aire.zmvm package

Description

Tools for downloading data from each of the pollution, wind and temperature measuring stations in the Zona Metropolitana del Valle de México (greater Mexico City).

Details

See the README on GitHub

Author(s)

Maintainer: Diego Valle-Jones diego@diegovalle.net

See Also

Useful links:

• https://hoyodesmog.diegovalle.net/aire.zmvm/

• https://github.com/diegovalle/aire.zmvm

• Report bugs at https://github.com/diegovalle/aire.zmvm/issues

Clean archive files

Description

Clean archive files

Usage

.clean_archive(df, include_hour)

Arguments

Value

data.frame

Hack to download 2016 WSP HORARIO data since the csv files were converted from mph to m/s (when the original data were already in m/s)

Description

Hack to download 2016 WSP HORARIO data since the csv files were converted from mph to m/s (when the original data were already in m/s)

Usage

.get_archive_wsp(syear)

Arguments

Value

data.frame

Recode pollutant abbreviations

Description

Recode pollutant abbreviations

Usage

.recode_pollutant(pollutant)

Arguments

Value

data.frame

Recode numeric codes to concentration units

Description

Recode numeric codes to concentration units

Usage

.recode_unit(pollutant)

Arguments

Value

data.frame

Recode numeric codes to concentration units (extended)

Description

Recode numeric codes to concentration units (extended)

Usage

.recode_unit_code(code)

Arguments

Value

data.frame

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)

Arguments

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

See Also

For the formulas on how to convert visit: AVISO POR EL QUE SE DA A CONOCER EL PROYECTO DE NORMA AMBIENTAL PARA EL DISTRITO FEDERAL

Other convert functions: convert_to_index()

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")

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)

Arguments

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

See Also

NADF-009-AIRE-2006

Other convert functions: convert_to_imeca()

Examples

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

Deprecated functions in package aire.zmvm.

Description

The functions listed below are deprecated and will be defunct in the near future. When possible, alternative functions with similar functionality are also mentioned.

Usage

get_zone_data(...)
get_latest_data(...)
get_station_single_month(...)

get_latest_data(...)

get_zone_data(...)

get_station_single_month(...)

Arguments

Value

A data.frame

A data.frame

A data.frame

A data.frame

Details

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())

Arguments

Value

A data.frame with pollution data.

Examples

## 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)

Arguments

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)

Download Lead Pollution Archives

Description

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

Usage

download_lead(type)

Arguments

Value

A data.frame with pollution data.

Examples

## 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())

Arguments

Value

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

Examples

## 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())

Arguments

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)

Download Atmospheric Pressure Archives

Description

The data comes from Presión Atmosférica

Usage

download_pressure(year, progress = interactive())

Arguments

Value

A data.frame with atmospheric pressure data.

Examples

## 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())

Arguments

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)

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()

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 values in IMECAs, the hour corresponds to the America/Mexico_City timezone (which changes with daylight saving time)

See Also

Reporte de calidad del aire

Other IMECA functions: get_station_imeca(), get_zone_imeca()

Examples

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())

Arguments

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

See Also

stations for a data.frame with the location and names of all pollution measuring stations,

Other raw data functions: get_station_month_data()

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)

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)

Arguments

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

See Also

Índice de calidad del aire por estaciones

Other IMECA functions: get_latest_imeca(), get_zone_imeca()

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)

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)

Arguments

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

See Also

stations for a data.frame with the location and names of all pollution measuring stations

Other raw data functions: get_station_data()

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)

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
)

Arguments

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

See Also

zones a data.frame containing the municipios belonging to each zone, and Índice de calidad del aire por zonas

Other IMECA functions: get_latest_imeca(), get_station_imeca()

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)

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)

Arguments

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)

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

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)

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

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

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.

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)