Impact of the Volkswagen emissions control defeat device on US public health

Uncertainty in input variables such as the total underreported NO_x is propagated throughout the calculation using Monte Carlo simulation. Ten thousand independent draws of each variable are performed, resulting in ten thousand independent estimates of the total mortality and morbidity impacts. The same draws are used to estimate the impacts in each year and under each scenario. Where not explicitly stated otherwise, reported results correspond to the median value of the the output distribution, i.e. there is equal probability that our central estimate is low or high. When monetizing mortality impacts, the economic value of statistical life is treated as an additional uncertain parameter with ten draws (i.e. 100 000 draws in total). Other sources of uncertainty, such as the shapes of exposure-response functions, are explored in sensitivity analyses which are discussed in the supplementary material.

We base our study on data from 2005 to 2015, using the most recent available data and correcting to the study period (from 2008) where necessary and possible. For example, gridded population data is for 2006, which is scaled for each year using changes in total US population. This introduces uncertainty in the population totals (which are forecasts from 2011 onwards), and also changes in the spatial distribution of population over time (which are not accounted for). Data sources and uncertainty associated with the different parts of our analysis are detailed in the following subsections. We further discuss sources of unquantified uncertainty in section 4.2, and where possible note the direction of the bias that may be introduced.

Total activity for the affected vehicles, expressed as vehicle-kilometers traveled (VKT), is determined using annual sales data for the affected vehicles and the Stochastic Transport Emissions Policy (STEP) light duty vehicle fleet model (Bastani et al 2012). The model uses assumed values for the average annual growth rate in kilometers traveled per vehicle and the decay rate with vehicle age. A logistic function is used to determine the vehicle retirement rate. The model uses triangular distributions to account for uncertainty in growth and decay rates and the median vehicle age at retirement. Parameters for each distribution are given in the supplementary material. Annual sales figures for affected VW vehicles between 2008 and August 2015 are obtained from WardsAuto Group (2015). These sales figures are scaled to match the total number of affected vehicles, 482 000, reported by the EPA which also includes affected Audi vehicles (US EPA, 2015c).

The measurements of Thompson et al (2014a) are used to calculate NO_x emissions factors for affected vehicles. Thompson et al (2014a) tested two affected vehicles, a 2012 Jetta (vehicle A) and a 2013 Passat (vehicle B), on several drive cycles characteristic of highway and urban driving. Each drive cycle was driven one or two times with each vehicle. We use the individual drive cycle NO_x emissions factors for all urban drive cycles using either vehicle (9 samples) and compute the mean and standard deviation of a truncated normal distribution for the urban driving emissions factor. We do the same with the highway drive cycle (4 samples) for the highway driving emissions factor. The distribution parameters for the baseline emissions factor are computed from the weighted FTP-75 chassis dynamometer tests for each vehicle (Thompson et al 2014a).

Excess emissions are allocated to 10 road types in the US consisting of rural and urban interstate, arterial, collector and local roads. Emissions based on highway test data are allocated to rural interstate roads, while those calculated from city drive cycles are allocated to the other road types. This is done since the urban drive cycles included portions of highway driving. We use spatial surrogates for light duty diesel vehicle NO_x emissions from the 2005 National Emissions Inventory (US EPA 2008) to map roads to model grid cells.

Excess emissions from 2008 through 2015 are computed as the difference between the real driving emissions and the FTP-75 emissions, representing the difference between actual and expected emissions. Excess emissions from 2016 onward are calculated as the difference between the estimated real driving emissions and the EPA Tier 2 Bin 5 standard of 0.043 g km⁻¹ (40 C.F.R. §86.1811–17), representing the difference in NO_x emissions that could be expected if these vehicles were brought into regulatory compliance.

Exposure to annual PM_2.5 and one-hour daily maximum ozone is estimated using updated versions of air quality sensitivity matrices developed as an air pollution policy assessment tool by Dedoussi and Barrett (2014). Sensitivity matrices derived from the GEOS-Chem adjoint have previously been used to assess the health impacts of emissions perturbations by Koo et al (2013) and Lee et al (2015). The sensitivity matrices map an arbitrary three-dimensional time-varying distribution of emissions to total US population exposure to inorganic PM_2.5 (on an annual average basis) and ozone (based on a one-hour daily maximum value during the ozone season). The sensitivity matrices were computed from the adjoint of the GEOS-Chem chemistry-transport model applied at a ∼50 km resolution to a domain encompassing the contiguous United States (CONUS). Further discussion of the air quality modeling approach relative to alternatives is provided in the supplementary material.

The GEOS-Chem adjoint model was developed by Henze et al (2007) and is based on the GEOS-Chem global tropospheric chemistry-transport model (Bey et al 2001). It calculates the sensitivities of quantities of interest (e.g. population exposure to PM_2.5 and ozone) with respect to various model parameters (e.g. emissions). GEOS-Chem performs transport, gas- and aerosol-phase chemistry and wet and dry deposition calculations. In the GEOS-Chem version that is applied in the present work the KPP chemical solver (Damian et al 2002) and the RPMARES aerosol equilibrium model (an implementation of the MARS-A scheme of Binkowski and Roselle (2003)) are used. GEOS5 meteorological data from the Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Centre for 2006 are used. We note that 2006 was a climatologically warm year in the US, with the annual average temperature 0.6 °C warmer than the 1995–2015 mean (NOAA 2015). The EPA National Emissions Inventory for 2005 is used for anthropogenic emissions in the US. We note that emissions have declined since, which will introduce an unquantified uncertainty to the size of sensitivities. In particular, decreasing SO₂ emissions will likely lead to a greater impact of NO_x emissions on ammonium nitrate particulate matter formation due to increased availability of free ammonia (Woody et al 2011). Decreasing NO_x emissions will likely lead to greater ozone sensitivity to NO_x emissions, as net ozone production is increased as a result of reduced titration of ozone at low NO_x concentrations (Seinfeld and Pandis 2006).

The GEOS-Chem adjoint simulations upon which the air quality sensitivity matrices are based are run for a 15-month period. The first 3 months of the (backward) simulation are used as the adjoint spin-up time, during which the model is run, but the outputs are not included in the analysis, to ensure that any initial conditions do not contribute significantly to the annual air quality impacts. We calculate the sensitivity of two objective functions (relevant to health impacts) with respect to NO_x emissions. The first objective function is defined as the annually averaged US population exposure to PM_2.5, and the second as the one-hour daily maximum US population exposure to ozone during the ozone season (May to September). We calculate exposure to one-hour daily maximum ozone given that this is the exposure metric linked to increased risk of premature mortality in the epidemiological work used in this study (Jerrett et al 2009). Morbidity outcomes, which are associated with the regulatory metric of eight-hour maxima, are calculated by assuming a constant correction factor between the one-hour and eight-hour maximum (Thurston and Ito 2001). The implications of this correction factor are discussed in the supplementary material. The population exposure impacts are quantified by performing an inner (Frobenius) product of the sensitivity matrix with the spatially resolved VW excess NO_x emissions map, obtained as described in section 2.3. This operation gives us the aggregate US air quality impacts change attributable to these emissions.

We estimate uncertainty in the PM_2.5 exposure calculations arising from the baseline atmospheric model (GEOS-Chem) as well as the uncertainty in applying the adjoint-based approach for capturing marginal impacts. Based on a comparison with annually averaged observational Federal Reference Method (FRM) PM_2.5 data from the EPA's Air Quality System (AQS) database from ∼1000 locations in the CONUS, the AQS PM_2.5 values are on average ∼40% higher than those predicted by GEOS-Chem (corrected to include particle bound water for comparison purposes using the Aerosol Inorganic Model by Clegg et al 1998). When comparing different atmospheric model approaches for capturing marginal impacts (Caiazzo et al (2013), Fann et al (2013) and Dedoussi and Barrett (2014) for the road transportation sector, correcting for the absence of secondary organic aerosols, and accounting for the different definitions of the mobile/road transportation sectors amongst studies), we find that these can be up to ∼40% lower than the adjoint-based approach. Given the biases from these modeling and measurement-based comparisons, we apply a normally distributed multiplicative factor to the PM_2.5 sensitivity matrix calculated values with mean 1 and standard deviation 0.4. The bias of grid resolution is not captured, as it has been shown that CONUS PM_2.5 health impacts are largely unaffected by grid resolution in fine resolutions (Thompson et al 2014b, Arunachalam et al 2011). We note that model uncertainty may not be fully captured given the interacting issues of resolution, emissions, meteorology, and chemical processes, as well as the limited coverage of monitors, mismatch in scales, and potential for canceling errors in the model.

Uncertainty in the ozone estimates are estimated by comparing modeled and measured one-hour daily maximum ozone concentrations at monitoring locations across the US. Observational data are obtained for ∼1200 sites from the EPA's AQS database. The normalized mean bias (NMB) from all locations is distributed with an average NMB of 25.3% and NMB standard deviation of 17.9%. We draw from this assuming a normal distribution and multiply the ozone impacts with its reciprocal as a corrective factor. This follows the uncertainty quantification done in Caiazzo et al (2013).

For a given estimate of total unreported NO_x emissions, the adjoint-based model described in section 2.4 is applied to determine the increase in total population exposure to both annual PM_2.5 and one-hour daily maximum ozone. Epidemiological concentration-response functions (CRFs) are applied to calculate the increase in premature mortality and morbidity (i.e. non-fatal) outcomes. Premature mortalities are calculated by assuming a log-linear CRF. Function parameters are derived from an American Cancer Society study (Krewski et al 2009, Jerrett et al 2009). Krewski et al (2009) correlated changes in annual average daily PM_2.5 exposure with increased premature mortality due to cardiopulmonary disease and lung cancer. Jerrett et al (2009) correlated increases in the daily one-hour maximum ozone exposure with premature mortality due to chronic obstructive pulmonary disease (COPD) and exacerbation of asthma. Alternative CRF shapes and parameters including the integrated exposure-response (IER) function applied in the 2010 Global Burden of Disease study (Burnett et al 2014, Lim et al 2012) are evaluated to determine the sensitivity of the result to the choice of CRF (presented in the supplementary material).

The rate of increase of relative risk with exposure is treated as two independent, uncertain variables for one-hour daily maximum ozone and annual average PM_2.5. We assume a triangular distribution with mode and 95% confidence interval taken from the epidemiological study results. Background levels of PM_2.5 and ozone are extracted from a previous forward ∼50 km resolution GEOS-Chem simulation of US air quality, with a population-weighted average background of 8.18 μgm⁻³ for PM_2.5 and 39.7 ppbv for ozone in 2006. The background distribution is taken from archived GEOS-Chem simulation data. Since the health impact functions are all assumed to be linear or log-linear with no lower threshold, increases in background exposure only have a significant effect at background levels higher than those in the US. Highly non-linear CRFs such as the integrated exposure response functions discussed in the supplementary material are more sensitive to background concentrations.

Morbidity impacts are estimated using linear CRFs from the 2005 update to the methodology for the European externalities of energy (ExternE) project (Bickel and Friedrich 2005). Morbidity outcomes include the increase in bronchodilator usage days, lower respiratory symptom days, minor restricted activity days (including work loss days), hospital admissions, and new cases of chronic bronchitis. Alternative response functions based on data from the EPA are also discussed in the supplementary material.

The gridded population distribution for the US applied in the GEOS-Chem adjoint-based model is taken from the Global Rural Urban Mapping Project (GRUMP) (Balk et al 2006) for 2006. This distribution is embedded in the adjoint sensitivity matrix, and the distribution is therefore assumed to be constant between target years. Estimated exposure is normalized by population to yield the population-weighted average change. Total population and population fractions by age are estimated for each year using data from UN forecasts (2013). Baseline mortality rates per 1000 people for each disease of interest are taken from the World Health Organization Global Burden of Disease report for 2012 (WHO 2014) and are assumed to remain constant.

Increase in premature mortality is monetized using estimates of the monetary value of changes in mortality risk, expressed as the value of statistical life (VSL). Following EPA recommendations (US EPA 2014), VSL is treated as an uncertain variable. In the supplementary material, we also present results if only the mean VSL value is used for monetization. We estimate a VSL distribution for 2015 by adjusting the original study values from previous years used by EPA in terms of inflation and real income growth. Inflation is considered using the US Bureau of Economic Analysis' implicit price deflator for gross domestic product (GDP) (US Bureau of Economic Analysis 2015). Real income growth is considered through changes in the full-time employed median usual weekly earnings (US Bureau of Labor Statistics 2015) and assuming a VSL income elasticity of 0.4, consistent with the recommended central value in EPA's Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model (RTI International 2015). A Weibull distribution is fitted to the augmented VSL data, yielding a shape factor of 1.5 and a scale factor of 8.9 for 2015. The resulting mean VSL is $8.1 million (2015 dollars). Alternative approaches for adjusting the EPA-recommended distribution of VSL values to 2015 include using the Consumer Price Index instead of the GDP deflator and using the high (1.0) and low (0.08) values of the EPA BenMap-CE VSL income elasticity recommendations. These alternative approaches yield mean 2015 VSLs within −1% to +4% of the mean value used in this study. The median VSL results of the alternative approaches are presented in the supplementary material.

For past years, the original VSL estimates considered by EPA are augmented by adjusting for income growth and expressing them in 2015 US dollars. For estimating the benefits associated with a return to compliance for the current fleet with the Clean Air Act after 2015, future year VSL distributions need to be estimated. We adjust 2015 VSL values by accounting for long-term annual real GDP growth in the US out to 2040 at a compound annual growth rate of 1.7% (OECD 2014) and assuming a VSL income elasticity of 0.4 as recommended in EPA BenMap-CE. The resulting VSL distributions for 2008–2040 are shown in the supplementary material.

Morbidity outcomes are not monetized. However, we expect mortality to capture >90% of monetized impacts (US EPA 2011). Social costs for each year are calculated by multiplying annual incidents of premature mortality with the VSL for the corresponding year, assuming the mortality lag structure recommended by the EPA for air-quality impacts. Total social costs for 2008 to 2015 from increased premature mortality are expressed in 2015 USD dollars using a social rate of time preference (discount rate) of 3% p.a. (US EPA 2014). Total social costs from additional increased premature mortality that occurs in future years if vehicles are not brought into compliance with EPA regulations are expressed in 2015 USD dollars using the same discount rate. The results for different discount rate choices are presented in the supplementary material.

Based on the drive cycle tests of Thompson et al (2014a), we estimate the NO_x emissions factors for urban driving for the affected vehicles to be 0.97 g km⁻¹ (95% CI: 0.31 to 1.62) and for highway driving to be 0.50 g km⁻¹ (95% CI: 0.06 to 0.94). The emissions factor based on the FTP-75 drive cycle is estimated to be 0.019 g km⁻¹ (95% CI: 0.0085 to 0.0294). Based on the 2005 emissions inventory, we estimate 8.6% of VKT to be driven on rural interstates, for which we use the highway emissions factor, while the urban emissions factor is used for all other VKT. This gives a weighted average NO_x emission factor for affected vehicles across all roads of 0.93 g km⁻¹ (95% CI: 0.33 to 1.53).

The excess NO_x emissions over time from the fleet of affected vehicles is shown in Figure 1. The total VKT by affected vehicles between 2008 and 2015 is estimated to be 40.5 billion km (95% CI: 39.4 to 41.6), corresponding to total excess NO_x emissions (above the emissions estimated based on the FTP-75 drive cycle) of 36.7 million kg (95% CI: 12.3 to 61.2). The total VKT expected for these vehicles from 2016 onward is estimated to be 93.4 billion km (95% CI: 80.2 to 109.9), corresponding to total excess NO_x emissions of 82.0 million kg (95% CI: 25.8 to 145.6). If these vehicles were brought into compliance with EPA regulations by the end of 2016 assuming a constant rate of repairs beginning in January 2016, we would expect the avoided VKT and NO_x emissions to be 87.3 billion km (95% CI: 74.4 to 103.6) and 76.6 million kg (95% CI: 24.1 to 136.6), respectively. Figure 2 shows the spatial distribution of excess VW light duty vehicle diesel NO_x emissions in the US, aggregated over 2008–2015, under the assumption that the excess NO_x emissions from the affected VW vehicles have the same spatial distribution as total NO_x emissions from light duty diesel vehicles.

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The exposure and health impacts results (including the 95% CI) are shown in table 1, both integrated over the time period 2008–2015 and the forecast impacts taking a reference case of no recall. The total US population exposure of people over 30 years old to annual PM_2.5 and one-hour daily maximum ozone due to the VW excess NO_x emissions during the years 2008–2015 is calculated to be 0.78 million people-μg/m³ (95% CI: 0.075–2.0) and 2.6 million people-ppbv (95% CI: 0.85–4.7), respectively. This results in a total of 59 (95% CI: 9.7–150) premature deaths, 87% of which are attributable to the PM_2.5 exposure and 13% to ozone exposure. Extended results are shown in the supplementary material.

In addition to increased premature mortalities, this increase in the population exposure to PM_2.5 and ozone is estimated to result in 31 (95% CI: −38 to 170) cases of chronic bronchitis and 34 (95% CI: −1.9 to 100) hospital admissions. The hospital admissions include respiratory and cardiac hospital admissions. We calculate that there will be ∼120 000 minor restricted activity days (including work loss days), ∼210 000 lower respiratory symptom days, and ∼33 000 additional bronchodilator usage days.

Assuming no new sales of the affected models from September 2015 and that no retrofitting of the affected VW vehicles occurs, we forecast the corresponding additional mortality and morbidity impacts until these vehicles are eventually disposed of. An additional 140 (95% CI: 23 to 370) premature deaths are forecast due to these NO_x emissions in excess of EPA compliant levels. In terms of the morbidity impacts we forecast 86 (95% CI: −91 to 460) additional chronic bronchitis cases and 87 (95% CI: −10 to 270) hospital admissions. Future minor restricted activity days are estimated to be increased by ∼270 000, lower respiratory symptom days by ∼490 000 and increased bronchodilator usage days by ∼77 000. However, we find that a full recall, beginning in January 2016 and ending in December 2016, would avoid 130 (95% CI: 18–350) of the 140 attributable future mortalities.

Monetized mortality costs from 2008 until the end of 2015 due to the excess VW NO_x emissions are estimated at $450m (95% CI: $72m to $1.2 bn), while future costs if there is no recall (but no further sales from September 2015) are forecast to be $910m (95% CI: $140m to $2.5bn). The total cost that will occur without recall is therefore expected to be ∼$1.4bn in 2015 USD, or ∼$2800 per vehicle. However, assuming that vehicles are recalled at a constant rate from the start of 2016 and all devices replaced by the end of 2016, the total cost of future mortality impacts could be reduced by 93% to $61m. This is equal to 62% of the total projected costs from 2008 to 2040.

We estimate the public health impacts and associated costs of the alleged CAA violations by VW due to defeat devices being present in model year 2009–2015 light duty diesel vehicles with 2.0 litre engines. An estimated ∼36.7 million kg of excess NO_x emissions occur from 2008 to 2015. Our computed excess NO_x emissions in 2015 are equivalent to ∼1% of the total light duty vehicle emissions.

We estimate that ∼59 early deaths will be caused by 2008–2015 excess emissions with a monetized cost of ∼$450m. (Some of the estimated deaths caused by historical emissions have not yet occurred due to the cessation lag structure assumed.) Morbidity impacts include ∼31 cases of chronic bronchitis, ∼34 hospital admissions, ∼120 000 minor restricted activity days, ∼210 000 lower respiratory symptom days, and ∼33 000 days of increased bronchodilator usage.

We compare our results to non-peer reviewed estimates that have appeared in the press in the month following EPA's Notice of Violation (NYT 2015, Vox 2015, AP 2015). These estimates consider excess NO_x emissions that have occurred from 2008 to 2015 only. Results from these studies, which consider only PM_2.5 exposure, range from 16 to 106 additional cases of premature mortality due to excess VW vehicle NO_x emissions, compared to our median result of 51, and 95% confidence interval of 4.6 to 130 cases for PM_2.5 due to NO_x only. For 2015 specifically, AP (2015) estimated 5 to 15 additional cases of premature mortality due to PM_2.5 compared to our median estimate of 14 cases in 2015.

The air pollution mortalities may also be compared to fatal accidents, another major adverse consequence of vehicle use. We estimate the vehicles of concern will have driven 40.5 billion km from 2008 to the end of 2015, which implies approximately 280 accident fatalities over this time period for a fleet of the size of the VW light duty diesel vehicles considered here when using average US fatality rates (ITF 2014, NHTSA 2015). Per kilometer driven, the air pollution death rate from the excess NO_x emissions is therefore ∼20% of the accident fatality rate for an average US passenger car.

If no recall is made we estimate that the existing affected vehicles will cause ∼140 early deaths from 2016, with a monetized cost of ∼$910m. However, if the vehicles are recalled and brought into compliance by the end of 2016 then 93% of these deaths and 92% of the costs can be avoided. Including the early deaths that have occurred or are already bound to occur, this means that bringing the vehicles into compliance in 2016 can avert 66% of total early deaths and 62% of monetized costs.

We now note unquantified uncertainties in this study, some of which it may be possible to quantify with further research. The fleet model uses US average car purchasing, use, and disposal trends. These may vary for the specific VW application. Any effect of the December 2014 voluntary recall has not been factored in to damage estimates for 2015 due to lack of data. The air quality sensitivity matrices are based on 2005–2006 data, but may have changed over time since then. Toxicity of different PM species is assumed equal, consistent with EPA practice. However, as NO_x emissions impact ammonium nitrate most strongly, this would amplify the impact of any differential toxicity relative to the basket of urban PM for which CRFs are derived. Secondary organic aerosol impacts have not been included in the health impact assessment. The time after exposure changes when changes in risk occurs is an uncertainty, where we have applied a cessation lag structure to PM_2.5 mortality impacts consistent with EPA practice. This lag structure reduces valuations by <10%, suggesting it is not a major contributor to uncertainty. The basis upon which to estimate excess emissions is limited to the number of available measurements. In terms of monetizing changes in premature mortality, social cost uncertainty exists beyond the EPA-recommended uncertainty quantification applied in this study. Additional uncertainty may stem from unknown heterogeneity in the valuation of mortality risk changes by age cohort and health status, and from an unknown degree of bias from benefits transfer.

Finally, we note that there may have been environmental benefits of the defeat device that we have not computed. For example, the reduced use of diesel exhaust fluid in selective catalytic reduction may potentially have also reduced ammonia slip, and therefore associated PM_2.5 exposure and health impacts. In particular, Dedoussi and Barrett (2014) find that total PM_2.5 exposure attributable to road transportation is caused approximately equally by NO_x and NH₃ emissions.

In the 18 September 2015 EPA letter to VW, EPA alleged that VW violated section 203 (a)(3)(B) of the CAA, 42 U.S.C. § 7522(a)(3)(B), because of the 'defeat devices' (ECM software). VW admitted the presence of defeat devices. This can incur a civil penalty of up to $3750 (or $2750 for violations prior to 13 January 2009) for each occurrence (CAA § 205(a), 42 U.S.C §7524(a), 40 C.F.R. §19.4). We refer to this as the 'defeat devices fine'. EPA also alleged that VW violated section 203(a)(1) of the CAA, 42 U.S.C. § 7522(a)(1) because the defeat devices mean that the vehicles 'do not conform in all material respects to the vehicle specifications described in the applications for the certificates of conformity that purportedly cover them'. Violation of section 203(a)(1) of the CAA, 42 U.S.C. § 7522(a)(1) can result in a civil penalty of up to $37 500 (or $32 500 for violations prior to 13 January 2009) for each occurrence (CAA § 205(a), 42 U.S.C §7524(a), 40 C.F.R. §19.4). We refer to this latter as the 'COC fine'.

Here we do not consider the legal strength of EPA's argument, but comment on the public policy aspects. For a policy that aims to increase social welfare, the level of fines for a given violation should be considerably higher than the marginal social cost of that violation (Polinsky and Shavell 1992). Our estimates suggest that the median monetized mortality cost of the alleged violations over the lifetime of a vehicle is ∼$2800, and may be as high as $7500. This suggests that the maximum defeat device fine alone would not be significantly higher than the social cost of the violation. However, if the COC fine can be assessed, then total maximum fines of $41 250 would be at least five times higher than the social costs of the fine, and likely 15 times higher. This suggests that the CAA and its implementing regulations provide sufficient scope to assess a fine that exceeds the social costs, potentially by a significant factor. We note that this is based on the expected full social costs from those vehicles already sold if the issue were not corrected. If instead only actual damages to be incurred from excess emissions until the end of 2015 are considered, then the social cost per vehicle is estimated to be ∼$940, or at most $2600. In this case the same broad conclusions would apply—that the defeat devices fine alone could not significantly exceed the social costs, but that the defeat devices and COC fines in combination do provide such scope.

Finally, we note that while the 18 September 2015 EPA letter to VW cites ozone exposure resulting from the excess NO_x emissions as a concern, we find that 87% of deaths are due to fine particulate matter exposure, with 13% due to ozone.