ICOS Science Conference, Session 2

1. ICOS-CH, 03 September 2020 1lukas.emmenegger@empa.ch
Observation of urban CO2 emissions using spatially dense low-cost
sensing and modelling
Michael Müller, Christoph Hüglin, Peter Graf, Dominik Brunner, Michael Jähn, Fernando Perez
Cruz, Pascal Salina, Jonas Meyer, Simone Baffelli, Stuart Grange, Lukas Emmenegger
11 September 2020

2. ICOS-CH, 03 September 2020 2lukas.emmenegger@empa.ch
The Carbosense network
LP8 network operational > two years
230 LP8 sensors deployed
HPP measurements > one year
15 HPP sensors deployed
Infrastructure access
Data transmission
Data hosting / access
Visualization tools
Deployment
Maintenance
Management
Visualization
Data science
partners
deployment

3. ICOS-CH, 03 September 2020 3lukas.emmenegger@empa.ch
Measurements and modelling
Sensor driven
statistical model
Atmospheric transport model
Sensor data
Drift correction based on
reference measurements
and meteo data
Drift correction based on
atmospheric modelling
Linking sensor based CO2
fields with physical model
CO2 maps
1 x 1 km
250 x 250 m •mixing layer
•assimilation

4. ICOS-CH, 03 September 2020 4lukas.emmenegger@empa.ch
Sensors: from high-end to low-cost
3 units
~100 kCHF
Picarro CRDS
30 units
~3 kCHF
SenseAir HPP
300 units
~0.5 kCHF
SenseAir LP8

5. ICOS-CH, 03 September 2020 5lukas.emmenegger@empa.ch
Sensor calibration and characterization
 Dependencies of the LP8 / HPP CO2 sensors on temperature, pressure, humidity
 Multi-factor model using Beer-Lambert’s law applied to IR detector output
CO2 (350 – 1000 ppm)
T (-5°C – 50°C)
CO2 (400 – 900 ppm)
p (770 – 1050 hPa), T
Climate chamber Pressure chamber
CO2, T, RH
Ambient measurements
HPP HPPLP8LP8LP8
Mueller, M., et al. (2019). AMTD, 2019: 1-25

6. ICOS-CH, 03 September 2020 6lukas.emmenegger@empa.ch
LP8 data quality
→ Outlier detection
Measurement yield ~ 70 %
→ drift correction
Accuracy ~10 ppm

7. ICOS-CH, 03 September 2020 7lukas.emmenegger@empa.ch
HPP LP8 data quality
Sensor drift and calibration
HPP 426 in ZUE
HPP 390 CO2,CYL – CO2,HPP ϵ calibration
RMSE: 2.4
→ Accuracy ~3 ppm
Comparison with
reference Instrument

8. ICOS-CH, 03 September 2020 8lukas.emmenegger@empa.ch
Data visualization on Decentlab dashboard
https://swiss.co2.live

9. ICOS-CH, 03 September 2020 9lukas.emmenegger@empa.ch
Data available on ICOS Carbon Portal

10. ICOS-CH, 03 September 2020 10lukas.emmenegger@empa.ch
Statistical model
(machine learning)
Random Forest
• Resolution:
20x20 m, 1 h
• Explanatory variables:
traffic
land use
topography
meteo
Random Forest simulation of CO2 in Zürich

12. ICOS-CH, 03 September 2020 12lukas.emmenegger@empa.ch
Predicted and measured NOx concentrations
The reduction of air pollutants by Covid-19 measures is calculated by comparing measured concentrations with
predicted «business as usual» values.
Prediction = Random Forest algorithm trained using 2 years of observations of time variables and meteorology
www.empa.ch/web/s503/covid-19

13. ICOS-CH, 03 September 2020 13lukas.emmenegger@empa.ch
Covid-19 effect on urban air quality
The reduction of air pollutants by Covid-19 measures is calculated by comparing measured concentrations with predicted «business as usual» values.
Prediction = Random Forest algorithm trained using 2 years of observations of time variables and meteorology; www.empa.ch/web/s503/covid-19NOxreduction(weeklymean/µgm-3)
Nitrogen oxides (NOx) weekly mean reduction at two representative NABEL stations
NOxreduction(weeklymean/µgm-3)

14. ICOS-CH, 03 September 2020 14lukas.emmenegger@empa.ch
GRAMM/GRAL model for Zurich
Catalogue of flow and
dispersion simulations for
discrete weather situations
Hourly data through
observation matching

15. ICOS-CH, 03 September 2020 15lukas.emmenegger@empa.ch
COSMO-GHG model for Switzerland
Exclude CO2 emissions and
fluxes over Zurich
Corrected for biases by
assimilating data
from background stations
(Picarro & HPP)

16. ICOS-CH, 03 September 2020 16lukas.emmenegger@empa.ch
Modelling, Observation and Inversion
COSMO-GHG model for Switzerland GRAMM/GRAL model for Zurich
Exclude CO2 emissions and fluxes over Zurich
Corrected for biases by assimilating data
from background stations (Picarro & HPP)
Catalogue of flow and dispersion
simulations for discrete weather situations
Hourly data through observation matching
Observations (y)
Picarro
HPP
LP8
Inversion framework
A posteriori (optimized) emissions
and fluxes x
A priori emissions xa,
simulated observations
Mx (above background)
Simulated background
part of Mx
( ) ( )     
priori-afromdeviation
1
nsobservatio-modelmisfit
1
)()(
2
1
2
1
aaao
T
xxSxxyMxSyMxJ −−+−−=
−−

17. ICOS-CH, 03 September 2020 17lukas.emmenegger@empa.ch
Preliminary GRAMM/GRAL simulations for Zürich
Model and Observations
DayNight
Dübendorf
August 2017

18. ICOS-CH, 03 September 2020 18lukas.emmenegger@empa.ch
Carbosense CO2 monitoring network is fully operational
Data is open access
Statistic models and visualization developed and validated
Covid-19 effect determined by comparing predicted and measured values
GHG transport simulation with COSMO and GAMM/GRAL implemented
Inversion and assimilation to be continued
Summary