Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling
Résumé
Highlights• We proposed an air quality evaluation framework using fixed and mobile sensing units.• It also integrates machine learning methods to predict the air quality from mobile data.• Three experimenting protocols for air pollution monitoring have been implemented.• NO2 pollution at human breathing levels was 3-5 times higher than those of static units.• Decision trees and neural networks can accurately predict mobile air quality.• Humidity and noise are the most important factors affecting the NO2 prediction.
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