COMPARING PIARC EMISSIONS WITH AUSTRALIAN TUNNEL MEASUREMENTS

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1 COMPARING PIARC EMISSIONS WITH AUSTRALIAN TUNNEL MEASUREMENTS C. Stacey, M. Meissner, P. Ridley, Stacey Agnew Pty Ltd, Australia ABSTRACT For the last two decades or so, Australian road tunnel ventilation design has relied on the emissions data published by PIARC, with little retrospective examination to ensure that the estimation method is current or to calibrate the method for future projects. Accurate, short term measurements in the M5 East tunnel were compared against the results from an IDA Tunnel model which used the PIARC prediction data, extrapolated to 2015, and applied to the observed traffic and airflows. For the M5 East geometry, when the modelled traffic flow and the observed traffic had closely the same behaviour, the carbon monoxide emissions were over-estimated by the PIARC method, with a factor of 0.6 to 0.85 to be applied to the prediction. The PIARC method predicts the total oxides of nitrogen (NO X ) rather than nitrogen dioxide (NO 2 ) directly. The fraction of the NO X which is NO 2 was estimated following data from Carslaw and Rhys-Tyler. The NO X predictions were then seen to be slightly low, requiring to be increased by a factor of 1.2 or 1.3 relative to the Australian method. The approach used for predicting the ratio NO 2 :NO X was found to reproduce the observed ratio well. Keywords: ventilation design, emission data, nitrogen dioxide. 1. INTRODUCTION All recent road tunnels in Australia have had their in-tunnel and external pollution assessed during design using the emission estimation methods given by PIARC (Road Tunnels: Vehicles, Emissions and Air Demand for Ventilation. Document 2012R05EN, 2012). The base PIARC figures were published in 2012, using data on the Australian fleet collected earlier. The data and methods are referred to here as PIARC or PIARC This report examines the accuracy of the PIARC pollution models when extrapolated to 2015, using recent real-time operating data and measurements from the M5 East tunnel in Sydney. The uncertainty in estimates of future emissions comes from both the uncertainty as to the current accuracy of models, and the uncertainty involved in the extrapolation of trends. The purpose of the comparison against recorded 2015 data is to give confidence in the current prediction, such that one source of uncertainty in estimating future emissions is greatly reduced. The PIARC 2012 document gives a detailed estimation method and also a simplified approach, tailored (at the time) to the Australian fleet. This paper compares both methods to the measured data Limitations The location of CSIRO monitoring equipment and the ventilation system of the M5 East tunnel mean that the Westbound tunnel between the Duff St supply point and the Western crossover dominate the pollution levels measured. This effectively limits the range of assessment to gradients in the range -1% to +6%. Refer to the shaded region of Figure 2.

2 TUNNEL SCHEMATIC AND MODEL PARAMETERS A dynamic model of the M5 East tunnel was constructed using the software package IDA Tunnel (EQUA AB, 2016), which is written specifically to simulate ventilation networks with traffic, pollution and thermodynamic behaviours. Model parameters were based on available drawings and technical information available from the tunnel operator. The overall schematic of the tunnel is shown in Figure 1, with the alignment of the westbound tube shown in Figure 2 (the eastbound tube runs beside the westbound tube). The tunnel is unusual in having air exchange effected near the middle of each tube, with crossover fans near the portals (segments 15 and 16 in Figure 1). The crossover fans take air that would otherwise leave the exit portal, and direct that air into the adjacent traffic tube. 23 TURRELLA EXHAUST Ch. 553 Ch PRINCESS HWY EXIT MARSH ST EXIT Ch Ch Ch Ch Ch Ch Ch Ch CSIRO STATION Ch Ch Ch Ch Ch Ch Ch DUFF STREET SUPPLY Ch. 290 MARSH ST ENTRY Figure 1: Tunnel ventilation schematic. Figure 2: Westbound vertical alignment. For the purposes of this work, calibration of the vehicle and tunnel aerodynamic properties has not been performed. Since the objective is to understand pollution emissions, it is more important to duplicate the observed dilution as closely as possible. To ensure that the simulated air flows transporting pollutants along the tunnel are closely matched to actual flows, the tunnel airflows have been driven by jet fans in the simulation to match the timevarying in-tunnel air flow measurements.

3 IN-TUNNEL MEASUREMENTS AND OPERATING DATA Data from a number of sources were used as inputs to the dynamic model and for comparison of the simulated results. The primary source of input data to the model was obtained from the tunnel SCADA system. Traffic loop data were used as vehicle flow inputs into the model at vehicle entrances and traffic diverges. Traffic loops are positioned at various locations through the tunnel and discriminate vehicles into three categories (small, medium and large) based on the vehicle length. Vehicle flows were aggregated across all lanes as IDA Tunnel does not provide the capability to simulate separate conditions for each lane in multiple-lane sections. Airflow sensors are positioned through the tunnel system and in ventilation supply and exhaust airways, providing a record of the network airflow conditions. Data were provided at random non-concurrent times and this was pre-processed by interpolating each sensor at concurrent 3-minute intervals. Noise in the signal was addressed by applying a least squares fit at each time-slice to ensure that continuity was preserved in the network airflow figures. The calibration status of airflow sensors is unknown, however the technique applied would avoid sensitivity to any one aberrant sensor. In-tunnel pollution measurements used for comparison against the simulated results were provided by the CSIRO. Measurement of NO, NO 2, CO, CO 2 and PM 2.5 were completed by the CSIRO on the 26 th and 27 th March 2015 in the westbound tunnel immediately upstream of the western end cross-over fan installation, see Figure 1. The results on the two days were similar, with only those from the 26 th March presented here for brevity. 4. VEHICLE FLOW MODEL CALIBRATION It is difficult to model traffic behaviours in the unstable flow region at the transition from free-flowing to congested flow. The IDA Tunnel parameters were adjusted, seeking to replicate the observed speed-flow relationship, noting also that the data only give speed at discrete locations. From 9 am up until approximately 11:30 am on the 26 th March 2015, there was good correlation between the simulated and measured traffic speed. Beyond that time, the model diverged from the measured conditions despite our calibration efforts. Pollution results have been interpreted only where both speed is well correlated with measurements. 5. POLLUTION MODEL PARAMETERS 5.1. Traffic mix Automated traffic network survey data collected during the period November to December 2014 were analysed to obtain overall fleet characteristics. Vehicles were categorised into a PIARC category based on the Vehicle Type Description in the data. The registration information also included vehicle build date, allowing the fleet age distribution to be compiled PIARC 2012 Euro classification Data from the Australian Government (Department of Infrastructure and Regional Development, 2016) on the introduction date of vehicle standards was used to estimate the proportion of vehicles in each Euro classification based on the age distribution profile shown in Figure 3. Due to the nature of the estimation method and available data, there is some uncertainty inherent in this methodology. Firstly, new model and existing model vehicles have differing years of implementation. Secondly, for the proportion of vehicles which are imported by global manufacturers, actual implementation of engine emissions standards may occur ahead of the legislated requirement.

4 Euro standards have been adopted for each vehicle category in accordance with Table 1. Figure 3: Traffic survey data vehicle age distribution. Table 1: Assumed average year of implementation for Euro standards in Australia. Category Pre-Euro Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 PC Petrol/Diesel - n/a LDV Petrol - n/a LDV Diesel - n/a HGV Diesel PIARC 2012 Australia Table 2 below shows the key parameters adopted in developing the pollution generation tables in accordance with PIARC 2012, based on PIARC s Australian fleet appendix (Appendix 3.1). Typical average fleet parameters are based on the traffic survey data outlined in Section NO2 emissions PIARC tables give NO X generation rates as a function of vehicle speed and road gradient. Since NO 2 is typically the dominant design pollutant, the mass ratio NO 2 :NO X is a key parameter in the PIARC method. It is highly desirable to have tables of NO 2 evolution rather than its proxy NO X, which bundles together the NO and NO 2. NO 2 :NO X mass ratios for the various vehicle classes were calculated using the fleet age profile. The vehicle classes are then further subdivided into Euro classes defined in Section 5.2. The input data for the estimates of NO 2 ratio come from (Carslaw & Rhys-Tyler, 2013) who used remote sensing of travelling surface vehicle s emissions (measured immediately after the vehicle tailpipe) to determine the ratio of vehicle pollutants NO 2, NO X and NH 3 to CO 2 emissions.

5 It is considered that applying Carslaw and Rhys Tyler s surface vehicle data to a tunnel scenario is valid. NO from the exhaust is converted to NO 2 in the presence of ozone (O 3 ) when it enters the atmosphere. However, in a tunnel environment (remote from the entry portal), where the ozone has been depleted, NO 2 levels should be unaffected by the further conversion of NO to NO 2. Ozone reactions are also catalyzed by any surfaces and so tunnel walls and fittings also eliminate ozone from the ingested air flow. Efforts to identify NO to NO 2 conversion by ozone just inside tunnel entry portals have not been able to show NO 2 :NO ratios different from the tailpipe ratios seen later in the tunnel 1. Hence readings taken at the tailpipe of a vehicle (prior to any atmospheric conversion of NO to NO 2 ) should allow reasonable estimation of the resulting NO 2 in a tunnel. Another issue which increases the uncertainty of the rate of NO 2 generation is that the evolution of pollutants is a function of the vehicle operating power. The work by Carslaw and Rhys Tyler does not attempt to quantify this relationship. NO 2 emissions for each of the three vehicle classes, are predicted from PIARC NO X tables multiplied by the appropriate NO 2 :NO X mass ratio predicted from the Carslaw Rhys-Tyler data. Table 2: Parameters applied with the PIARC emission factors and CO 2 model. Vehicle Category PC LDV HGV % Diesel in Category 7.3% 46.3% % Vehicle Mass (kg) 3 1,650 3,150 15,500 Idle heat release (W) 3 10,000 15,000 35,000 Overall thermal efficiency (%) 3 31% 31% 31% Frontal Area (m 2 ) Drag Coefficient Coefficient of rolling resistance PIARC Australian emission factors Future Years (ft) factor CO - gasoline 0.59 Future Years (ft) factor CO - diesel Mass factor CO n/a n/a 0.80 Future Years (ft) factor NOx - gasoline 0.55 Future Years (ft) factor NOx - diesel Mass factor NOx n/a n/a 0.80 Future Years (ft) factor Opacity - diesel Mass factor Opacity n/a n/a 0.80 Factor non-exhaust particulate matter Carslaw - Rhys Tyler NO 2 :NO x ratios Petrol 2.7% 2.7% n/a Diesel 26.1% 26.5% 18.4% 1 Sturm P., Personal communication, October PIARC 2012 Australian appendix only provides emissions tables assuming a fixed proportion of diesel vehicles in both LDV (50%) and HGV (100%0 categories. 3 Used only for the purposes of estimating vehicle CO 2 emissions.

6 CO 2 emissions Vehicle characteristics were used to estimate the vehicle CO 2 emissions on the basis of vehicle speed and road gradient, adopting an average CO 2 production rate of g/j for petrol engines and g/j for diesel engines. Aerodynamic drag has been calculated assuming conditions of zero wind velocity, which will over-estimate the drag. 6. RESULTS The figures below compare simulated results using pollution generation tables based on PIARC 2012 with in-tunnel measurements on 26 th March Two results are provided for each pollution model, with the fleet mix treated differently to address the uncertainty in vehicle types. In the first approach, vehicle flows reported by tunnel traffic loops are unaltered, with small, medium and large vehicles simulated as PC, LDV and HGV respectively. In the second approach, vehicle flows reported by the tunnel traffic loops are adjusted ( adj added to trace name) such that the daily total vehicles in small and medium are redistributed to match the PC and LDV proportions as per the automated traffic network survey; o Small (adj) = small 0.95 o Medium (adj) = medium small Scaling factors were applied to emissions tables for each pollutant, to achieve a better match between simulated and measured data. No attempt has been made to calibrate individual vehicle categories separately and the same scaling factor has been applied to PC, LDV and HGV emissions tables for each pollutant. The resulting scaling factors are shown in Table 3. Similarly, parameters within the heat and CO 2 emissions model were adjusted until a more appropriate fit to measured data was obtained, the resulting factors are incorporated in Table 2. Table 3: Global 2015 calibration factors for PIARC emissions models in M5 East. Australia Australia (adj) Euro Euro (adj) NO X NO 2 :NO X ratio CO Total particulates With the above scaling factors applied, the simulated and measured results are compared in the plots below. Calibration of the CSIRO sensors was performed between 9:45 am and 9:55 am. The following observations are made based on the calibration necessary to fit the curves. NO X is underestimated by PIARC s methodologies, while CO is overestimated. The simulated ratio of NO 2 :NO X is consistent with measured values, indicating that the data from Carslaw and Rhys Tyler are directly applicable to the Australian fleet. The combined effects of exhaust and non-exhaust particulate emissions is estimated with reasonable accuracy based only on tailpipe emissions, with the Australian methodology slightly under-predicting and the Euro methodology slight overpredicting. CO 2 is slightly over-predicted by the approach developed for this study. Correcting the vehicle air speed may correct that. The close match gives confidence that the fleet mass is well reflected in the model.

7 Figure 4: Comparison of calibrated NO X model, 26th March Figure 5: Comparison of calibrated NO 2 model, 26th March Figure 6: Comparison of calibrated CO model, 26 th March 2015.

8 Figure 7: Comparison of calibrated particulate matter model, 26th March Figure 8: Comparison of calibrated CO 2 model, 26 th March ACKNOWLEDGEMENTS The authors thank Dr Brendan Halliburton and CSIRO for the detailed site measurements, and NSW Roads and Maritime Services for permission to publish. REFERENCES Carslaw, D., & Rhys-Tyler, G. (2013). New insights from comprehensive on-road measurements of NOx, NO 2 and NH 3 from vehicle emission remote sensing. Atmospheric Environment, Department of Infrastructure and Regional Development. (2016, March 15). Vehicle Emissions Standards. Retrieved from EQUA AB. (2016, March 15). IDA Tunnel. Retrieved from EQUA AB: PIARC. (2012). Road Tunnels: Vehicles, Emissions and Air Demand for Ventilation. Document 2012R05EN. PIARC Technical Committee C4 Road Tunnels Operation.