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

Journal Article · · Journal of Cleaner Production
ORCiD logo [1];  [2];  [3]
  1. University of North Texas, Denton, TX (United States)
  2. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  3. Indiana University, Bloomington, IN (United States); Harvard University, Cambridge, MA (United States)
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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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1995231
Report Number(s):
PNNL-SA-157007
Journal Information:
Journal of Cleaner Production, Journal Name: Journal of Cleaner Production Vol. 333; ISSN 0959-6526
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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54 ENVIRONMENTAL SCIENCES
Chemical Dynamics Initiative
Delay embedding
Nonlinear dynamics
Operations Research in environmental modeling
Sparse regression
Stability analysimistry
atmosphere modeling
atmospheric chemodeling
chemical dynamics and kinetics
machine leaning
model discovery