This document discusses using recurrent neural networks (RNNs) to estimate air quality and atmospheric chemical composition. RNNs can provide estimates much more cheaply than traditional chemical transport models run on supercomputers. The presented Chemistry Estimating Recurrent Neural Network (CERNN) model takes in meteorological data and outputs estimates of atmospheric compounds like ozone and ethane that match estimates from chemical transport models. While CERNN requires large amounts of training data, it can still provide approximations of air quality changes during the COVID-19 pandemic lockdown period when direct chemical modeling data is limited.

1. Chemistry Emulating Recurrent
Neural Network(CERRN)
Andrey Vlasenko

2. Agenda
• What is air quality?
• Why Neural networks?
• Examples of atmospheric chemical state estimates
with neural networks.
• Does COVID-19 lockdown is the only factor for air
quality improvement?

3. What is Air Quality?
(Why do we need it? )
Health risk Global warming Oil and gas mining
Atmospheric compound determines numerous aspects of our life and business: health risk,
global warming, or indicate malfunctions in oil and gas production plants. Air quality modeling
predicts the atmospheric compound and helps in designating hazards.

4. Air Quality estimates are expensive
Computing atmospheric compound requires supercomputers with cost more than 100k $

5. Why Neural Networks?

6. The strongest argument
Compare the computational costs of air quality estimates
Chemical transport model (CMAQ) on a Supercomputer
V.S.
NN on a Dell Notebook

7. Our goal is to estimate the atmospheric chemical state from
the meteorological data by means of the NN

8. Recurrent Neural Network, O3 example
The Recurrent Neural
Network
Percip
Cloud
WIND
Temper
Humidity
The
First
Layer
O3
Output
The chemical transport
model
Percip
Cloud
WIND
Temper
Humidity
The
Propagator
O3
Output

9. Chemistry Estimating Recurrent Neural Network (CERNN).
Principal scheme of the NN Overlapp
Meteorol
ogical
data
Recurrent
layer
Recursion
output
Recursion
Dense
layer
Dense
layer
Output
Input Black Box Output

10. The only problem……
NN require a lot of data

11. Data Preparation
• Estimation of the atmospheric chemical data for 1979-2012 with CMAQ chemical
transport model
• Subtract climatological mean (we do not need to predict it), and get the rest: anomalies
• Split the obtained anomalies into training (1979-2006), test (2007-2008) and validation
(2009-2012) sets
Climatological means of concentrations (left) and emissions (right)

12. Examples: NO2 anomaly
CERNN Estimates CMAQ Estimates
Figure taken from Simulation of Chemical Transport Model Estimates by means of Neural Network using Meteorological Data. Atmospheric
Environment 254(80-):118236 . DOI: 10.1016/j.atmosenv.2021.118236

13. Examples: C2H6 anomaly
CERNN Estimates CMAQ Estimates

14. So, what about the COVID-19 case
• Data is taken from European Centre for Medium-
Range Weather Forecasts (ECMWF) center. It is
available only for 2012-2020 (with gaps!).
Consequences
• Less data – worse training.
• We cannot separate pure weather effect. Only
some approximation of it
Does only the “lockdown” results in cleaner air?

15. Summer O3 anomalies (R2=0.35)
ppmV ppmV
Overall O3 changes (CAMS) Weather induced changes (NN)

16. Outcome
• The NN is a fast and efficient tool for estimating chemical air
compound
• It is at least 720 times faster than chemical transport model
CMAQ
• It always converge and its estimated do not depend on any time-
stepping and parameter conditions, which are typical for the
numerical modelling.
• The difference between CMAQ estimates NN is not bigger than
the difference between CMAQ estimates and other chemical
transport models.
• The NN can be also applied for the fluid flow reconstruction
• See more on CERNN here: DOI: 10.1016/j.atmosenv.2021.118236

17. Thank you