This package downloads pollutant data from the SENAMHI air quality network, meteorological data from SENAMHI automatic weather staions, and sounding data from Jorge Chávez airport located in Lima, capital of Peru.
The returned data.frame is a complete dataset.
This means that missing data by missing hours is padded out by NA.
To install limaair you need first to install devtools.
You can do it by:
install.package("devtools")Then install limaair by:
devtools::install_github("quishqa/limaair")limaair has three functions:
download_senamhi_pol().download_senamhi_met().donwload_airport_sounding()
To use download_senamhi_pol(), you need the air quality station (AQS) code (aqs_code),
the pollutant code (pol_code),
and the start and end date of the download (start_date and end_date).
And, to use download_senmahi_met(), you need to know the automatic weather station (AWS) code
(aws_code)
limaair has three datasets to check aqs_code, pol_code, and aws_code values:
library(limaair)
# To check SENAMHI AQS code, name, latitude and longitude
senamhi_aqs
# To check SENAMHI AQS pollutant code, name and units
senamhi_params
# To check SENAMHI AWS code, name latitude and longitude
senamhi_awsIn this example we'll download fine particle concentrations (PM2.5) from Campo de Marte station, from January 1st to January 7th of 2022. Here how you do it.
library(limaair)
senamhi_aqs # To check Campo de Marte
senamhi_params # To check PM25 code
cm_code <- 112194
pm25_code <- "N_PM25"
start_date <- "01/01/2022"
end_date <- "07/01/2022"
cm_pm25 <- download_senamhi_pol(aqs_codes = cm_code,
pol_codes = pm25_code,
start_date = start_date,
end_date = end_date)
Maybe you need to compare data from different stations for different pollutant. You can do it with the same download_senamhi_pol function. aqs_codes and pol_codes can be a vector with the AQS and pollutant codes!.
In this example we'll download PM25 and PM10 from Campo de Marte and San Borja stations.
library(limaair)
senamhi_aqs # To check Campo de Marte and San Borja station codes
senamhi_params # To check PM25 and PM10 codes
cm_sb_code <- c(112194, 112193)
pm_code <- c("N_PM25", "N_PM10")
start_date <- "01/01/2022"
end_date <- "07/01/2022"
cm_pm25 <- download_senamhi_pol(aqs_codes = cm_sb_code,
pol_codes = pm_code,
start_date = start_date,
end_date = end_date)
Voilà! In this case download_senamhi_polreturns a list with two data frames (one for each station). Each data frame has a column for each pollutant. If you prefer one data.frame returned instead of a list (i.e. both data frames combined by row), you can just use to_df = TRUE argument:
cm_sb_code <- c(112194, 112193)
pm_code <- c("N_PM25", "N_PM10")
start_date <- "01/01/2022"
end_date <- "07/01/2022"
cm_pm25 <- download_senamhi_pol(aqs_codes = cm_sb_code,
pol_codes = pm_code,
start_date = start_date,
end_date = end_date,
to_df = TRUE) # Look here!Following the same philosophy as download_senamhi_pol function, you can use download_senamhi_met
to download data for Temperature (°C), Precipitation (mm/hour), Relative Humidity (%), Wind speed (m/s) and direction (°). You only need to know the aws_code, which for some cases is the same as the aqs_code (except for Campo de Marte). In this example, We download meteorological data
for Campo de Marte AWS
cm_code <- 112181 # senamhi_aws
start_date <- "01/01/2019"
end_date <- "28/02/2019"
cm_met <- download_senamhi_met(112181, "01/01/2019", "28/02/2019")Like download_senamhi_pol, you can download data for multiple AWS.
The download_airport_sounding function download sounding data from Wyoming University sounding
repository. Default values are set for Jorge
Chavez International Airport, so you can also use this function to download
sounding data for other airports ;)
# Downloading sounding for 15/01/2019 at 12z
jc_sounding <- download_airport_sounding("15/01/2019")If you prefer to process the data in another software,
you can export the downloaded data to a csv.
You just need to add the to_csv and csv_path arguments in download_senamhi_pol function.
If to_df = TRUE, then it will write a csv with the following name convention {aqs_code1}_{aqs_code2}...{aqs_codeN}-{pol_code1}...{pol_codeN}-{start_date}-{end_date}.csv
If to_df = FALSE, then it will create a csv for each station with the followingname convention {aqs_code1-{pol_code1}...{pol_codeN}-{start_date}-{end_date}.csv.
If csv_path = "" (the default value) then the csv will be saved in the working directory.
cm_sb_code <- c(112194, 112193)
pm_code <- c("N_PM25", "N_PM10")
start_date <- "01/01/2022"
end_date <- "07/01/2022"
cm_pm25 <- download_senamhi_pol(aqs_codes = cm_sb_code,
pol_codes = pm_code,
start_date = start_date,
end_date = end_date,
to_df = TRUE,
to_csv = TRUE,
csv_path = "~/")It will create the 112194_112193-N_PM25_N_PM10-01012022-07012022.csv in the home directory.
According to SENAMHI, the data displayed in their site is not validated data, so you need to be careful and perform a data quality control methodology. You can do it!
Thanks to SENAMHI for posting on-line their air quality data.
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