Knowing the amount of air pollutants in our cities is of great importance to help decision makers in the definition of effective strategies aimed at maintaining a good air quality, which is a key factor for a healthy life, especially in urban environments. Using a data set from a big metropolitan city, we realize the uAQE: urban Air Quality Evaluator, which is a supervised machine learning model able to estimate air pollutants values using only weather and traffic data. We evaluate the performance of our solution by comparing the predicted pollutant values with the real measurements provided by professional air monitoring stations. We use the predicted pollutants to compute a standard Air Quality Index (AQI) and we map it into a set of five qualitative AQI classes, which can be used for decision making at the city level. uAQE is able to predict the AQI class value with an accuracy of 0.8.

A Machine Learning Approach to Monitor Air Quality from Traffic and Weather data / Rossi, C.; Farasin, A.; Falcone, G.; Castelluccio, C.. - ELETTRONICO. - 2492:(2019), pp. 66-74. ((Intervento presentato al convegno 2019 Joint Poster and Workshop Sessions of AmI, AmI 2019 and 2019 European Conference on Ambient Intelligence tenutosi a (IT) nel 2019.

A Machine Learning Approach to Monitor Air Quality from Traffic and Weather data

Rossi C.;Farasin A.;Castelluccio C.
2019

Abstract

Knowing the amount of air pollutants in our cities is of great importance to help decision makers in the definition of effective strategies aimed at maintaining a good air quality, which is a key factor for a healthy life, especially in urban environments. Using a data set from a big metropolitan city, we realize the uAQE: urban Air Quality Evaluator, which is a supervised machine learning model able to estimate air pollutants values using only weather and traffic data. We evaluate the performance of our solution by comparing the predicted pollutant values with the real measurements provided by professional air monitoring stations. We use the predicted pollutants to compute a standard Air Quality Index (AQI) and we map it into a set of five qualitative AQI classes, which can be used for decision making at the city level. uAQE is able to predict the AQI class value with an accuracy of 0.8.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2771792
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