Böhm M., Nanni M., Pappalardo L.
Computational social science Data science Applied data science Sustainable development goals AI for social good
Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.
Source: NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020
@inproceedings{oai:it.cnr:prodotti:456584, title = {Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution}, author = {Böhm M. and Nanni M. and Pappalardo L.}, booktitle = {NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020}, year = {2021} }
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