Borken-Kleefeld, J.

Chen, Y.

Hausberger, S.

Jerksjo, M.

Kupiainen, K.

Rexeis, M.

Sjodin, A.

Tate, J.

International Institute for Applied Systems Analysis (IIASA)

Remote sensing for  identifying high emitters and  validating emission models J.Borken-Kleefeld1, K. Kupiainen1, Y.Chen1,2, S.Hausberger3, M.Rexeis3, A.Sjodin4, M.Jerksjo4, J.Tate5    1MAG-Program, IIASA – International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria  2Institute of Transportation Studies, University of California, Davis, CA 95616 United States  3Institute for Internal Combustion Engines and Thermodynamics, Graz University of Technology, Austria 4IVL Swedish Environmental Research Institute Ltd., Gothenburg, Sweden  5Institute for Transportation Studies, Leeds University, UK   We gratefully acknowledge the provision of remote sensing data by Gian-Marco Alt (AWEL, Baudirektion Zürich).  Jens Borken-Kleefeld  International Institute for Applied Systems Analysis  (IIASA) IIASA core competence:  Analysis of emissions, environmental and health impacts & identification of cost-effective measures for whole Europe for all sectors up to 2035 e.g. for Review of EU Strategy on Air Pollution Future emissions of NOx from light-duty diesel vehicles in EU27 as function of performance of Euro 6 diesel cars & light trucks  Therefore we are concerned to get emissions & emission factors right. Main findings Method:  • High emitting vehicles ≠ vehicles with highest instantaneous emissions  Base emission factors:  • Some high emitters included in ARTEMIS DB, hence implicitly in HBEFA!  – Are levels and shares, hence average emission factors correct?  • Share of high emitters estimated for several European sites – Preliminary results (and some problems) for Gothenburg & Zurich  • Comparison of instantaneous emission factors from RSD with PHEM model (=average emission factor) – Trends reproduced well for NOx but difficulties for CO  Emission modeling • High emitters important for both urban and highway fleet emissions Traditional interpretation of RSD “….a small number of high emitting vehicles responsible for a disproportionately large fraction of mobile emissions …”   (Kuhns et al. 2004 citing (Y. Zhang, Bishop, and Stedman 1994).      Emission spikes part of normal operation Modal CO emissions over CADC – PC G4 That’s a high emitter!  Modal emission measurements: TUG     Emission spikes part of normal operation Modal CO emissions over CADC – PC G4 That’s a high emitter!  Modal emission measurements: TUG New approach • Establish a reference distribution from chassis dynamometer data • Identify high-emitters from the difference between Remote Sensing Data and clean reference chassis data   Working definition for a high emitting vehicle:  A vehicle whose average emissions are  by at least 2 standard deviation  higher than the average emissions of the sample tested.   New approach: RSD vs. Chassis benchmark using CO from PC-G4 to illustrate method 𝑥𝑆𝑆(𝐶𝐶) = 5% (1 )RSD normal HighEmitterEF x EF x EF= − + PC-G4: CO g/kg EF_RSD 4.6 EF_normal  3.5 EF_HE  16.5 Key: Mean EF for “normal” and “high emitting” vehicle => sufficient sample needed!  Real-world (RSD) High emitters (chassis) Clean vehicles (chassis) SE RSD for Gothenburg: IVL NOx EF: PHEM vs. chassis dyno vs. RSD PC Gasoline Euro 3 & 4 (no HE data for other techologies)  Gothenburg 2007 & Zurich 2011 For PC Gasoline Euro 3:  • PHEM lower than RSD,  • opposite load behavior • Chassis dyno relatively stable • Some NOx HE in Gothenburg!?  For PC Gasoline Euro 4:  • PHEM -20%/+40% vs. RSD,  • Opposite load behavior • Chassis data and RSD at same levels   no NOx HE at these sites?  SE RSD for Gothenburg: IVL; CH RSD for Zurich: Baudirektion Zurich; PHEM simulation: TUG NOx Gothenburg (2007): 0-2° grade, NO+NO2 Zurich (avg. 2000-2011): 9° uphill, NO measured, NO2 calculated from HBEFA 3.1 shares NOx EF: PHEM vs. chassis dyno vs. RSD PC Gasoline Euro 3 & 4 (no HE data for other technologies)  Gothenburg 2007 & Zurich 2011 For PC Gasoline Euro 3:  • PHEM   >> RSD (?) • Chassis clean << RSD (?) => Many CO HE PC-G3 (?)  For PC Gasoline Euro 4:  • PHEM   >> RSD (?) • Chassis clean > RSD Gothenburg   << RSD Zurich ⇒ Many CO HE in Zurich (?)   PHEM CO for these urban driving conditions not correct.  SE RSD for Gothenburg: IVL; CH RSD for Zurich: Baudirektion Zurich; PHEM simulation: TUG CO Gothenburg (2007): 0-2° grade,  Zurich (avg. 2000-2011): 9° uphill Approach depends on credibility of input data modal data with high emitters only for PC-G3 & G4 PC-Gasoline Share HE: NOx Share HE: CO Chassis dyno RSD Zurich  (2000-2011) RSD Gothenb. (2007) Chassis dyno RSD Zurich  (2000-2011) RSD Gothenb. (2007) EURO 3 33%  (3 in 9) (neg.)-1% 18%-24% 33% (3 in 9) 22%-29% 22% EURO 4 17% (4 in 24) (neg.) (neg.) 17% (4 in 24) 23%-33% (neg.)-5% Method nice (?) but not yet robust as devil is in details • Modal chassis data available and reliable !!! • Correct data treatment, e.g.  • match records form speed and emissions instruments • conversion volume increments to fuel specific EF • Correct filtering for comparing RSD and Chassis data  Note: Here, RSD indicate different shares than in base data! Anything suitable for work program 2013!? Strongly affects calculated share of HE NOx: PHEM simulated EF vs. mean RSD EF calibrated to 30-160 vehicles each incl. unknown high emitters Gothenburg/SE, 0-2% grade (2007) Zurich/CH, 9% uphill (2000-2011) PHEM very good • For PC gasoline at both sites • For PC diesel somewhat lower   CO: PHEM simulated EF vs. mean RSD EF calibrated to 30-160 vehicles each incl. unknown high emitters Gothenburg/SE, 0-2% grade (2007) Zurich/CH, 9% uphill (2000-2011) PHEM higher for Euro G2 – G5,  calibration to engine maps difficult %difference mean EF: PHEM simul. vs. RSD PHEM for (urban) driving situations  • gasoline cars much higher,  • diesel cars lower.    PHEM for (urban) driving situations  • gasoline E3-E4 30-40% lower,  • diesel E1-E3 20-30% lower.    Extended comparisons warranted for 2013!? Outlook Identifying high emitters:  • Some high emitters included in ARTEMIS DB, hence implicitly in HBEFA!  – Are levels and shares, hence average emission factors correct? – More modal emission measurements available?   Validation of average emission factors:  • Share of high emitters estimated for several European sites • Comparison of instantaneous emission factors from RSD with PHEM model  – We continue with data from UK (ITS Leeds)  – More RSD sites? NL?  – Analyse aging effects from RSD spanning 2000 to 2011/2? – Analyse cross-country effects between CH-SE-UK – NL?! sites?      If share high emitters is known,  we can generalize on whole driving cycle High emitter Clean vehicle 𝑈𝑈𝑈𝑈𝑈 𝐷𝑈𝐷𝐷𝐷𝑈𝐷 𝑅𝑅𝑈𝑈𝑅 𝐷𝑈𝐷𝐷𝐷𝑈𝐷 𝑀𝑀𝑀𝑀𝑈𝑀𝑈𝑀 𝐷𝑈𝐷𝐷𝐷𝑈𝐷 𝐸𝐹2 = 12 ∗ 𝐸𝐹1 𝐸𝐹2 = 7 ∗ 𝐸𝐹1 𝐸𝐹2 = 6 ∗ 𝐸𝐹1 

Remote sensing for identifying high emitters and validating emission models

Remote sensing for identifying high emitters and validating emission models

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