AQ-CNEA-CAC Air quality dataset (2019-2020): "A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina"

Published: 21 September 2021| Version 1 | DOI: 10.17632/h9y4hb8sf8.1
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Description

This dataset provides hourly air quality data collected from the National Atomic Energy Commission of Argentina (CNEA-CAC: 34° 24' S, 58° 31' W). The data spans from February 2019 to May 2020. The pollutants included are carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and ozone (O₃). Meteorological Variables of the Observatory Buenos Aires (OBS: 34° 35' S , 58° 29' W) used to run the Random Forest simulations (Wind speed, wind direction, 2 m temperature, 2 m relative humidity, sea level pressure and calm condition) are also provided. A full list of variables is shown below. "date" Datetime; "NO" nitrogen oxide concentration [ppm]; "NO2" nitrogen dioxide concentration [ppm]; "NOx" nitrogen oxides concentration [ppm]; "CO" carbon monoxide concentration [ppm]; "O3" ozone concentration [ppm]; "SO2" sulfur dioxide concentration [ppm]; "aqsite" air quality site; "t2" 2 m temperature (°C); "rh2" 2 m relative humidity [%]; "slp" sea level pressure [hPa]; "wspd" wind speed [degrees]; "wdir" wind direction [km/hr]; "calm" calm condition; "meteosite" meteorological site; "gas_emcycle" gas emission cycle; "aer_emcycle" aerosol emission cycle; "month" month; "U" U wind component; "V" V wind component; "daynight" [morning, day or night]; "datatype" [training: Data used for training and testing, evaluation: Data used to evaluate model skills (BLD) , lockdown: Data used to estimate relative changes during the lockdown phases (LD and PLD)]. An introductory code to use the data is also provided. The code will be run from the R script AQ_RandomForestCNEA.R. AQ_RF_functions are the auxiliary functions used and RF_parameters.csv is the file you need to provide in order to set the predictors and other parameters needed.

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Institutions

Comision Nacional de Energia Atomica

Categories

Air Quality, Machine Learning, South America, Carbon Monoxide, Nitrogen Dioxide, Nitrogen Oxide, Urban Air Quality, Ambient Air Quality Monitoring, Effect of Air Pollutants on Climate, Argentina, Sulphur Dioxide

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