Easy-to-use functions for downloading air quality data from the Mexican National Air Quality Information System (SINAICA). With this R package you can download pollution and meteorological parametrs from the more than a hundred monitoring stations located throughout Mexico. The package allows you to query for crude real-time air quality data, validated data, or manually collected data.


To install the most recent package version from CRAN type:


You can always install the development version from GitHub:

if (!require(devtools)) {


Suppose you wanted to download pollution data from the Centro station in Guadalajara. First, we load the necessary packages and look up the numeric code for the station in the stations_sinaica data.frame:

## Auto-install required R packages
packs <- c("ggplot2", "maps", "mapproj", "rsinaica")
success <- suppressWarnings(sapply(packs, require, character.only = TRUE))
if (length(names(success)[!success])) {
  sapply(names(success)[!success], require, character.only = TRUE)

knitr::kable(stations_sinaica[which(stations_sinaica$station_name == "Centro"), 1:6])
station_id station_name station_code network_id network_name network_code
12 33 Centro CEN 30 Aguascalientes AGS
42 54 Centro CEN 38 Chihuahua CHIH1
75 102 Centro CEN 63 Guadalajara GDL

It looks like there are three stations named Centro, the one we are looking for is the one in Guadalajara with a numeric code (station_id) of 102. The stations_sinaica data.frame also includes the latitude and longitude of all the measuring stations in Mexico (including some that have never reported any data!).

mx <- map_data("world", "Mexico")
stations_sinaica$color <- "Others"
stations_sinaica$color[stations_sinaica$station_id == 102] <- "Centro (102)"
ggplot(stations_sinaica[order(stations_sinaica$color, decreasing = TRUE),], aes(lon, lat)) +
  geom_polygon(data = mx, aes(x= long, y = lat, group = group)) +
  geom_point(alpha = .9, size = 3, aes(fill = color), shape = 21) +
  scale_fill_discrete("station") +
  ggtitle("Air quality measuring stations in Mexico") +
  coord_map() +

Then we query the start and end dates for which SINAICA has received data from the station:

#> [1] "1997-01-01" "2020-07-31"

It’s currently reporting data (this document was built on 2020-07-31), and has been doing so since 1997. We can also query which type of parameters (pollution, wind, solar radiation, etc) the station has sensors for. Note that the package also includes a parameters data.frame with the complete set of supported parameters, but not all stations support all of them.

cen_params <- sinaica_station_params(102)
param_code param_name
CN Carbono negro
SO2 Dióxido de azufre
NO2 Dióxido de nitrógeno
DV Dirección del viento
HR Humedad relativa
CO Monóxido de carbono
NO Óxido nítrico
NOx Óxidos de nitrógeno
O3 Ozono
PM10 Partículas menores a 10 micras
PM2.5 Partículas menores a 2.5 micras
PP Precipitación pluvial
RS Radiación solar
TMPI Temperatura interior
VV Velocidad del viento

Finally, we can download and plot hourly concentrations of particulate matter with a diameter smaller than 10 micrometers (μm) (PM10) during the month of January.

# Download all PM10 data for January 2018
df <-  sinaica_station_data(102, # station_id
                         "PM10", # can be one of parameters$parameter_code
                         "2018-01-31", # Maximum of one month
                         "Crude" # Crude, Manual or Validated

ggplot(df, aes(hour, value, group = date)) +
  geom_line(alpha=.9) +
                           " pollution during January 2018 in Centro, Guadalajara, by hour"))) +
  xlab("hour") +
  ylab(expression(paste(mu,"g/", m^3))) +

the hours are in the local Guadalajara time zone of UTC-6 since we plotted January data.

stations_sinaica$timezone[which(stations_sinaica$station_id == 102)]
#> [1] "Tiempo del centro, UTC-6 (UTC-5 en verano)"

You can find a handy map of Mexico’s time zones from Wikipedia to help you with any time conversions you might need.