1) The document describes an urban greenhouse gas monitoring program in the Greater Toronto Area of Canada, which has a population of over 7 million people and aims for ambitious emission reduction targets. 2) The program utilizes various atmospheric observation techniques including total column measurements from ground-based sites and mobile surveys to measure gases like CO2, CH4, and CO to better understand urban emissions. 3) Preliminary findings show the program can detect changes in atmospheric CO2 levels associated with reduced traffic and activity during COVID lockdowns, demonstrating its ability to track policy-driven emission changes over time.

1. Urban greenhouse gas monitoring in the
Greater Toronto Region, Canada
S. Ars1, N. Mostafavipak1,2, S. Daymond1,
E. Chan1, D. Worthy1, D. Wunch2 and F. Vogel1
1Environment and Climate Change Canada
2University of Toronto
Special thanks to S. Racki for last-minute data processing

2. Why a testbed in the Greater Toronto and Hamilton Area?
In 2018 about 7 Million people lived in the Greater Toronto Area and the population is
predicted to rise to 8.5 Million in 2030 – it is the fastest growing metropolitan area in
US&CA, while pursuing ambitious greenhouse gas mitigation goals.
Transform TO emission reduction
targets compared to 1990
-30% in 2020
-65% in 2030
-80% in 2050

3. Introduction to the program – modelling tools
and Sharon Gourdji (NIST)

4. Introduction to the program – atmospheric observations
Lower-cost CO2 sensors
Photo: R. Cruz
Mobile survey platforms
Photos by Phillips and Cruz
Solar-tracking FTIRs
Photo by N. Pak
Tree core sampling
High-precision GHG instruments
In-situ sites are integrated in ECCC’s national GHG
monitoring network and ground-based remote sensing sites
are maintained in collaboration with UoToronto
Photo by C. Crann

5. Urban total column observing network of ECCC and UofT
Downsview site:
automated and
running
Also used as test
site for new
instruments
UofT Mississauga site:
manual operations
UofT Downtown site:
automated and runningUofT Scarborough site:
manual operations

6. XCH4 enhancements at downwind sites
Wind:
 Total column values reflect larger scale footprints
 XCH4 gradients/differences reflect local emission
Time in UTC Time in UTC
Mostafavipak et al. in review

7. Total Column Mixing Ratio Time Series at Downtown
 Clear seasonality observed for all XGHGs
 Short-term variability often related to meteorological conditions
 Long-term observations will allow tracking trends in
atmospheric composition
 Investigating co-emitted species can be valuable for source
apportionment in the future (XCO/XCO2).
 Co-located in-situ observations to investigate impact of strong
difference in footprints.
U of Toronto – Downtown site

8.  By subtracting 10 minute averaged XGas values from the
reference site (Downtown) we compute the anomalies for
each site.
 DXCO2/DXCH4 and DXCO/DXCH4 values from different
pairs are consistent within the uncertainty levels and can
be used to estimate CH4 emissions when using reported
emissions from CO and CO2 from inventories.
Tracer-tracer ratio method

9. Previously: Methane fluxes in Toronto and urban methane enhancement trends
 Atmospheric observation-based methods have identified discrepancies with activity-based emission reporting
 Long-term monitoring allows to track the slow decrease in urban CH4 excess in Toronto
1900
1950
2000
2050
2010 2012 2014 2016 2018
0
50
ObservedCH4(ppb)
Egbert (rural)
Downsview (urban)
UrbanexcessCH4(ppb)
Year
DCH4 trend: -1.4 ± 0.5 ppb a-1

10. Mobile surveys of atmospheric methane in and around Toronto
Photo: R. Cruz
Mobile survey platforms
Photos by Phillips and Cruz
 Over 8000 km surveyed since 2018 using bike (UofT) and car platforms (ECCC)
 Frequent revisits downtown, near major facilities and interesting natural sources
Ars et al. 2020 in review
40 km

11. *Bicycle mostly deployed downtown
Ars et al. 2020 in review
 New category for smaller leaks introduced compared to previous studies in the US
 Toronto has few leak indications (similar or better than cleanest US cities in previous studies)
 Data seems to indicate that natural gas network leakage rates below the suggested provincial emission factors
 Large leaks and point source facilities contribute significantly
Mobile surveys of atmospheric methane in and around Toronto
4 km

12. Quantification of methane emissions at facility level
Example: Keating Channel
Estimate: ~ 0.09 ± 0.03 Gg/yr
Reported: NA
 Landfill emissions lower than expected from emission reporting
 Natural gas transmission/compressor station emissions are very variable
 Noticeable natural sources found related to rivers and lake Ontario

13. Tracking atmospheric CO2 enhancements during COVID lockdown

14. Tracking atmospheric CO2 enhancements during COVID lockdown
January 1, 2020 March 1, 2020 May 1, 2020 July 1, 2020
0
1000
2000
3000
4000
5000
DVP Southbound
DVP Northbound
Gardiner Westbound
Gardiner Eastbound
Dailytrafficcounts
Regular Lockdown Recovery

15. Tracking atmospheric CO2 enhancements during COVID lockdown
2012-01-01 2012-04-01 2012-07-01 2012-10-01 2013-01-01
-12
-10
-8
-6
-4
-2
0
ChangeinurbanFFCO2enhancement(ppm)
Reference
Emission reduction target
30% traffic reduction
30% heating reduction
100% renewable energy
Semi-quantitative estimate of expected signalModelling impact on weekly DFFCO2 due to mitigation
0 20 40 60 80 100
-10
-8
-6
-4
-2
0
ModelledchangeinDFFCO2(ppm)
Emission reduction from traffic (%)
January 1, 2020 March 1, 2020 May 1, 2020 July 1, 2020
0
1000
2000
3000
4000
5000
DVP Southbound
DVP Northbound
Gardiner Westbound
Gardiner Eastbound
Dailytrafficcounts
Regular Lockdown Recovery

16. 2020-01-01 2020-03-01 2020-05-012020-03-20
0
30
60
90
2019-01-01 2019-03-01 2019-05-01
0
30
60
90
2018-01-01 2018-03-01 2018-05-01
0
30
60
90
CO2enhancement(ppm)
A B
Reference Mean Median
2020B to 2019B&2018B -3.8ppm -1.3ppm
2020A to 2020B -2.3ppm -1.3ppm
0
10
20
30
40
UrbanCO2enhancement(ppm)
2020B 2020A 2019B 2019A 2018B 2018A
 We see a decrease in urban CO2 enhancements similar to the expected 1-2 ppm drop
 However, we do need other indicators to ensure it is DFFCO2 that has changed
Tracking atmospheric CO2 enhancements during COVID lockdown

17. Lockdown (mid-March to mid-May) Recovery (mid-May to July)Regular (Jan to mid-March)
2020 2020 2020
CO2enhancement(ppm)
Tracking atmospheric CO2 enhancements during COVID lockdown

18. Regular
Clean air sector (NW) as well as sectors with strong urban influence (SW, SE) similar in 2019 and 2020
Atmospheric CO2 enhancements in 2020 vs. 2019
UrbanCO2enhancement(ppm)

19. Lockdown
Clean air sector (NW) remains similar. SW and SE show clear decrease compared to 2019
Atmospheric CO2 enhancements in 2020 vs. 2019
UrbanCO2enhancement(ppm)UrbanCO2enhancement(ppm)

20. Recovery
Clean air sector (NW) remains similar. SW and SE still show slight decrease compared to 2019
Atmospheric CO2 enhancements in 2020 vs. 2019
UrbanCO2enhancement(ppm)UrbanCO2enhancement(ppm)

21. Take home message
 Urban (and subnational) greenhouse gas emissions and mitigation measures matter
=> science-based information has a role to play
 Our testbed allows to experiment with new atmospheric monitoring techniques (observations & models) and identify
their value added as well as find new science questions (non-localized CH4 from waste, anthropogenic versus
natural processes, etc.)
 Total column greenhouse gradients can be seen across Toronto and used to better understand atmospheric
composition changes as well as compared to satellite observations (OCO-2, OCO-3, GOSAT-2, maybe TROPOMI
in future?)
 Mobile survey platforms have helped to better understand methane emissions in the region, which are fairly low
from natural gas infrastructure
 Even short-term emission changes in CO2 seem to be detectable in atmospheric observations (COVID lockdown),
which is cause for optimism about our ability to track long-term changes due to policy-driven mitigation