Package 'aire.zmvm' reference manual

Title:	Download Mexico City Pollution, Wind, and Temperature Data
Description:	Tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.
Authors:	Diego Valle-Jones [aut, cre]
Maintainer:	Diego Valle-Jones <[email protected]>
License:	BSD_3_clause + file LICENSE
Version:	1.0.1
Built:	2026-05-11 09:18:16 UTC
Source:	https://github.com/diegovalle/aire.zmvm

Convert pollution values to IMECA

Description

This function converts pollution running averages in the original units (ppb, µg/m³, etc) to IMECA

Usage

convert_to_imeca(value, pollutant, showWarnings = TRUE)
convert_to_imeca(value, pollutant, showWarnings = TRUE)

Arguments

value

a numeric vector of values to convert to IMECAs. Note that the concentration of pollutants can be measured in different ways, for NO2, and O3 a 1 hour average is used, for CO, an 8 hour average, and for SO2, PM10 and PM25 a 24 hour average is used.

pollutant

type of pollutant. A vector of one or more of the following options:

SO2 - Sulfur Dioxide - ppb (24 hour average)
CO - Carbon Monoxide - ppm (8 hour average)
NO2 - Nitrogen Dioxide - pbb (1 hour average)
O3 - Ozone ppb (1 hour average)
PM10 - Particulate matter 10 micrometers or less (24 hour average)
PM25 - Particulate matter 2.5 micrometers or less (24 hour average)

showWarnings

deprecated; you can use the function suppressWarnings instead.

Details

Air quality in Mexico City is reported in IMECAs (Índice Metropolitano de la Calidad del Aire), a dimensionless scale where all pollutants can be compared.

Note that each pollutant has different averaging periods (see the arguments section). Because of rounding error results may be off by a couple of points.

Value

A vector containing the converted value in IMECAs

Examples

## IMECA is a dimensionless scale that allows for the comparison of
## different pollutants
convert_to_imeca(157, "O3")
convert_to_imeca(c(450, 350, 250), rep("NO2", 3))
## Since this is PM10 the 80 is supposed to be the 24 hour average
convert_to_imeca(80, "PM10")

## warning about recycling elements in a vector
convert_to_imeca(c(157, 200), c("O3", "O3"))

convert_to_imeca(67, "O3")
convert_to_imeca(77, "O3")
convert_to_imeca(205, "O3")
convert_to_imeca(72, "O3")
convert_to_imeca(98, "O3")

## IMECA is a dimensionless scale that allows for the comparison of
## different pollutants
convert_to_imeca(157, "O3")
convert_to_imeca(c(450, 350, 250), rep("NO2", 3))
## Since this is PM10 the 80 is supposed to be the 24 hour average
convert_to_imeca(80, "PM10")

## warning about recycling elements in a vector
convert_to_imeca(c(157, 200), c("O3", "O3"))

convert_to_imeca(67, "O3")
convert_to_imeca(77, "O3")
convert_to_imeca(205, "O3")
convert_to_imeca(72, "O3")
convert_to_imeca(98, "O3")

Convert a pollutant concentration to its air quality category

Description

This functions converts a pollutant value in its original units into one of the 5 categories used by the Mexican government to communicate to the public how polluted the air currently is and its health risks.

Usage

convert_to_index(value, pollutant)
convert_to_index(value, pollutant)

Arguments

value

a numeric vector of values to convert to index

pollutant

type of pollutant. A vector of one or more of the following options:

SO2 - Sulfur Dioxide - ppb (24 hour average)
CO - Carbon Monoxide - ppm (8 hour average)
NO2 - Nitrogen Dioxide - pbb (1 hour average)
O3 - Ozone ppb (1 hour average)
PM10 - Particulate matter 10 micrometers or less (24 hour average)
PM25 - Particulate matter 2.5 micrometers or less (24 hour average)

Value

the IMECA value of the concentration indexed into 5 categories

BUENA - Good: 0-50 minimal health risk
REGULAR - Regular: 51-100 moderate health effects
MALA - Bad: 101-150 sensitive groups may suffer adverse heatlh effects
MUY MALA - Very Bad: 151-200 everyone can experience negative health effects
EXTREMADAMENTE MALA - Extremely Bad: > 200 serious health issues

Examples

convert_to_index(c(12.1, 215, 355), c("PM25", "PM10", "PM10"))
convert_to_index(c(12.1, 215, 355), c("PM25", "PM10", "PM10"))

Download archives of the 24 hour averages of pollutants

Description

Data comes from Promedios de 24 horas de partículas suspendidas(PM10 Y PM2.5) and Promedios de 24 horas de Dióxido azufre

Usage

download_24hr_average(type, year, progress = interactive())
download_24hr_average(type, year, progress = interactive())

Arguments

type

type of data to download.

SO2 - Sulfur Dioxide (parts per billion)
PS - Suspended solids

year

a numeric vector containing the years for which to download data (the earliest possible value is 1986 for SO2 and 1995 for PS)

progress

whether to display a progress bar (TRUE or FALSE). By default it will only display in an interactive session.

Value

A data.frame with pollution data.

Examples

## Not run: 
head(download_24hr_average("PS", 2017))

## End(Not run)
## Not run: 
head(download_24hr_average("PS", 2017))

## End(Not run)

Download Acid Rain Measurements Archives

Description

Download data on rainfall samples collected weekly during the rainy season, available at Depósito and Depósito

Usage

download_deposition(deposition, type)
download_deposition(deposition, type)

Arguments

deposition

type of deposition to download

TOTAL - Total deposition (1988-2000)
HUMEDO - Wet and dry deposition (1997-)

type

type of ion measurement

DEPOSITO - ion quantity deposition
CONCENTRACION - ion concentration

Value

A data.frame with deposition data.

Examples

## Not run: 
## Download rainfall in mm
df <- download_deposition(deposition = "HUMEDO", type = "CONCENTRACION") %>%
        filter(pollutant == "PP")
head(df)

## End(Not run)
## Not run: 
## Download rainfall in mm
df <- download_deposition(deposition = "HUMEDO", type = "CONCENTRACION") %>%
        filter(pollutant == "PP")
head(df)

## End(Not run)

Download Lead Pollution Archives

Description

Download data on lead pollution from the archives available at Plomo and Partículas suspendidas

Usage

download_lead(type)
download_lead(type)

Arguments

type

type of data to download.

PbPST
PST, PM10, PM25

Value

A data.frame with pollution data.

Examples

## Not run: 
head(download_lead("PbPST"))

## End(Not run)
## Not run: 
head(download_lead("PbPST"))

## End(Not run)

Download Meteorological Data Archives

Description

Download the files available at Meteorología

Usage

download_meteorological(year, progress = interactive())
download_meteorological(year, progress = interactive())

Arguments

year

a numeric vector containing the years for which to download data (the earliest possible value is 1986)

progress

whether to display a progress bar (TRUE or FALSE). By default it will only display in an interactive session.

Value

a data.frame with meterological information: "RH","TMP","WDR","WSP","PBa"

Examples

## Not run: 
head(download_meteorological(2017))

## End(Not run)
## Not run: 
head(download_meteorological(2017))

## End(Not run)

Download Pollution Archives

Description

Download the pollution files available at Contaminante

Usage

download_pollution(year, progress = interactive())
download_pollution(year, progress = interactive())

Arguments

year

a numeric vector containing the years for which to download data (the earliest possible value is 2009)

progress

whether to display a progress bar (TRUE or FALSE). By default it will only display in an interactive session.

Value

a data.frame with pollution information for the following pollutants "CO", "NO", "NO2", "NOX", "O3", "PM10", "SO2", "PM25", and "PMCO"

Examples

## Not run: 
head(download_pollution(2017))

## End(Not run)
## Not run: 
head(download_pollution(2017))

## End(Not run)

Download Atmospheric Pressure Archives

Description

The data comes from Presión Atmosférica

Usage

download_pressure(year, progress = interactive())
download_pressure(year, progress = interactive())

Arguments

year

a numeric vector containing the years for which to download data (the earliest possible value is 2009)

progress

whether to display a progress bar (TRUE or FALSE). By default it will only display in an interactive session.

Value

A data.frame with atmospheric pressure data.

Examples

## Not run: 
head(download_pressure(2017))

## End(Not run)
## Not run: 
head(download_pressure(2017))

## End(Not run)

Download Ultraviolet Radiation Archives

Description

Download data on UVA and UVB from the pollution archives available at Radiación Solar (UVA) and Radiación Solar (UVB)

Usage

download_radiation(type, year, progress = interactive())
download_radiation(type, year, progress = interactive())

Arguments

type

type of data to download.

UVA - long wave ultraviolet A
UVB - short wave ultraviolet B

year

a numeric vector containing the years for which to download data (the earliest possible value is 2000)

progress

whether to display a progress bar (TRUE or FALSE). By default it will only display in an interactive session.

Value

A data.frame with pollution data. The hours correspond to the Etc/GMT+6 timezone, with no daylight saving time

Examples

## Not run: 
head(download_radiation("UVA", 2017))

## End(Not run)
## Not run: 
head(download_radiation("UVA", 2017))

## End(Not run)

Get the latest pollution values for each station

Description

Download the latest hourly values for the pollutants with the highest values for each station as measured in IMECAs

Usage

get_latest_imeca()
get_latest_imeca()

Details

Note that in 2015 it was determined that the stations with codes ACO, AJU, INN, MON and MPA would no longer be taken into consideration when computing the pollution index because they didn't meet the objectives of monitoring air quality, and are no longer included in the index, even if they are still part of the SIMAT (Sistema de Monitoreo Atmosférico de la Ciudad de México). Thus, even if they are located inside a zone, they are not included in the pollution values for that zone.

Value

A data.frame with pollution air quality, the hours are in the America/Mexico_City timezone

Examples


df <- get_latest_imeca()
head(df)

df <- get_latest_imeca()
head(df)

Download pollution data by station

Description

Retrieve pollution data by station, in the original units, from the air quality server at Consulta de Concentraciones, or for earlier years use the archive files available from Contaminante, or Meteorología for meteorological data. There's a mistake in the 2016 wind speed data, so for this year, and only this year, the alternative Excel file was used.

Usage

get_station_data(criterion, pollutant, year, progress = interactive())
get_station_data(criterion, pollutant, year, progress = interactive())

Arguments

criterion

Type of data to download.

HORARIOS - Hourly data
MAXIMOS - Daily maximums
MINIMOS - Daily minimums

pollutant

The type of pollutant to download.

SO2 - Sulfur Dioxide (parts per billion)
CO - Carbon Monoxide (parts per million)
NOX - Nitrogen Oxides (parts per billion)
NO2 - Nitrogen Dioxide (parts per billion)
NO - Nitric Oxide (parts per billion)
O3 - Ozone (parts per billion)
PM10 - Particulate matter 10 micrometers or less (micrograms per cubic meter)
PM25 - Particulate matter 2.5 micrometers or less (micrograms per cubic meter)
WSP - Wind velocity (meters per second)
WDR - Wind direction (degrees)
TMP - Temperature (degrees Celsius)
RH - Relative humidity (percentage)

year

a numeric vector containing the years for which to download data (the earliest possible value is 1986)

progress

whether to display a progress bar (TRUE or FALSE). By default it will only display in an interactive session.

Details

Temperature (TMP) archive values are correct to one decimal place, but the most recent data is only available rounded to the nearest integer.

Value

A data.frame with pollution data. When downloading "HORARIOS" the hours correspond to the Etc/GMT+6 timezone, with no daylight saving time

Warning

The data for the current month is in the process of being validated

Examples

## Not run: 
## Download daily maximum PM10 data (particulate matter 10 micrometers or
## less in diameter) from 2015 to 2016
df <- get_station_data("MAXIMOS", "PM10", 2015:2016)
head(df)

## Download ozone concentration hourly data for 2016
df2 <- get_station_data("HORARIOS", "O3", 2016)

## Convert to local Mexico City time
df2$mxc_time <- format(as.POSIXct(paste0(df2$date, " ", df2$hour, ":00"),
                                  tz = "Etc/GMT+6"),
                       tz = "America/Mexico_City")
head(df2)

## End(Not run)
## Not run: 
## Download daily maximum PM10 data (particulate matter 10 micrometers or
## less in diameter) from 2015 to 2016
df <- get_station_data("MAXIMOS", "PM10", 2015:2016)
head(df)

## Download ozone concentration hourly data for 2016
df2 <- get_station_data("HORARIOS", "O3", 2016)

## Convert to local Mexico City time
df2$mxc_time <- format(as.POSIXct(paste0(df2$date, " ", df2$hour, ":00"),
                                  tz = "Etc/GMT+6"),
                       tz = "America/Mexico_City")
head(df2)

## End(Not run)

Download pollution data by station in IMECAs

Description

Retrieve hourly averages of pollution data, by station, measured in IMECAs

Usage

get_station_imeca(pollutant, date, show_messages = TRUE)
get_station_imeca(pollutant, date, show_messages = TRUE)

Arguments

pollutant

The type of pollutant to download

SO2 - Sulfur Dioxide
CO - Carbon Monoxide
NO2 - Nitrogen Dioxide
O3 - Ozone
PM10 - Particulate matter 10 micrometers or less
PM25 - Particulate matter 2.5 micrometers or less

date

The date for which to download data in YYYY-MM-DD format (the earliest possible date is 2009-01-01).

show_messages

show a message about issues with excluded stations

Details

Value

A data.frame with pollution data measured in IMECAs, by station. The hours correspond to the Etc/GMT+6 timezone, with no daylight saving time

Examples

## Not run: 
## There was an ozone pollution emergency on May 15, 2017
df_o3 <- get_station_imeca("O3", "2017-05-15", show_messages = FALSE)

## Convert to local Mexico City time
df_o3$mxc_time <- format(as.POSIXct(paste0(df_o3$date,
                                           " ",
                                           df_o3$hour,
                                           ":00"),
                                    tz = "Etc/GMT+6"),
                         tz = "America/Mexico_City")
head(df_o3[order(-df_o3$value), ])

## End(Not run)
## Not run: 
## There was an ozone pollution emergency on May 15, 2017
df_o3 <- get_station_imeca("O3", "2017-05-15", show_messages = FALSE)

## Convert to local Mexico City time
df_o3$mxc_time <- format(as.POSIXct(paste0(df_o3$date,
                                           " ",
                                           df_o3$hour,
                                           ":00"),
                                    tz = "Etc/GMT+6"),
                         tz = "America/Mexico_City")
head(df_o3[order(-df_o3$value), ])

## End(Not run)

Download monthly pollution data

Description

Retrieve hourly averages, daily maximums, or daily minimums of pollution data in the original units, by station, from the air quality server at Consulta de Concentraciones

Usage

get_station_month_data(criterion, pollutant, year, month)
get_station_month_data(criterion, pollutant, year, month)

Arguments

criterion

Type of data to download.

HORARIOS - Hourly data
MAXIMOS" - Daily maximums
MINIMOS - Daily minimums

pollutant

The type of pollutant to download.

SO2 - Sulfur Dioxide (parts per billion)
CO - Carbon Monoxide (parts per million)
NOX - Nitrogen Oxides (parts per billion)
NO2 - Nitrogen Dioxide (parts per billion)
NO - Nitric Oxide (parts per billion)
O3 - Ozone (parts per billion)
PM10 - Particulate matter 10 micrometers or less (micrograms per cubic meter)
PM25 - Particulate matter 2.5 micrometers or less (micrograms per cubic meter)
WSP - Wind velocity (meters per second)
WDR - Wind direction (degrees)
TMP - Temperature (degrees Celsius)
RH - Relative humidity (percentage)

year

an integer indicating the year for which to download data (the earliest possible value is 1986)

month

month number to download

Details

Temperature (TMP) data was rounded to the nearest integer, but the get_station_data function allows you to download data accurate to one decimal point in some cases (i.e. for old data).

Value

A data.frame with pollution data, the hours correspond to the Etc/GMT+6 timezone, with no daylight saving time

Warning

The data for the current month is in the process of being validated

Examples

## Not run: 
## Download daily hourly PM10 data (particulate matter 10 micrometers or
## less in diameter) from March 2016
df_pm10 <- get_station_month_data("HORARIOS", "PM10", 2016, 3)
head(df_pm10)

## Download daily hourly O3 data from October 2017
df_o3 <- get_station_month_data("HORARIOS", "O3", 2018, 1)
## Convert to local Mexico City time
df_o3$mxc_time <- format(as.POSIXct(paste0(df_o3$date,
                                           " ",
                                           df_o3$hour, ":00"),
                                    tz = "Etc/GMT+6"),
                         tz = "America/Mexico_City")
head(df_o3)

## End(Not run)
## Not run: 
## Download daily hourly PM10 data (particulate matter 10 micrometers or
## less in diameter) from March 2016
df_pm10 <- get_station_month_data("HORARIOS", "PM10", 2016, 3)
head(df_pm10)

## Download daily hourly O3 data from October 2017
df_o3 <- get_station_month_data("HORARIOS", "O3", 2018, 1)
## Convert to local Mexico City time
df_o3$mxc_time <- format(as.POSIXct(paste0(df_o3$date,
                                           " ",
                                           df_o3$hour, ":00"),
                                    tz = "Etc/GMT+6"),
                         tz = "America/Mexico_City")
head(df_o3)

## End(Not run)

Download pollution data by zone in IMECAs

Description

Retrieve pollution data in IMECAs by geographic zone from the air quality server at Consultas

Usage

get_zone_imeca(
  criterion,
  pollutant,
  zone,
  start_date,
  end_date,
  showWarnings = TRUE,
  show_messages = TRUE
)
get_zone_imeca(
  criterion,
  pollutant,
  zone,
  start_date,
  end_date,
  showWarnings = TRUE,
  show_messages = TRUE
)

Arguments

criterion

The type of data to download. One of the following options:

HORARIOS - Hourly data
MAXIMOS" - Daily maximums

pollutant

The type of pollutant to download. One or more of the following options:

SO2 - Sulfur Dioxide
CO - Carbon Monoxide
NO2 - Nitrogen Dioxide
O3 - Ozone
PM10 - Particulate matter 10 micrometers or less
TC - All the pollutants

zone

The geographic zone for which to download data. One or more of the following:

NO - Noroeste
NE - Noreste
CE - Centro
SO - Suroeste
SE - Sureste
TZ - All zones

start_date

The start date in YYYY-MM-DD format (earliest possible value is 2008-01-01).

end_date

The end date in YYYY-MM-DD format.

showWarnings

deprecated; you can use the function suppressWarnings instead.

show_messages

show a message about issues with performing the conversion

Details

Note that in 2015 it was determined that the stations with codes ACO, AJU, INN, MON and MPA would no longer be taken into consideration when computing the pollution index because they didn't meet the objectives of monitoring air quality. They are no longer included in the index, even if they are still part of the SIMAT (Sistema de Monitoreo Atmosférico de la Ciudad de México). Thus, even if they are located inside a zone, they are not included in the pollution values for that zone.

The different geographic zones were defined in the Gaceta Oficial de la Ciudad de México No. 230, 27 de Diciembre de 2016.

Zona Centro: Benito Juárez, Cuauhtémoc, Iztacalco and Venustiano Carranza.

Zona Noreste: Gustavo A. Madero, Coacalco de Berriozábal, Chicoloapan, Chimalhuacán, Ecatepec de Morelos, Ixtapaluca, La Paz, Nezahualcóyotl and Tecámac.

Zona Noroeste: Azcapotzalco, Miguel Hidalgo, Atizapán de Zaragoza, Cuautitlán, Cuautitlán Izcalli, Naucalpan de Juárez, Nicolás Romero, Tlalnepantla de Baz and Tultitlán.

Zona Sureste: Iztapalapa, Milpa Alta, Tláhuac, Xochimilco, Chalco and Valle de Chalco.

Zona Suroeste: Álvaro Obregón, Coyoacán, Cuajimalpa, Magdalena Contreras, Tlalpan and Huixquilucan.

Value

A data.frame with pollution data measured in IMECAs, by geographic zone. The hours correspond to the Etc/GMT+6 timezone, with no daylight saving time

Examples


## There was a regional (NE) PM10 pollution emergency on Jan 6, 2017
get_zone_imeca("MAXIMOS", "PM10", "NE", "2017-01-05", "2017-01-08",
               show_messages = FALSE)

## There was an ozone pollution emergency on May 15, 2017
get_zone_imeca("MAXIMOS", "O3", "TZ", "2017-05-15", "2017-05-15",
               show_messages = FALSE)


## Not run: 
## Download daily maximum PM10 data (particulate matter 10 micrometers or
## less in diameter) from 2015-01-01 to 2016-03-20 for all geographic zones
df <- get_zone_imeca("MAXIMOS", "PM10", "TZ", "2015-01-01", "2016-03-20")
head(df)

## Download hourly O3 pollution data for May 15, 2017. Only the suroeste zone
df2 <- get_zone_imeca("HORARIOS", "O3", "SO", "2017-05-15", "2017-05-15")

## Convert to local Mexico City time
df2$mxc_time <- format(as.POSIXct(paste0(df2$date, " ", df2$hour, ":00"),
                                  tz = "Etc/GMT+6"),
                       tz = "America/Mexico_City")

head(df2)

## End(Not run)

## There was a regional (NE) PM10 pollution emergency on Jan 6, 2017
get_zone_imeca("MAXIMOS", "PM10", "NE", "2017-01-05", "2017-01-08",
               show_messages = FALSE)

## There was an ozone pollution emergency on May 15, 2017
get_zone_imeca("MAXIMOS", "O3", "TZ", "2017-05-15", "2017-05-15",
               show_messages = FALSE)


## Not run: 
## Download daily maximum PM10 data (particulate matter 10 micrometers or
## less in diameter) from 2015-01-01 to 2016-03-20 for all geographic zones
df <- get_zone_imeca("MAXIMOS", "PM10", "TZ", "2015-01-01", "2016-03-20")
head(df)

## Download hourly O3 pollution data for May 15, 2017. Only the suroeste zone
df2 <- get_zone_imeca("HORARIOS", "O3", "SO", "2017-05-15", "2017-05-15")

## Convert to local Mexico City time
df2$mxc_time <- format(as.POSIXct(paste0(df2$date, " ", df2$hour, ":00"),
                                  tz = "Etc/GMT+6"),
                       tz = "America/Mexico_City")

head(df2)

## End(Not run)

Inverse Distance Weighting with Directional Data

Description

Function for inverse distance weighted interpolation with directional data. Useful for when you are working with data whose unit of measurement is degrees (i.e. the average of 35 degrees and 355 degrees should be 15 degrees). It works by finding the shortest distance between two degree marks on a circle.

Usage

idw360(values, coords, grid, idp = 2)
idw360(values, coords, grid, idp = 2)

Arguments

values

the dependent variable

coords

the spatial data locations where the values were measured. First column x/longitude, second y/latitude

grid

data frame or Spatial object with the locations to predict. First column x/longitude, second y/latitude

idp

The inverse distance weighting power

Value

data.frame with the interpolated values for each of the grid points

Examples

library("sp")
library("ggplot2")

## Could be wind direction values in degrees
values <- c(55, 355)

## Location of sensors. First column x/longitud, second y/latitude
locations <- data.frame(lon = c(1, 2), lat = c(1, 2))
coordinates(locations) <- ~lon+lat

## The grid for which to extrapolate values
grid <- data.frame(lon = c(1, 2, 1, 2), lat = c(1, 2, 2, 1))
coordinates(grid) <- ~lon+lat

## Perform the inverse distance weighted interpolation
res <- idw360(values, locations, grid)
head(res)

## Not run: 
df <- cbind(res, as.data.frame(grid))
## The wind direction compass starts where the 90 degree mark is located
ggplot(df, aes(lon, lat)) +
  geom_point() +
  geom_spoke(aes(angle = ((90 - pred) %% 360) * pi / 180),
             radius = 1,
             arrow=arrow(length = unit(0.2, "npc")))

library("mapproj")
## Random values in each of the measuring stations
locations <- stations[, c("lon", "lat")]
coordinates(locations) <- ~lon+lat
crs_string <- "+proj=longlat +ellps=WGS84 +no_defs +towgs84=0,0,0"
proj4string(locations) <- CRS(crs_string)
values <- runif(length(locations), 0, 360)
pixels <- 10
grid <- expand.grid(lon = seq((min(coordinates(locations)[, 1]) - .1),
                              (max(coordinates(locations)[, 1]) + .1),
                              length.out = pixels),
                    lat = seq((min(coordinates(locations)[, 2]) - .1),
                              (max(coordinates(locations)[, 2]) + .1),
                              length.out = pixels))
grid <- SpatialPoints(grid)
proj4string(grid) <- CRS(crs_string)
## bind the extrapolated values for plotting
df <- cbind(idw360(values, locations, grid), as.data.frame(grid))
ggplot(df, aes(lon, lat)) +
  geom_point(size = .1) +
  geom_spoke(aes(angle = ((90 - pred) %% 360) * pi / 180),
             radius = .07,
             arrow=arrow(length = unit(0.2,"cm"))) +
  coord_map()

## End(Not run)
library("sp")
library("ggplot2")

## Could be wind direction values in degrees
values <- c(55, 355)

## Location of sensors. First column x/longitud, second y/latitude
locations <- data.frame(lon = c(1, 2), lat = c(1, 2))
coordinates(locations) <- ~lon+lat

## The grid for which to extrapolate values
grid <- data.frame(lon = c(1, 2, 1, 2), lat = c(1, 2, 2, 1))
coordinates(grid) <- ~lon+lat

## Perform the inverse distance weighted interpolation
res <- idw360(values, locations, grid)
head(res)

## Not run: 
df <- cbind(res, as.data.frame(grid))
## The wind direction compass starts where the 90 degree mark is located
ggplot(df, aes(lon, lat)) +
  geom_point() +
  geom_spoke(aes(angle = ((90 - pred) %% 360) * pi / 180),
             radius = 1,
             arrow=arrow(length = unit(0.2, "npc")))

library("mapproj")
## Random values in each of the measuring stations
locations <- stations[, c("lon", "lat")]
coordinates(locations) <- ~lon+lat
crs_string <- "+proj=longlat +ellps=WGS84 +no_defs +towgs84=0,0,0"
proj4string(locations) <- CRS(crs_string)
values <- runif(length(locations), 0, 360)
pixels <- 10
grid <- expand.grid(lon = seq((min(coordinates(locations)[, 1]) - .1),
                              (max(coordinates(locations)[, 1]) + .1),
                              length.out = pixels),
                    lat = seq((min(coordinates(locations)[, 2]) - .1),
                              (max(coordinates(locations)[, 2]) + .1),
                              length.out = pixels))
grid <- SpatialPoints(grid)
proj4string(grid) <- CRS(crs_string)
## bind the extrapolated values for plotting
df <- cbind(idw360(values, locations, grid), as.data.frame(grid))
ggplot(df, aes(lon, lat)) +
  geom_point(size = .1) +
  geom_spoke(aes(angle = ((90 - pred) %% 360) * pi / 180),
             radius = .07,
             arrow=arrow(length = unit(0.2,"cm"))) +
  coord_map()

## End(Not run)

Pollution measuring stations in Mexico City

Description

This dataset contains all pollution measuring stations in Mexico City. The station with code SS1 was added manually since it was missing from the official source dataset (its location was found in the Audit of Ambient Air Monitoring Stations for the Sistema de Monitoreo Atmosférico de la Ciudad de México).

Usage

stations
stations

Format

A data frame with 63 rows and 7 variables:

station_code: abbreviation of the station
station_name: name of the station
lon: longitude of the station
lat: latitude of the station
altitude: altitude of the station
comment: comment
station_id: id of the station

Source

‘⁠http://148.243.232.112:8080/opendata/catalogos/cat_estacion.csv⁠’

Examples

head(stations)
head(stations)

Pollution zones in Mexico City

Description

This data set contains the municipios (counties) that make up the 5 geographic zones into which Mexico City was divided for the purpose of disseminating information about the IMECA.

Usage

zones
zones

Format

A data frame with 36 rows and 6 variables:

region: INEGI code of the region (state_code + municipio_code)
state_code: INEGI code of the state
state_abbr: state abbreviation
municipio_code: INEGI code of the municipio
municipio_name: name of the municipio
zone: zone

Details

A transparency request was used to determine the zone to which the municipios of Acolman, Texcoco and Atenco belong.

Source

Gaceta Oficial de la Ciudad de México No. 230, 27 de Diciembre de 2016, and Solicitud de Información FOLIO 0112000033818

Examples

head(zones)
head(zones)

Package 'aire.zmvm'

Help Index

Convert pollution values to IMECA

Description

Usage

Arguments

Details

Value

See Also

Examples

Convert a pollutant concentration to its air quality category

Description

Usage

Arguments

Value

See Also

Examples

Download archives of the 24 hour averages of pollutants

Description

Usage

Arguments

Value

Examples

Download Acid Rain Measurements Archives

Description

Usage

Arguments

Value

Examples

Download Lead Pollution Archives

Description

Usage

Arguments

Value

Examples

Download Meteorological Data Archives

Description

Usage

Arguments

Value

Examples

Download Pollution Archives

Description

Usage

Arguments

Value

Examples

Download Atmospheric Pressure Archives

Description

Usage

Arguments

Value

Examples

Download Ultraviolet Radiation Archives

Description

Usage

Arguments

Value

Examples

Get the latest pollution values for each station

Description

Usage

Details

Value

See Also

Examples

Download pollution data by station

Description

Usage

Arguments

Details

Value

Warning

See Also

Examples

Download pollution data by station in IMECAs

Description

Usage

Arguments

Details