Recommendations on fine scale emissions derived from HERMESv3 M. - - PowerPoint PPT Presentation
Recommendations on fine scale emissions derived from HERMESv3 M. Guevara, Tena, C., Jorba, O., Prez Garca-Pando, C. FAIRMODE technical meeting 09/10/2019 October 07 - 09, 2019 Madrid, Spain Motivation Modelled emissions Gridded
09/10/2019
FAIRMODE technical meeting October 07 - 09, 2019 – Madrid, Spain
Gridded emission inventories Modelled emissions √ Gridded (fixed grid) X Not hourly (annual, monthly) X Not speciated √ Gridded (any grid or source level) √ Hourly √ Speciated
Emission processing systems Adapt the emission data to the air quality model’s requirement Emission models Implement detailed bottom-up emission estimation methodologies
(a) (b) (c) (d)
A python-based, parallel and multiscale emission modelling framework that processes and estimates gas and aerosol emissions for use in atmospheric chemistry models.
A python-based, parallel and multiscale emission modelling framework that processes and estimates gas and aerosol emissions for use in atmospheric chemistry models. global-regional module (HERMESv3_GR) bottom-up module (HERMESv3_BU)
A processing system that calculates emissions through an automatic combination of existing inventories and user defined vertical, temporal and speciation profiles An emission model that estimates emissions at the source level combining state-of-the-art bottom- up methods with local activity and emission factors
Guevara et al. (2019a, GMD) Guevara et al. (in preparation)
HERMESv3_GR output Emission data library
Criteria pollutants: NOx, CO, SO2, NMVOC, NH3, PM10, PM2.5 Greenhouse gases: CO2, CH4
A_PublicPower B_Industry C_OtherStationary Comb F_Roadtransport K_AgriLivestock L_AgriOthers G_Shipping H_Aviation I_Offroad
user-dependent input data internal input data meteorology
Bottom-up and process-based estimation methodologies
0.0 1.0 2.0 3.0 4.0 1 3 5 7 9 11 13 15 17 19 21 23
C_OtherStationaryCombution - Default C_OtherStationaryCombution - PM
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 1 20 39 58 77 96 115 134 153 172 191 210 229 248 267 286 305 324 343 362
Athens Barcelona Oslo
PM10 – EIONET Rural stations (December 2016)
𝐹𝑚,𝑗 ℎ =
𝑤=1 𝑜
𝐵𝐵𝐸𝑈(ℎ)𝑤,𝑚 ∗ 𝐹𝐺(ℎ)𝑤,𝑚,𝑗
Estimation of link-level vehicle emissions
hot, cold-start, wear, evaporative, resuspension (speed and meteo dependent)
0.0 0.2 0.4 0.6 0.8 1.0
Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Diesel passenger cars @ 28km/h [g/km]
COPERT IV COPERT V COPERT V (with degradation factor) RSD
Amato et al. (2012)
0.0 0.2 0.4 0.6 0.8 1.0
Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Petrol passenger cars @ 28km/h [g/km]
COPERT IV COPERT V COPERT V (with degradation factor) RSD
𝐹𝑚,𝑗 ℎ =
𝑤=1 𝑜
𝐵𝐵𝐸𝑈(ℎ)𝑤,𝑚 ∗ 𝐹𝐺(ℎ)𝑤,𝑚,𝑗
Estimation of link-level vehicle emissions
hot, cold-start, wear, evaporative, resuspension (speed and meteo dependent) Amato et al. (2012)
Low temperature NOx diesel emission penalty Grange et al. (2019)
𝐹𝑚,𝑗 ℎ =
𝑤=1 𝑜
𝐵𝐵𝐸𝑈(ℎ)𝑤,𝑚 ∗ 𝐹𝐺(ℎ)𝑤,𝑚,𝑗
Estimation of link-level vehicle emissions
0.0E+00 5.0E+03 1.0E+04 1.5E+04 2.0E+04 2.5E+04 3.0E+04 3.5E+04
NOx SO2 CO NMVOC PM10 PM25 t/year
Total annual emissions
Port activities Small boats
contribution to CO and NMVOC emissions in coastal areas
summer season
being treated in EMEP 0.1x01?
0% 20% 40% 60% 80% 100%
NOx CO PM10 PM2.5 NMVOC
apu taxi takeoff climbout approach landing
Main engines Brake and tyre wear Auxiliary Power Unit
Not included in the EMEP/EEA guidelines
Netcen (2004) Morris (2007)
Consideration of the spatial and temporal dynamical component of the emission processes.
𝐹 𝑦, 𝑒 =
𝑑=1 𝑜
൯ ) 𝐵(𝑦 𝑑 ∗ ) Г(𝑦 𝑑 ∗ 𝐹𝐺(𝑦 𝑑 ∗ (𝑓0.022∗𝑈(𝑒)+0.042∗𝑋𝑇(𝑒)) ∗
𝑏=1 3
𝛾𝑏,𝑑 𝜏𝑑,𝑏 ∗ 2 ∗ 𝜌 ∗ 𝑓
(𝑒− 𝜐𝑑,𝑏)2 −2∗𝜏𝑑,𝑏
2
Local cultural techniques
Crop distribution
Soil properties
NH3 volatilization
Local cultural techniques
Skjøth et al. (2011)
Consideration of the spatial and temporal dynamical component of the emission processes.
𝐹 𝑦, 𝑒 =
𝑑=1 𝑜
൯ ) 𝐵(𝑦 𝑑 ∗ ) Г(𝑦 𝑑 ∗ 𝐹𝐺(𝑦 𝑑 ∗ (𝑓0.022∗𝑈(𝑒)+0.042∗𝑋𝑇(𝑒)) ∗
𝑏=1 3
𝛾𝑏,𝑑 𝜏𝑑,𝑏 ∗ 2 ∗ 𝜌 ∗ 𝑓
(𝑒− 𝜐𝑑,𝑏)2 −2∗𝜏𝑑,𝑏
2
20 40 60 80 100 120 1 18 35 52 69 86 103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358 t day-1
Lleida
20 40 60 80 100 120 1 18 35 52 69 86 103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358 t day-1
South of Castilla Leon Barley Maize Wheat Others HERMESv3 – NH3 L_AgriOthers (2015)
Use of the gridded Livestock of the World version 3 (GLWv3; Gilbert et al., 2018) GLWv3 versus location of farms HERMESv3 – NH3 G_Livestock (2015)
5 10 15 20 25 30 35 40 45 50 55 1 19 37 55 73 91 109 127 145 163 181 199 217 235 253 271 289 307 325 343 361 t day-1
Murcia Pigs Cattle Others
5 10 15 20 25 30 35 40 45 50 55 1 19 37 55 73 91 109 127 145 163 181 199 217 235 253 271 289 307 325 343 361 t day-1
Galicia Pigs Cattle Others
Van Damme et al. (2018, Nature) 0.0E+00 3.0E+04 6.0E+04 9.0E+04 1.2E+05
Aragon - Catalonia Murcia
Total NH3 [t/year] HERMESv3 IASI HERMESv3 – NH3 G_Livestock (2015) Use of the gridded Livestock of the World version 3 (GLWv3; Gilbert et al., 2018) IASI-derived total NH3
improvement/refinement of the emissions (e.g. temperatura effect)
emission processes is a key component.
wear)
sector should be treated separately and not as part as the “OtherMobileSources”