Impact of excess NO_x emissions from diesel cars on air quality, public health and eutrophication in Europe

Since the late 1990s the sales share of LDDVs (light duty diesel vehicles) has risen sharply at the expense of petrol in most European countries (see the online supplementary data available at stacks.iop.org/ERL/12/094017/mmedia). This development has been intentional, as diesel engines emit less CO₂ than comparable petrol engines. However, they emit significantly more NO_x (NO + NO₂). Road transport contributes about 40% of the land based NO_x emissions in the EU28+ countries (EU28 plus Norway and Switzerland), and is one of the main reasons why several countries, including Germany, France, Austria, Belgium, and Ireland, consistently exceed their internationally agreed NO_x emission caps (Wankmüller et al 2015). Exceedances of Europe's ambient NO₂ air quality limit values have to a large part been attributed to the emissions from diesel cars (Kiesewetter et al 2014, Carslaw et al 2016, Degraeuwe et al 2016).

Euro 3 and newer LDDVs should emit less NO_x than the older vehicles, both petrol and diesel, that they are replacing. However, unlike petrol cars, on the road these cars do not comply with Euro limit values. (Chen and Borken-Kleefeld 2014, Fontaras et al 2014, Hagman et al 2015, Yang et al 2015). In fact, measurements indicate that Euro 3, 4 and 5 LDDVs may have higher emissions under actual driving conditions than older diesel vehicles (Chen and Borken-Kleefeld 2014). Furthermore Euro 5 LDDVs may have higher emissions than previous Euro class vehicles under actual driving conditions (Ligterink et al 2013).

The difference between expected and actual emissions of NO_x has gained a lot of attention following the discovery of Volkswagen's (VW) diesel 'defeat devices' (US-EPA 2015, BMVI 2016, UK Department for Transport 2016), where software is installed in the vehicles which reduces NO_x emissions to comply with emissions tests in the US and Europe. In Europe about 8.5 million VW diesel cars have been sold with defeat devices installed, a far greater number than in the US where the defeat device was first discovered. As a result, the effects on health in Europe should be much larger than that calculated for the US (Hou et al 2016). Chossière et al (2017) have calculated the effects of excess NO_x emissions from VW-only diesel passenger vehicles in Germany, and found that these vehicles have resulted in about 1200 premature deaths integrated over the sales period 2008–2015 in Germany and neighbouring countries. However, large discrepancies between test cycle NO_x emissions and on-road emissions have been found across all manufacturers (UK Department for Transport 2016, Transport and Environment 2016). Therefore, in this study, we analyse the consequences of excess NO_x emissions from the whole fleet of LDDVs (and not just a single brand) across all of Europe.

Here we calculate concentrations and depositions of air pollutants in Europe with emissions and meteorology from 2013 focusing on the effects of excess NO_x emissions from LDDVs. NO_x emissions from traffic assumed to reflect real driving conditions are used as a reference case. Additional model calculations are made assuming emissions from LDDVs are compliant with the EU and national regulations and assuming all LDDVs emit as petrol vehicles. Finally we also include a model calculation without LDDVs. Based on these scenarios, the effects on the concentrations of the atmospheric pollutants NO₂, PM_2.5 and ozone, as well as the deposition of nitrogen, are estimated.

The relationship between NO₂ and health is scientifically not as well founded as for PM_2.5 and ozone (WMO 2013). In this paper we therefore assess changes in PM_2.5 and ozone as the most important indicators of human health impacts. Furthermore the effects of increased nitrogen deposition are quantified by the computation of exceedances of critical loads for eutrophication of semi-natural soils.

Emissions as officially reported by European countries under the Convention on Long-range Transport of Air Pollutants for the year 2013 are used as input for the calculations (Wankmüller et al 2015) (see also WebDab (www.ceip.at/ms/ceip_home1/ceip_home/webdab_emepdatabase/). As the discrepancy between Euro standards and real-world emissions for LDDVs has been known for some time already, the emission factors used in European emission inventories are not based upon Euro standards, but rather already reflect the high NO_x emissions expected in real-world conditions (Ntziachristos and Samaras 2009, HBEFA 2010, Katsis et al 2012). According to ERMES (European Research Group on Mobile Emission Sources, www.hbefa.net/d/pdf/ERMES_NOX_EF_V20151009.pdf), the emissions from passenger cars estimated with such tools should be at least a factor of four larger than emissions would have been if the vehicles had been complying with the Euro emissions standards.

Here we conduct four runs:

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• 'Reference case': Here the officially reported emissions are used (including real driving emissions from LDDVs) in the calculation of the health and environmental impacts of the current situation. It is thus the backdrop against which the other scenarios are to be evaluated.Reported emissions are however only given for all LDDVs together. They are neither disaggregated by fuel type nor by emission control stage (i.e. Euro class) as needed for the scenario calculations. Therefore we employ the GAINS model that is calibrated to the reported emissions and represents the necessary details for all countries (Amann et al 2015), notably how much NO_x is emitted by diesel cars of a certain emission control stage⁶.

For the scenario calculations we leave emissions from all other sectors unchanged and only adjust NO_x emissions from LDDVs as follows:

• Scenario 'WhatIf-Dlim': This scenario assumes that average on-road NO_x emissions of LDDVs are at the level of the respective diesel limit values. This could represent emissions as intended by the legislation under 'normal conditions of use'. We apply the respective emission rates to the whole fleet of passenger cars and light duty diesel vehicles, as measurements show that high on-road NO_x emissions are not limited to a certain manufacturer (BMVI 2016, UK Department for Transport 2016).
• Scenario 'WhatIf-Petrol': This scenario assumes that average on-road NO_x emissions of diesel cars are as low as the respective on-road emissions of petrol cars of the same age and emission standard. Petrol powered cars comply with stricter NO_x emission limits in the European Union than diesel cars. Here we assume that no discrimination would be made by propulsion type as is the case in the US legislation.

• Scenario 'WhatIf-NoLDDV': Lastly, we set NO_x emissions from LDDVs to zero. This model run is used to calculate the impacts of NO_x emissions from all other sources except LDDVs. The difference between the Reference case and NoLDDVs can then be interpreted as the health and environmental burden from these vehicles, and as such represents a scale against which the effects of WhatIf-Petrol and WhatIf-Dlim can be measured.

Figure 1 illustrates the distributions between non traffic emissions, and traffic emissions split between reference driving emissions and the scenario emissions of NO_x for the EU28+ countries, both in Gg and in percent. The contributions from the different sources/scenarios are stacked so that the total emissions from LDDVs from the individual countries are shown as the sums of the 'WhatIf Petrol', 'WhatIf Dlim' and 'Reference' emissions. Thus, emissions exceeding 'WhatIf Dlim' are shown in red labelled 'Add reference' in the figure. Further reductions that could have been achieved with 'WhatIf Petrol' are shown in yellow labelled 'Add WhatIf-Dlim'. In the EU28+ countries we find that excess NO_x emissions from LDDVs account for up to 15% of national emissions. Remaining traffic emissions are essentially trucks and petrol cars.

We calculate the 'WhatIf' NO_x emissions accounting for different shares, ages and annual mileages of diesel cars in each European country. These 'WhatIf' emissions replace the LDDV emissions in the Reference case (data documented in table 1). Their spatial and temporal distribution is equal to the Reference case, even though in reality this percentage is likely to differ from one region to another within the countries. Emissions from all other sources are left unchanged, i.e. as officially reported by the countries. Concentrations and impacts are calculated through for these counterfactual scenarios and results compared to the reference case with actual driving emissions. (It can be noted that the GAINS and EMEP LDDV emissions differ slightly, but the discrepancy of about 4% has little impact on our calculations.)

In this study we use the EMEP/MSC-W chemical transport model, version rv4.8. This model, available as open source (www.emep.int), has been described in detail in Simpson et al (2012), with various updates, see (Simpson et al 2016) and references within.

For Europe the model is regularly evaluated against measurements in the EMEP annual reports, available at www.emep.int. In addition the EMEP model has been included in model intercomparisons and model validations in a number of peer reviewed publications (Jonson et al 2006, Jonson et al 2010, Simpson et al 2006, Simpson et al 2006, Colette et al 2011, Colette et al 2012, Angelbratt et al 2011, Dore et al 2015, Stjern et al 2016).

The model is run with a 0.1 × 0.1 degrees resolution driven by ECMWF-IFS meteorology and emissions as described in section 2. A comprehensive description, including model evaluations, of the model results with the 0.1 × 0.1 degrees application of the EMEP model for 2013 can be found in Tsyro et al (2015). All model runs have been made for a single year, 2013. More details about the model, including specifications of the model validation, is included in the supplementary data.

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3.1. Health impacts from PM_2.5

Following the methodology recommended for European health impact assessments by the HRAPIE (Health risks of air pollution in Europe) project (HRAPIE 2013) of the World Health Organization, the assessment of PM_2.5 health effects is based on estimates of the impact of long-term (annual average) exposure to PM_2.5 on all-cause (natural) mortality in adult populations (age >30 years). For the PM_2.5 concentrations prevailing in Europe, a linear concentration–response function is employed, with a relative risk coefficient of 1.062 (95% confidence interval (CI) 1.040−1.083) per 10μg m⁻³. This relationship is applied to PM_2.5 concentrations arising from anthropogenic emission sources, while impacts from natural sources of PM_2.5 remain unquantified.

In calculating the population exposure to PM_2.5 it is recognized that the resolution of the atmospheric dispersion calculation is insufficient to reproduce accurately measured urban background PM_2.5 concentrations. To account for this, an urban increment related to primary PM emissions from local low-level sources is estimated by applying a downscaling scheme based on a redistribution of primary PM concentrations according to the primary PM emission densities from the domestic and transport sectors. A detailed description of the methodology is given by Kiesewetter et al (2015). The annual mean population-weighted PM_2.5 concentrations are used to calculate premature mortality attributable to PM_2.5 in country i as:

$$\begin{equation} {\rm mort}_{i} = {\rm PM}2.5_{i}\, \times \,{\rm RR}_{\rm PM}\, \times \,{\rm deaths }30_{i} \end{equation} \tag{ }$$

where: mort_i             cases of premature mortality per year in country i;

deaths30_i    baseline mortality (number of natural adult deaths per year) in country i;

RRPM          relative risk per μg m⁻³ annual mean PM_2.5;

PM_2.5i          annual mean population-weighted PM_2.5 in country i.

3.2. Health impacts from ozone

Following the methodology recommended for European health impact assessments by the HRAPIE project (HRAPIE 2013), the assessment of effects due to ozone is based on estimates of the impact of short-term (daily maximum eight-hour mean) exposure to ozone on all-cause mortality for all ages. This is calculated by applying a linear function with a relative risk coefficient of 1.0029 (95% CI 1.0014−1.0043) per 10 μg m⁻³. Using the modelled estimates of the SOMO35 indicator, which quantifies the yearly sum of the daily maximum eight-hour ozone concentrations exceeding a 35 ppb threshold, the annual cases of premature mortality in country i attributable to ozone are then calculated as:

$$\begin{equation} {\rm mort}_{i} = ({\rm SOMO}35_{i}/365)\, \times \, {\rm RR}_{{\rm{O}}3}\, \times \, {\rm deaths}_{i} \end{equation} \tag{ }$$

where mort_i and deaths30_i are as given above, and:

RR_O3                relative risk per ppb 8 h maximum ozone concentration per day;

SOMO35_i     population-weighted SOMO35 in country i.

3.3. Exceedances of critical loads for eutrophication

Here we briefly describe the Reference situation, i.e. pollution and their impacts from our best estimate of emissions for the year 2013. Then we identify what part of this pollution can be attributed to emissions from light duty diesel vehicles as the difference between the Reference and WhatIf scenarios. Finally, we estimate the additional effects on human health and the environment from LDDV NO_x emissions.

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4.1. LDDVs: effects on ambient concentrations

4.2. Health effects

4.3. Eutrophication effects

Model calculations are of course uncertain, but the EMEP model has been extensively evaluated for many pollutants in many different climates (see references in section 3), and is among the best models operating in Europe today, e.g. Dore et al (2015). With a 0.1 × 0.1 degrees resolution we are not able to fully resolve pollution levels at locations close to major sources of traffic (Schaap et al 2015). Dispersion of primary compounds such as NO_x or primary PM_2.5 could in principle be modelled with urban-scale models. However, such models are usually not capable of modelling the medium to long range transport and chemical formation of oxidants and aerosols. In any case, fine-scale meteorology and emissions are not generally available for European-scale assessments such as ours.

Methods extrapolating regional model results, as the CITY-DELTA methods (http://ies-webarchive.jrc.ec.europa.eu/citydelta/) or the Air Quality Re-gridder Model (Theobald et al 2016), could be an alternative, but such methods require an individual treatment of each city, beyond the scope of this study.

Further, model uncertainties tend to be systematic rather than random, with small perturbations in for instance emission inputs, leading to well-behaved changes in output concentrations. In the scenario calculations several additional assumptions are made that may affect the uncertainty. As already discussed, the emissions from diesel vehicles differ substantially depending on make and model (Transport and Environment 2016). The European emission reporting guidelines are the same for all countries, but the vehicle model mix is not. Therefore the exact emissions in the individual countries will depend on the local fleet mix. Subsequently the calculated effects are likely to be a lower estimate in countries where the most polluting brands and makes are popular. In addition NO_x emissions as officially reported (i.e. as used in our Reference calculation) are likely to be revised upwards in the light of recent findings (Keller et al 2017). Thus excess NO_x emissions may be higher than reported here. As a result we believe that the calculated impacts are likely to be a lower estimate.

As noted above, we are not able to resolve locations close to heavy traffic. However, the formation of secondary PM and ozone happens on spatial scales of several tens of kilometers, and concentrations are relatively uniform across larger regions. Therefore a finer spatial resolution would likely not change our result much.

The link between air pollution and health is well established through projects such as the HRAPIE project (HRAPIE 2013), but additional processing and use of the model results will inevitably increase the uncertainty to some extent.

In this paper health effects from LDDVs are only considered for ozone and secondary particles from NO_x. The effects of primary emitted particles are not included, as these emissions are within the emission limits. Health effects from increased NO₂ concentrations are not included for lack of agreed risk factors and uncertain overlap with PM health impacts.

Furthermore we assume that all populations have the same health response to air pollution and that there exists no interaction between ozone, PM_2.5 and other air pollutants. The paper does not account for other health effects beyond adult mortality.

The calculations in this paper demonstrate how excess NO_x emissions from LDDVs lead to increased levels of NO₂, PM_2.5, ozone and depositions of eutrophying nitrogen in Europe. This increase in pollution levels affects human health and the environment. The effects as summed up for the EU28 countries as a whole are as follows:

• Up to 10 000 premature deaths due to PM_2.5 and ozone formation can be attributed to high NO_x emissions from LDDVs in Europe in the year 2013.
• About half of these cases could have been avoided if these vehicles would in real driving emit no more than the EU limit value (as possibly will be achieved with forthcoming legislation).
• About 80% of the impacts could have been avoided if these vehicles would emit no more than petrol vehicles (as achieved in the US with technology neutral emission standards).
• Excess premature deaths will continue into the future until LDDVs with high on-road NO_x emissions have been replaced, possibly from 2021 and onwards when the final stage of the Euro 6 legislation is intended to close the gap between the test cycle and on-road emissions.
• NO_x emissions from LDDVs contribute to the exceedance of the critical loads of eutrophication in Europe. About half of this additional exceedance could have been avoided if LDDVs would emit no more than the EU limit value, and about 80% if they would emit no more than petrol cars.

There are signs that diesel penetration in Europe is now going down, partly as a result of the 'Dieselgate' scandal. Dieselgate may also have the effect of expediting the transition to electric vehicles and other alternative fuel modes of transport. Several economic studies, such as Bloomberg (2016) and the Fitch report (www.fitchratings.com/site/pr/1013282), predict a strong growth in the electrification of the car industry in the next decade that will inevitably reduce all types of direct traffic emissions.

It is interesting to note that even though the calculation chain, from emissions to pollution levels to effects on human health and the environment, involve several potentially nonlinear steps, the calculations are close to linear. The 50%–80% changes in the emissions of NO_x from LDDVs studied here result in similar percentage changes in concentrations of NO₂ and PM_2.5, and depositions of nitrogen, and furthermore in the number of premature deaths and in the exceedances of critical loads of eutrophication (both in amount and ecosystem area). The exception is ozone, where the percentage changes in ozone (as SOMO35) and in premature deaths are smaller than the changes in NO_x emissions as result of NO_x titration in some highly polluted regions.

This work has been partially funded by EMEP under UNECE. We would like to thank Katarina Mareckova and Robert Wankmüller, Deptartment for Emissions and Climate Change, UBA Austria and Rolf Hagman, Institute of Transport Economics—Norwegian Centre for Transport Research for valuable help and advice. Computer time for EMEP model runs was supported by the Research Council of Norway through the NOTUR project EMEP (NN2890K) for CPU, and NorStore project European Monitoring and Evaluation Programme (NS9005K) for storage of data.