plot = plot, path = 'maps/', height = 5, width = 15, units='cm')
rm(plot, nox_group_one_sailing, colours, i, labels)
nox_group_one_berth <- filter(grid_emissions, pollutant == 'NOx' & group == 1 & !is.na(berth) & berth > 0) %>%
dplyr::select(berth)
labels <-  list()
for (i in 1:5) {
labels[[i]]                   <- paste(round(quantile(nox_group_one_berth$berth,seq(0,1,0.2))[i],3),
'-',
round(quantile(nox_group_one_berth$berth,seq(0,1,0.2))[(i+1)],3))
}
labels                          <- unlist(labels)
nox_group_one_berth$emissions <- cut(nox_group_one_berth$berth,
breaks=c(0,quantile(nox_group_one_berth$berth,seq(0,1,0.2))[2:10]),
labels = labels)
colours <- c('#ffffd4','#fed98e','#fe9929','#d95f0e','#993404')
plot <- ggplot() +
geom_sf(data = temp) +
geom_sf(data = nox_group_one_berth,colour = 'black', aes(fill = emissions)) +
coord_sf() +
scale_fill_manual(values = colours, name = "NOx emissions (kg/year)") +
#scale_fill_discrete(name = expression(paste("NOx emissions (", mu, m^2, "/", m^3, ")", sep=""))) +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank())
ggsave('large_grid_berth_group_one_nox.png', plot = plot, path = 'maps/', height = 5, width = 15, units='cm')
rm(plot, nox_group_one_berth, colours, i, labels)
head(grid_emissions)
filter(large_grid_emissions, large_grid_id == 9559)
filter(grid_emissions, large_grid_id == 9559)
small_grid                         <- unique(grid_emissions[,c('large_grid_id','geometry')]) %>%
st_make_grid(cellsize = 20, what = 'polygons') %>%
st_sf() %>%
st_join(unique(grid_emissions[,c('large_grid_id','geometry')]), join = st_within) %>%
filter(!is.na(large_grid_id)) %>%
mutate(small_grid_id = row_number())
head(small_grid)
small_grid[1:100,]
ggplot(data = small_grid[1:100,]) + geom_sf()
ggplot(data = small_grid[1:10,]) + geom_sf()
small_grid_result         <- rbind(small_grid %>% mutate(group = 1),
small_grid %>% mutate(group = 2),
small_grid %>% mutate(group = 3),
small_grid %>% mutate(group = 4))
plot <- ggplot() +
geom_sf(data=filter(small_grid_result, large_grid_id %in% c(10399,10400,10401) & group == 1), aes(colour = as.factor(large_grid_id)), size=0.5) +
geom_sf(data=st_crop(st_transform(the_thames,27700),
filter(small_grid_result, large_grid_id %in% c(10399,10400,10401) & group == 1)), fill=NA, colour = 'blue') +
theme(legend.position = 'none', axis.text = element_blank(), axis.ticks = element_blank()) +
ggtitle('Small grid example')
ggsave('small_grid_example.png', plot = plot, path = 'maps/', height = 5, width = 15, units='cm')
rm(plot)
list.files('gps/', full.names=T, pattern = 'Rdata')
## Get GPS data
## These files are produced by Andrew. He processed the AIS data in Python.
## THe scripts to do this, are here: https://github.com/JimShady/laei_river_data/tree/master/ais_processing
list_of_gps_data             <- list.files('gps/', full.names=T, pattern = 'Rdata')
list_of_gps_data             <- data.frame(filename         = list_of_gps_data,
actual_date      = NA,
stringsAsFactors = F)
list_of_gps_data
head(list_of_gps_data)
list_of_gps_data$actual_date <- substr(x     = list_of_gps_data$filename,
start = 24,
stop  = nchar(list_of_gps_data$filename)-6)
list_of_gps_data$actual_date <- as.Date(list_of_gps_data$actual_date, format = '%d_%b_%Y')
list_of_gps_data             <- list_of_gps_data[order(list_of_gps_data$actual_date),]
list_of_gps_data             <- as.list(list_of_gps_data$filename)
head(list_of_gps_data)
list_of_gps_data             <- list.files('gps/', full.names=T, pattern = 'Rdata')
list_of_gps_data             <- data.frame(filename         = list_of_gps_data,
actual_date      = NA,
stringsAsFactors = F)
list_of_gps_data$actual_date <- substr(x     = list_of_gps_data$filename,
start = 24,
stop  = nchar(list_of_gps_data$filename)-6)
list_of_gps_data$actual_date <- as.Date(list_of_gps_data$actual_date, format = '%d_%b_%Y')
list_of_gps_data             <- list_of_gps_data[order(list_of_gps_data$actual_date),]
head(list_of_gps_data)
list_of_gps_data             <- as.list(list_of_gps_data$filename)
list_of_gps_data
load('gps/Gravesend_ANSData_01_Apr_2016.Rdata')
test_gps <- filter(data, !is.na(VESSEL_TYPE)) %>%
st_as_sf(coords = c('lon', 'lat'), crs = 4326) %>%
st_transform(27700) %>% #27
st_crop(st_bbox(small_grid)) %>% #61
left_join(vessel_class, by = c('VESSEL_TYPE' = 'code')) %>%
dplyr::select(group)
plot <- ggplot() +
geom_sf(data = temp) +
geom_sf(data = test_gps, alpha=0.3, size=0.5, colour='blue') +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank())
ggsave('one_day_gps_example.png', plot = plot, path = 'maps/', height = 5, width = 15, units='cm')
list_of_gps_data
load(list_of_gps_data[[1]])
head(data)
head(vessel_class)
gps_data                                <-    filter(data, !is.na(VESSEL_TYPE)) %>%
st_as_sf(coords = c('lon', 'lat'), crs = 4326) %>%
st_transform(27700) %>% #27
st_crop(st_bbox(small_grid)) %>% #61
left_join(vessel_class, by = c('VESSEL_TYPE' = 'code')) %>%
dplyr::select(group)
gps_data
gps_data
rm(data)
gps_per_small_grid_id                   <- st_join(gps_data, small_grid, join = st_intersects) %>%
filter(!is.na(small_grid_id))
head(small_grid_id)
head(gps_per_small_grid_id)
# Remove geoms we don't need from this count
gps_per_small_grid_id$geometry          <- NULL
head(gps_per_small_grid_id)
# Sum up by the grid square
gps_per_small_grid_id                   <- gps_per_small_grid_id %>%
dplyr::select(group, small_grid_id) %>%
group_by(small_grid_id, group) %>%
summarise(count = length(group))
head(gps_per_small_grid_id)
paste0('grids/small_result_', substr(x = x,
start = 24,
stop = nchar(x)-6), '.Rdata')
save(gps_per_small_grid_id, file = paste0('grids/small_result_', substr(x = x,
start = 24,
stop = nchar(x)-6), '.Rdata'))
x <- list_of_gps_data[[1]]
paste0('grids/small_result_', substr(x = x,
start = 24,
stop = nchar(x)-6), '.Rdata')
save(gps_per_small_grid_id, file = paste0('grids/small_result_', substr(x = x,
start = 24,
stop = nchar(x)-6), '.Rdata'))
rm(gps_per_small_grid_id)
a <- a+1
print(a)
sfInit(parallel=TRUE, cpus=parallel:::detectCores()-1)
sfLibrary(sf)
sfLibrary(tidyverse)
sfExport(list=list("small_grid", "vessel_class", "a"))
sfStop()
## Tidy
rm(process_gps_data, list_of_gps_data)
process_gps_data <-  function(x) {
load(x)
gps_data                                <-    filter(data, !is.na(VESSEL_TYPE)) %>%
st_as_sf(coords = c('lon', 'lat'), crs = 4326) %>%
st_transform(27700) %>% #27
st_crop(st_bbox(small_grid)) %>% #61
left_join(vessel_class, by = c('VESSEL_TYPE' = 'code')) %>%
dplyr::select(group)
rm(data)
# Count, over the year in total, how many GPS points there are in each small grid square
gps_per_small_grid_id                   <- st_join(gps_data, small_grid, join = st_intersects) %>%
filter(!is.na(small_grid_id))
# Remove geoms we don't need from this count
gps_per_small_grid_id$geometry          <- NULL
# Sum up by the grid square
gps_per_small_grid_id                   <- gps_per_small_grid_id %>%
dplyr::select(group, small_grid_id) %>%
group_by(small_grid_id, group) %>%
summarise(count = length(group))
save(gps_per_small_grid_id, file = paste0('grids/small_result_', substr(x = x,
start = 24,
stop = nchar(x)-6), '.Rdata'))
rm(gps_per_small_grid_id)
a <- a+1
print(a)
}
list.files('grids/', full.names=T, pattern = 'small')
list_of_small_grid_gps_result_data             <- list.files('grids/', full.names=T, pattern = 'small')
for (i in 1:length(list_of_small_grid_gps_result_data)) {
load(list_of_small_grid_gps_result_data[i])
if (i == 1) {
gps_per_small_grid_bind <- gps_per_small_grid_id
} else {
gps_per_small_grid_bind <- bind_rows(gps_per_small_grid_bind,gps_per_small_grid_id)
}
}
rm(list_of_small_grid_gps_result_data, gps_per_small_grid_id, i, small_grid)
head(gps_per_small_grid_bind)
filter(gps_per_small_grid_bind, small_grid_id == 11410)
gps_per_small_grid        <- gps_per_small_grid_bind %>%
group_by(small_grid_id, group) %>%
summarise(count = sum(count))
rm(gps_per_small_grid_bind)
gps_per_small_grid
small_grid_result         <- left_join(small_grid_result, gps_per_small_grid,
by = c("small_grid_id" = "small_grid_id", "group" = "group"))
rm(gps_per_small_grid)
small_grid_result
small_grid_result
tail(small_grid_result)
berths <- read_csv('berths/berths_v1.csv') %>%
st_as_sf(coords = c('x', 'y')) %>%
st_set_crs(4326) %>%
filter(berth_name != 'Coldharbour Jetty') %>%
st_transform(27700)
ggplot(data=berths) + geom_Sf()
ggplot(data=berths) + geom_sf()
small_grid_result
small_grid_result <- small_grid_result %>%
st_join(berths, join = st_intersects, left = TRUE)
small_grid_result$berth_name <- as.character(small_grid_result$berth_name)
small_grid_result
small_grid_result
## remove data where there's very few GPS points
small_grid_result    <- filter(small_grid_result, count > 20)
large_grid_sailing_counts <- aggregate(data=small_grid_result[!is.na(small_grid_result$count) &
is.na(small_grid_result$berth_name),],
count ~ group + large_grid_id, FUN=sum)
large_grid_berth_counts   <- aggregate(data=small_grid_result[!is.na(small_grid_result$count) &
!is.na(small_grid_result$berth_name),],
count ~ group + large_grid_id, FUN=sum)
names(large_grid_sailing_counts)[3] <- 'sailing_count'
names(large_grid_berth_counts)[3]   <- 'berth_count'
large_grid_berth_counts
small_grid_result <- small_grid_result %>% left_join(large_grid_sailing_counts, by = c("large_grid_id" = "large_grid_id",
"group" = "group")) %>%
left_join(large_grid_berth_counts,   by = c("large_grid_id" = "large_grid_id",
"group" = "group"))
rm(large_grid_sailing_counts, large_grid_berth_counts)
small_grid_result
# Calculate the contribution percentages
small_grid_result$contribution <- NA
small_grid_result[is.na(small_grid_result$berth_name),'contribution'] <-  small_grid_result[is.na(small_grid_result$berth_name),]$count /
small_grid_result[is.na(small_grid_result$berth_name),]$sailing_count
small_grid_result[!is.na(small_grid_result$berth_name),'contribution'] <- small_grid_result[!is.na(small_grid_result$berth_name),]$count /
small_grid_result[!is.na(small_grid_result$berth_name),]$berth_count
head(small_grid_result)
small_grid_result    <-   rbind(small_grid_result %>% mutate(pollutant = 'NOx'),
small_grid_result %>% mutate(pollutant = 'PM'),
small_grid_result %>% mutate(pollutant = 'PM2.5'))
small_grid_result
grid_emissions
# Join the small grid result to the original emissions
small_grid_result         <-  left_join(small_grid_result, st_drop_geometry(grid_emissions),
by = c("large_grid_id" = "large_grid_id",
"group" = "group",
"pollutant" = "pollutant"))
small_grid_result$emissions <- NA
small_grid_result[is.na(small_grid_result$berth_name),'emissions'] <-  small_grid_result[is.na(small_grid_result$berth_name),]$contribution *
small_grid_result[is.na(small_grid_result$berth_name),]$sailing
small_grid_result[!is.na(small_grid_result$berth_name),'emissions'] <-  small_grid_result[!is.na(small_grid_result$berth_name),]$contribution *
small_grid_result[!is.na(small_grid_result$berth_name),]$berth
small_grid_result    <- filter(small_grid_result, !is.na(emissions))
small_grid_result
#################################
rm(berths, vessel_class, the_thames)
small_grid_result   <- st_centroid(small_grid_result)
### Re-organise for David
small_grid_result$x         <- st_coordinates(small_grid_result)[,1]
small_grid_result$y         <- st_coordinates(small_grid_result)[,2]
small_grid_result$geometry  <- NULL
small_grid_result           <- as.tibble(small_grid_result)
small_grid_result
result <- small_grid_result %>%
dplyr::select(x,y,group, pollutant, emissions, berth_name) %>%
spread(pollutant, emissions) %>%
rename_all(tolower) %>%
mutate(no2 = nox * 0.05,
year = 2016)
head(result)
rm(result)
nox <- grid_emissions %>%
st_drop_geometry() %>%
dplyr::select(pollutant, large_grid_id, group, sailing, berth) %>%
group_by(pollutant, group, large_grid_id) %>%
summarise(sailing_emissions = sum(sailing, na.rm=T),
berth_emissions   = sum(berth, na.rm=T)) %>%
filter(pollutant == 'NOx')
no2 <- nox %>% mutate(sailing_emissions = sailing_emissions * 0.05,
berth_emissions   = berth_emissions * 0.05) %>% ungroup() %>% mutate(pollutant = 'NO2')
pm <- grid_emissions %>% st_drop_geometry() %>% dplyr::select(pollutant, large_grid_id, group, sailing, berth) %>%
group_by(pollutant, group, large_grid_id) %>%
summarise(sailing_emissions = sum(sailing, na.rm=T),
berth_emissions   = sum(berth, na.rm=T)) %>%
filter(pollutant == 'PM')
pm25 <- grid_emissions %>% st_drop_geometry() %>% dplyr::select(pollutant, group, large_grid_id, sailing, berth) %>%
group_by(pollutant, group, large_grid_id) %>%
summarise(sailing_emissions = sum(sailing, na.rm=T),
berth_emissions   = sum(berth, na.rm=T)) %>%
filter(pollutant == 'PM2.5')
result <- bind_rows(nox, pm, pm25, no2) %>% rename_all(tolower)
result
rm(result, no2, nox, pm, pm25)