Recent evidence concerning higher NOx emissions from passenger cars and light duty vehicles

Atmospheric Environment 45 (2011) 7053e7063

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Recent evidence concerning higher NOx emissions from passenger cars and light duty vehicles David C. Carslaw a, *, Sean D. Beevers a, James E. Tate b, Emily J. Westmoreland a, Martin L. Williams a a b

King’s College London, Environmental Research Group, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, UK Institute for Transport Studies, University of Leeds, LS2 9JT, UK

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 June 2011 Received in revised form 19 September 2011 Accepted 22 September 2011

Ambient trends in nitrogen oxides (NOx) and nitrogen dioxide (NO2) for many air pollution monitoring sites in European cities have stabilised in recent years. The lack of a decrease in the concentration of NOx and in particular NO2 is of concern given European air quality standards are set in law. The lack of decrease in the concentration of NOx and NO2 is also in clear disagreement with emission inventory estimates and projections. This work undertakes a comprehensive analysis of recent vehicle emissions remote sensing data from seven urban locations across the UK. The large sample size of 84,269 vehicles was carefully cross-referenced to a detailed and comprehensive database of vehicle information. We find that there are significant discrepancies between current UK/European estimates of NOx emissions and those derived from the remote sensing data for several important classes of vehicle. In the case of light duty diesel vehicles it is found that NOx emissions have changed little over 20 years or so over a period when the proportion of directly emitted NO2 has increased substantially. For diesel cars it is found that absolute emissions of NOx are higher across all legislative classes than suggested by UK and other European emission inventories. Moreover, the analysis shows that more recent technology diesel cars (Euro 3e5) have clear increasing NOx emissions as a function of Vehicle Specific Power, which is absent for older technology vehicles. Under higher engine loads, these newer model diesel cars have a NOx/CO2 ratio twice that of older model cars, which may be related to the increased use of turbo-charging. Current emissions of NOx from early technology catalyst-equipped petrol cars (Euro 1/2) were also found to be higher than emission inventory estimates e and comparable with NOx emissions from diesel cars. For heavy duty vehicles, it is found that NOx emissions were relatively stable until the introduction of Euro IV technology when emissions decreased by about 30%. The more limited data available for urban buses shows that there has been little change in NOx emissions from Euro I to Euro IV. There is general much better consistency across the different estimates of heavy duty vehicle NOx emissions than for light duty vehicles. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Vehicle emissions Remote sensing Primary NO2 Emissions inventory Ambient trends

1. Introduction 1.1. Background Emissions of oxides of nitrogen (NOx) from road vehicles have received considerable attention over the years. Emissions of NOx and its subsequent fate in the environment are central to many important environmental impacts including regional ozone formation, eutrophication and secondary particle formation. However, perhaps the principal concern in recent years in Europe is meeting the annual mean limit value (40 mg m
3) and the hourly

* Corresponding author. Tel.: þ44 (0) 1904 709967. E-mail address: [email protected] (D.C. Carslaw). 1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2011.09.063

limit value (200 mg m
3 not to be exceeded for more than 18 h a year) for NO2. Exceedances of the NO2 limit values tend to be dominated by urban areas close to roads and hence understanding the characteristics of vehicular emissions of NOx is essential if these limit values are to be met. In Europe, like most of the rest of the world, vehicle emissions are regulated. European emissions legislation has progressively reduced the allowable amount of NOx and other pollutants that a vehicle can emit (EC, 2007, 2009). These emissions standards are commonly referred to as ‘Euro classes’ e Euro 0e6 for light duty vehicles and Euro 0eVI for heavy duty vehicles. Considering emissions of NOx specifically, European standards have reduced the limits considerably over the years. For example, for Euro 3 cars introduced in January 2000 the amount of NOx emitted should have been below 0.50 g km
1, whereas a Euro 5 car introduced in

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September 2009 should emit less than 0.18 g km
1. These limits must be met for new model cars over a specific drive cycle called the NEDC. It has been recognised for some time that the NEDC does not adequately capture actual ‘real-world’ driving patterns (Williams and Carslaw, 2011). For this reason considerable effort has been dedicated to developing alternative drive cycles that are designed to be more representative of actual conditions e.g. Andre (2004). Rexeis and Hausberger (2009), for example, clearly showed for NOx that emissions over the NEDC are both lower than actual in-use emissions and that projecting emissions reductions based on legislative limits gives an overly optimistic view of likely future NOx reduction. It is these more realistic driving patterns that are used as the basis for developing emission factors that are used in the compilation of atmospheric emissions inventories including UK and European inventories (Murrells et al., 2010). While emissions of total NOx are of importance, the proportion that is directly emitted as NO2 is also important; particularly close to the source i.e. close to roads where exceedances of the annual mean limit value of NO2 are more likely. Recent research shows that the proportion of NOx in the form of NO2 from diesel vehicles has increased markedly over the past few years, with important effects on ambient concentrations of NO2 (Carslaw, 2005; Anttila and Tuovinen, 2010; Alvarez et al., 2008). These increases have been due to the types of after-treatment used on diesel vehicles which result in the increased oxidation of NO to NO2. The current work has several aims with respect to these issues. First, we consider the evidence relating to ambient trends in NOx and NO2 in the UK and Europe to provide an up to date understanding of how concentrations of NOx and NO2 have changed recently. Second, we analyse data from several vehicle emission remote sensing campaigns that have recently been conducted in the UK. The remote sensing data provides a comprehensive database of vehicle emissions that is complimentary to dynamometertype studies. Of particular importance is that the database is large

(>80,000 vehicles), up to date and (critically) provides information on actual in-use emissions. 1.2. Recent trends in NOx and NO2 in the UK and Europe We have considered the recent trends in ambient NOx and NO2 concentrations in the UK and Europe. UK data are available through the Automatic Urban and Rural Network (AURN, http://uk-air.defra. gov.uk/data/). In London there are a greater number of air pollution monitoring sites available through the London Air Quality Network (LAQN, http://www.londonair.org.uk/). These data are generally up to date and provide a comprehensive resource of data from which trends can be calculated. More widely in Europe, the Airbase database provides data for most European countries (http://airclimate.eionet.europa.eu/databases/airbase), but is not as up to date as the two UK databases. The UK sites are particularly useful to the current study because they can be linked with emissions inventory estimates of NOx to check the consistency with ambient trends. Deseasonalised monthly trends in NOx concentration by type of location are shown in Fig. 1. The monthly data were deasonalised according to the method of Cleveland et al. (1990). For these trend estimates a non-parametric smooth fit has been applied because it was clear the trends were mostly non-linear. Fig. 1 shows that trends, on average, have shown similar patterns at most sites; characterised by a reduction in NOx concentration from the mid1990s to the early 2000s, followed by a period of stable or gently decreasing concentrations. Table 1 summarises the trends in NOx and NO2 concentrations from 2004 to 2009 for a range of site types. The motorway sites have on average shown greater reductions than at other site types for both NOx and NO2; although there were only three sites. There is also a relatively small range of slope estimates for roadside sites for NOx e.g. from
1.7% yr
1 for outer London to
0.6% yr
1 for inner London sites. As expected, trends in NO2 concentration are less than those for NOx. While inner London may

Fig. 1. Mean trends in deseasonalised monthly mean NOx for a range of different site types. A non-parametric smooth fit has been applied to the data and the shading shows the 95% confidence intervals related to the fit.

D.C. Carslaw et al. / Atmospheric Environment 45 (2011) 7053e7063 Table 1 Median Sen slope estimates for trends in NOx and NO2 concentrations by site type/ location expressed as percentage change per year (2004e2009). The median trend is shown in each case and the number of sites analysed are shown in square brackets. Location

NOx trend

NO2 trend

Inner London roadside [10] Motorway [3] Outer London roadside [13] UK roadside [12] UK rural [10] UK urban background [17] UK urban centre [11]


0.6
3.4
1.7
1.4
1.9
2.1
0.8


0.5
0.8
0.8
0.6
1.4
0.8
0.4

have showed less of a decrease in NOx and NO2 compared with other sites, there is actually consistency across the UK. Trend estimates were undertaken using the openair data analysis tools (Carslaw and Ropkins, in press). The trends in NOx shown in Table 1 can be compared with recent, detailed estimates of UK NOx emission trends (Murrells et al., 2010). While not directly comparable, the roadside ambient trends in NOx are typically around
1 to 2% yr
1 from 2004 to 2009. In contrast, total UK road transport emissions of NOx over a similar period are z
5 to 6% yr
1. Trends in NO2 concentrations at roadside sites for a number of European countries are shown in Fig. 2. This Figure provides a somewhat aggregate view of the trends, but it does in general reveal that NO2 concentrations have tended to stabilise across many European countries, similar to the UK. There is more evidence

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that NO2 concentrations have decreased over the past 10e15 years in Greece and Italy; perhaps reflecting specific vehicles fleets e.g. dominated more by petrol-engined vehicles rather than diesel. However, even here concentrations have tended to stabilise in the past few years. We have also considered the extent to which the UK is similar to other European countries by quantifying the proportion of sites that exceed the annual mean NO2 Limit Value of 40 mg m
3. Of the 2728 sites analysed for 2008 the UK had a total of 18.0% that exceeded the Limit Value. In the rest of Europe 18.9% of all sites exceeded the annual mean NO2 limit value in 2008, which is very similar to that of the UK. Overall therefore, the UK seems to show similar characteristics to most other European countries on the basis of recent trends and the proportion of sites exceeding the Limit Value.

2. Data and methods 2.1. Vehicle remote sensing data The vehicle emission remote sensing measurements were carried out by the University of Leeds and Enviro Technology plc between 2007 and 2010 using a AccuScan RSD-4600 instrument supplied by Environmental Systems Products (ESP, Arizona, US) as a dedicated across-road vehicle emissions monitoring system. Individual exhaust plumes trailing vehicles are measured by casting a focused beam of Non-Dispersive Infrared (NDIR) and Ultraviolet (UV) light across the plume. A corner cube mirror reflects the IR/UV

Fig. 2. Mean trends in deseasonalised monthly trends in NO2 for roadside sites at for select European countries. Hourly data were downloaded and processed from Airbase (http:// air-climate.eionet.europa.eu/databases/airbase).

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beam back to the remote sensing detector (spectrophotometer and opacity) module. Open-path NDIR spectroscopic techniques were first used to measure CO vehicle emissions by Bishop et al. (1989). Enhancements to measure a portion of HC emissions (Cadle and Stephens, 1994), the addition of a high-speed UV spectrometer capable of measuring NO (Popp et al., 1999; Zhang et al., 1996) and a UV opacity meter (wavelength 230 nm) to provide a PM10 proxy (Stedman et al., 1997) followed. The RSD-4600 measurements include the concentration ratios of NO, CO, HC and a PM10 proxy to the concentration of CO2. The measurement ratios therefore reflect the pollutant emissions per unit of fuel used. The use of CO2 as a reference gas facilitates quantitative measurements of exhaust species without knowledge of the plume location or extent of dilution. The emission measurements are supported by a speed/ acceleration module, temperature and barometric pressure sensors, a camera system to capture a clear digital image of the vehicles number plate for post-processing, and a control/data logging PC. This approach is commonly used in the US and Canada in large-scale pre-screening testing for vehicle inspection and maintenance emission programs (e.g. Bishop and Stedman, 2008). In accordance with the manufacturer operating procedures, the remote sensing beam is located in a position where it will intersect a significant proportion of exhaust gas, with the beam aligned between 250 and 300 mm from the road surface. It is important the source/detector module (SDM) is well aligned with the corner cube mirror. An alignment laser beam and digital level are initially used to coarsely align the IR/UV beam before a real-time beam intensity measure is used to fine-tune the setup. The system was powered on for a minimum of 45 min prior to an initial on-site calibration to gain thermal stability in the circuitry, source and detector elements. On-site quality assurance procedures include calibration of the SDM to the known concentration of gas in a reference cell that is placed (automated) in the beam path. The calibration audit is verified by a blended calibration gas (1000 ppm propane, 2% CO, 13.6% CO2, NO 1000 ppm) released into the sensing beam. These calibrations and audits are conducted hourly during normal operation. All routine calibrations were within normal performance tolerances. A measurement is defined as a beam block (by a vehicle) followed by a half second of data collection. If the data collection is interrupted by another beam block, i.e. a following vehicle with a headway less than 0.5 s, the measurement attempt is aborted. A measurement is declared ‘valid’ when the size of the observed CO2 emission plume is sufficient to allow emission ratios to be calculated; a speed/acceleration measure is available with the speed in the range 5e60 km h
1 and the vehicle is accelerating; and a clear ‘static’ digital image of the number plate is captured. The 5 consecutive 50 Hz measurements prior to a beam block are considered as the background concentration for that pass-by. The collection of a high proportion of ‘valid’ measurements requires selected monitoring sites to be single lane operation, the optical beam path distance is limited to less than 10 m, the majority of vehicle engine’s are under load as they drive through the measurement site. This is to ensure significant emission plumes are available for measurement. Sites are therefore recommended to have an uphill grade; weather and environmental conditions to be favourable as high wind speeds rapidly disperse exhaust plumes. An uphill gradient has the potential to bias the emission results. However, as it will be seen later, the campaigns used in this study have a median slope of only 0.7. The equipment is also not weather-proof, so cannot be operated in rain or snow. We have compiled data from seven field campaigns from around the UK from 2007 to 2010. In total the final data set consists of 84,269 valid records. A summary by year of manufacture, vehicle type and fuel type is given in Table 2. It is interesting to note that

Table 2 Vehicle numbers sampled by vehicle emission remote sensing split by vehicle type and fuel. Note that LGVs includes all goods vehicles up to 3.5 tonnes gross vehicle weight and HGVs are those vehicles greater than 3.5 tonnes. Year

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Car

HGV

LGV

Bus

Diesel

Petrol

Diesel

Diesel

Diesel

0 0 1 1 7 10 17 54 108 189 221 331 440 520 565 635 1072 1627 1847 2373 2396 2557 2758 2044 1350 327

18 23 33 78 123 166 217 298 588 836 1123 1841 2400 3142 3505 3844 4298 4279 4301 3910 3589 3370 3076 1940 1183 350

0 0 0 0 0 0 0 0 0 8 11 31 16 10 17 16 23 40 81 69 66 62 60 47 13 3

1 1 1 1 2 1 5 10 13 34 74 118 214 294 430 503 693 804 1024 1207 1584 1718 1763 1176 538 78

0 0 0 0 0 0 0 0 0 0 0 14 38 21 162 318 30 362 38 30 57 116 143 43 23 0

older cars are clearly dominated by petrol-fuelled vehicles but in more recent years the number of diesel cars is comparable with petrol. These data highlight the clear shift towards the use of diesel fuel at the expense of petrol; a pattern that is also observed in many other European countries. The other observation to note from Table 2 is that there are relatively far fewer numbers of HGVs and buses compared with light vehicles (cars and LGVs). These proportions are more typical of those observed in urban areas, than for example on motorways. It is important to understand some of the key characteristics of the remote sensing data. In particular, characterising the driving conditions is important if comparisons are to be made with other data sources. Overall, these data represent typical urban-type driving conditions. The median speed across the whole data set is 31 km h
1 and the 95th percentile speed 60 km h
1. The median gradient of the roads used for the experiments was 0.7. The issues related to the comparison of RSD and laboratory measurements of emissions is considered in Smit and Bluett (2011), who show for example that laboratory-based measurements of emissions do not adequately capture higher emitting vehicles. Further information concerning emission factor validation of relevance to the current study can be found in Smit et al. (2010). There are several characteristics or limitations of the RSD that should be noted. First, the measurements represent a mix of urbantype conditions and not roads such as motorways etc. The RSD measures ratios of pollutant concentrations to CO2 and therefore does not provide an absolute emission measure as used in emission inventories i.e. in g km
1. However, pollutant ratios are very useful measures and can be used to derive absolute emissions given an estimate of an emissions of CO2 in g km
1. The equipment was set up to measure exhaust from vehicle plumes at a height of 30 cm. As such, the measurements will not include vehicles where the exhaust exits at height; such as on large HGVs. This again is not considered to be a significant limitation because there tend to be few of these vehicles in urban areas.

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The RSD uses an automatic number plate recognition system (ANPR) to record the vehicle registration number of individual vehicles. All data were manually checked and any ANPR errors corrected. The number plate information can be used to query databases that contain information on individual vehicles. We commissioned Carweb (http://www.carwebuk.co.uk/) to match the RSD number plate information with specific vehicle characteristics. Note that Carweb are able to provide over 100 different variables related to vehicle information e.g. relating to physical characteristics (length, width, engine size, number of gears etc.), performance characteristics (e.g. time taken to accelerate from 0 to 60 mph etc.) and many other items of information. Also available was the Euro class designation of the vehicle where available. This information was available for almost all cars but was only partially available for HGVs/buses. A key advantage of the CarweB data over other potential data sources is that vehicle manufacturer databases are queried and cross-checked for quality assurance purposes. It is our understanding that these data are the most comprehensive, reliable data available in the UK. These data have been used extensively in the analysis of the vehicle emissions information e.g. for Euro class designation. 2.2. Assumptions regarding primary NO2 emissions The RSD data provides a measure of NO emissions and not NO2. It is therefore necessary to apply assumptions regarding the proportion of NO2/NOx in vehicle exhausts for different vehicle types. Emissions of total NOx have been calculated by applying the NO2/NOx values from Grice et al. (2009), shown in Table 3. These assumptions are broadly consistent with other data sources including AQEG (2008) and recent remote sensing campaigns from Sweden using remote sensing measurements of both NO and NO2 (Jerksjö et al., 2008). The Grice et al. (2009) and Jerksjö et al. (2008) results are generally consistent with one another. For example, there is good agreement that all petrol cars have very low NO2/NOx ratios and HGVs are around 10e15%. They are in agreement that early diesel cars (pre-Euro 3) have relatively low NO2/NOx values (z10%) and that Euro 3/4 are much higher (z30%). The bus data are more inconsistent, but these values will depend very much on the specific bus fleet in question and the type of after-treatment used. For example, in London almost all buses are fitted with continuously regenerating particle filters which result in high (z40%) NO2/NOx ratios (Carslaw, 2005). However, only a few urban Table 3 Percentage of NO2 assumed by vehicle type used to calculate total NOx emissions from the RSD NO data (Grice et al., 2009; Jerksjö et al., 2008). The numbers in square brackets give the number of vehicles sampled for the Jerksjö et al. (2008) data. Note that for diesel LGVs the Grice et al. (2009) values are assumed to be the same as for diesel cars. Vehicle class

Euro class

% NO2 (by volume) (Grice et al., 2009)

% NO2 (by volume) (Jerksjö et al., 2008)

Petrol cars Diesel cars

All Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro

3 11 30 55

z1 [12,551] 14 [177] 47 [538] 55 [881] 10 [42] 20 [179] 30 [816] 60 [49] 7 [218] 9 [353] 13 [52] 10 [78] 30 [93] 25e52 [45] 48

Diesel LGVs

HGVs

Buses

2 and earlier 3 4e6 1 2 3 4e6 II and earlier III IVeVI II and earlier III (no trap) III (trap) IVeVI

11 30 55 11 14 10 11 14 35 10

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areas in the UK are known to have bus fleets with these types of after-treatment fitted. While these assumptions are regarded as reasonable, the total NOx emission estimates could be refined through measurements of NO and NO2 as described by Bishop et al. (2010). Emission factors used to compile emission inventories generally express emissions as a mass per unit distance travelled (typically g km
1 in the UK and Europe). The RSD data provides estimates of ratios of different species to CO2. Therefore, in order to provide absolute emission estimates it is necessary to calculate g km
1 estimates from NOx/CO2 ratios derived from the RSD. We have used the UK emission factor estimate of CO2 in g km
1 as a means of estimating the total NOx emission in g km
1. The key assumption therefore is that the UK emission factor estimates are accurate for CO2. While there is likely to be some uncertainty in these factors, the estimates for CO2 should be more reliable than those for other non-fuel related emissions such as NOx. The emission factors for CO2 for most classes of vehicle do tend to show progressive reductions in CO2 emissions through the Euro classes. These reductions in CO2 also mean that total mass emissions of NOx reduce in a proportionate way to absolute reductions in CO2. It should also be mentioned that the ratios of NOx/CO2 from the RSD generally show consistency within a particular vehicle class. For example, there is little variation in NOx/CO2 for petrol vehicles of different engine sizes; although the absolute emissions of CO2 (and hence derived absolute NOx emissions) do vary. For Euro 3 diesel cars it is generally the case that smaller-engined vehicles have a higher NOx/CO2 ratio compared with larger-engined vehicles. For example, Euro 3 diesel cars with engine sizes <2.0 L have a NOx/CO2 ratio of 76.8  7.8 whereas vehicles with engine sizes >2.0 L have a NOx/CO2 ratio of 71.3  2.1. The RSD data therefore act to simplify the estimate of absolute NOx emissions because the ratios often vary little across different engine sizes, and to some extent vehicle speed. Three different methodologies/data sources have been compared for estimating emission factors: current UK methods Boulter et al. (2009), the Handbook Emission Factors for Road Transport (HBEFA, 2010) and the RSD data. It is not possible to compare these data sources on exactly the same basis due to different methodologies used in both measurement (RSD) and approach. We have however taken care to ensure that the emissions are as consistent as possible. The RSD has been taken as the basis of comparing the emissions. These emissions best represent urban-type driving conditions and the mean speed across all campaigns was 31 km h
1. This speed was used directly in the UK emission factor calculations since these factors use vehicle speed as an input. The HBEFA data are somewhat more complex because the emission depends on one of many ‘traffic situations’. We have chosen ‘URB/Trunk-City/50/Satur’ where the average speed is 36 km h
1 (and 29 km h
1 for HGVs). These types of road and traffic condition are most likely consistent with the other data sources. Note also, that much of the interest in this paper is concerned with how emissions change in a relative way through the different vehicle technologies. These relative changes are not very sensitive to the precise assumptions concerning vehicle speed or other choices affecting the emission estimate. 3. Results and discussion 3.1. Analysis of remote sensing data A summary of the remote sensing emissions expressed as NOx/ CO2 ratios is shown in Fig. 3. The error bars show the 95% confidence interval in the mean, with wider uncertainties generally relating to smaller sampler sizes; particularly for very old or new vehicles.

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Fig. 3. NOx/CO2 ratio for major classes of vehicle based on the analysis of the remote sensing data. The error bars show the 95% confidence interval in the mean.

Considering the NOx emissions for petrol cars (upper left panel of Fig. 3) it can be seen that NOx emissions from recent model cars (2005e2010) are substantially lower than early model cars. In particular, it is apparent from Fig. 3 that there is a step change in emissions for petrol car NOx emissions from 1992 to 1993, which corresponds to the introduction of three-way catalysts on petrol cars in the UK. Indeed, it can be shown that cars manufactured from 2005 to 2010 emit around 96% less NOx compared with petrol cars without catalysts. These results show that for new petrol vehicles the three-way catalyst and emissions control system in general is highly effective for controlling emissions of NOx. However, as will be considered in subsection 3.2, there is evidence that older generation catalyst-equipped vehicles are relatively high emitters of NOx. The diesel car NOx emissions have tended to increase over the period 1987e2010. Note that CO2 emissions from diesel cars would also have decreased over this period (see Table 4) and therefore emissions expressed in g km
1 would not have increased to the same extent. Nevertheless, the RSD results clearly show that under actual driving conditions, diesel car NOx emissions are high and that recent technologies used by Euro 4/5 vehicles have not decreased emissions of NOx compared with older generations of vehicles. This is in clear contrast with the successively stringent emission limits in the Euro standards (Williams and Carslaw, 2011). Since diesel cars would have to comply with the Euro emission limits over the test cycle, it suggests that the regulatory test cycle is a poor indicator of emissions under actual driving conditions. This is of major significance for air quality management in Europe and in other countries in the world who have adopted Euro standards for vehicle emission regulation. The LGV emissions shown in the bottom-left panel of Fig. 3 tend to track those for diesel cars, which is expected given the similarities between these two vehicles types. Emissions from HGVs were relatively stable until the introduction of Euro IV vehicles. Compared with Euro III vehicles, Euro IV vehicles emit about 30% less NOx e seen by the reduction from 2006 to 2007 in the upper right panel of Fig. 3.

NOx emissions from buses have tended to increase over time. However, these results should be treated with caution because, as will be shown, the emissions are sensitive to the particular bus fleet sampled. Furthermore, because the numbers of HGVs and buses sampled are much lower than for cars or LGVs (see Table 2), the uncertainty in the emissions is higher for these vehicles for all years compared with light duty vehicles. 3.2. Emissions control degradation on petrol vehicles To understand the performance of the emissions control system on petrol vehicles further, we have considered how the distribution of NOx emissions from these vehicles has changed over time. Fig. 4 shows several percentile emission levels over time. It is apparent for the median emission level i.e. the 50th percentile, that emissions decreased considerably from pre-catalyst vehicles to post catalysts vehicles. Note that the vertical dashed line shows when catalyst vehicles were introduced to the UK. The introduction of catalyst vehicles to the UK is most apparent for the 75th, 90th and 95th percentile emissions. For the higher percentile levels the shape of the relationship over time differs from the median line. For example, considering the 99th percentile line the emission level is relatively constant from 1986 to 2000. This behaviour suggests that there has been little change in the level of emissions for the highest emitting vehicles for pre-Euro 3 cars. Despite being equipped with catalysts these vehicles behave like non-catalyst vehicles, which would be consistent with degraded emissions control systems. However, Fig. 4 shows that similar patterns are also observed for the other high percentile levels (90th percentile and higher). There is therefore a distribution of emissions control equipment degradation. 3.3. Light duty vehicle emissions of NOx as a function of Vehicle Specific Power There are many approaches available that aim to characterise how emissions from vehicles vary by various metrics such as

D.C. Carslaw et al. / Atmospheric Environment 45 (2011) 7053e7063 Table 4 Emission factor estimates based on current UK emission factors, HBEFA and those estimated from the RSD. The CO2 values shown relate to those currently used in UK emission factor estimates and have been used to scale appropriate NOx/CO2 estimates from the RSD to derive absolute g km
1 estimates of NOx. The RSD uncertainties relate to the 95% confidence intervals in the mean. Euro classification

CO2 (g km
1)

NOx emission factor (g km
1) UK

HBEFA (2010)

RSD

Diesel LGV Diesel LGV Diesel LGV Diesel LGV Diesel LGV Diesel LGV Diesel LGV Diesel car Diesel car Diesel car Diesel car Diesel car Diesel car Diesel car Petrol car Petrol car Petrol car Petrol car Petrol car Petrol car Petrol car Rigid HGV Rigid HGV Rigid HGV Rigid HGV Rigid HGV Rigid HGV Rigid HGV Bus Bus Bus Bus Bus Bus Bus

Euro-0 Euro-1 Euro-2 Euro-3 Euro-4 Euro-5 Euro-6 Euro-0 Euro-1 Euro-2 Euro-3 Euro-4 Euro-5 Euro-6 Euro-0 Euro-1 Euro-2 Euro-3 Euro-4 Euro-5 Euro-6 Euro-0 Euro-I Euro-II Euro-III Euro-IV Euro-V Euro-VI Euro-0 Euro-I Euro-II Euro-III Euro-IV Euro-V Euro-VI

203 250 240 216 216 216 216 186 181 171 158 152 136 122 228 212 204 193 178 159 143 425 340 321 342 321 327 327 1277 1132 1110 1164 1102 1143 1143

1.98 1.76 1.37 0.52 0.40 0.25 0.11 0.71 0.74 0.71 0.49 0.36 0.23 0.10 1.36 0.63 0.13 0.05 0.05 0.03 0.03 4.61 3.20 3.42 2.71 1.63 0.97 0.19 15.95 12.06 13.05 10.66 6.42 3.90 0.78

1.38 1.25 1.12 1.04 0.67 0.66 0.23 0.60 0.60 0.65 0.67 0.49 0.49 0.17 1.64 0.34 0.20 0.06 0.06 0.06 0.05 4.31 3.01 3.32 2.74 1.82 1.08 0.30 15.03 11.59 12.78 11.05 7.08 4.28 1.30

1.17  0.28 1.3  0.09 1.33  0.05 1.53  0.03 1.31  0.04 NA NA 1.04  0.09 0.98  0.06 0.94  0.04 1.12  0.02 0.96  0.02 1.12  0.08 NA 2.38  0.14 1.15  0.06 0.74  0.03 0.22  0.01 0.08  0.01 0.06  0.04 NA NA 3.44  0.4 4.03  0.42 3.43  0.21 2.18  0.3 2.81  1.22 NA NA 11.13  0.99 9  0.34 10.2  0.33 10.34  0.81 NA NA

vehicle speed. This is an important issue for several reasons, including the extent to which the RSD data reflects actual driving conditions. One commonly used approach to characterise vehicle operating conditions and relate them to emissions is to use Vehicle Specific Power, VSP (Jiménez et al., 1999). VSP is a measure of the power required by an engine to overcome forces including friction, aerodynamic drag, internal engine friction and the effect of road gradient. VSP is expressed in kW t
1 and simple analytical expressions have been derived for different categories of vehicle. VSP has the advantage over other metrics such as vehicle speed in that it is related to the fundamental forces a vehicle must overcome. VSP forms the basis of the US-EPA Motor Vehicle Emission Simulator (MOVES) approach, which replaced the older MOBILE emission estimate approaches.1 The relevance of VSP to this study is mostly in understanding how emissions of NOx vary as a function of VSP. The Jiménez et al. (1999) approach provides a simple algorithm for calculating VSP for light duty vehicles:

VSP ¼

Power z0:22$v$a þ 4:39$sinðslopeÞ$v þ 0:0954$v Mass þ 0:0000272$v3

(1)

1 See http://www.epa.gov/oms/models/moves/movesback.htm for information on the use of VSP by the US-EPA MOVES.

400

300

NOX/CO2 * 10000

Vehicle type

7059

200

100

0 1985

1990

1995

year

2000

2005

2010

percentile 50th percentile 75th percentile 90th percentile

95th percentile 99th percentile

Fig. 4. Different percentile emissions of the NOx/CO2 ratio for petrol cars. The dashed vertical line shows the date when catalysts vehicles were introduced to the UK.

VSP is in kW/Metric tonne, v is the vehicle speed in mph, a is the vehicle acceleration in mph s
1 and the slope is expressed in degrees. In the RSD surveys used in this study, the mean roadway slope was 1 and the median slope 0.7, and these data have been used in evaluating Equation (1). Jiménez et al. (1999) provides some examples of vehicle usage and typical VSP values. For example, a car accelerating from 0 to 60 mph in 15 s requires a VSP of 33 kW t
1. The mean value from the RSD data was 6.7 kW t
1 (typical of RSD surveys). By contrast the urban part of the ARTEMIS cycle with a mean speed of 17 km h
1 is 0.9 kW t
1 and the urbaneregional cycle with a mean speed of 57 km h
1 is 5.1 kW t
1. However, values for actual urban-type driving are not known. As it will be shown, modern diesel cars tend to emit high NOx/CO2 ratios as the VSP increases. Therefore, it is not sufficient to know what a ‘typical’ or ‘average’ VSP value is because of the disproportionate effect that higher engine loads have on emissions of NOx. For example, a small proportion of higher load conditions e.g. through ‘aggressive’ driving could have an important effect on overall emissions of NOx. Fig. 5 show the results for diesel cars. It is clear that there has been a tendency for NOx/CO2 ratios to increase with increasing VSP for newer model diesels. There is little evidence that the NOx/ CO2 ratio increased for Euro 1 vehicles with increasing VSP and only weak evidence for Euro 2 vehicles. However, for Euro 3e5 there is a clear increasing relationship between NOx/CO2 and VSP. These relationships for diesel vehicles could have important implications for emission trends. The results show that under higher engine loads, modern diesels (Euro 3e5) can emit considerably higher NOx/CO2 ratios than older vehicles. This increasing relationship of NOx emissions with VSP might be related to the increased use of turbo-charging on diesel engines. The data show that diesel cars have become increasingly powerful through the Euro classes with pre-Euro to Euro 2 cars having a maximum rated power output of 68e70 kW increasing to 85, 98 and 113 kW for Euro 3e5, respectfully. Petrol vehicle maximum rated power has remained about 80 kW through all Euro classes. Note also that under higher loads the absolute emission of CO2 would also be

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Fig. 5. VSP versus NOx/CO2 ratio for diesel cars split by Euro status. The error bars show the 95% confidence interval in the mean.

higher and hence the absolute emission of NOx would also be proportionately higher. For diesel LGVs, it is only Euro 4 vehicles that showed a clear increasing relationship of NOx/CO2 with VSP. There was very little evidence of any consistent relationship between NOx/CO2 and VSP from petrol vehicles. 3.4. Comparsion with alternative emission factor estimates The RSD emission factor estimates have been compared with two alternative methods used to estimate vehicle emissions: the current method used in the UK and the most recent release of HBEFA (HBEFA, 2010). The results for principal vehicle categories are shown in Fig. 6 and tabulated in Table 4. The comparison with UK factors is of importance because these are used as the basis of emission inventory projections and assessments of ambient concentrations as required by Directive 2008/50/EC. For petrol vehicles (top left panel of Fig. 6), all emission factor estimates suggest that emissions of NOx from petrol vehicles have decreased substantially from pre-Euro 1 (i.e. pre-catalyst vehicles) through to Euro 5 vehicles. It is clear however that older catalystequipped vehicles (Euro 1/2) emit much higher levels of NOx according to the RSD data compared with the other emission factor estimates. The higher estimates for the RSD data would suggest that emissions degradation on catalyst-equipped petrol cars is higher than current estimates. The results for diesel cars show important differences between the different estimates. The most striking feature of the top, middle panel of Fig. 6 is that the RSD data is in absolute terms higher than either the UK or HBEFA estimates. Note that the higher estimates would not be caused by erroneous CO2 estimates because the RSD data has been scaled to current UK emission factor estimates of CO2 i.e. the NOx/CO2 ratio of the RSD and UK emission factors are in clear disagreement. The other feature of importance is that the RSD data

show that emissions of NOx in absolute terms from diesel cars has changed little from pre-1993 vehicles to those entering the fleet in 2010 (Euro 5). Although not shown in Fig. 6, emissions of NO2 from diesel cars would show a substantial increase from pre-Euro 3 to Euro 4/5 due to the increased use of oxidation catalysts fitted to diesel vehicles. The HBEFA estimates tend to capture the variation better across the Euro classes than current UK emission factor estimates e but are lower in absolute terms compared with the RSD data. Note also that early (Euro 1) petrol cars currently emit similar levels of NOx compared with all generations of diesel car. For the diesel LGVs there is a closer correspondence in NOx estimates for the three emission factors compared with diesel cars, but like diesel cars, the trend across the Euro classes for the RSD data differs markedly with current UK factors. There is much better agreement in terms of absolute emissions and trend across the Euro classes between the three emission factor estimates for rigid HGVs, shown in the lower left panel of Fig. 6. The RSD data had a strong bias towards smaller HGVs for the reasons discussed previously. However, if the NOx/CO2 ratio from the RSD is applied to much larger vehicles e.g. 18 tonne articulated vehicles, the agreement is very similar to that shown in Fig. 6. This finding suggests therefore that the RSD data is likely to provide reasonable estimates of NOx/CO2 emissions ratio from a wide range of HGVs. We have found little evidence that heavy duty vehicles (trucks) emit higher amount of NOx than expected from a consideration of emission inventories as has been found by Velders et al. (2011). Velders et al. (2011) used a portable emissions measurement system (PEMS) to show that NOx emissions from this class of vehicles have changed little from Euro I (introduced z1993) to Euro V (introduced z2010). Of the seven vehicles tested, six were fitted with selective catalytic reduction (SCR) and one with exhaust gas recirculation (EGR). Compared with legislative test cycle emission limits, Euro V vehicles were found to emit about three times the NOx. Velders et al. (2011) suggest that the exhaust gas

D.C. Carslaw et al. / Atmospheric Environment 45 (2011) 7053e7063

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Fig. 6. Comparison of different emission factor estimates. Three emission sources are compared: current UK factors, HBEFA (2010) and estimates based on the analysis of remote sensing data. The uncertainties for the remote sensing data are the 95% confidence intervals in the mean.

temperature is likely to have been too low for SCR to work efficiently. In our work it was not possible to confirm whether the heavy duty vehicles were fitted with SCR or EGR, or even both. Data relating to the use of SCR on HGVs in the UK is scarce. Currently it is thought about 20% of the HGV fleet in UK use SCR, although these estimates are considered approximate.2 The proportion using SCR in urban areas will be somewhat lower than 20% due to the types of HGV fleet in urban areas. It is thought that about 80% of new vehicles in the long-haul sector are vehicles with SCR. However, it seems that manufacturers frequently offer EGR on their distribution and delivery vehicles for Euro IV/V i.e. lorries of the type typically used in urban areas. Below 16 tonnes GVW about 80% of new sales are for vehicles with EGR. Given that the remote sensing data were collected in urban-type areas where the weight of these vehicles is typically much lower than 16 tonnes, it is likely that very few SCR-equipped vehicles were measured and hence the issue of poor SCR performance not detected. Furthermore, the sample size for remote sensing data Euro V vehicles is too small to confirm whether these vehicles emit higher than expected emissions of NOx, but this clearly is an issue that should be considered further experimentally for in-use vehicles.

2 Personal Communication. Simon Davies, Cleaner Fuels and Vehicles, Department for Transport, 28th May 2010.

The agreement between the emission estimates for buses is reasonable. As previously discussed, these results will be susceptible to specific bus fleet technologies used in different urban areas. Almost all the Euro II buses sampled, for example, were from the London bus fleet e and all of those vehicles would have been fitted with continuously regenerating particle filters (Carslaw, 2005). It is possible therefore that a bias has been introduced to Fig. 6; although it is difficult to know the extent to which Euro II buses in London differ from other UK urban areas. The results for buses should therefore be considered with more caution. Detailed inventory calculations will be made in future work using the assumptions in the current work. However, it is possible to estimate the relative importance of the differences in emission factors. There are two issues of importance here. First, there is the absolute estimate of the emission factors themselves in g km
1 i.e. the estimates shown in Table 4. Second, there are the assumptions relating to the numbers of different vehicles in the vehicle fleet i.e. the data shown in Table 2. Between them, these two data sources allow an estimate of the relative difference in absolute vehicle emissions to be made. It should be stressed however, that these estimates will not be representative of national totals, but will be biased by the types of road surveyed during the remote sensing campaigns i.e. would tend to better reflect urban areas. Table 5 summarises the estimated emission totals of NOx, scaled such that the sum of base case UK emission is equal to 100. For the UK emission factors, it can be seen that diesel cars for example

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Table 5 Relative absolute emissions of NOx calculated by vehicle type using three different emission factor sources. The estimates take account of both the emission factor (in g km
1) and numbers of vehicles by type and age according to the RSD surveys. Note that the data have been scaled such that the sum of UK emission estimates is 100. Vehicle type

Diesel car Petrol car LGV HGV þ bus Sum

Emission estimate UK

HBEFA

RSD

20.4 14.2 20.0 45.4 100.0

26.1 15.4 29.8 46.0 117.3

46.4 43.4 46.7 41.9 178.3

account for around 20% of total emissions and roughly half the total is due to HGVs and buses. The HBEFA estimates show that diesel cars and LGVs are relatively more important than the UK estimates. In total the HBEFA suggests that emissions are about 17% more than is suggested by the UK emission factors. By contrast, the estimates based on the RSD are substantially higher than either the UK or HBEFA estimates for all light duty vehicles. The HGV þ bus sum is very similar across all emission factor estimates. Interestingly, the RSD estimates differ most from the other two methods for petrol vehicles; due to the higher emissions assumed for Euro 1/2 vehicles in particular. It should be noted that the RSD data suggests a much higher proportion of Euro 1/2 vehicles in the fleet than that assumed in the emissions inventory. For example, for the UK emissions inventory for 2009 it was assumed that Euro 1/2 comprised 21.5% of the petrol vehicle fleet, whereas the RSD data suggests 33.4%. The UK NAEI does not differentiate between the age of vehicle fleets by area or road type e.g. urban areas versus motorways, and this could be an important factor affecting emission estimates. There are also large differences in the assumptions for the percentage of Euro 4 petrol cars e 60.0% versus 22.3% of the total. Indeed, it is very likely that the differences assumed in the age of the vehicle fleet for the UK emissions inventory and the observed vehicle fleet from the RSD data are important factors affecting the trend estimates in NOx over the past decade. 4. Conclusions Vehicle emission remote sensing is a powerful technique which has not been applied to a large extent in Europe. This work suggests that there is a large amount of information which could be obtained from this technique which would be of great importance for the assessment of the accuracy of emission inventories e a key component of atmospheric modelling, and also for the evaluation of probably the most important policy measures in Europe relating to urban air quality and public exposure. Furthermore, linking vehicle registration data to detailed databases of vehicle information also has the benefit of providing actual vehicle stock information that is of direct use in the compilation of emission inventories, and which in this case reveals that current estimates of vehicle stock profiles are erroneous in the UK. Regular surveys of remote sensing as in the case of Bishop and Stedman (2008) would provide robust and up to date information on in-use vehicles emissions, which would complement emissions measurements obtained on dynamometers. It is clear from the current work that the Euro standard limits have not been effective in reducing real-world emissions for diesel cars and LGVs post Euro 2. While controls and Euro standard limits for new petrol cars have been extremely effective in reducing emissions of NOx from these vehicles, degradation in emission reduction performance appears to have been greater than emission inventories suggest. Emission factors for NOx currently in use in the UK are overly optimistic in terms of emission trends through the Euro classes for

diesel cars and LGVs. In addition, the RSD data shows that there is both a greater number of older catalyst-equipped petrol vehicles in the fleet than assumed in the emissions inventories and that these vehicles emit higher levels of NOx than previously thought. While the trends through the Euro classes shown by the HBEFA factors are more closely aligned with observations from RSD, the latter observations suggest that the HBEFA factors could be low in absolute terms for diesel cars and LGVs. This work has important implications regarding the ability of European member states meeting the annual mean NO2 limit value of 40 mg m
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