Emission factors, Non-regulated pollutants Air pollution assessment methodologies

Transcription

1 Emission factors, Non-regulated pollutants Air pollution assessment methodologies - works in link with ERMES Issues: ERMES Graz, 17 September 2014 Non-regulated pollutants, Guidance on and benchmarking of models Yao LIU, Michel ANDRÉ Transports and Environment Laboratory 1

2 ERMES Non-regulated-pollutants issue A high concern, due to health and environment impacts with emerging technologies and fuels Different topics were suggested in ERMES 1. Identify key non-regulated pollutants with evolution of vehicle technologies 2. Collect literature and data into a database of emission factors 3. Literature review on current sampling and analysis methods for each family of compounds (PAH, VOC, Aldehyde ) 4. Design a test program on diesel and gasoline vehicles (more or less recent Euro 2-6), with several partner laboratories (JRC, EMPA, LAT ), to measure emissions data around the key NRP 5. Data synthesis and elaboration of emission factors 2

3 ERMES Non-regulated-pollutants issue Preliminary works by IFSTTAR (Ademe funding) 1. Identify key NR pollutants with evolution of vehicle technologies 2. Collect literature and data into a database of emission factors 3. Literature review on current sampling and analysis methods A literature review and laboratory experiments enabled identification of non-regulated pollutants BTEX: Benzene, Toluene, Ethylbenzene, Xylenes PAH: Naphtalene, Phenanthrene, Fluoranthene, Pyrene, Benzo(a) antracene, Chrysene, Benzo(b+j) fluorenthene, Benzo(a) pyrene Carbonyl compounds: formaldehyde, acetaldehyde, acetone, benzaldehyde, black carbon (BC) NO2, NH3. A short report in French on PAH, VOC and black carbon EF A synthesis could be written in English, if needed Sampling and analysis methods reported by Pillot (2006) to be completed / updated 3

4 ERMES Non-regulated-pollutants issue Preliminary works by IFSTTAR (Ademe funding) Emission factors (mg/km) 4. Design a test program, to measure key NRP Sampling definition (2014): 4 BTEX cartridge-types tested with Euro 4 gasoline vehicle Tenax, Tenax/Carboxen 1000 with 70/30% or 85/15% and Air Toxic Tenax: best sampling and efficiency for all BTEX Tenax TA/Carb (70/30) TA/Carb (85/15) Air Toxic

5 ERMES Non-regulated-pollutants issue Preliminary works by IFSTTAR (Ademe funding) 4. Design a test program, to measure key NRP Sampling flow and system optimisation (2014), for BTEX, PAH, Carbonyl compounds, black carbon and particle number Different sampling conditions tested with high polluting (Euro 4 diesel), and low polluting vehicles (Euro 5 diesel with DPF and Euro 5 gasoline with direct injection system) Suitable sampling systems designed, Detailed results : a report in French, possible synthesis in English 5

6 ERMES Non-regulated-pollutants issue Preliminary works by IFSTTAR (Ademe funding) 4. Design a test program, to measure key NRP Emission factors measurements on 3 vehicles Detailed results : a report available in French, possible synthesis Data Vehicle Cycle NRP Regulated Compounds Euro 4: - Diesel Euro 5: - Diesel DPF - Gasoline (DI) Artemis: Urban Motorway PAH, BTEX, Carbonyl compounds, NO 2, BC HC, NOx, CO, CO 2, NMHC, Particle number Date and Lab May - June 2014 IFSTTAR 6

7 ERMES Non-regulated-pollutants issue Future works (Ademe, French Research Agency funding) Emission factors measurements 6-8 cars to be tested by IFSTTAR ( ) Vehicle Cycle NRP Regulated compounds Date Euro 5: Diesel DPF (additive filter) Diesel DPF (catalysed filter) Gasoline Euro 4: Diesel DPF (additive filter) Diesel DPF (catalysed filter) Gasoline Euro 6 Diesel DPF Gasoline Artemis: Urban Road Motorway PAH, BTEX, Carbonyl compounds, NO2, BC HC, NOx, CO, CO2, NMHC, Particle mass and number Oct 2014 Mars

8 ERMES Non-regulated-pollutants issue to go further To complete the review More vehicles for larger samples, by other labs? A cooperative process is needed Data synthesis and elaboration of emission factors for their taking into account in the emissions models 8

9 models, uncertainty, test cases Four phases were proposed in ERMES 1. Emission models : accuracy, validity, review, intercomparisons. 2. Data and assumptions for emission estimation Fleet and traffic data, prospective scenario, etc. and their variability Sensitivity of the emissions calculation. 3. Use of emission models within larger chains of models. Assumptions and interfaces. Validity of the coupling with micro or macro traffic models. 4. Practices for the assessment of the air pollution from transports Lessons from applications, real-world practices, benefits from the implementation of transport / traffic measures 5. Expected results : a better knowledge of the contexts, recommendation around the assessment approaches. 9

10 models - IFSTTAR preliminary works Different Case studies : City of Nantes and urban area Eval-PDU project, funding by the French Research Agency (ANR) simulated hourly traffic Grenoble ring, MOCOPO res. proj., funding by Dept of Sust. Dev. Traffic and fleet, and air quality monitoring Paris area - Assessment of Low Emission Zones (ZaParC, Ademe) Fleet monitoring in different places, trafic and emission simulation over the whole Île-de-France An urban district in Villeurbanne (CoerT-P, Dept of Sust. Dev.) Micro and macro traffic simulation An urban district in Paris area (Trafipollu, French ANR) Heavy experiment including air and water quality, traffic and fleet monitoring; macro and micro simulation 10

11 models - IFSTTAR preliminary works Different Case studies : City of Nantes and area (Eval-PDU) Hourly traffic (DAVISUM travel and traffic modelling - 4 steps static approach) 2002 and 2008 reference situations as well as different scenarios Urban mobility plan implementation (actual status) +20% of mobility ; -25% passenger cars ; +30 and +50% public transport Busway implementation: high service bus lines along a main road Voluntary urban mobility plan Speed limits (90 -> 70 km/h on ring / motorway, 30 km/h in city centre) Fleet renewal scenarios Emission calculation using 2 plate-forms derived from COPERT4 Analysis of the whole chain of models (from travel to air quality), assumptions and data, assessment practices Development of a Health impact indicator 11

12 models - IFSTTAR preliminary works City of Nantes and urban area - a few results A review of emission models Different implementations of the same COPERT4 methodology can induce strong differences for certain pollutant estimation 20 to 40% for PM, Cd, Benzene, CO, while others are within 1% Due to different interpretation, updates, etc. Weaknesses of the overall approach Due to its principles and data, the Static Traffic model addresses weakly the congestion (hourly step), the speed level (overestimated), the heavy vehicles, the spatial and temporal distribution of the traffic Cold start and evaporative emission : difficult to distribute spatially Insufficient taking into account of local specificities (fleet, driving and use conditions) 12

13 models - IFSTTAR preliminary works City of Nantes and urban area - a few results Sensitivity to Vehicle fleet is high A 2-years fleet evolution induces a difference of the pollutant estimation by 15 to 27% of most pollutants (except CO2, N2O, PAH) a 4-years fleet evolution induces differences by 30 to 45% Low differences in Car Diesel rate (by 2%) and of recent cars (by 1%) induce variations of the estimations (CO by 26%, COV by 9%) However the taking-into account of local fleet specificities is difficult : Lack of data NGV Bus (Natural Gas Veh) which are predominant cannot be computed City-centre, peak-hour, passenger cars (which are the focus of most public actions) do not represent the main of the emission quantities Heavy duty vehicles - although poorly assessed - are significant (8% of the traffic, but 25% CO2, 18% PM, 42% NOx) Cold start (CO, VOC, ), and Non-Exhaust emissions (PM) are highly significant 13

14 models - IFSTTAR preliminary works Different Case studies Grenoble ring, (MOCOPO): A frequently congested sector Traffic monitoring (6 minutes counting and speeds) Air quality monitoring (near the road and urban background) Fleet composition monitoring through 4 video cameras Around 1,7 Million of observations during one month 350,000 identified French registration Emission calculation by steps of 6 min, using the COPCETE plateform derived from COPERT4 Coupling with Dispersion / deposition models was also realised 14

15 models - IFSTTAR preliminary works Grenoble ring, (MOCOPO research project) - a few results Local fleet Significant differences with national estimation (less Diesel and recent cars) Strong variability week / week-end (HGV, LCV traffics) Lighter variations between peak (older cars, less Diesel) and offpeak hours Important temporal variability (6 minutes steps) See influence on emissions Congestion 5-8% of the time, 9-15% of the traffic But only 13 to 20% of the total emissions Limited influence on the emissions as speeds are rarely very low (under 40 km/h) and emission vary few over km/h 15

16 models - IFSTTAR preliminary works Grenoble ring, (MOCOPO research project) - a few results Incidence of the fleet variability on the emissions Current observed variations of HGV, LCV traffic rate induce emissions variations by 30 to 70% (CO2, NOx, PM) Car Diesel rate variations influence the overall CO, COV by 10% Current observed variations in the EURO distribution of cars induce quite limited variations of the overall emission (4 to 5%) Time resolution Emissions were computed at 6, 15 min and 1 hour time-resolutions When estimations are aggregated over long periods (1 day, 1 week) or when estimations concern stable periods as regards traffic, the time resolution does not induce significant influence (1-2%) When estimations are focused on congested periods or when traffic is varying (from free-flow to congestion), 15min and 1h resolutions underestimates emissions by 4 to 14% 16

17 models - IFSTTAR preliminary works Different Case studies In the Paris area - Assessment of Low Emission Zones Large scale measurements of air quality, black carbon Tunnel experiment to assess traffic emissions Vehicle fleet monitoring; 9 places ; around 500,000 observations, Detailed technological data identified through the National registration file Analysis of the spatial vriability of the fleet composition from a large-scale mobility survey (15,000 households in Île-de-France) From the in-situ video monitoring Île de France area: traffic simulation (morning peak hour) 20,200 km of road, 37,700 road segments Analysis of the sensitivity of the emission calculation Emission calculation using COPERT4 methodology 17

18 models - IFSTTAR preliminary works Paris area and Low emissions zones - Results Significant fleet variability according to territories Well-off territories have a younger car fleet, with less Diesel and would be also less affected by selective driving restriction measures High interest of mobility surveys to apprehend differences in car buying and renewal behaviours according to the areas Results confirmed by the video observations Cars Diesel rate : from 57 to 70% according to areas Cars Euro 4+5 rate : from 58 to 44% Induced differences in the overall emission estimations: 7% CO2, 13% PM, 35% CO et COV, 30% NOx 18

19 models - IFSTTAR works in progress Case study : An urban district of Villeurbanne (near Lyon, France) 110 permanent traffic counting points and 70 directional counting at junctions 37 junctions monitored by video and survey Macroscopic traffic model SIMBAD tool, static approach Lyon area and focus on the district Dynamic traffic simulation AIMSUN tool District area, input from the SIMBAD Emission simulation using COPERT4, HBEFA emission factors, and PHEM model 20

20 models - to go further Application of HBEFA and PHEM to the above case studies Inter-comparison of tools at different spatial - temporal scales Sensitivity studies : A simulation plan is already drafted input data; local versus national data temporal / spatial aggregation Real-world versus simulated speeds Parameters specific to the different emission calculation tools Synthesis Other European application cases Integration of emission models within chains of models State-of-the-art, review, synthesis (a PhD at IFSTTAR) Methodologies for assessing air pollution from transport and measures to limit it (PhD at IFSTTAR) 21

21 Conclusions Currently, a strong concern around the 2 ERMES issues Non-regulated pollutants and emissions factors Guidance on and benchmarking of models Other topics of interest Fleet and traffic data (update of HBEFA with French data) Ultrafine particles emission and evolution in atmosphere Black carbon characterization from chassis dyno and in-situ measurements (tunnel, urban and rural), summer and winter High-Emitters Their detection on the road, and number assessment Dedicated emission factors Their taking into account in fleet-model and emission models 22