In ropensci/qualR: An R package to download São Paulo and Rio de Janeiro air pollution data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Context

Both the State of São Paulo and Rio de Janeiro have one of the most extensive air quality stations network in South America. CETESB QUALAR System provide to the user the air quality data from the State of São Paulo. QUALAR System limits the download to one parameter from one air station for one year in a simple query (three parameter in advance query). The data can have missing hours (e.g. due to calibration), the decimal separator is ",", and the output is a CSV file. data.rio hosts the air quality information from Monitor Ar Program. It is not an user-friendly API and the data needs the same preprocessor as the data from QUALAR System.

qualR surpasses these limitations and brings to your R session ready-to use data frames with the information of air quality station from the State of São Paulo and the city of Rio de Janeiro.

Approach

qualR has the following functions:

• cetesb_retrieve_param: Download a list of different parameter from one air quality station (AQS) from CETESB QUALAR System.
• cetesb_retrieve_pol: Download criteria pollutants from one AQS from CETESB QUALAR System.
• cetesb_retrieve_met: Download meteorological parameters from one AQS from CETESB QUALAR System.
• cetesb_retrieve_met_pol: Download meteorological parameters and criteria pollutants from one AQS from CETESB QUALAR System.
• monitor_ar_retrieve_param: Download a list of different parameters from MonitorAr - Rio program.
• monitor_ar_retrieve_pol: Download criteria pollutants from one AQS from MonitorAr - Rio program.
• monitor_ar_retrieve_met: Download meteorological parameters from one AQS from MonitorAr - Rio program.
• monitor_ar_retrieve_met_pol: Download meteorological parameters and criteria pollutants from one AQS from MonitorAr - Rio Program.

Example to download data from Rio de Janeiro

In this example we want to download one year PM10 concentration from an air quality station located in Rio de Janeiro downtown. We need to do the following:

1. Check for the code or abbreviation of the station.

library(qualR)
monitor_ar_aqs

1. Check for the code or abbreviation of the parameters.

monitor_ar_param

1. We have that the air quality station code is CA (Estação Centro), and PM10 code is PM10. So we use the function monitor_ar_retrieve_param().

rj_centro <- monitor_ar_retrieve_param(start_date = "01/01/2019",
                                       end_date = "31/12/2019",
                                       aqs_code = "CA",
                                       parameters = "PM10")

head(rj_centro)

1. We can download multiple parameters too. For example, maybe we need to know the relationship between PM10 and Wind Speed. To do that we just need to define a vector with the parameters we need.

to_dwld <- c("PM10", "Vel_Vento")

rj_ca_params <- monitor_ar_retrieve_param(start_date = "01/01/2019",
                                    end_date = "31/12/2019",
                                    aqs_code = "CA",
                                    parameters = to_dwld)
head(rj_ca_params)

1. Now we can make a simple plot

plot(rj_ca_params$ws, rj_ca_params$pm10,
     xlab = "Wind speed (m/s)",
     ylab = "",
     xlim = c(0,4),
     ylim = c(0,120))
mtext(expression(PM[10]~" ("*mu*"g/m"^3*")"), side = 2, line = 2.5)

An example using tidyverse

tidyverse is a powerful collection of R package. Here is an example using purrrto download data from multiple stations and ggplot2to visualize the relation between Ozone and air temperature. As we know Ozone is formed by photochemical reaction which means the participation of solar radiation.

library(qualR)
library(purrr)

# Retrieve data from all stations in Rio
rj_params <- purrr::map_dfr(.x = qualR::monitor_ar_aqs$code, 
                            .f = monitor_ar_retrieve_param,
                            start_date = "01/01/2020",
                            end_date = "31/12/2020",
                            parameters = c("O3", "Temp")
)

Now we can visualize all the data simultaneity using ggplot2 facet:

library(magrittr)
library(ggplot2)

# making the graph with facet
rj_params %>% 
  ggplot() +
  geom_point(aes(x = tc, y = o3), size = 0.5,  alpha = 0.5) +
  labs(x = "Air temperature (º)",
       y = expression(O[3]~" ("*mu*"g/m"^3*")"),
       caption = "Source: Data from MonitorAr - Rio, retrieved with qualR R package. "
       )+
  theme_bw()+
  facet_wrap(~aqs)

PS: Special thanks to @beatrizmilz for inspiring this example.

Compatibility with openair

qualR functions returns a completed data frame (i.e. missing hours padded out with NA) with a date column in POSIXct. This ensure compatibility with the openairpackage.

Here is the code to use openair timeVariation() function. Note that no preprocessing is needed.

#install.package("openair")
library(openair)
openair::timeVariation(rj_centro, pollutant = "pm10")

Example to download data from São Paulo State stations

To use cetesb_retrieve you first need to create an account in CETESB QUALAR System. The cetesb_retrieve functions are similar as monitor_ar_retrieve_param functions, but they require the username and password arguments. Check this section on qualR README to safely configure your user name and password on your R session.

In this example, we download Ozone concentration from an air quality station located at Universidade de São Paulo (USP-Ipen) for August, 2021. 1. Check the station code or name

head(cetesb_aqs, 15)

1. Check ozone code or abbreviation

head(cetesb_param, 15)

1. The air quality station is 95 and ozone code is 63. So to retrieve the data we should use the cetesb_retrieve_paramfunction like this:

usp_o3 <- cetesb_retrieve_param(username = my_user,
                                password = my_password,
                                parameters = "O3", # or 63
                                aqs_code = "Cid.Universitaria-USP-Ipen",  # or 95
                                start_date = "01/08/2021",
                                end_date = "31/08/2021")      

More information

• You can check qualR README for more examples and good practices.
• You can also check this tutorial for more examples of qualR and how it works with openair.

ropensci/qualR documentation built on June 13, 2025, 3:06 a.m.