Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

Published

March 9, 2019

Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

Journal of Cleaner Production - Volume 221, 1 June 2019, Pages 398-418

Please find a Link providing 50 days’ free access to our last paper with our colleagues from Data61/CSIRO Australia and UL-ERPI Laboratory, France

https://authors.elsevier.com/c/1YhI73QCo9UqCv

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.