tests/testthat/_test_data.R
In hubert-thieriot/crea_r_package: Query And Plot Air Quality And Weather Data from CREA Database
library(testthat)
# Testing CREA data vs official or semi-official sources
test_that("Numbers match previous studies", {
within_range <- function(value_crea, value_official, max_relative_error ){
return(abs((value_crea-value_official)/value_official) <= max_relative_error)
}
#-------------
# Beijing
#-------------
obs <- data.frame("country"=character(), "city"=character(), "location_id"=character(), "poll"=character(),
"year"=integer(), "month"=integer(), "day"=integer(), "value"=double(), "source"=character(),
stringsAsFactors = F)
# The 2019 average concentration of 42 micrograms per cubic metre was 53 per cent lower
# than the 2013 figure of 89.5, according to the municipal ecology and environment bureau.
# https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war
link <- "https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war"
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2019, NA, NA, 42, link)
# Average PM2.5 level in Beijing was 52 micrograms per cubic meter in January,
# up 52.9 percent from a year ago, according to the MEE data.
# https://www.reuters.com/article/us-china-pollution-beijing/chinas-capital-beijing-vows-air-quality-improvement-but-gives-no-target-idUSKCN1Q90TU
link <- "https://www.reuters.com/article/us-china-pollution-beijing/chinas-capital-beijing-vows-air-quality-improvement-but-gives-no-target-idUSKCN1Q90TU"
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2019, 1, NA, 52, link)
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2018, 1, NA, 52/(1+0.529), link)
# Average concentrations of PM2.5, or particulate matter that measures 2.5 microns, in 2018
# fell 12.1 percent from 2017 to 51 micrograms per cubic meter
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2018, NA, NA, 51, link)
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2017, NA, NA, 51/(1-0.121), link)
# This has also resulted in the concentration of sulphur dioxide in the atmosphere dropping by 85 per cent
# from 28 microgrammes per cubic metre in 2013 to 4 in 2019.
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, SO2, 2019, NA, NA, 4, link)
#-------------
# China
#-------------
# According to central government figures, in 2018, the national average concentration of PM2.5
# was 39 micrograms per cubic meter, 9.3 per cent lower than the previous year
# https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war
link <- "https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war"
obs[nrow(obs) + 1,] <- list("CN", NA, NA, PM25, 2019, NA, NA, 39, link)
obs[nrow(obs) + 1,] <- list("CN", NA, NA, PM25, 2018, NA, NA, 39/(1-0.093), link)
# The average concentration of PM10 particles and nitrogen dioxide were 68 and 37 micrograms per cubic metre,
# both in line with national targets.
obs[nrow(obs) + 1,] <- list("CN", NA, NA, PM10, 2019, NA, NA, 69, link)
obs[nrow(obs) + 1,] <- list("CN", NA, NA, NO2, 2019, NA, NA, 37, link)
#-------------
# India
#-------------
# # 2017 data
# # http://www.cpcbenvis.nic.in/air_quality_data.html
# # SO2 NO2 PM10 PM2.5
# # Hyderabad 6 28 108 54
# # Kolkata 6 41 120 71
# # Mumbai 3 18 151 40
# # Delhi 7 68 241 101
# link <- "http://www.cpcbenvis.nic.in/air_quality_data.html"
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, SO2, 2017, NA, NA, 6, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, NO2, 2017, NA, NA, 28, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2017, NA, NA, 108, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM25, 2017, NA, NA, 54, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, SO2, 2017, NA, NA, 6, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, NO2, 2017, NA, NA, 41, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2017, NA, NA, 120, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM25, 2017, NA, NA, 71, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, SO2, 2017, NA, NA, 3, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, NO2, 2017, NA, NA, 18, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2017, NA, NA, 151, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM25, 2017, NA, NA, 40, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, SO2, 2017, NA, NA, 7, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, NO2, 2017, NA, NA, 68, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2017, NA, NA, 241, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM25, 2017, NA, NA, 101, link)
#
# # 2016 data
# # http://www.cpcbenvis.nic.in/air_quality_data.html
# # SO2 NO2 PM10 PM2.5
# # Hyderabad 4 27 100 49
# # Kolkata 4 49 113 70
# # Mumbai 6 30 119 -
# # Delhi 7 66 278 118
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, SO2, 2016, NA, NA, 4, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, NO2, 2016, NA, NA, 27, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2016, NA, NA, 100, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM25, 2016, NA, NA, 49, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, SO2, 2016, NA, NA, 4, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, NO2, 2016, NA, NA, 49, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2016, NA, NA, 113, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM25, 2016, NA, NA, 70, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, SO2, 2016, NA, NA, 6, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, NO2, 2016, NA, NA, 30, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2016, NA, NA, 119, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, SO2, 2016, NA, NA, 7, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, NO2, 2016, NA, NA, 66, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2016, NA, NA, 278, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM25, 2016, NA, NA, 118, link)
# CPCB CCR data
# North Campus Delhi
# S.No From Date To Date PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) CO (mg/m3) Ozone (ug/m3) PM10 (ug/m3) NOx (ppb)
# 2 01-Jan-2016 - 00:00 01-Jan-2017 - 00:00 66.07 11.68 13.85 1.48 26.72 167.13 25.53
link <- 'https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-view-data-report/'
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM10, 2016, NA, NA, 167.13, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM25, 2016, NA, NA, 66.07, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO, 2016, NA, NA, 11.68, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO2, 2016, NA, NA, 13.85, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NOX, 2016, NA, NA, 25.53, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', CO, 2016, NA, NA, 1480, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', O3, 2016, NA, NA, 26.72, link)
# S.No From Date To Date NO (ug/m3) PM10 (ug/m3) PM2.5 (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3)Ozone (ug/m3)
# 1 01-Nov-2018 - 00:00 02-Nov-2018 - 00:00 171.68 379.09 283.77 127.78 299.3 4.16 28.74
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM25, 2018, 11, 1, 283.77, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO, 2018, 11, 1, 171.68, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO2, 2018, 11, 1, 127.78, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', CO, 2018, 11, 1, 4160, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', O3, 2018, 11, 1, 28.74, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM10, 2018, 11, 1, 379.09, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NOX, 2018, 11, 1, 299.3, link)
# Siri fort, Delhi
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-131', PM25, 2018, 2, 1, 125.2, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-131', NO, 2018, 2, 1, 28.06, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-131', NO2, 2018, 2, 1, 73.73, link)
# Victoria, Kolkata - WBPCB
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2017 - 00:00 01-Jan-2018 - 00:00 88.34 - 49.66 50.11 100.07 4.86 31.68
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM10, 2017, NA, NA, 88.34, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM25, 2017, NA, NA, NA, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO, 2017, NA, NA, 49.66, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO2, 2017, NA, NA, 50.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NOX, 2017, NA, NA, 100.07, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', CO, 2017, NA, NA, 4860, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', O3, 2017, NA, NA, 31.68, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', SO2, 2017, NA, NA, NA, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) Ozone (ug/m3) SO2 (ug/m3) NO2 (ug/m3) NO (ug/m3) CO (mg/m3) NOx (ppb)
# 1 01-Jan-2018 - 00:00 01-Jan-2019 - 00:00 84.57 51.51 19.58 4.95 40.69 29.3 1.06 69.57
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM25, 2018, NA, NA, 51.51, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO, 2018, NA, NA, 29.3, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO2, 2018, NA, NA, 40.69, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', CO, 2018, NA, NA, 1060, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', O3, 2018, NA, NA, 19.58, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM10, 2018, NA, NA, 84.57, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NOX, 2018, NA, NA, 69.57, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', SO2, 2018, NA, NA, 4.95, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) Ozone (ug/m3) SO2 (ug/m3) NO2 (ug/m3) NO (ug/m3) CO (mg/m3) NOx (ppb)
# 1 20-Oct-2019 - 00:00 21-Oct-2019 - 00:00 107.9 69.54 37.38 3.28 54.67 20.08 1.44 74.75
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM25, 2019, 10, 20, 69.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO, 2019, 10, 20, 20.08, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO2, 2019, 10, 20, 54.67, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', CO, 2019, 10, 20, 1440, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', O3, 2019, 10, 20, 37.38, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM10, 2019, 10, 20, 107.9, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NOX, 2019, 10, 20, 74.75, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', SO2, 2019, 10, 20, 3.28, link)
# Adarsh Nagar, Jaipur - RSPCB "IN-297"
# "Bandra, Mumbai - MPCB" "IN-22"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 62.81 177.22 2.83 21.88 101.11 27.95 54.33 82.29
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', PM25, 2019, 2, 20, 62.81 , link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', PM10, 2019, 2, 20, 177.22, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', CO, 2019, 2, 20, 2830, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', SO2, 2019, 2, 20, 21.88, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', O3, 2019, 2, 20, 101.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NO2, 2019, 2, 20, 27.95, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NO, 2019, 2, 20, 54.33, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NOX, 2019, 2, 20, 82.29, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2016 - 00:00 01-Jan-2017 - 00:00 40.2 0.0
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NOX, 2016, NA, NA, 40.2, link)
# "Ardhali Bazar, Varanasi - UPPCB" "IN-84"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Mar-2017 - 00:00 16-Mar-2017 - 00:00 86.81 202.84 12.71 13.25 21.71 1.13 8.45 30.16
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2017, 3, 15, 86.81, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2017, 3, 15, 202.84, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2017, 3, 15, 1130, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2017, 3, 15, 8.45, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2017, 3, 15, 12.71, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2017, 3, 15, 13.25, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2017, 3, 15, 21.71, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2017, 3, 15, 30.16, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Mar-2018 - 00:00 16-Mar-2018 - 00:00 145.06 356.74 34.52 30.98 104.11 1.25 34.09 53.1
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2018, 3, 15, 145.06, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2018, 3, 15, 356.74, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2018, 3, 15, 1250, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2018, 3, 15, 34.09, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2018, 3, 15, 34.52, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2018, 3, 15, 30.98, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2018, 3, 15, 104.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2018, 3, 15, 53.1, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 154.41 305.38 1.1 31.78 66.59 117.26 43.49 75.22
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2019, 2, 20, 154.41, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2019, 2, 20, 305.38, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2019, 2, 20, 1100, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2019, 2, 20, 43.49, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2019, 2, 20, 31.78, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2019, 2, 20, 66.59, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2019, 2, 20, 117.26, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2019, 2, 20, 75.22, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 22-Jan-2020 - 00:00 23-Jan-2020 - 00:00 137.17 14.29 40.75 50.62 8.76 20.11
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2020, 1, 22, 137.17, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2020, 1, 22, NA, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2020, 1, 22, NA, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2020, 1, 22, 8.76, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2020, 1, 22, 14.29, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2020, 1, 22, 40.75, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2020, 1, 22, 50.62, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2020, 1, 22, 20.11, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2016 - 00:00 01-Jan-2017 - 00:00 266.36 154.12 11.14 20.92 32.12 1.13 12.36
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2016, NA, NA, 266.36, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2016, NA, NA, 154.12, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2016, NA, NA, 11.14, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2016, NA, NA, 20.92, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2016, NA, NA, 32.12, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2016, NA, NA, 1130, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2016, NA, NA, 12.36, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2016, NA, NA, 5.76, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb) SO2 (ug/m3) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2017 - 00:00 01-Jan-2018 - 00:00 105.54 217.7 36.5 11.48 41.55 6.91 0.57 11.54
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2017, NA, NA, 105.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2017, NA, NA, 217.7, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2017, NA, NA, 36.5, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2017, NA, NA, 11.48, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2017, NA, NA, 41.55, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2017, NA, NA, 6.91, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2017, NA, NA, 570, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2017, NA, NA, 11.54, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb) SO2 (ug/m3) CO (mg/m3) Ozone (ug/m3)
# 2 01-Jan-2018 - 00:00 01-Jan-2019 - 00:00 103.25 219.62 77.84 19.41 35.8 22.1 1.0 42.82
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2018, NA, NA, 103.25, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2018, NA, NA, 219.62, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2018, NA, NA, 77.84, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2018, NA, NA, 19.41, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2018, NA, NA, 35.8, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2018, NA, NA, 22.1, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2018, NA, NA, 1000, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2018, NA, NA, 42.82, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb) SO2 (ug/m3) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2019 - 00:00 01-Jan-2020 - 00:00 97.96 207.15 55.33 14.17 25.45 22.88 0.68 61.97
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2019, NA, NA, 97.96, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2019, NA, NA, 207.15, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2019, NA, NA, 55.33, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2019, NA, NA, 14.17, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2019, NA, NA, 25.45, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2019, NA, NA, 22.88, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2019, NA, NA, 680, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2019, NA, NA, 61.97, link)
# "Central University, Hyderabad - TSPCB" "IN-63"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Mar-2018 - 00:00 16-Mar-2018 - 00:00 43.25 104.67 6.75 69.79 49.54 1.01 6.48 32.27
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM25, 2018, 3, 15, 43.25, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM10, 2018, 3, 15, 104.67, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', CO, 2018, 3, 15, 1010, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', SO2, 2018, 3, 15, 6.75, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', O3, 2018, 3, 15, 69.79, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO2, 2018, 3, 15, 49.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO, 2018, 3, 15, 6.48, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NOX, 2018, 3, 15, 32.27, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 28.11 83.2 0.42 3.83 38.56 28.72 3.18 17.87
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM25, 2019, 2, 20, 28.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM10, 2019, 2, 20, 83.2, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', CO, 2019, 2, 20, 420, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', SO2, 2019, 2, 20, 3.83, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', O3, 2019, 2, 20, 38.56, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO2, 2019, 2, 20, 28.72, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO, 2019, 2, 20, 3.18, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NOX, 2019, 2, 20, 17.87, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Jan-2020 - 00:00 16-Jan-2020 - 00:00 33.94 70.54 2.67 36.14 20.41 0.0 4.06 14.14
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM25, 2020, 1, 15, 33.94, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM10, 2020, 1, 15, 70.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', CO, 2020, 1, 15, 0, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', SO2, 2020, 1, 15, 2.67, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', O3, 2020, 1, 15, 36.14, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO2, 2020, 1, 15, 20.41, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO, 2020, 1, 15, 4.06, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NOX, 2020, 1, 15, 14.14, link)
# Sanathnagar, Hyderabad - TSPCB IN-111
# S.No From Date To Date NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3) SO2 (ug/m3) PM2.5 (ug/m3)
# 1 01-Jan-2019 - 00:00 01-Jan-2020 - 00:00 6.56 32.51 22.61 0.7 28.37 8.24 48.05
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', PM25, 2019, NA, NA, 48.05, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', CO, 2019, NA, NA, 700, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', SO2, 2019, NA, NA, 8.24, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', O3, 2019, NA, NA, 28.37, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', NO2, 2019, NA, NA, 32.51, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', NO, 2019, NA, NA, 6.56, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', NOX, 2019, NA, NA, 22.61, link)
# "Ashok Nagar, Udaipur - RSPCB" "IN-139"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 65.69 185.01 1.75 14.74 20.38 53.95 68.24 122.24
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM25, 2019, 2, 20, 65.69, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM10, 2019, 2, 20, 185.01, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', CO, 2019, 2, 20, 1750, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', SO2, 2019, 2, 20, 14.74, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', O3, 2019, 2, 20, 20.38, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO2, 2019, 2, 20, 53.95, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO, 2019, 2, 20, 68.24, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NOX, 2019, 2, 20, 122.24, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) SO2 (ug/m3) Ozone (ug/m3) CO (mg/m3)
# 1 01-Feb-2020 - 00:00 02-Feb-2020 - 00:00 90.46 40.37 25.88 38.6 64.48 9.57 30.5 1.61
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM25, 2020, 2, 1, 40.37, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM10, 2020, 2, 1, 90.46, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', CO, 2020, 2, 1, 1610, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', SO2, 2020, 2, 1, 9.57, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', O3, 2020, 2, 1, 30.5, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO2, 2020, 2, 1, 38.6, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO, 2020, 2, 1, 25.88, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NOX, 2020, 2, 1, 64.48, link)
# Carbon Copy Dashboard
link <- "https://ncap.carboncopy.info/ncap-cities/"
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2016, NA, NA, 113, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2017, NA, NA, 120, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2016, NA, NA, 119, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2017, NA, NA, 151, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2016, NA, NA, 276, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2017, NA, NA, 240, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2016, NA, NA, 101, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2017, NA, NA, 108, link)
#------------------------
# Calculate crea values
#------------------------
# For one row
get_crea_value <- function(row) {
city <- if(is.na(row$city)) NULL else strsplit(row$city,"\\|")[[1]]
month_from <- ifelse(is.na(row$month), 1,row$month)
day_from <- ifelse(is.na(row$day), 1, row$day)
date_from = lubridate::ymd(row$year*10000 + month_from*100 + day_from)
month_to <- ifelse(is.na(row$month),12,row$month)
day_to <- ifelse(is.na(row$day), days_in_month(month_to), row$day)
date_to = lubridate::ymd(row$year*10000 + month_to*100 + day_to)
meas <- measurements(country=row$country,
city=city,
location_id = row$location_id,
poll=row$poll,
date_from=date_from,
date_to=date_to,
collect=F)
# Average per city if we want a national figure
if(is.na(row$city) && is.na(row$location_id)){
meas <- meas %>% group_by(city) %>% summarize(value=mean(value, na.rm = T)) %>% ungroup()
}
# Finally average all measurements
meas <- meas %>% summarize(crea_value=mean(value, na.rm = T))
meas <- meas %>% collect()
return(meas)
}
# # For each row
# obs_crea <- obs %>% by_row(..f = get_crea_value, .to = "crea_value", .collate = "cols")
#
# # Reshaping
# obs_crea <- obs_crea %>%
# dplyr::mutate(rel_diff=sprintf("%1.2f%%", 100*abs(value-crea_value1)/value)) %>%
# dplyr::select(-source,source) %>%
# dplyr::rename(value_official=value, value_crea=crea_value1)
})
hubert-thieriot/crea_r_package documentation built on June 11, 2025, 11:31 p.m.
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library(testthat)
# Testing CREA data vs official or semi-official sources
test_that("Numbers match previous studies", {
within_range <- function(value_crea, value_official, max_relative_error ){
return(abs((value_crea-value_official)/value_official) <= max_relative_error)
}
#-------------
# Beijing
#-------------
obs <- data.frame("country"=character(), "city"=character(), "location_id"=character(), "poll"=character(),
"year"=integer(), "month"=integer(), "day"=integer(), "value"=double(), "source"=character(),
stringsAsFactors = F)
# The 2019 average concentration of 42 micrograms per cubic metre was 53 per cent lower
# than the 2013 figure of 89.5, according to the municipal ecology and environment bureau.
# https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war
link <- "https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war"
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2019, NA, NA, 42, link)
# Average PM2.5 level in Beijing was 52 micrograms per cubic meter in January,
# up 52.9 percent from a year ago, according to the MEE data.
# https://www.reuters.com/article/us-china-pollution-beijing/chinas-capital-beijing-vows-air-quality-improvement-but-gives-no-target-idUSKCN1Q90TU
link <- "https://www.reuters.com/article/us-china-pollution-beijing/chinas-capital-beijing-vows-air-quality-improvement-but-gives-no-target-idUSKCN1Q90TU"
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2019, 1, NA, 52, link)
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2018, 1, NA, 52/(1+0.529), link)
# Average concentrations of PM2.5, or particulate matter that measures 2.5 microns, in 2018
# fell 12.1 percent from 2017 to 51 micrograms per cubic meter
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2018, NA, NA, 51, link)
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, PM25, 2017, NA, NA, 51/(1-0.121), link)
# This has also resulted in the concentration of sulphur dioxide in the atmosphere dropping by 85 per cent
# from 28 microgrammes per cubic metre in 2013 to 4 in 2019.
obs[nrow(obs) + 1,] <- list("CN", "Beijing|北京市", NA, SO2, 2019, NA, NA, 4, link)
#-------------
# China
#-------------
# According to central government figures, in 2018, the national average concentration of PM2.5
# was 39 micrograms per cubic meter, 9.3 per cent lower than the previous year
# https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war
link <- "https://www.scmp.com/news/china/society/article/3044747/beijings-air-quality-shows-significant-improvements-war"
obs[nrow(obs) + 1,] <- list("CN", NA, NA, PM25, 2019, NA, NA, 39, link)
obs[nrow(obs) + 1,] <- list("CN", NA, NA, PM25, 2018, NA, NA, 39/(1-0.093), link)
# The average concentration of PM10 particles and nitrogen dioxide were 68 and 37 micrograms per cubic metre,
# both in line with national targets.
obs[nrow(obs) + 1,] <- list("CN", NA, NA, PM10, 2019, NA, NA, 69, link)
obs[nrow(obs) + 1,] <- list("CN", NA, NA, NO2, 2019, NA, NA, 37, link)
#-------------
# India
#-------------
# # 2017 data
# # http://www.cpcbenvis.nic.in/air_quality_data.html
# # SO2 NO2 PM10 PM2.5
# # Hyderabad 6 28 108 54
# # Kolkata 6 41 120 71
# # Mumbai 3 18 151 40
# # Delhi 7 68 241 101
# link <- "http://www.cpcbenvis.nic.in/air_quality_data.html"
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, SO2, 2017, NA, NA, 6, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, NO2, 2017, NA, NA, 28, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2017, NA, NA, 108, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM25, 2017, NA, NA, 54, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, SO2, 2017, NA, NA, 6, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, NO2, 2017, NA, NA, 41, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2017, NA, NA, 120, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM25, 2017, NA, NA, 71, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, SO2, 2017, NA, NA, 3, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, NO2, 2017, NA, NA, 18, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2017, NA, NA, 151, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM25, 2017, NA, NA, 40, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, SO2, 2017, NA, NA, 7, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, NO2, 2017, NA, NA, 68, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2017, NA, NA, 241, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM25, 2017, NA, NA, 101, link)
#
# # 2016 data
# # http://www.cpcbenvis.nic.in/air_quality_data.html
# # SO2 NO2 PM10 PM2.5
# # Hyderabad 4 27 100 49
# # Kolkata 4 49 113 70
# # Mumbai 6 30 119 -
# # Delhi 7 66 278 118
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, SO2, 2016, NA, NA, 4, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, NO2, 2016, NA, NA, 27, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2016, NA, NA, 100, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM25, 2016, NA, NA, 49, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, SO2, 2016, NA, NA, 4, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, NO2, 2016, NA, NA, 49, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2016, NA, NA, 113, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM25, 2016, NA, NA, 70, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, SO2, 2016, NA, NA, 6, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, NO2, 2016, NA, NA, 30, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2016, NA, NA, 119, link)
#
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, SO2, 2016, NA, NA, 7, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, NO2, 2016, NA, NA, 66, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2016, NA, NA, 278, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM25, 2016, NA, NA, 118, link)
# CPCB CCR data
# North Campus Delhi
# S.No From Date To Date PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) CO (mg/m3) Ozone (ug/m3) PM10 (ug/m3) NOx (ppb)
# 2 01-Jan-2016 - 00:00 01-Jan-2017 - 00:00 66.07 11.68 13.85 1.48 26.72 167.13 25.53
link <- 'https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-view-data-report/'
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM10, 2016, NA, NA, 167.13, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM25, 2016, NA, NA, 66.07, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO, 2016, NA, NA, 11.68, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO2, 2016, NA, NA, 13.85, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NOX, 2016, NA, NA, 25.53, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', CO, 2016, NA, NA, 1480, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', O3, 2016, NA, NA, 26.72, link)
# S.No From Date To Date NO (ug/m3) PM10 (ug/m3) PM2.5 (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3)Ozone (ug/m3)
# 1 01-Nov-2018 - 00:00 02-Nov-2018 - 00:00 171.68 379.09 283.77 127.78 299.3 4.16 28.74
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM25, 2018, 11, 1, 283.77, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO, 2018, 11, 1, 171.68, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NO2, 2018, 11, 1, 127.78, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', CO, 2018, 11, 1, 4160, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', O3, 2018, 11, 1, 28.74, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', PM10, 2018, 11, 1, 379.09, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-82', NOX, 2018, 11, 1, 299.3, link)
# Siri fort, Delhi
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-131', PM25, 2018, 2, 1, 125.2, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-131', NO, 2018, 2, 1, 28.06, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', 'IN-131', NO2, 2018, 2, 1, 73.73, link)
# Victoria, Kolkata - WBPCB
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2017 - 00:00 01-Jan-2018 - 00:00 88.34 - 49.66 50.11 100.07 4.86 31.68
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM10, 2017, NA, NA, 88.34, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM25, 2017, NA, NA, NA, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO, 2017, NA, NA, 49.66, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO2, 2017, NA, NA, 50.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NOX, 2017, NA, NA, 100.07, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', CO, 2017, NA, NA, 4860, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', O3, 2017, NA, NA, 31.68, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', SO2, 2017, NA, NA, NA, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) Ozone (ug/m3) SO2 (ug/m3) NO2 (ug/m3) NO (ug/m3) CO (mg/m3) NOx (ppb)
# 1 01-Jan-2018 - 00:00 01-Jan-2019 - 00:00 84.57 51.51 19.58 4.95 40.69 29.3 1.06 69.57
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM25, 2018, NA, NA, 51.51, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO, 2018, NA, NA, 29.3, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO2, 2018, NA, NA, 40.69, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', CO, 2018, NA, NA, 1060, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', O3, 2018, NA, NA, 19.58, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM10, 2018, NA, NA, 84.57, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NOX, 2018, NA, NA, 69.57, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', SO2, 2018, NA, NA, 4.95, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) Ozone (ug/m3) SO2 (ug/m3) NO2 (ug/m3) NO (ug/m3) CO (mg/m3) NOx (ppb)
# 1 20-Oct-2019 - 00:00 21-Oct-2019 - 00:00 107.9 69.54 37.38 3.28 54.67 20.08 1.44 74.75
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM25, 2019, 10, 20, 69.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO, 2019, 10, 20, 20.08, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NO2, 2019, 10, 20, 54.67, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', CO, 2019, 10, 20, 1440, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', O3, 2019, 10, 20, 37.38, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', PM10, 2019, 10, 20, 107.9, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', NOX, 2019, 10, 20, 74.75, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', 'IN-142', SO2, 2019, 10, 20, 3.28, link)
# Adarsh Nagar, Jaipur - RSPCB "IN-297"
# "Bandra, Mumbai - MPCB" "IN-22"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 62.81 177.22 2.83 21.88 101.11 27.95 54.33 82.29
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', PM25, 2019, 2, 20, 62.81 , link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', PM10, 2019, 2, 20, 177.22, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', CO, 2019, 2, 20, 2830, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', SO2, 2019, 2, 20, 21.88, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', O3, 2019, 2, 20, 101.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NO2, 2019, 2, 20, 27.95, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NO, 2019, 2, 20, 54.33, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NOX, 2019, 2, 20, 82.29, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2016 - 00:00 01-Jan-2017 - 00:00 40.2 0.0
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', 'IN-22', NOX, 2016, NA, NA, 40.2, link)
# "Ardhali Bazar, Varanasi - UPPCB" "IN-84"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Mar-2017 - 00:00 16-Mar-2017 - 00:00 86.81 202.84 12.71 13.25 21.71 1.13 8.45 30.16
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2017, 3, 15, 86.81, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2017, 3, 15, 202.84, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2017, 3, 15, 1130, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2017, 3, 15, 8.45, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2017, 3, 15, 12.71, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2017, 3, 15, 13.25, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2017, 3, 15, 21.71, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2017, 3, 15, 30.16, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Mar-2018 - 00:00 16-Mar-2018 - 00:00 145.06 356.74 34.52 30.98 104.11 1.25 34.09 53.1
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2018, 3, 15, 145.06, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2018, 3, 15, 356.74, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2018, 3, 15, 1250, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2018, 3, 15, 34.09, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2018, 3, 15, 34.52, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2018, 3, 15, 30.98, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2018, 3, 15, 104.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2018, 3, 15, 53.1, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 154.41 305.38 1.1 31.78 66.59 117.26 43.49 75.22
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2019, 2, 20, 154.41, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2019, 2, 20, 305.38, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2019, 2, 20, 1100, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2019, 2, 20, 43.49, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2019, 2, 20, 31.78, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2019, 2, 20, 66.59, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2019, 2, 20, 117.26, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2019, 2, 20, 75.22, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 22-Jan-2020 - 00:00 23-Jan-2020 - 00:00 137.17 14.29 40.75 50.62 8.76 20.11
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2020, 1, 22, 137.17, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2020, 1, 22, NA, link)
# obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2020, 1, 22, NA, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2020, 1, 22, 8.76, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2020, 1, 22, 14.29, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2020, 1, 22, 40.75, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2020, 1, 22, 50.62, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2020, 1, 22, 20.11, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2016 - 00:00 01-Jan-2017 - 00:00 266.36 154.12 11.14 20.92 32.12 1.13 12.36
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2016, NA, NA, 266.36, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2016, NA, NA, 154.12, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2016, NA, NA, 11.14, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2016, NA, NA, 20.92, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2016, NA, NA, 32.12, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2016, NA, NA, 1130, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2016, NA, NA, 12.36, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2016, NA, NA, 5.76, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb) SO2 (ug/m3) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2017 - 00:00 01-Jan-2018 - 00:00 105.54 217.7 36.5 11.48 41.55 6.91 0.57 11.54
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2017, NA, NA, 105.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2017, NA, NA, 217.7, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2017, NA, NA, 36.5, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2017, NA, NA, 11.48, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2017, NA, NA, 41.55, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2017, NA, NA, 6.91, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2017, NA, NA, 570, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2017, NA, NA, 11.54, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb) SO2 (ug/m3) CO (mg/m3) Ozone (ug/m3)
# 2 01-Jan-2018 - 00:00 01-Jan-2019 - 00:00 103.25 219.62 77.84 19.41 35.8 22.1 1.0 42.82
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2018, NA, NA, 103.25, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2018, NA, NA, 219.62, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2018, NA, NA, 77.84, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2018, NA, NA, 19.41, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2018, NA, NA, 35.8, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2018, NA, NA, 22.1, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2018, NA, NA, 1000, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2018, NA, NA, 42.82, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb) SO2 (ug/m3) CO (mg/m3) Ozone (ug/m3)
# 1 01-Jan-2019 - 00:00 01-Jan-2020 - 00:00 97.96 207.15 55.33 14.17 25.45 22.88 0.68 61.97
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM25, 2019, NA, NA, 97.96, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', PM10, 2019, NA, NA, 207.15, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO2, 2019, NA, NA, 55.33, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NO, 2019, NA, NA, 14.17, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', NOX, 2019, NA, NA, 25.45, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', SO2, 2019, NA, NA, 22.88, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', CO, 2019, NA, NA, 680, link)
obs[nrow(obs) + 1,] <- list("IN", 'Varanasi', 'IN-84', O3, 2019, NA, NA, 61.97, link)
# "Central University, Hyderabad - TSPCB" "IN-63"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Mar-2018 - 00:00 16-Mar-2018 - 00:00 43.25 104.67 6.75 69.79 49.54 1.01 6.48 32.27
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM25, 2018, 3, 15, 43.25, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM10, 2018, 3, 15, 104.67, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', CO, 2018, 3, 15, 1010, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', SO2, 2018, 3, 15, 6.75, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', O3, 2018, 3, 15, 69.79, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO2, 2018, 3, 15, 49.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO, 2018, 3, 15, 6.48, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NOX, 2018, 3, 15, 32.27, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 28.11 83.2 0.42 3.83 38.56 28.72 3.18 17.87
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM25, 2019, 2, 20, 28.11, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM10, 2019, 2, 20, 83.2, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', CO, 2019, 2, 20, 420, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', SO2, 2019, 2, 20, 3.83, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', O3, 2019, 2, 20, 38.56, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO2, 2019, 2, 20, 28.72, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO, 2019, 2, 20, 3.18, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NOX, 2019, 2, 20, 17.87, link)
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) CO (mg/m3) NO (ug/m3) NOx (ppb)
# 1 15-Jan-2020 - 00:00 16-Jan-2020 - 00:00 33.94 70.54 2.67 36.14 20.41 0.0 4.06 14.14
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM25, 2020, 1, 15, 33.94, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', PM10, 2020, 1, 15, 70.54, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', CO, 2020, 1, 15, 0, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', SO2, 2020, 1, 15, 2.67, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', O3, 2020, 1, 15, 36.14, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO2, 2020, 1, 15, 20.41, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NO, 2020, 1, 15, 4.06, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-63', NOX, 2020, 1, 15, 14.14, link)
# Sanathnagar, Hyderabad - TSPCB IN-111
# S.No From Date To Date NO (ug/m3) NO2 (ug/m3) NOx (ppb) CO (mg/m3) Ozone (ug/m3) SO2 (ug/m3) PM2.5 (ug/m3)
# 1 01-Jan-2019 - 00:00 01-Jan-2020 - 00:00 6.56 32.51 22.61 0.7 28.37 8.24 48.05
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', PM25, 2019, NA, NA, 48.05, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', CO, 2019, NA, NA, 700, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', SO2, 2019, NA, NA, 8.24, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', O3, 2019, NA, NA, 28.37, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', NO2, 2019, NA, NA, 32.51, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', NO, 2019, NA, NA, 6.56, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', 'IN-111', NOX, 2019, NA, NA, 22.61, link)
# "Ashok Nagar, Udaipur - RSPCB" "IN-139"
# S.No From Date To Date PM2.5 (ug/m3) PM10 (ug/m3) CO (mg/m3) SO2 (ug/m3) Ozone (ug/m3) NO2 (ug/m3) NO (ug/m3) NOx (ppb)
# 1 20-Feb-2019 - 00:00 21-Feb-2019 - 00:00 65.69 185.01 1.75 14.74 20.38 53.95 68.24 122.24
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM25, 2019, 2, 20, 65.69, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM10, 2019, 2, 20, 185.01, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', CO, 2019, 2, 20, 1750, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', SO2, 2019, 2, 20, 14.74, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', O3, 2019, 2, 20, 20.38, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO2, 2019, 2, 20, 53.95, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO, 2019, 2, 20, 68.24, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NOX, 2019, 2, 20, 122.24, link)
# S.No From Date To Date PM10 (ug/m3) PM2.5 (ug/m3) NO (ug/m3) NO2 (ug/m3) NOx (ppb) SO2 (ug/m3) Ozone (ug/m3) CO (mg/m3)
# 1 01-Feb-2020 - 00:00 02-Feb-2020 - 00:00 90.46 40.37 25.88 38.6 64.48 9.57 30.5 1.61
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM25, 2020, 2, 1, 40.37, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', PM10, 2020, 2, 1, 90.46, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', CO, 2020, 2, 1, 1610, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', SO2, 2020, 2, 1, 9.57, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', O3, 2020, 2, 1, 30.5, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO2, 2020, 2, 1, 38.6, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NO, 2020, 2, 1, 25.88, link)
obs[nrow(obs) + 1,] <- list("IN", 'Udaipur', 'IN-139', NOX, 2020, 2, 1, 64.48, link)
# Carbon Copy Dashboard
link <- "https://ncap.carboncopy.info/ncap-cities/"
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2016, NA, NA, 113, link)
obs[nrow(obs) + 1,] <- list("IN", 'Kolkata', NA, PM10, 2017, NA, NA, 120, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2016, NA, NA, 119, link)
obs[nrow(obs) + 1,] <- list("IN", 'Mumbai', NA, PM10, 2017, NA, NA, 151, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2016, NA, NA, 276, link)
obs[nrow(obs) + 1,] <- list("IN", 'Delhi', NA, PM10, 2017, NA, NA, 240, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2016, NA, NA, 101, link)
obs[nrow(obs) + 1,] <- list("IN", 'Hyderabad', NA, PM10, 2017, NA, NA, 108, link)
#------------------------
# Calculate crea values
#------------------------
# For one row
get_crea_value <- function(row) {
city <- if(is.na(row$city)) NULL else strsplit(row$city,"\\|")[[1]]
month_from <- ifelse(is.na(row$month), 1,row$month)
day_from <- ifelse(is.na(row$day), 1, row$day)
date_from = lubridate::ymd(row$year*10000 + month_from*100 + day_from)
month_to <- ifelse(is.na(row$month),12,row$month)
day_to <- ifelse(is.na(row$day), days_in_month(month_to), row$day)
date_to = lubridate::ymd(row$year*10000 + month_to*100 + day_to)
meas <- measurements(country=row$country,
city=city,
location_id = row$location_id,
poll=row$poll,
date_from=date_from,
date_to=date_to,
collect=F)
# Average per city if we want a national figure
if(is.na(row$city) && is.na(row$location_id)){
meas <- meas %>% group_by(city) %>% summarize(value=mean(value, na.rm = T)) %>% ungroup()
}
# Finally average all measurements
meas <- meas %>% summarize(crea_value=mean(value, na.rm = T))
meas <- meas %>% collect()
return(meas)
}
# # For each row
# obs_crea <- obs %>% by_row(..f = get_crea_value, .to = "crea_value", .collate = "cols")
#
# # Reshaping
# obs_crea <- obs_crea %>%
# dplyr::mutate(rel_diff=sprintf("%1.2f%%", 100*abs(value-crea_value1)/value)) %>%
# dplyr::select(-source,source) %>%
# dplyr::rename(value_official=value, value_crea=crea_value1)
})
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