Get air quality data from a single measuring station
Source:R/sinaica_station_data.R
sinaica_station_data.RdDownload data from a single station by specifying a parameter and a date range
Usage
sinaica_station_data(
station_id,
parameter,
start_date,
end_date,
type = "Crude",
remove_extremes = FALSE
)Arguments
- station_id
the numeric code corresponding to each station. See
stations_sinaicafor a list of stations and their ids.- parameter
type of parameter to download
BEN - Benceno
CH4" - Metano
CN - Carbono negro
CO - Monóxido de carbono
CO2 - Dióxido de carbono
DV - Dirección del viento
H2S - Acido Sulfhídrico
HCNM - Hidrocarburos no metánicos
HCT - Hidrocarburos Totales
HR - Humedad relativa
HRI - Humedad relativa interior
IUV - Índice de radiación ultravioleta
NO - Óxido nítrico
NO2 - Dióxido de nitrógeno
NOx - Óxidos de nitrógeno
O3 - Ozono
PB - Presión Barométrica
PM10 - Partículas menores a 10 micras
PM2.5 - Partículas menores a 2.5 micras
PP - Precipitación pluvial
PST - Partículas Suspendidas totales
RS - Radiación solar
SO2 - Dióxido de azufre
TMP - Temperatura
TMPI - Temperatura interior
UVA - Radiación ultravioleta A
VV - Radiación ultravioleta B
XIL - Xileno
- start_date
start of range in YYYY-MM-DD format
- end_date
end of range from which to download data in YYYY-MM-DD format
- type
The type of data to download. One of the following:
Crude - Crude data that has not been validated
Validated - data which has undergone a validation process during which it was cleaned, verified, and validated
Manual - Manually collected data that is sent to an external lab for analysis (may no be collected daily). Mostly used for suspend particles collected by pushing air through a filter which is later sent to a lab to be weighted
- remove_extremes
whether to remove extreme values. For O3 all values above .2 are set to NA, for PM10 those above 600, for PM2.5 above 175, for NO2 above .21, for SO2 above .2, and for CO above 15. This is done so that the values match exactly those of the SINAICA website, but it is recommended that you use a more complicated statistical procedure to remove outliers.
Value
data.frame with air quality data. Care should be taken when working
with hourly data since
each station has their own timezone (available in the
stations_sinaica data.frame)
and some stations reported the timezome in which they are located
erroneously.
See also
Crude data comes from https://sinaica.inecc.gob.mx/data.php, validated data from https://sinaica.inecc.gob.mx/data.php?tipo=V, and manual data from https://sinaica.inecc.gob.mx/data.php?tipo=M
Examples
stations_sinaica[which(stations_sinaica$station_name == "Xalostoc"), 1:5]
#> station_id station_name station_code network_id network_name
#> 196 271 Xalostoc XAL 119 Valle de México
df <- sinaica_station_data(271, "O3", "2015-09-11", "2015-09-11", "Crude")
head(df)
#> id date hour value valid unit station_id station_name
#> 1 271O315091100 2015-09-11 0 0.013 1 ppm 271 Xalostoc
#> 2 271O315091101 2015-09-11 1 0.015 1 ppm 271 Xalostoc
#> 3 271O315091102 2015-09-11 2 0.006 1 ppm 271 Xalostoc
#> 4 271O315091103 2015-09-11 3 0.014 1 ppm 271 Xalostoc
#> 5 271O315091104 2015-09-11 4 0.010 1 ppm 271 Xalostoc
#> 6 271O315091105 2015-09-11 5 0.003 1 ppm 271 Xalostoc