Modeling atmospheric data and identifying dynamics Temporal data-driven modeling of air pollutants

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. Furthermore, with the...

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Bibliographic Details
Published in:Journal of cleaner production 2021-12, Vol.333
Main Authors: Rubio-Herrero, Javier, Marrero, Carlos Ortiz, Fan, Wai-Tong
Format: Article
Language:English
Subjects:
atmosphere modeling
atmospheric chemodeling
chemical dynamics and kinetics
Chemical Dynamics Initiative
Delay embedding
ENVIRONMENTAL SCIENCES
machine leaning
model discovery
Nonlinear dynamics
Operations Research in environmental modeling
Sparse regression
Stability analysimistry
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Summary:Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. Furthermore, with the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our model using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.
ISSN:0959-6526
1879-1786