A Brisbane tunnel study to validate motor vehicle emission models
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1 A Brisbane tunnel study to validate motor vehicle emission models Asia Pacific Automotive Engineering Conference 1-12 March 215, Melbourne Robin Smit and Phil Kingston
2 Tunnel study conducted for about one week in Aug-Sep 214 to provide accurate vehicle emissions data for a portal emissions trial
3 Measurement of key air pollutants relevant for road traffic: CO, NOx, NO2, PM2.5, PM1, SO2, PAHs and VOCs (speciated) plus CO2 Other parameters: temperature, humidity, pressure, tunnel air speed/flow, background concentrations, in tunnel AQ sensors, tolling statistics by vehicle type, license plate data, in-tunnel VKT (mass balance) ~ one week sampling period minimum to capture variability in traffic flows and emissions (5 min/hourly, weekday/weekend)
4 (Clem Jones) CLEM7 tunnel in Brisbane ~ 5 km of twin two-lane tunnels linking major Brisbane roads north ventilation vent
5 Accurate tunnel and motor vehicle emission predictions required to ensure sound policy decisions Focus on Australian vehicle emission model validation Urban Background Tunnel Stack 25/8/214 26/8/214 27/8/214 28/8/214 29/8/214 3/8/214 31/8/214 1/9/214 2/9/214 3/9/ NOx 3 g/m (1 atm, C, NO2-eq) 5 For tunnel assessments models are essential emission estimates for a range of pollutants, including surrounding road network + possibly future situations not feasible to measure! SAEA paper presents results for one important pollutant: NOx Comprehensive report is being finalised now and will be published
6 Vehicle emission software is well-developed in Europe and the US They do not adequately reflect Australian conditions (fleet mix, vehicle technology, fuel quality and climate) Large prediction errors of up to a factor of 2 have been reported when overseas models are directly applied to Australian conditions without calibration Two software packages recently (213) developed for Australian conditions using comprehensive empirical data from major Australian emission testing programs: Extensive background information and scientific papers available
7 P P (engine) power-delta-power vehicle emission software (free) high resolution: second-by-second emission simulation for all major Australian vehicle classes including cars, SUVs, light-commercial vehicles, rigid trucks and articulated trucks quantifies impacts of factors that affect engine load/power, i.e. speed, acceleration, road gradient, vehicle loading, wind speed, air conditioning multivariate time-series models using: - instantaneous engine power (P) - the change in engine power ( P), both expressed as Watts t et 1 Pt 2 Pt 3 Pt 2 4 Pt 2 5 Pt Pt ARMA p, q t Australian empirical data: 2,5 modal emission tests (1 Hz), ~ 12,5 bag emission tests
8 COPERT = globally used software to calculate road transport emissions European Commission manages scientific development Dedicated Australian version developed by DSITI in cooperation with an EU partner and released in 213 is recommended for motor vehicle emission inventories in Australia by National Pollutant Inventory Some features: - average speed model vehicle classes air pollutants and greenhouse gases - all types of emissions (cold start, hot running, non-exhaust) - several corrections e.g. for fuel quality, climate, ageing,
9 Elapsed Time (s) aggregation LDV 5 1 In-tunnel air speed (km/h) tunnel road gradient: ~2% increase of in-tunnel NOx emissions (air speed is zero km/h) - piston air flow roughly compensates road gradient at high air speeds - mean correction factor for the sampling period is 1.1 and 1.8 for LDVs and HDVs, respectively - COPERT Australia base emission factors were multiplied with these values base Correction factor NOx (g/s) 1 Hz input: - vehicle speed - road gradient - vehicle loading - in-tunnel air speed - airco on/off 15 HDV
10 Normalised fleet averaged emissions for validation g/km EF vs. PHDV WOLS regression: e = (h + a ) + b PHDV. Around the world fleet emissions dominated by small portion of high emitters e.g. 1% of vehicles in USA now make up 3% of the emissions valid points so should be retained in model, but affect proper model fit Two step approach: 1) Robust weighted linear regression to identify outliers (M+3SD), and used for high emitter offset calculation 2) weighted OLS regression NOx emission factor (g/veh.km) Outliers 5 1 PHDV (%) 15 2
11 5 4 - difference is statistically significant 3 - magnitude of difference appears to be relatively small review of 5 international vehicle emission model validation studies shows reported prediction errors generally within a factor of 2 for NOx +23% NOx emission factor (g/km) % LDV (COPERT Australia) LDV (tunnel) HDV (COPERT Australia) HDV (tunnel)
12 Discussion tunnel studies Strengths: o large sample of on-road vehicles o capturing inter-vehicle variability in emissions behaviour o relatively controlled environment o spatial resolution aligns with distance based emission models Weaknesses: o limited range of traffic conditions (free-flow high speed) o significant road gradient effects o significant piston flow effects o dusty loads (PM) o unknown portion of cold start vehicles o unknown vehicle loads Therefore, partial model validation, and other methods required to confirm results, e.g. near road AQ measurements, remote sensing studies, on-board emission measurements (PEMS), etc.
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14 Analysis license plate data Queensland fleet vs. CLEM7 in-tunnel fleet Significant differences: o proportion diesel vehicles (both LDV and HDV) substantially higher in the CLEM7 tunnel (3% versus 18% in the Queensland fleet) o CLEM7 lower proportion of small/large passenger cars, but a higher proportion of SUVs o vehicles in the CLEM7 tunnel are substantially younger with better engine and emission control technology CLEM7 Queensland UNCNTRL ADR27 ADR3 ADR36 ADR37- ADR37-1 ADR7- ADR79- ADR79-1 ADR79-2 ADR79-3
15 PHDV (%) CO emission factor (g/km) CO emission factor (g/km) CO emission factor (g/km) LDV QLD (COPERT Australia) LDV CLEM7 (PIARC) LDV CLEM7 (COPERT Australia) LDV CLEM7 (Measured) HDV QLD (COPERT Australia) HDV CLEM7 (PIARC) HDV CLEM7 (COPERT Australia) HDV CLEM7 (Measured)
16 .5 PM2.5 emission factor (g/km) PM2.5 emission factor (g/km) 1 15 PHDV (%) 2 LDV QLD (COPERT Australia) LDV CLEM7 (PIARC) LDV CLEM7 (COPERT Australia) LDV CLEM7 (Measured) PM2.5 emission factor (g/km).2. HDV QLD (COPERT Australia) HDV CLEM7 (PIARC) HDV CLEM7 (COPERT Australia) HDV CLEM7 (Measured)
17 measurement of traffic conditions using a phone app ATLAS II (UniQ)
18 Speed (km/h) 8 ATLAS speed Imputed Stationary or signal loss 9:24:23 9:25: 9:26: 9:27: 9:28: 9:29: 9:3: 9:31: 4 2 Speed (km/h) 6 8 ATLAS speed Imputed Smoothed trace 9:24:23 9:25: 9:26: 9:27: 9:28: 9:29: 9:3: 9:31:
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21 Emission factor (ng/km) 5 phenanthrene pyrene fluoranthene anthracene PAHs_BaPeq benzo(a)anthrancene chrysene benzo(e)pyrene benzo(b)fluoranthene benzo(g,h,i)perylene benzo(a)pyrene benzo(k)fluoranthene indeno(1,2,3-cd)pyrene dibenzo(a,h)anthracene
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23 Thank you for your attention
24 kwh CO2 NOx FC
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