CO2 emissions and uptake in urban ecosystems Integrating models and measurements

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1 CO2 emissions and uptake in urban ecosystems Integrating models and measurements Andreas Christen Department of Geography, University of British Columbia, Vancouver, BC, Canada Workshop on Carbon dioxide in the urban atmosphere December 1, 2011

2 Why modelling or monitoring CO2 emissions? Knowledge on emissions and uptake of carbon-dioxide (CO2) are relevant for decision making on different scales: Building per area Neighborhood City 5-50 m Region / Nation per capita km x 1000s km Globe x s km x s km x Christen et al. (2010)

3 Energy-related global CO2 emissions 6 t C km -2 yr -1 New York 4 Los Angeles London Moscow Tokyo 2 Mexico City 0 Sao Paulo Dhaka Annual CO2 emissions from fossil-fuel Burning, Hydraulic Cement Production, and Gas Flaring Only grid cells with more than 0.1 t C m -2 yr -1 are shown Auckland Data from ORNL

4 Per-capita CO2 emissions from selected cities (a) Heating and industrial fuel (b) Ground transportation Warm / mild Continental Compact Extensive GHG emissions (t CO 2 e year 1 cap 1 ) Denver Geneva Toronto Bangkok New York Prague London Los Angeles Cape Town Barcelona New York Cape Town 50 Bangkok Barcelona Geneva Prague London Toronto Denver Los Angeles Heating degree days ( days) Space consumption (m 2 cap -1 ) North-America Europe Asia Africa Data from Kennedy et al. (2010) Note- CO2 equivalents are shown, those include greenhouse warming effects due to emitted N2O and CH4

5 Integral vs. sector specific Challenge 1 Urban metabolism Models quantify sector-specific emissions Transportation models Building energy models Resource and waste flow models Urban biosphere models Transportation Buildings Human body, food, and waste Vegetation and soils Flux and concentration measurements quantify net effect (integral effect) of all sources and sinks Can we partition flux and concentration measurements to inform or validate sector-specific model results?

6 Measuring carbon-dioxide fluxes Atmosphere F CO2 C - Combustion R - Respiration P - Photosynthesis Urban Ecosystem S C z top ΔS - Storage change in air within balancing volume P R FCO2 - Measured net flux z bot FCO2 = C + R - P + ΔS

7 Local vs. external Challenge 2 External emissions due to local activities Local emissions due to local activities Local emissions due to external activities Model results Typically expressed in kg C cap -1 year -1 Measurements Typically expressed in kg C m -2 year -1 Christen et al., 2011, Atmos. Environ.

8 Overview How can we combine atmospheric measurements of CO2 and greenhouse gas emission models, given the different spatial, temporal and processual representations? Hour of day Part 1 - Partitioning measured concentrations and fluxes using relations to temporal and environmental controls. 50% 90% Part 2 - Partitioning measured concentrations and fluxes using inverse modelling / source area attribution Part 3 - Partitioning measured concentrations and fluxes using chemical tracers including isotopes.

9 Part 1 - Partitioning measured emissions using relations to temporal and environmental controls measured CO2 flux measured CO2 flux Hour of day 1. Use temporal patterns of fluxes and concentrations of CO2 based on human activity cycles. Diurnal, weekday-weekend, seasonal, interannual? control 2. Empirically relate fluxes or concentrations to postulated environmental and anthropogenic controls of the exchange e.g. HDD, traffic counts, photosynthetic active radiation, soil temperatures

10 Fingerprints of measured urban CO2 fluxes Time of day (b) Basel - Klingelbergstrasse (48 N, LCZ 5) J F M A M J J A S O N D Time of day (a) Baltimore Cub-Hill (38 N, LCZ 6) J F M A M J J A S O N D 4 4 F CO2 (µmol m -2 s -1 ) Net-Uptake Net-Emission Long-term measurements by Eddy Covariance (EC) over 6 years each See talk by Matthias Roth on how to measure fluxes by EC Data from R. Vogt (Univ. of Basel) and S. Grimmond (KCL London)

11 Transportation - Weekend vs. weekday emissions Vancouver Sunset (Tower) Measured monthly fluxes using EC approach On average 24% emission reduction on weekends Weekend (g C m-2 day-1) SW NE SE 10 NW Different wind sectors Weekday (g C m-2 day-1) Christen et al., 2011, Atmos. Environ.

12 Traffic counts vs. measured fluxes Nemitz et al. 2002

13 Montréal Suburban 25 m EC tower Montréal Urban 25 m EC tower Vancouver 30 m EC tower

14 Annual total measured emissions Montréal Suburban Montréal Urban Vancouver Population Density (Inh. km -2 ) Yearly HDD (ºC day) 2,400 8,400 6,410 4,319 4,315 2,697 Plan area fractions Buildings Vegetated 50% 12% 37% 29% 27% 34% 29% Impervious 44% 37% Annual total (local) emissions (kg C m -2 year -1 )

15 Measured emission profiles by month Montréal Suburban Vancouver Montréal Urban Average daily carbon emission (g C m -2 d -1 ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Data from montreal sites provided by I. Strachan and O. Bergeron

16 Emissions vs. HDD - different responses Average monthly carbon emission (g C m -2 month -1 ) Measured fluxes using EC approach Fc = 0.35 HDD R² = 0.82 Vancouver Fc = 0.28 HDD + 13 R² = 0.96 Fc = 0.53 HDD R² = 0.91 Montréal Urban Montréal Suburban Monthly total heating degree days (ºC day)

17 Emissions due to (likely) space heating Montréal Suburban Montréal Urban Vancouver Per urban area (g C m -2 HDD -1 ) Per building volume (g C m -3 HDD -1 ) Per capita (g C Inh -1 HDD -1 ) This assumes that other components (traffic, human respiration, biosphere) do not change between months!

18 Part 2 - Partitioning measured emissions using inverse modelling and spatial data 50% 90% 1. Relate EC measurements to land-cover and urban metabolism in the turbulent source area 2. Relate concentration measurements using regional inverse modelling e.g. upwind - downwind differences of a city

19 Summertime FCO2 from 30 flux towers North America Europe Asia Australasia Africa Compact midrise (LCZ 2) Compact lowrise (LCZ 3) Open midrise (LCZ 5) Open lowrise (LCZ 6) Scattered trees (LCZ B) Daily total F CO2 (g C m -2 d -1 ) Mo08s Es07[p] Sl05 V08s Mo08u Tk01 Ba04 Lo06 Mb03l V08s[r] Si06 Ch95 V08o Bm02 Ma01 Me06 Ba02u1 Me03 Ro04 Mb03m Es07[u] Fl05 Source Sink Low density High density Plan area fraction of buildings λ b (%)

20 Turbulent source areas EC system wind 50% 90% turbulent source area isopleths

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22 Fraction of arterial roads in source area Daytime values (summer) FCO2 µmol m -2 sec -1 in turbulent source area B. Crawford, UBC

23 Spatial emission modelling Urban weather station Satellite data LiDAR Census and assessment data Tansportation data SUBMODELS MODEL INPUTS 1 x 1 m 1 year 1 x 1 m Parcel or DA Arterial roads 2.4 x 2.4 m Air and soil temperatures Water content of soil Solar radiation Vegetation and soils Ecosystem carbon model accounts for soil respiration and photosynthesis of lawns and trees. Lawn extent Leaf area Tree location Leaf area Shading Buildings Building typologies Building volumes Building shell area LIDAR-informed building energy models quantify carbon emissions in a bottom-up approach. Population Land-use Employment data Waste, food, and human body Estimated waste production and human respiration based on census data. Trasportation Traffic counts Trip diaries Top-down modelling of traffic emissions based on splitting-up traffic counts and trip-diaries.

24 A building typology approach post pre W m W m W m -3 Systematic approach to document, classify and estimate carbon emissions attributable to buildings Bottom-up modelling Describes neighbourhood through a series of building types 4558 buildings in study area - so key is to describe diversity of building types and energy performance through representative samples

25 Modelling emissions from buildings Annual simulations for 4 orientations and primary and secondary heating types (HOT-2000, NRCAN OEE) Emissions from buildings E 41st Ave 50 m raster map 10 kg C m -2 year Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr E 49th Ave Tower 5 4 Gordon Park E 54th Ave m N

26 Modelling emissions from transportation Top-down modelling of transportation emissions based on traffic counts and trip diary data Emissions from transportation E 41st Ave 50 kg C m -2 year Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr E 49th Ave Tower 25 Knight / 49 th Ave. seen from flux tower Gordon Park E 54th Ave m N

27 Modelling emissions from human respiration Emissions from human respiration Estimation based on night-time population density (census) E 41st Ave 5 kg C m -2 year -1 4 Census areas (population) Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr E 49th Ave Tower 2 Gordon Park 1 50 x 50 m raster of population density land-use and LiDAR volume E 54th Ave m N

28 Modelling emissions from vegetation and soils LiDAR subset of 1m urban surface and cover model Net-emissions from urban vegetation E 41st Ave -1.6 kg C m -2 year Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr Based on soil respiration and leaf chamber measurements E 49th Ave Tower Gordon Park E 54th Ave m N

29 Emission modelling methodology Vegetation and soils Buildings Waste, food, and human body Trasportation modelling MODEL OUTPUTS Maps Per area emissions 50 x 50 m raster Adding Components MODEL VALIDATION All? Flux tower data Independent, direct measurement of carbon emissions (2 years) on a tall tower using the eddy-covariance approach.

30 Net-emissions from all sources Emissions from all sources kg C m -2 year -1 Human respiration Vegetation and soils 8% % 0.33 Buildings 40% Fraser St. E 41st Ave Memorial S. Park E 49th Ave Tower Knight St. Tecumseh Park Victoria Dr % 2.93 Gordon Park Transportation Emissions only (no uptake) E 54th Ave m N

31 Relative modelled flux contributions (a) Integral turbulent source area m -2 (b) Relative CO 2 flux contribution kg C m -2 year -1 m x E 41st Ave E 41st Ave Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr. 0.6 Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr E 49th Ave E 49th Ave 0.40 Gordon Park Gordon Park E 54th Ave m N Emissions from all sources E 41st Ave Fraser St. Memorial S. Park Knight St. Tecumseh Park Victoria Dr kg C m -2 year each element multiplied E 54th Ave Human R Vegetation and soils 4% 5% 27% Buildings E 49th Ave Tower % Gordon Park Transportation E 54th Ave

32 Source area weighted model vs. measured fluxes kg C m -2 year % Difference Model 7.5 EC 6.7 Model 9.5 EC 6.6 Model 11.4 EC 13.2 Model 6.2 EC 4.3 Measured Soil and vegetation Transportation Buildings Human respiration Model 2.9 EC All sectors NW SE SW NW Wind sector

33 Part 3 - Partitioning of emissions using isotopic or chemical tracers Using additional chemical tracers for combustion to CO2 to complement concentrations or fluxes Example: CO 2. Using the radioisotope 14 C to determine source of carbon 3. Using stable isotopologues of carbon-dioxide (δ 13 C and δ 18 O) to infer sources of carbon and oxygen in CO2

34 Radioisotopes - 14 C High atmosphere Cosmic radiation 14 C is formed constantly in the upper atmosphere by cosmic radiation impacting 14 N and is also a residual of atmospheric nuclear weapon tests in the 1950s and 60s. Atmospheric 14 C is oxidized to 14 CO2 which is taken up by plants. 14 C 14 CO2 14 N Oxidation to CO2 Uptake by plants Neutron Proton It decays with a half-life of about years (radiocarbon dating of organic matter) 14 C beta-decay 14 N

35 Use of Δ 14 C signature 14 C is an excellent tracer for the source of CO2, as fossil fuels contain virtually no 14 C, so CO2 from fossil-fuel combustion is 14 C free. CO2 from gasoline combustion contains no 14 C CO2 from natural gas combustion CO2 from wood burning contains some 14 C CO2 from respiration However, 14 CO2 concentrations are in the ppt and cannot be easily measured in-situ in the atmosphere.

36 Radioisotopes as tracer - 14 C C in plant material 2 14 C of plant material ( ) Highway Data from Lichtfouse et al. (2005) Distance to highway (m) Fossil fuel C in plant material (%)

37 Δ 14 C in annual grass in California more fossil-fuel CO2 assimilated San Francisco less fossil-fuel CO2 assimilated Los Angeles Los Angeles Riley et al., JGR, 2008

38 Stable isotopologues of CO2 Isotopologue - same molecule (here CO2), but different isotopes contained. Isotopologue Estimated abundance In-situ measurement in atmosphere possible? 12 C 16 O % TGA 200, G2131-i 13 C 16 O2 1.10% TGA 200, G2131-i 12 C 16 O 18 O 0.39% TGA C 16 O 17 O 0.07% 13 C 16 O 18 O >0.01%

39 Reporting ratios of CO2 isotopologues 13 [ 13 C 16 O 2 ]/[ 12 C 16 O 2 ] C = R VPDB Ratio of a pre-defined standard sample 18 [ 12 C 16 O 18 O]/[ 12 C 16 O 2 ] O = R SMOW Ratio of a pre-defined standard sample

40 Isotopic signatures of CO2 from combustion more 18 O δ 18 O in CO2 (,VPDB-CO2) Kraków, Poland without catalytic converter with converter Diesel Gasoline LPG Methane (natural gas) Coal less 18 O less 13 C δ 13 C in CO2 (, VPDB) more 13 C Data from Zimnoch (2009)

41 Use of δ 13 C signature δ 18 O in CO2 (,VPDB-CO2) Natural Gas ~-55 Petroleum ~-30 Coal ~ δ 13 C in CO2 (, VPDB) Diesel Gasoline LPG Methane (natural gas) Coal δ 13 C is in particular useful to distinguish between CO2 emitted from natural gas vs. petroleum (diesel, gasoline, LPG).

42 Use of δ 18 O signature δ 18 O in CO2 (,SMOW) δ 13 C in CO2 (, VPDB) Above-ground respiration ~48 Natural gas and petroleum ~27 Below-ground respiration ~26 δ 18 O can be useful to distinguish between CO2 emitted in biogenic respiration vs. fossil fuels, because of evaporative enrichment of H2 18 O imparts the respired CO2. Data from Djuricin et al. (2010)

43 Using a mixing model to infer sources of CO2 in urban BL Current concentration reflects background + local sources: concentration in urban atmosphere concentration contribution of local sources c u = c b + c s background concentration Mass conservation let us write for example for δ 13 C: 13 C u c u = 13 C b c b + 13 C s c s

44 Keeling plot Background CO2 δ 13 C in CO2 (, VPDB) Source CO2 Sampled CO / cu (µmol mol -1 ) Based on Pataki et al. (2003)

45 Keeling plot with two sources Background CO2 δ 13 C in CO2 (, VPDB) Petroleum Sampled CO2 Natural gas / cu (µmol mol -1 )

46 Mixing-models involving several isotopologues c u = c n + c g + c ra + c rb + c b (1) total urban atmospheric concentration contribution by natural gas contribution by gasoline contribution by above-ground respiration contribution by below-ground respiration background concentration 13 C u c u = 13 C n c n + 13 C g c g + 13 C ra c ra + 13 C rb c rb + 13 C b c b 18 O u c u = 18 O n c n + 18 O g c g + 18 O ra c ra + 18 O rb c rb + 18 O b c b 14 C u c u = 14 C n c n + 14 C g c g + 14 C ra c ra + 14 C rb c rb + 14 O b C b (2) (3) (4) 4 unknowns 6 measurements 12 lab-analysis Djuricin et al. (2010)

47 Using flux measurements of isotopologues to infer sources (1/2) Griffis et al. (2008, JGR) have shown that isofluxes (i.e. fluxes of isotopologues) can be measured using the eddycovariance approach (requires a fast-response analyzer, such as the CSI TGA 200). They have validated the approach above cropland. For an urban ecosystem, we could write: F u = F n + F g + F r (1) Flux contribution by natural gas Flux contribution by respiration Total urban flux Flux contribution by gasoline

48 Campbell Scientific TGA 200 Tunable diode laser spectrometer measures at a narrow range 2308 cm -1

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50 Summary The huge number of local and non-local sources of CO2 make a partitioning of fluxes and concentrations into individual emission sectors the primary goal. A set of approaches is available to first partition flux or concentration measurements of CO2 into emission sectors, and combine them with models (e.g. atmospheric inverse models, chemical mixing models). A promising monitoring or measurement campaign is likely a combination of several presented methods, and choice of approaches and measurement locations depends on scale of interest for emission monitoring / measurement.

51 Contact Dr. Andreas Christen, UBC Thank you