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ulez_data_collection_normalisation.R
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library(openair)
library(dplyr)
library(rmweather)
library(zoo)
require(RcppRoll)
library(ggplot2)
library(plotly)
library(tidyr)
library(purrr)
#Processing code----------------------------------------------
london_kcl_meta = importMeta(source = "kcl") %>%
drop_na()
london_urb_sites <- filter(
kcl_noaa_nearest,
site_type %in% c("Urban Background", "Roadside"))
directory_met_data = "D:/cpdav/UK_met_data/noaa_UK_met_data_"
met_london_df_all = map2_dfr(.x = london_urb_sites$code,
.y = london_urb_sites$met_code,
.f = ~read_met_sites_london(site_code = .x, metcode = .y,
"2016-01-01", "2020-12-31"))
met_aq_london_urban_background_no2=met_aq_prepared_rm_vs_de(met_london_df_all,
"no2", "Urban Background", TRUE)
met_aq_london_roadside_no2=met_aq_prepared_rm_vs_de(met_london_df_all,
"no2", "Roadside", TRUE)
met_aq_london_roadside_pm25=met_aq_prepared_rm_vs_de(met_london_df_all,
"pm25", "Roadside", TRUE)
London_code_no2_urban_background = unique(as.character(met_aq_london_urban_background_no2$code))
London_code_no2_roadside = unique(as.character(met_aq_london_roadside_no2$code))
London_code_pm25_roadside = unique(as.character(met_aq_london_roadside_pm25$code))
ULEZ_no2_urban_background_sites = c("BL0", "CT3", "KC1", "WM0")
ULEZ_no2_urban_roadside_sites = c("CD9", "CT4", "CT6", "NB1")
ULEZ_pm25_urban_roadside_sites = c("CD9")
#Note, ULEZ was initially implemented 8th April 2019, ran code a month prior to implementation
normalised_urban_background_no2_london = map(.x = London_code_no2_urban_background,
.f = ~rmweather_normalised_observed(df = met_aq_london_urban_background_no2,
site = .x, 300, "2016-01-01", "2019-12-31", 0.85, 300))
normalised_roadside_no2_london = map(.x = London_code_no2_roadside,
.f = ~rmweather_normalised_observed(df = met_aq_london_roadside_no2,
site = .x, 300, "2016-01-01", "2019-12-31", 0.85, 300))
normalised_roadside_pm25_london = map(.x = London_code_pm25_roadside,
.f = ~rmweather_normalised_observed(df = met_aq_london_roadside_pm25,
site = .x, 300, "2016-01-01", "2019-12-31", 0.85, 300))
normalised_urban_background_no2_ULEZ_reformat =
urban_reformat_data_mean_sd_no_normal(normalised_urban_background_no2_london,
London_code_no2_urban_background, ULEZ_no2_urban_background_sites,
normal=TRUE, ULEZ=TRUE)
normalised_urban_background_no2_all_sites_reformat =
urban_reformat_data_mean_sd_no_normal(normalised_urban_background_no2_london,
London_code_no2_urban_background, ULEZ_no2_urban_background_sites,
normal=TRUE, ULEZ=FALSE)
normalised_roadside_no2_ULEZ_reformat =
urban_reformat_data_mean_sd_no_normal(normalised_roadside_no2_london,
London_code_no2_roadside, ULEZ_no2_urban_roadside_sites,
normal=TRUE, ULEZ=TRUE)
normalised_roadside_no2_all_sites_reformat =
urban_reformat_data_mean_sd_no_normal(normalised_roadside_no2_london,
London_code_no2_roadside, ULEZ_no2_urban_roadside_sites,
normal=TRUE, ULEZ=FALSE)
normalised_roadside_pm25_ULEZ_reformat =
urban_reformat_data_mean_sd_no_normal(normalised_roadside_pm25_london,
London_code_pm25_roadside, ULEZ_pm25_urban_roadside_sites,
normal=TRUE, ULEZ=TRUE)
normalised_roadside_pm25_all_sites_reformat =
urban_reformat_data_mean_sd_no_normal(normalised_roadside_pm25_london,
London_code_pm25_roadside, ULEZ_pm25_urban_roadside_sites,
normal=TRUE, ULEZ=FALSE)
normalised_urban_background_no2_all_sites_reformat %>%
filter(date >= as.Date("2019-01-01") & date <= as.Date("2019-06-30")) %>%
ggplot(aes(x = date, y = d7_rollavg_normal_mean))+
annotate("rect", xmin = as.POSIXct(as.Date("2019-04-08")),
xmax = as.POSIXct(as.Date("2019-06-30")), ymin = -Inf, ymax = Inf,
alpha = .2) +
geom_line(colour="red", lwd = 1.5) +
facet_grid(delta~., scales = "free_y")+
labs(x= "Date", y = "Various Units", colour = "Human impact comparison")+
geom_vline(xintercept = as.POSIXct(as.Date("2019-03-15")),
color = "black",
lwd = 1,
linetype = "dashed")+
theme_bw(base_size = 20)