Raphael E Stern
email: raphael.stern@vanderbilt.edu

Yuche Chen

Miles Churchill

Fangyu Wu

Maria Laura

Delle Monache

Benedetto Piccoli

Benjamin Seibold

Jonathan Sprinkle

Daniel B Work

Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic

Keywords: Vehicle emissions, Traffic waves, Autonomous vehicles, Traffic stability

It is anticipated that in the near future, the penetration rate of vehicles with some autonomous capabilities (e.g., adaptive cruise control, lane following, full automation, etc.) will increase on roadways. This work investigates the potential reduction of vehicular emissions caused by the whole traffic stream, when a small number of autonomous vehicles (e.g., 5% of the vehicle fleet) are designed to stabilize the traffic flow and dampen stop-and-go waves. To demonstrate this, vehicle velocity and acceleration data are collected from a series of field experiments that use a single autonomous-capable vehicle to dampen traffic waves on a circular ring road with 20 to 21 human-piloted vehicles. From the experimental data, vehicle emissions (hydrocarbons, carbon monoxide, carbon dioxide, and nitrogen oxides) are estimated using the MOVES emissions model. This work finds that vehicle emissions of the entire fleet may be reduced by between 15% (for carbon dioxide) and 73% (for nitrogen oxides) when stop-andgo waves are reduced or eliminated by the dampening action of the autonomous vehicle in the flow of human drivers. This is possible if a small fraction (∼5%) of vehicles are autonomous and designed to actively dampen traffic waves. However, these reductions in emissions apply

Introduction

It is expected that in the next few years vehicles will be developed with enhanced automation capabilities, and soon autonomous vehicles (AVs) will begin entering the vehicle fleet in small numbers. Even at low penetration rates (e.g., as low as 5% under ideal circumstances), these vehicles may be capable of dampening traffic waves caused by human driving behavior, resulting in smoother driving profiles (e.g., reduced acceleration/deceleration and speed variability) and consequently smoother traffic flow conditions compared to entirely human-piloted traffic [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. Smooth driving profiles result in lower fuel consumption and emissions, which are damaging to the environment and to human health [START_REF] Dockery | An association between air pollution and mortality in six us cities[END_REF].

The main focus of this article is to quantify the potential reduction of vehicle emissions of the total traffic flow when a small fraction of vehicles are automated and designed to dampen humangenerated stop-and-go traffic. This work uses experimental traffic data collected during a series of experiments in which a single autonomous vehicle is carefully controlled to dampen stop-and-go waves that arise when human-piloted traffic is sufficiently dense [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. The data used to assess the potential emissions impact of AVs stabilizing the traffic flow is found in the work by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. The primary contribution of the work by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF] was the design of controllers to dampen traffic waves.

The reduction in emissions of the total traffic flow are estimated from the experimental data using the MOtor Vehicle Emissions Model (MOVES) [START_REF]Moves2014 and moves2014a technical guidance: Using moves to prepare emission inventories for state implementation plans and transportation conformity[END_REF]. While the introduction of AVs at large penetration rates is certain to induce large effects such as changes in land use [START_REF] Kockelman | An assessment of autonomous vehicles: Traffic impacts and infrastructure needs[END_REF], travel demand [START_REF] Harb | Projecting travelers into a world of self-driving vehicles: Estimating travel behavior implications via a naturalistic experiment[END_REF], mode choice [START_REF] Lamondia | Shifts in long-distance travel mode due to automated vehicles: Statewide mode-shift simulation experiment and travel survey analysis[END_REF], and vehicle ownership [START_REF] Fagnant | Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations[END_REF], here we consider the impact that a small penetration rate of carefully controlled AVs can have on emissions due to the stability of the resulting traffic flow. By holding constant other large but longer term effects, we are able to highlight that the effects on the flow stability and consequently the emissions of the flow is itself significant (i.e., between 15% and 73% depending on the pollutant). To the best of our knowledge, this is the first of its kind study to use experimental data to demonstrate that that even at a low market penetration rate, the presence of AVs in the traffic flow may have the potential to reduce vehicle emissions of the overall traffic flow.

Related work on air quality and vehicle emissions

Motor vehicle emissions are a primary source of greenhouse gasses and contribute to global climate change [START_REF] Chapman | Transport and climate change: a review[END_REF]. These emissions are made worse by congestion and stop-and-go traffic [START_REF] Barth | Real-world carbon dioxide impacts of traffic congestion[END_REF]. Recently, a broad range of efforts have been made to curb vehicle emissions [START_REF] Shaheen | Reducing greenhouse emissions and fuel consumption: sustainable approaches for surface transportation[END_REF]. These efforts include vehicle improvements to increase the efficiency of combustion engines [START_REF] Reitz | Review of high efficiency and clean reactivity controlled compression ignition (rcci) combustion in internal combustion engines[END_REF][START_REF] Schäfer | Reduced emissions and fuel consumption in automobile engines[END_REF] and a transition to hybrid vehicles [START_REF] Fontaras | Experimental evaluation of hybrid vehicle fuel economy and pollutant emissions over real-world simulation driving cycles[END_REF][START_REF] De Haan | Reducing energy consumption in road transport through hybrid vehicles: investigation of rebound effects, and possible effects of tax rebates[END_REF]. It also includes traffic network management strategies that have focused on more efficient vehicle routing and traffic control [START_REF] Yang | Eco-cooperative adaptive cruise control at signalized intersections considering queue effects[END_REF][START_REF] Boriboonsomsin | Eco-routing navigation system based on multisource historical and real-time traffic information[END_REF][START_REF] Vreeswijk | Energy efficient traffic management and control-the ecomove approach and expected benefits[END_REF]. It is anticipated that AVs will also impact vehicle emissions, though it is unclear whether they will increase or decrease overall traffic emissions [START_REF] Chen | Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach[END_REF][START_REF] Stephens | Estimated bounds and important factors for fuel use and consumer costs of connected and automated vehicles[END_REF].

Direct measurement of vehicle emissions has been an area of intense research interest for several decades [START_REF] Schuetzle | Sampling of vehicle emissions for chemical analysis and biological testing[END_REF][START_REF] Tong | On-road motor vehicle emissions and fuel consumption in urban driving conditions[END_REF][START_REF] Frey | On-road measurement of vehicle tailpipe emissions using a portable instrument[END_REF][START_REF] Harrington | Fuel economy and motor vehicle emissions[END_REF]. However, due to the large cost associated with emissions measurement equipment, there has been a significant push [START_REF] Rakha | Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions[END_REF][START_REF] Rakha | Comparison of mobile5a, mobile6, vt-micro, and cmem models for estimating hot-stabilized light-duty gasoline vehicle emissions[END_REF][START_REF] An | Development of comprehensive modal emissions model: operating under hot-stabilized conditions[END_REF] to develop models that are able to estimate vehicle emissions based on easier to measure quantities such as vehicle speed and acceleration. These models estimate the vehicle emissions for a variety of pollutants (e.g., carbon dioxide, carbon monoxide, hydrocarbons, and nitrogen oxides).

These emissions models generally fall into one of two categories: aggregate and microscopic. Aggregate models are used to assess the environmental impact during project planning and use inputs such as average link-level speed and distance traveled to assess emissions. These models are often useful for assessing the impact of a large change in land use and traffic patterns on city or regional emissions. Popular aggregate models include the US Environmental Protection Agency (EPA) MOVES [START_REF]Moves2014 and moves2014a technical guidance: Using moves to prepare emission inventories for state implementation plans and transportation conformity[END_REF] and MOBILE6 [START_REF]User's Guide to MOBILE6.1 and MOBILE6[END_REF] as well as the European Environment Agency COPERT [START_REF] Kouridis | COPERT III: Computer Programme to Calculate Emissions from Road Transport: User Manual (version 2.1)[END_REF] and ARTEMIS [START_REF] André | The artemis european tools for estimating the transport pollutant emissions[END_REF] models. In contrast, microscopic emissions models use instantaneous measurements at the vehicle level to estimate emissions for a specific trip. Common microscopic emissions models include CMEM [START_REF] An | Development of comprehensive modal emissions model: operating under hot-stabilized conditions[END_REF], EMIT [START_REF] Cappiello | A statistical model of vehicle emissions and fuel consumption[END_REF], POLY [START_REF] Teng | Statistical microscale emission models incorporating acceleration and deceleration[END_REF], and VT-Micro [START_REF] Ahn | Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels[END_REF][START_REF] Rakha | Comparison of mobile5a, mobile6, vt-micro, and cmem models for estimating hot-stabilized light-duty gasoline vehicle emissions[END_REF][START_REF] Rakha | Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions[END_REF]. These models are typically used for estimating emissions of individual vehicles under some specified drive cycle or test procedure.

The MOVES model offers both an aggregate analysis as discussed above as well as a vehiclespecific power (VSP) based analysis that allows for instantaneous emissions modelling at the individual vehicle level. It is a state-of-the-science emission modeling system that estimates emissions for mobile sources for air pollutants. The aggregate approach provided by MOVES estimates vehicle emissions based on a mapping between average travel speed and emission rates. The VSP-based approach estimates vehicle emissions by utilizing relationship between engine load and vehicle emissions at a high time-resolution (1Hz), and is capable of assessing the influence of transient vehicle dynamics on engine load and emissions. Therefore, this approach is suitable to analyze vehicles emissions on an ad-hoc road link or segment as has been done in previous studies integrating vehicle travel profiles with the MOVES model to investigate air quality benefits of various traffic management or control technologies. For examples, see the works by [START_REF] Alam | A simulation of transit bus emissions along an urban corridor: Evaluating changes under various service improvement strategies[END_REF][START_REF] Xu | Estimating project-level vehicle emissions with vissim and moves-matrix[END_REF][START_REF] Wang | Characterizing near-road air pollution using local-scale emission and dispersion models and validation against in-situ measurements[END_REF], and [START_REF] Zhao | Computationally-efficient approaches to integrating the moves emissions model with traffic simulators[END_REF]. In these studies, vehicle travel profiles were obtained through either traffic simulation or realworld data collection. Furthermore, the MOVES model is regularly maintained and updated by EPA to reflect emission characteristics and improvements of emissions control techniques future vehicles. The analysis in this article relies on emissions estimates from a VSP analysis conducted in MOVES.

Related work on human-generated traffic waves and wave dampening

Traffic waves are a common phenomenon on urban highways. They have been shown to arise even in the absence of external bottlenecks such as merges or reduction in lanes. This has been experimentally demonstrated by [START_REF] Sugiyama | Traffic jams without bottlenecks-experimental evidence for the physical mechanism of the formation of a jam[END_REF], where 22 vehicles drive on a ring-road track as well as by [START_REF] S.-I. Tadaki | Phase transition in traffic jam experiment on a circuit[END_REF], who conduct similar experiments with between 10 and 40 vehicles. In both cases the vehicles begin with uniform speed and spacing, but the flow quickly devolves into a stop-and-go wave that travels upstream. These results have been independently validated in the experiments of [START_REF] Wu | Tracking vehicle trajectories and fuel rates in oscillatory traffic[END_REF] and [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF], which replicated the [START_REF] Sugiyama | Traffic jams without bottlenecks-experimental evidence for the physical mechanism of the formation of a jam[END_REF] experimental design, and confirmed that human driving behavior alone is sufficient to trigger traffic instabilities such as stop-and-go waves. These traffic instabilities are often referred to as phantom jams [START_REF] Kerner | Cluster effect in initially homogeneous traffic flow[END_REF] and can reduce the throughput and increase the fuel consumption of all vehicles on the roadway [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF].

The ability of AVs to reduce emissions has been considered by several simulation-based works reviewed below [START_REF] Liu | Anticipating the emissions impacts of autonomous vehicles using the MOVES model[END_REF][START_REF] Yang | A control theoretic formulation of green driving strategies based on intervehicle communications[END_REF]. [START_REF] Liu | Anticipating the emissions impacts of autonomous vehicles using the MOVES model[END_REF] modified a typical vehicle speed profile, and applied smoothing techniques to produce a plausible synthetic AV driving profile. The emissions estimates of both the original (oscillatory) and the smoothed velocity profile are compared using MOVES, and it is found that AV emissions may be substantially reduced. Compared to the present work, [START_REF] Liu | Anticipating the emissions impacts of autonomous vehicles using the MOVES model[END_REF] do not consider field data captured from vehicles. Moreover, they only considers the direct benefits of a smooth driving profile on the emissions of the AV, and do not capture the potential of AVs to also reduce the emissions of human piloted vehicles due to the smoother driving profile the AVs that may propagate to human drivers. [START_REF] Yang | A control theoretic formulation of green driving strategies based on intervehicle communications[END_REF] propose a control framework to provide advisory speeds to a subset of vehicles with the goal to smooth the traffic flow. The framework is modeled in simulation at varying AV market penetration rates ranging from 1% to 100%. Human-piloted traffic is simulated using a car-following model, and some vehicles implement a green driving strategy to dampen traffic waves a feedbackbased cooperative adaptive cruise control (CACC). The output from the simulation is analyzed using the CMEM model to estimate the fuel consumption and emissions. They find that at a 5% penetration rate of CACC-equipped vehicles in the traffic, hydrocarbon and carbon monoxide emissions are reduced by about 60%, while carbon monoxide emissions are reduced by as much as 73% and carbon monoxide emissions are reduced by 9%. In agreement with the experimental findings presented in this article, the simulation results of [START_REF] Yang | A control theoretic formulation of green driving strategies based on intervehicle communications[END_REF] indicate a reduction in emissions and fuel consumption is possible with the introduction of a small number (e.g., ∼5%) vehicles that actively dampen the traffic flow.

In contrast to enforcing specific speed profiles using AVs or ACC vehicles the ability of advisory speed limits to calm traffic and reduce emissions has also been studied. [START_REF] Servin | An energy and emissions impact evaluation of intelligent speed adaptation[END_REF] study the use of an advisory speed to smooth traffic and quantify the effect of the advisory speed on vehicle emissions of the traffic flow. Using traffic simulation and the CMEM model, they study varying rates of vehicles that follow the advisory speed, and determine that while the advisory speed may not significantly impact the travel time, it can have a significant impact in reducing vehicle emissions and fuel consumption. [START_REF] Servin | An energy and emissions impact evaluation of intelligent speed adaptation[END_REF] find a 35% reduction in carbon dioxide emissions, a 85% reduction in carbon monoxide emissions, a 69% reduction in hydrocarbon emissions, and a 74% reduction in nitrogen oxide emissions when all vehicles follow the advisory speed limit.

Contribution and outline

The works discussed above provide simulation results to give insight into the possible reduction in emissions that may result from even just a small portion of the vehicle fleet becoming autonomous. In contrast, this article uses experimental data from [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF], to analyze the impact of a single AV on the vehicle emissions of all of the vehicles in the traffic flow. Thus, this work goes beyond the previously mentioned simulation results since it uses experimental data to demonstrate the ability of AVs to reduce emissions. While the experimental data used for this study consider a uniform AV penetration rate of roughly 5%, higher AV penetration rates will likely be required for deployments of such systems on real freeways.

This article presents experimental evidence of the impact of oscillatory traffic on emissions, and quantifies the potential emissions reductions possible if the waves are mitigated using automated vehicles. This is accomplished by measuring vehicle trajectories in a series of experiments conducted by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF] on a circular ring road similar those of [START_REF] Sugiyama | Traffic jams without bottlenecks-experimental evidence for the physical mechanism of the formation of a jam[END_REF], and estimating the microscopic vehicle emissions using the VSP analysis in MOVES. The use of the ring road is experimentally beneficial since it allows for the isolation of car following behavior alone, without capturing confounding factors such as lane changing. Furthermore, three control strategies are implemented on a single AV in the flow of mostly human-piloted vehicles to control the flow and dampen traffic waves. The three control strategies give an indication of the variability of the potential benefits due to the implementation of the precise control law implemented by the AV. The reduction in emissions due to the control action of a single autonomous vehicle (∼5% of the traffic stream in the experiments) is presented in this article. To identify if the benefits observed are due to the vehicles used in the experiments, or if there will still be benefits in the future when the fleet mix changes, we consider four fleet scenarios that include the vehicle fleet tested in the experiments as well as projected fleets in the future. Full details of the four scenarios considered are presented in Table 1.

This work is part of a larger body of research that explores how traffic waves develop and the impacts of a small number of AVs actively dampening the traffic flow. This includes an article that presents a dataset with vehicle trajectories and a data collection methodology by [START_REF] Wu | Tracking vehicle trajectories and fuel rates in oscillatory traffic[END_REF] as well as work on the traffic flow implications of AVs [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. The main contribution of this work is to quantify the reduction in total traffic emissions possible when a single autonomous vehicle in a flow of 20 to 21 human-piloted vehicles is driven to dampen the traffic waves present in the flow. We find that driving a single autonomous vehicle in such a way as to dampen traffic waves can reduce traffic emissions of the entire traffic flow between 15% and 73%. The remainder of the article is organized as follows. We first present the design of the experiment to collect data using human drivers and an AV, and discuss strategies to dampen traffic waves using an AV as well as review methods for estimating vehicle emissions in Section 2. The results are presented in Section 3 and we conclude that a small number (∼5%) of AVs in the traffic flow may significantly reduce vehicle emissions for all vehicles on the roadway in Section 4.

Experimental setup

The vehicle trajectories used to estimate vehicle emissions in this article are obtained from the experiments described in [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. The experiments consist of between 21 and 22 vehicles driving on a single lane ring track, 82.8 m in diameter (260 m track length), located in a large parking lot in Tucson, AZ as seen in Figure 1. The experiments are designed to have a similar vehicle density as the experiment conducted by [START_REF] Sugiyama | Traffic jams without bottlenecks-experimental evidence for the physical mechanism of the formation of a jam[END_REF].

Vehicles are obtained from the University of Arizona motor pool, and are all recent model year passenger vehicles. At the beginning of each experiment, vehicles are spaced evenly on the track, with the number of vehicles between experiments varying from 21 to 22 vehicles depending on the desired density. Hired drivers are instructed to drive as they would in regular traffic, and to follow the vehicle in front of them safely. Individual tests last between 5 and 10 minutes, with breaks between to reset the track and allow for drivers to rest.

A single vehicle in the experiment, the University of Arizona's Cognitive and Autonomous Test (CAT) Vehicle, is a highly instrumented and actuated vehicle that can be switched from being human-piloted to autonomous. In each experiment, the CAT Vehicle begins under human control with a driver who is given the same instructions as all other drivers. Once traffic waves develop, the driving behavior of the CAT Vehicle is changed by either switching the CAT Vehicle into an autonomous driving mode (Experiments A and C), or by instructing the driver of the CAT Vehicle to drive with a specific velocity (Experiment B). These experiments allow data collection on traffic in which stop-and-go waves appear due to human driving behavior, and are subsequently dampened or eliminated via control of a single vehicle on the track, which represents a scenario in which roughly 5% of vehicles are either autonomous, or driving an a way that is substantially different from the human drivers. A brief overview of the experimental procedure is presented below.

1. Set vehicles on track at starting positions (even spacing) 2. Begin driving, all vehicles under human control 3. Intervene by instructing driver of CAT Vehicle to engage AV mode (Experiment A and C) or change driving behavior (Experiment B)

End experiment, all vehicles come to rest

The experiment is recorded by a 360-degree panoramic camera placed at the center of the circular track. Video recordings from the center 360-degree camera are used to extract vehicle trajectory data through computer vision algorithms. The computer vision algorithm uses background subtraction and pixel clustering, and template tracking to identify the position of each vehicle on the track in each frame. The resulting vehicle trajectories are verified against human-labeled data to an accuracy of 0.11 m. The full data are provided by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF], and details of the algorithms used to process the data are provided by [START_REF] Wu | Tracking vehicle trajectories and fuel rates in oscillatory traffic[END_REF].

Dampening traffic waves

A total of three control approaches implemented on the CAT Vehicle are tested. All controllers share the goal to stabilize the entire traffic flow such that all vehicles, including the human-piloted vehicles, drive with as little velocity variation as possible. The general strategy employed by all three methods is to command the AV to drive with a properly selected velocity, and to drive with as uniform of a velocity profile as possible while still operating the vehicle safely. If this uniform velocity is close to the equilibrium velocity [START_REF] Cui | Stabilizing traffic flow via a single autonomous vehicle: Possibilities and limitations[END_REF] of the traffic flow, the AV allows a small gap to open up as the vehicles race away when they exit a traffic wave, and is able to use this gap to avoid braking when the vehicles in front enter a traffic wave. While the equilibrium velocity may depend on a number of factors, practically, the local average speed may be used as a good approximation of the equilibrium velocity. This allows the AV to approach the lead vehicle just as it is leaving a traffic wave, and thus dampens the wave. The controllers that are run on the CAT Vehicle are briefly summarized in this section and a detailed explanation of the controllers used on the CAT Vehicle to dampen traffic waves is provided by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF].

Controller A: FollowerStopper controller

The premise of this controller is to command exactly the desired velocity whenever safe (i.e., as in a standard cruise controller), but to command a suitable lower velocity whenever safety requires, e.g., to avoid colliding with the lead vehicle. Using the gap to the lead vehicle (defined as the distance from the front bumper of the AV and the rear bumper of the lead vehicle) and the velocity difference between the lead vehicle and the AV, three regions are defined: (i) a safe region, where the AV drives at the desired speed, (ii) a stopping region, where a zero velocity is commanded, and (iii) an adaptation region, where some average of desired and lead vehicle velocity is commanded. These regions allow the AV to select a safe velocity, while driving as smoothly as possible. The full details on the calibration and implementation of the FollowerStopper controller are provided in the work by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF].

Controller B: Traffic control with a trained human driver

One experiment is conducted where a trained human driver is instructed to drive at a specified speed and only deviate when safety mandates. The speed at which the driver is instructed to drive is computed externally by experimental staff observing the experiment. It is computed as the total length of the track divided by the time for the CAT Vehicle to make one complete pass around the track. This speed is then communicated to the driver of the CAT Vehicle via two-way radio.

Controller C: PI controller with saturation

The idea behind this controller is that the CAT Vehicle may estimate the average speed of the vehicles in front, and drive as close to that average speed as safely possible. When stop-and-go waves are present, it allows a gap to open up in front of the CAT Vehicle when the lead vehicle accelerates, which is then closed when the lead vehicle decelerates as it enters the next wave. An estimate of the average speed required by the controller can be obtained through connectivity with other AVs in the flow ahead of the CAT Vehicle. The controller determines a command velocity for the AV following a standard proportional integral (PI) control logic where the deviation from the average speed is treated as the error signal in the PI controller. More information about the general structure of PI controllers can be found in [START_REF] Åström | Feedback Systems: An Introduction for Scientists and Engineers[END_REF]. The PI controller is modified to include a saturation effect, in which the CAT Vehicle should command the velocity of the lead vehicle for safety reasons when the gap is small.

Estimating vehicle emissions

In this work, the operating mode based project level analysis module in MOVES is used to evaluate vehicle emissions and assess emission benefits of replacing roughly 5% of the traffic flow with AVs that are specifically designed to stabilize the traffic flow. We adopt MOVES since the EPA regularly maintains and updates the MOVES model to reflect emissions characteristics and improvements in emission control technologies in new and future vehicles. This feature of MOVES is important since the scenarios we consider include the impacts that AVs may have both currently and in future years.

The MOVES model predicts vehicle emissions based on five different parameter values. These parameters are the humidity and temperature, the road link that the modelling is being conducted on, the vehicle fleet mix, the vehicle fleet age distribution, and the VSP distribution during the drive cycle. These parameters are explained in more detail below.

1. Humidity and Temperature: Meteorological conditions influence vehicle engine performance and thus will effect vehicle emissions. The simulation time and location are set to the same as the experiment time and experiment location: Tucson, Arizona in July. This takes into account average high temperatures and precipitation in Tucson, Arizona in July.

2. Road Link: We assume each experiment is one road link. The length of each road link is computed as the average distance each vehicle drove during the experiment. It is important to note that since the total emissions on that road link are normalized by the length of the road link, the link length does not impact emission rates per distance, but does impact the total emissions of the fleet on the link. 4. Vehicle fleet age distribution: Emissions of vehicles vary based on age of vehicles due to an effect known as emissions deterioration [START_REF] Zachariadis | The effect of age and technological change on motor vehicle emissions[END_REF]. Specifically, emissions per distance tend to increase as vehicles age. Therefore, it is important to know the age distribution of vehicle fleet to account for aging effect in our emission analysis. We adopted the default age distribution in MOVES, which specifies vehicle age distribution projections through the year 2050. The MOVES model assumes a distribution between 0 and 30 years of age.

5. VSP Distribution: For each experiment, the instantaneous velocity and acceleration is obtained from the experiments by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. Using these measurements, the corresponding VSP is calculated at 1 Hz VSP and then aggregated to obtain VSP distribution over time. For typical light-duty vehicles and trucks, VSP in units of kW/ton can be approximated using [START_REF] Ahn | Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels[END_REF] where v is the vehicle velocity in mph, a is the vehicle acceleration in mph/s. This equation models work required due to acceleration of the mass of the vehicle, rolling friction, and air drag, but does not consider non-driving related energy consumption such as air conditioning.

VSP ≈ φ 1 va + φ 2 v + φ 3 v 3 , (1)
where φ 1 = 0.22, φ 2 = 0.0954, φ 3 = 0.0000272 [START_REF] Jimenez | Vehicle specific power: A useful parameter for remote sensing and emission studies[END_REF]. Using this approximation for all vehicles neglects small differences in air resistance due to vehicle form or rolling resistance due to tire pressure.

Vehicle fleet scenarios considered

The effect on vehicle emissions of a small number of AVs in the traffic flow will depend to a large extent on the vehicle fleet on the road, and the levels of emissions they produce. Therefore, a total of four scenarios with different fleets are considered. These scenarios use the same experimentally-collected vehicle trajectories for all scenarios and represent different vehicle fleet mixes as described below. These scenarios are detailed in Table

Experimental results

For completeness we briefly describe the three traffic experiments that are conducted. A full description of the experimental setup is provided in Section 2. The goal of the experiments is to demonstrate the ability of a single AV to dampen traffic waves that naturally occur from human driving behavior. A total of three wave-dampening strategies are used in the three ring road experiments.

The vehicles can be seen on the track at various stages in the experiment in Figure 1. Specifically, in Figure 1a, the vehicles are observed with uniform spacing at the start of the experiment. After 95 s, a stop-and-go wave is present in the traffic flow and the configuration of vehicles on the ring road is seen in Figure 1b. Finally, when the AV is actively dampening the traffic flow, the uniform traffic flow 328 s after the start of the experiment is seen in Figure 1c.

In Experiment A, the FollowerStopper controller is used to dampen traffic waves, while in Experiment B a human driver is instructed to drive the CAT Vehicle at a constant speed, and in Experiment C a PI controller with saturation is used to dampen traffic waves. In all experiments, the result of the control algorithm is to reduce the velocity standard deviation as seen in the vehicle trajectories in Figures 2,3 and nitrogen oxides (NO x ) are obtained through MOVES for each of the three experiments. While there are several controller periods tested in each experiment, for the purposes of this analysis only the period when waves are present and the period where the AV controller is most effective are used.

MOVES operating mode distribution

The MOVES analysis is based on the percentage of time in each drive schedule that is spent in a particular operating mode. These operating modes are specified by defining specific ranges of speed and VSP. Consequently, it is possible to classify each moment of the drive schedule as one of a number of discrete operating modes, which correspond to vehicle emissions. Specifically, the percent of time spent in each operating mode over the course of a drive schedule along with the total distance traveled and the average travel speed determine the vehicle emissions estimate. The operating modes are outlined in the MOVES documentation [START_REF]Moves2014 and moves2014a technical guidance: Using moves to prepare emission inventories for state implementation plans and transportation conformity[END_REF]. The operating mode distributions for each experiment are separated for the period when traffic waves are present and the period when the AV is actively dampening the traffic flow, and plotted in Figures 5a through 5c. A greater percentage of the time is spent in higher operating modes when waves are present than when the AV is actively dampening the waves.

As seen in Figure 5a through Figure 5c, when comparing the MOVES operating mode distribution under uncontrolled conditions when traffic waves are present (left) to the operating mode distribution when the CAT Vehicle is actively dampening the traffic flow (right), the operating mode distribution shifts from higher operating modes to lower operating modes. These lower operating modes are indicative of lower engine demand, and thus generally correspond to lower vehicle emissions. This is because there is less positive acceleration across the vehicle fleet when the CAT Vehicle is actively dampening the traffic flow, and thus lower VSP. Note that these distributions do not change under the scenarios considered.

Scenario 1: Estimating emissions of the experiment fleet

We estimate the emissions of the experimental fleet as tested in three different experiments. We consider the reduction of emissions when a single vehicle in the traffic flow is an AV, and consider three different control strategies, one in each of the three experiments. In Experiment A, the average per-vehicle emissions in the experiment (Scenario 1) in the presence of traffic waves for hydrocarbons is 0.010 g/mi, for carbon monoxide are 2.430 g/mi, for nitrogen oxides are 0.107 g/mi, and for carbon dioxide are 1245 g/mi. When the traffic is under the control of the AV in the control period when the set speed is 7.50 m/s, the hydrocarbon emissions are reduced by 51.5% to 0.005 g/mi, the carbon monoxide emissions are reduced by 39.1% to 1.481 g/mi, the nitrogen oxide emissions are reduced by 73.5% 0.028 g/mi, and the carbon dioxide emissions are reduced by 30.7% to 863.1 g/km.

In Experiment B, the average per-vehicle emissions when a traffic wave is present in the vehicle fleet tested in the experiment (Scenario 1) are as follows: hydrocarbon emissions are 0.010 g/mi, carbon monoxide emissions are 2.380 g/mi, nitrogen oxide emissions are 0.095 and the carbon dioxide emissions are 1260 g/mi. When the AV is actively dampening the traffic waves, the hydrocarbon emissions are reduced by 38.7% to 0.006 g/mi, the carbon monoxide emissions are reduced by 36.1% to 1.520 g/mi, the nitrogen oxide emissions are reduced by 60.8% to 0.037 g/mi, and the carbon dioxide emissions are reduced by 27.2% to 916.0 g/mi.

In Experiment C, the average per-vehicle emissions in the presence of a traffic wave for Scenario 1 is 0.101 g/mi for hydrocarbons, 2.420 g/mi for carbon monoxide, 0.101 g/mi for nitrogen oxides, and 1240 g/mi for carbon dioxide. When the AV is actively dampening the traffic flow hydrocarbon emissions are reduced by 63.8% to 0.006 g/mi, carbon monoxide emissions are reduced by 26.9% to 1.770 g/mi, nitrogen oxide emissions are reduced by 63.3% to 0.037 g/mi, and carbon dioxide emissions are reduced by 14.8% to 1060 g/mi. The results can also be seen in Tables 3 through 6.

Scenario 2: Emissions of the 2016 vehicle fleet

In Scenario 2 which represents the average US fleet in 2016, the average per-vehicle emissions in Experiment A in the presence of traffic waves are 0.191 g/mi HC, 7.843 g/mi CO, 0.933 g/mi NO x , and 1413 g/mi CO2. When the AV is actively dampening the traffic flow, the HC emissions are reduced by 38.4% to 0.125 g/mi, the CO emissions are reduced by 38.1% to 4.854 g/mi, the NO x emissions are reduced by 64% to 0.336 g/mi, and the CO2 emissions are reduced by 31.4% to 970 g/mi.

In Experiment B, in the presence of a traffic wave the average per-vehicle HC emissions are 0.189 g/mi, the average carbon monoxide emissions are 7.740 g/mi, the average nitrogen oxide emissions are 0.872 g/mi, and the average carbon dioxide emissions are 1420 g/mi. When the AV is actively dampening the traffic waves hydrocarbon emissions are reduced by 28.0% to 0.136 g/mi, carbon monoxide emissions are reduced by 34.6% to 5.060 g/mi, nitrogen oxide emissions are reduced by 53.0% to 0.411 g/mi, and carbon dioxide emissions are reduced by 27.5% to 1030 g/mi.

In Experiment C, when traffic waves are present the average per-vehicle hydrocarbon emissions are 0.191 g/mi, the carbon monoxide emissions are 7.760 g/mi, the nitrogen oxide emissions are 0.884 g/mi, and the carbon dioxide emissions are 1410 g/mi. When the AV is actively dampening traffic waves hydrocarbon emissions are reduced by 17.8% to 0.157 g/mi, carbon monoxide emissions are reduced by 24.7% to 5.840 g/mi, nitrogen oxide emissions are reduced by 52.0% to 0.424 g/mi, and carbon dioxide emissions are reduced by 15.5% to 1190 g/mi. The results can also be seen in Tables 3 through 6.

Figures 6a through 6d show that the hydrocarbon, carbon monoxide, and nitrogen oxide emissions for Scenario 2 are substantially higher than in Scenario 1. This is because the vehicle fleet used in Scenario 1 was mostly 2016 and 2015 model year vehicles (full details on the vehicles used can be found in [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]) while Scenario 2 is composed of the US age distribution of vehicles in 2016. Since most of the vehicles are less than three years old, very little emissions deterioration has occurred, and the estimated emissions for that scenario were very low. Scenarios 2 through 4 use projected fleet age distributions, which assume a mix of new and old vehicles on the road. In the scenarios tested, the older vehicles may be polluting more than the newer vehicles due to emissions deterioration. This is not the case in the vehicle fleet tested in the 2016 experiment since most vehicles are new.

Scenarios 3 and 4: Emissions of projected future fleets

The projected future vehicle fleet scenarios presented in Table 1 are also considered. The emissions for these scenarios are presented below. In Scenario 3, the projected 2030 US vehicle fleet us used for estimation. In Experiment A, B, and C the hydrocarbon emissions in the presence of traffic waves are 0.019 g/mi, 0.018 g/mi, and 0.019 g/mi, respectively. When the Autonomous vehicle is actively dampening the traffic waves, the reductions in hydrocarbon emissions for Experiments A, B, and C are 38.4% to 0.010 g/mi, 37.8% to 0.011 g/mi, and 35.1% to 0.012 g/mi, respectively. Similarly, for Experiments A, B, and C, the carbon monoxide emissions in the presence of traffic waves are 2.984 g/mi, 2.920 g/mi, and 2.960 g/mi, respectively. When the AV is actively dampening the traffic flow, carbon monoxide emissions are reduced by 39.3%, 36.1%, and 27.1% to 1.812 g/mi, 1.860 g/mi, and 2.160 g/mi for Experiments A, B, and C, respectively. In the presence of a traffic wave, the nitrogen oxide emissions for Experiments A, B, and C are 0.114 g/mi, 0.101 g/mi, and 0.107 g/mi, respectively. By actively dampening the traffic flow, the AV is able to reduce the average vehicle emissions for Experiments A, B, and C by 72.5%, 59.8%, and 61.0% to 0.031 g/mi, 0.041 g/mi, and 0.041 g/mi, respectively. Finally, when traffic waves are present, the average per-vehicle emissions of carbon dioxide for Experiments A, B, and C are 1019 g/mi, 1030 g/mi, and 1010 g/mi, respectively. When the AV is actively dampening the traffic flow, these are reduced by 31.0%, 27.3%, and 15.1% to 703.0 g/mi, 748.0 g/mi, and 15.1 g/mi, respectively.

In Scenario 4, the projected average US vehicle fleet for 2050 is used for estimation. However, a high electric vehicle adoption rate of 80% electric vehicles is also assumed. This is to test how significant the impact of AVs dampening the traffic flow will be on emissions with a high vehicle fleet electrification rate. When traffic waves are present, the average per-vehicle hydrocarbon emissions rate for Experiments A, B, and C, respectively are 0.003 g/mi, 0.003 g/mi, and 0.003 g/mi. When the AV is actively dampening the traffic flow, these are reduced by 51.6%, 38.7%, and 36.8% to 0.002 g/mi, 0.002 g/mi, and 0.002 g/mi, respectively. The carbon monoxide emissions in the presence of traffic waves are 0.503 g/mi, 0.491 g/mi, and 0.500 g/mi, for Experiments A, B, and C, respectively. When the AV is dampening the traffic waves, the carbon monoxide emissions are reduced by 39.3%, 36.2%, and 27.1% to 0.305 g/mi, 0.314 g/mi, and 0.364 g/mi, for Experiments A, B, and C, respectively. When a traffic wave is present, the average per-vehicle nitrogen oxide emissions for Experiments A, B, and C are 0.006 g/mi, 0.005 g/mi, and 0.005 g/mi. These are reduced by 73.6%, 60.8%, and 63.4% to 0.002 g/mi, 0.002 g/mi, and 0.002 g/mi, for experiments A, B, and C, respectively, when the AV is actively dampening traffic waves. Finally, in when traffic waves are present the carbon dioxide emissions are 246.7 g/mi, 249.0 g/mi, and 245.0 g/mi, for Experiments A, B, and C, respectively. When the AV is actively dampening the traffic flow, the carbon dioxide emissions are reduced by 31.0%, 27.3% and 15.1% to 179.2 g/mi, 181.0 g/mi, and 208.0 g/mi, for Experiments A, B, and C, respectively. The results for Scenarios 3 and 4 can also be seen in Tables 3 through 6.

The trend observed in Figures 6a through 6d is a decrease in per-vehicle emissions both when waves are present and when the AV is actively dampening the traffic waves. This reflects the anticipated stringent emissions requirements in the future. When considering Scenario 4 where the projected 2050 vehicle fleet with an assumed 80% EV market penetration rate is used for estimation, the reduction in average per-vehicle emissions rate has two sources: the increased efficiency of combustion engines and the electrification of the fleet, which in Scenario 4 assumes that 80% of the vehicles have zero tailpipe emissions. This is also true to a lesser extent in Scenario 3 where the 2030 vehicle fleet with a projected 3.4% EV penetration rate is assumed. The experimental results for average per-vehicle emissions rates in Tables 4 through 6 are also represented graphically in Figure 6. These results show that in all three experiments (A, B, and C) the emissions rate in the presence of a wave is very similar. This indicates that in all three experiments, similar traffic conditions are observed as seen in Figures 2 through 4. Furthermore, while the details of the specific controllers used in Experiments A, B, and C are different, they all have a similar effect on reducing the emissions rate in each of the thee experiments for each fleet scenario, indicating that there is a variety of possible controllers that may be able to achieve similar reductions in vehicle emissions caused by stop-and-go traffic.

When looking at the emissions reduction, we observe that the emissions reduction for each emissions category is approximately the same across the different scenarios for each experiment. Thus, while the overall emissions are expected to reduce dramatically (Figures 6a through 6d), the impact that a low penetration rate of autonomous vehicles has on the traffic stream remains approximately unchanged.

Interestingly, as seen in Table 3, the percent reduction in NO x emissions is substantially greater than the percent reduction in other quantities across all scenarios and experiments. This is because high NO x emissions are correlated with transient engine behavior, while CO and CO 2 emissions are correlated more closely to the fuel burn rate [START_REF] Mei | Comparative study of the transient emission profiles of a light-duty vehicle powered by petrodiesel and a light-duty vehicle powered by biodiesel (jatropha curcas)[END_REF].

Conclusions

The results presented in this article indicate that a single autonomous vehicle can have a substantial impact on reducing traffic emissions if properly controlled to dampen traffic waves and stabilize the traffic flow. While a single autonomous vehicle out of 22 vehicles is autonomous in the experiments presented in this article, this should be thought of as a uniform AV penetration rate of roughly 5%. These results represent the reduction in vehicle emissions that is possible due to the smoother traffic flow that may result from AVs dampening traffic waves, and the impact this has on the underlying traffic dynamics. These results are based on experimental data that is collected in a controlled environment to isolate the wave-amplifying car following behavior of human drivers in the absence of lane changing or other external factors. [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. Reduction (R) is computed as difference between period with waves and period when the AV is actively dampening traffic waves and smoothing the traffic flow. Note that the percent reduction is computed based on the projected emissions, while the emissions rates are only presented to at most three decimal places in this table. [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. Reduction (R) is computed as difference between period with waves and period when the AV is actively dampening traffic waves and smoothing the traffic flow. Note that the percent reduction is computed based on the projected emissions, while the emissions rates are only presented to at most three decimal places in this table.

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Pollutant W (g/mi) C (g/mi) R (%) W (g/mi) C (g/mi) R (%) W (g/mi) C (g/mi) R (%) W (g/mi) C (g/mi) R (
Putting these numbers into perspective, the impact that vehicle electrification has on the overall vehicle (tailpipe) emissions is roughly proportional to the number of combustion engine vehicles being replaced with electric vehicles. This is because, from a modelling perspective, EVs do not impact source emissions, and MOVES does not include power plant emissions to produce the electricity elsewhere. By replacing combustion engine vehicles with human-piloted electric vehicles, source polluters are being replaced with vehicles that have no tailpipe emissions but still contribute to congestion. Note that this does not include a possible increase in vehicle miles traveled by electric vehicles because of the perceived reduced environmental impact or decrease in costs that consumers may feel when driving electric vehicles [START_REF] Vivanco | The remarkable environmental rebound effect of electric cars: a microeconomic approach[END_REF].

Thus, when looking at pollutants such as NO x , replacing 5% of the vehicle fleet with properly-designed AVs has the same impact on emissions reduction as replacing roughly 75% of vehicles with electric vehicles. Importantly though, this only applies to driving conditions under which stop-and-go waves are present.

This work is limited in that it considers the emissions during stop-and-go traffic waves. These traffic waves are a common phenomenon in urban highway traffic but may only represent a small percentage of overall vehicle miles travelled by a typical driver. Therefore, it is unlikely that such significant reductions in emissions would be realized across an entire drive. However, there are additional benefits such as smoother driving and fewer braking events that are realized, even with partial vehicle fleet automation [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. Additionally, the experiment was limited to a single AV and a limited track size. Therefore, only a small range of AV penetration rates could be experimentally tested. Regardless, the findings of this article indicate that significant reductions in vehicle emissions may be possible if only a small number of vehicles on the road are replaced with more technologically-advanced vehicles.

A limitation of the experimental setup used in this article is that there is only one lane of traffic, which is not typical for highway settings. However, importantly, the types of instabilities observed in this experimental setting are the same types of instabilities often observed on highways. Factors such as lane changing and merging, which are present on highways, may make it more difficult for an AV to dampen traffic waves. However, since the instabilities are the same types of instabilities observed in this experimental setting, the experimental results obtained in this experiment are applicable, though a higher AV penetration rate may ultimately be required to obtain the same reductions in emissions observed in this article.

Generally speaking, the FollowerStopper controller (Experiment A) is the most effective controller since it is able to reduce the vehicle emissions the most. This finding is consistent with the results reported by [START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF] where the FollowerStopper controller was able to achieve the greatest reduction in velocity standard deviation.

While the overall per-vehicle emissions are expected to decrease over the next several decades as vehicles are modernized and more stringent emissions standards are imposed, the impact that autonomous vehicles can have on reducing emissions remains relatively constant. This article finds that at a penetration rate of roughly 5%, AVs will be able to reduce hydrocarbon emissions by as much as 51.6%, carbon monoxide emissions by as much as 39.3%, nitrogen oxide emissions by as much as 73.6%, and carbon dioxide emissions by as much as 31.0% when comparing the smooth traffic under the control of AVs to the oscillatory traffic conditions observed with only human drivers. These AVs will enter our roadways in the near future regardless, so if properly designed to dampen traffic waves, this reduction in emissions comes at relatively little additional cost. Moreover, this reduction in emissions is not only realized by the AV, but manifested over the entire vehicle fleet, since all vehicles in the flow experience smoother driving behavior when the AV is actively dampening traffic waves.

  (a) Alignment of vehicles at start of Experiment A. (b) Alignment of vehicles 95 seconds into Experiment A when wave is present in back right. (c) Alignment of vehicles 328 seconds into Experiment A when the CAT Vehicle is actively dampening the wave.

Figure 1 :

 1 Figure 1: Experimental track with 21 vehicles during Experiment A. The position of the CAT Vehicle is shown with a red arrow.

3 .

 3 Vehicle fleet mix: In this study, we only consider the light-duty vehicle fleet, which consists of sedans and light-duty trucks (e.g., SUVs and small pickup trucks). These two types of vehicle are different in size, weight, engine capacity, etc., and therefore have different emission characteristics. The vehicle fleet mix used in this study is either the actual fleet mix used to experimentally collect the vehicle trajectories (Scenario 1 discussed in Section 2.4) or based on fleet mix projections (Scenarios 2-4) as discussed in Section 2.4.

Figure 2 :Figure 3 :

 23 Figure 2: Vehicle trajectories for all vehicles in Experiment A color coded by, velocity (top), and acceleration (bottom). The time under which traffic waves are present is shaded blue, while the control period used for analysis, where the AV is actively dampening traffic waves is shaded red.

Figure 4 :

 4 Figure 4: Vehicle trajectories for all vehicles in Experiment C color coded by, velocity (top), and acceleration (bottom). The time under which traffic waves are present is shaded blue, while the control period used for analysis, where the AV is actively dampening traffic waves is shaded red.

  (a) Distribution of MOVES operating modes in Experiment A. (b) Distribution of MOVES operating modes in Experiment B. (c) Distribution of MOVES operating modes in Experiment C.

Figure 5 :

 5 Figure 5: Distribution of MOVES operating modes in each of the three experiments when waves are present (left) and when the AV is actively dampening the traffic waves (right). Note that while the MOVES model defines operating modes above 25, these operating modes are not observed in any of the experiments, and thus left off of these figures for plotting purposes. Additionally, note that these distributions are applicable to all scenarios.

  (a) Hydrocarbon emissions rate. (b) Carbon monoxide emissions rate. (c) Nitrogen oxides emissions rate. (d) Carbon dioxide emissions rate.

Figure 6 :

 6 Figure 6: Emissions results for all four emissions categories for each experiment and fleet scenario considered showing both the emissions rate when waves are present and emissions rate when the traffic is under the control of the autonomous vehicle.

  (a) Emissions reductions for all four fleet scenarios for Experiment A. (b) Emissions reductions for all four fleet scenarios for Experiment B. (c) Emissions reductions for all four fleet scenarios for Experiment C.

Figure 7 :

 7 Figure 7: Emissions reduction for each experiment by scenario. Lighter shaded bar represents the average per-vehicle emissions rate in the presence of traffic waves for a specific experiment while the darker bar represents the reduced average per-vehicle emissions rate when the AV is actively dampening the traffic. Note the generally consistent reduction in emissions for each emissions category across the four fleet scenarios considered.

Table 1 :

 1 1. Four scenarios considered including 1) the vehicle fleet tested in the 2016 experiment, 2) US national vehicle fleet in 2016, 3) the projected 2030 vehicle fleet, and 4) the projected 2050 vehicle fleet, assuming an 80% vehicle electrification, spread evenly across both vehicle classes.In Scenario 1, the vehicle fleet used during the experiments is considered, where 45.5% of the vehicles are sedans, 54.5% are SUVs, and no electric vehicles (EVs) are present. In Scenario 2, the average 2016 vehicle fleet in the US is considered where 51.2% of the vehicles are sedans, 48.8% are SUVs, and a negligible percentage of the vehicles are electrified. Scenario 3 considers the projected 2030 vehicle fleet in the US where 44.5% of the vehicles are sedans and an estimated 3.4% of the vehicle fleet is electrified, distributed evenly between sedans and SUVs, as projected by the US Energy Information Administration's 2017 Annual Energy Outlook[START_REF]Annual Energy Outlook[END_REF]. Finally, in Scenario 4 the projected 2050 vehicle fleet in the US is considered with 42.8% sedans assuming a very high electric vehicle adoption rate of 80%. This fleet also takes into account the vehicle age distribution for Scenarios 2, 3, and 4, while the vehicle age distribution in Scenario 1 is obtained from the vehicle fleet used to conduct the experiments.

	Scenario Electric vehicle fraction (%) Sedan fraction (%) Small truck fraction (%)
	1	0.0	45.5	54.5
	2	0.0	51.2	48.8
	3	3.4	44.5	55.6
	4	80.0	42.8	57.2

Table 2 :

 2 , and 4 for Experiments A, B, and C, respectively. Summary statistics of each experiment are provided in Table2. Experiment summary statistics.

	Experiment Total time (s) Avg. speed no control (m/s) Avg. speed with control (m/s)
	A	567		6.33			7.04
	B	409		6.29			6.86
	C	413		6.05			5.57
		Waves start	Control period 1	Control period 2	Control period 3	Control period 4	Control period 5	End control
		Waves start	Control period 1	Control period 2	Control period 3	Control period 4	Control period 5	End control

Vehicle emissions rates for hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO 2 ),

Table 3 :

 3 Percent reduction in emissions from period with waves to period when the AV is actively dampening the traffic.

			Experiment A			Experiment B			Experiment C
			(% reduction)			(% reduction)			(% reduction)
	Scenario:	1	2	3	4	1	2	3	4	1	2	3	4
	HC	51.5 38.4 50.4 51.6 38.7 28.0 37.8 38.7 36.8 17.8 35.1 36.8
	CO	39.1 38.1 39.3 39.3 36.1 34.6 36.1 36.1 26.9 24.7 27.1 27.1
	NO x	73.5 64.0 72.5 73.6 60.8 52.9 59.8 60.8 63.3 52.0 61.9 63.4
	CO 2	30.7 31.4 31.0 31.0 27.2 27.5 27.3 27.3 14.8 15.5 15.1 15.1

Table 4 :

 4 Experimental results from Experiment A for period with waves (W) and best control period (C) as identified in[START_REF] Stern | Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments[END_REF]. Reduction (R) is computed as difference between period with waves and period when the AV is actively dampening traffic waves and smoothing the traffic flow. Note that the percent reduction is computed based on the projected emissions, while the emissions rates are only presented to at most three decimal places in this table.

		Scenario 1	Scenario 2	Scenario 3	Scenario 4
	Pollutant	W (g/mi)	C (g/mi)	R (%)	W (g/mi)	C (g/mi)	R (%)	W (g/mi)	C (g/mi)	R (%)	W (g/mi)	C (g/mi)	R (%)
	HC	0.010 0.005 51.5 0.191 0.125 38.4 0.019 0.010 50.4 0.003 0.002 51.6
	CO	2.430 1.481 39.1 7.843 4.854 38.1 2.984 1.812 39.3 0.503 0.305 39.3
	NO x	0.107 0.028 73.5 0.933 0.336 64.0 0.114 0.031 72.5 0.006 0.002 73.6
	CO 2	1246 863.1 30.7 1413 970.0 31.4 1019 703.0 31.0 246.7 179.2 31.0
		Scenario 1	Scenario 2	Scenario 3	Scenario 4
	Pollutant	W (g/mi)	C (g/mi)	R (%)	W (g/mi)	C (g/mi)	R (%)	W (g/mi)	C (g/mi)	R (%)	W (g/mi)	C (g/mi)	R (%)
	HC	0.010 0.006 38.7 0.189 0.136 28.0 0.018 0.011 37.8 0.003 0.002 38.7
	CO	2.380 1.520 36.1 7.740 5.060 34.6 2.920 1.860 36.1 0.491 0.314 36.2
	NO x	0.095 0.037 60.8 0.872 0.411 52.9 0.101 0.041 59.8 0.005 0.002 60.8
	CO 2	1260 916.0 27.2 1420 1030 27.5 1030 748.0 27.3 249.0 181.0 27.3

Table 5 :

 5 Experimental results from Experiment B for period with waves (W) and best control period (C) as identified in

Table 6 :

 6 Experimental results from Experiment C for period with waves (W) and best control period (C) as identified in

	%)

MethodologyIn this section, the methodology used in the experimental design and execution, as well as data analysis is discussed. First, we describe the experimental setup, next we describe the wavedampening controllers tested, and finally we briefly summarize the model used to estimate vehicle emissions.

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grants No. CNS-1446715 (Piccoli), CNS-1446690 (Seibold), CNS-1446435 (Sprinkle), and CNS-1446702 (Work). This research was supported by the Inria associated team "ModEling autonoMous vEhicles iN Traffic flOw (ME-MENTO)".


Lauriane Lecoq

Shishan Wang

Thomas Wiegand

Stéphane Bressanelli

Beat H Meier Michael Nassal
email: michael.nassal@uniklinik-freiburg.de

Anja Böckmann
email: a.bockmann@ibcp.fr

Beat H Meier

• A Böckmann

Solid-state [ 13 C-15 N] NMR resonance assignment of hepatitis B virus core protein

Keywords: Hepatitis B virus, Core protein, Nucleocapsid, Solid-state NMR, Assignments

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Biological Context

More than 2 billion people have been infected with hepatitis B virus (HBV), and more than 250 million thereof have become chronic carriers of the virus. Each year, close to 900,000 people die from hepatitis B as they develop chronic liver disease, including cancer (World Health Organization 2017). While an effective prophylactic vaccine is available to prevent infection, current treatments for chronic HBV infection are rarely curative and subject to the emergence of resistant virus variants [START_REF] Zoulim | Antiviral Therapies and Prospects for a Cure of Chronic Hepatitis B[END_REF]. Hence new therapeutic options are urgently sought [START_REF] Revill | Global strategies are required to cure and eliminate HBV infection[END_REF]).

The HBV virus particle (virion) is composed of an outer envelope bearing three related surface antigen (HBsAg) proteins and an inner nucleocapsid formed by multiple copies of a single about 20 kDa core protein (Cp) species. Cp forms stable dimers capable of selfassembling the capsid shell. The predominant form consists of 120 Cp dimers arranged in T=4 icosahedral symmetry. In infection, newly forming nucleocapsids initially copackage a complex of one copy of the viral pregenomic (pg) RNA plus one copy of the viral polymerase (Pol); maturation involves capsid-assisted reverse transcription of the pgRNA into a relaxed circular DNA by Pol [START_REF] Nassal | HBV cccDNA: viral persistence reservoir and key obstacle for a cure of chronic hepatitis B[END_REF]. The N terminal ~140 amino-acids of Cp, modeled by C terminally truncated variants like Cp149, are required and sufficient for capsid shell formation, even in bacteria [START_REF] Gallina | A recombinant hepatitis B core antigen polypeptide with the protamine-like domain deleted self-assembles into capsid particles but fails to bind nucleic acids[END_REF][START_REF] Birnbaum | Hepatitis B virus nucleocapsid assembly: primary structure requirements in the core protein[END_REF]). The downstream arginine-rich C-terminal domain (CTD) generally binds nucleic acids [START_REF] Birnbaum | Hepatitis B virus nucleocapsid assembly: primary structure requirements in the core protein[END_REF]) but is also required for specific viral pgRNA packaging [START_REF] Nassal | The arginine-rich domain of the hepatitis B virus core protein is required for pregenome encapsidation and productive viral positive-strand DNA synthesis but not for virus assembly[END_REF]). 3D structures, first by cryo-electron microscopy (EM) reconstruction, revealed mostly an 𝛼helical fold [START_REF] Conway | Visualization of a 4-helix bundle in the hepatitis B virus capsid by cryo-electron microscopy[END_REF][START_REF] Böttcher | Determination of the fold of the core protein of hepatitis B virus by electron cryomicroscopy[END_REF]. The crystallographic structure of Cp149 later solved at a resolution of 3.3 Å [START_REF] Wynne | The crystal structure of the human hepatitis B virus capsid[END_REF] gave an atomic-level view of the protein. Since then, further structures of the HBV capsid have been solved either by cryo-EM [START_REF] Crowther | Three-dimensional structure of hepatitis B virus core particles determined by electron cryomicroscopy[END_REF][START_REF] Dryden | Native Hepatitis B Virions and Capsids Visualized by Electron Cryomicroscopy[END_REF][START_REF] Chen | A Kinase Chaperones Hepatitis B Virus Capsid Assembly and Captures Capsid Dynamics in vitro[END_REF][START_REF] Yu | 3.5 Å cryo-EM Structure of Hepatitis B Virus Core Assembled from Full-Length Core Protein[END_REF][START_REF] Selzer | Hepatitis B Virus Core Protein Phosphorylation Sites Affect Capsid Stability and Transient Exposure of the C-terminal Domain[END_REF][START_REF] Patel | HBV RNA pre-genome encodes specific motifs that mediate interactions with the viral core protein that promote nucleocapsid assembly[END_REF] or crystallography [START_REF] Bourne | Global Structural Changes in Hepatitis B Virus Capsids Induced by the Assembly Effector HAP1[END_REF][START_REF] Heger-Stevic | Assembly-Directed Antivirals Differentially Bind Quasiequivalent Pockets to Modify Hepatitis B Virus Capsid Tertiary and Quaternary Structure[END_REF]). Yet, despite these structural studies, many steps along the viral life cycle involving the core protein

are not yet fully understood; namely, as the conformation of the capsids is believed to be a function of the different maturation states [START_REF] Ning | Capsid Phosphorylation State and Hepadnavirus Virion Secretion[END_REF], it is central to structurally analyze the capsids under a larger variety of conditions. In this context, solid-state NMR is an attractive technique since it allows the study of large molecular assemblies as viral capsids at physiological temperature, including those not forming crystalline states, which enables the study of different constructs, formed in various environments. First applications of solid-state NMR to virus capsids have been described in the literature and made significant advances in the understanding of different viral cycles. For example, Polenova and coworkers have determined a structural model of the human immunodeficency virus 1 (HIV-1) capsids and have shown that the internal flexibility of the capsid protein is essential for its assembly and interaction with host partners [START_REF] Han | Solid-State NMR Studies of HIV-1 Capsid Protein Assemblies[END_REF][START_REF] Quinn | Magic angle spinning NMR of viruses[END_REF][START_REF] Suiter | MAS NMR of HIV-1 protein assemblies[END_REF]. Solidstate NMR also allowed the characterization of the dynamics of fd bacteriophage, both of the filamentous and icosahedral, as well as to probe the capsid regions involved in the interaction with the DNA genome of the virus [START_REF] Morag | Complete Chemical Shift Assignment of the ssDNA in the Filamentous Bacteriophage fd Reports on Its Conformation and on Its Interface with the Capsid Shell[END_REF][START_REF] Abramov | Magic-angle spinning NMR of intact bacteriophages: Insights into the capsid, DNA and their interface[END_REF]. More recently, the structure of the Acinetobacter phage 205 has been solved [START_REF] Andreas | Structure of fully protonated proteins by proton-detected magicangle spinning NMR[END_REF]) using the advances in NMR technology with faster spinning frequencies (110 kHz) and 1 H detection techniques [START_REF] Penzel | Protein resonance assignment at MAS frequencies approaching 100 kHz: a quantitative comparison of J-coupling and dipolar-coupling-based transfer methods[END_REF][START_REF] Stanek | NMR Spectroscopic Assignment of Backbone and Side-Chain Protons in Fully Protonated Proteins: Microcrystals, Sedimented Assemblies, and Amyloid Fibrils[END_REF]. All these studies have paved the way towards the analysis of a large variety of viral capsid assemblies using magic-angle spinning (MAS)-NMR, in order to understand the viral life cycle.

Here we report the 13 C and 15 N resonance assignments of the HBV core protein Cp149 in the capsid form as assembled in E. coli using 2D and 3D solid-state NMR experiments. The spectra obtained show narrow lines (between 70 and 100 Hz in 13 C) typical for highly ordered assemblies. We compare the obtained chemical shifts and secondary structures to previous structural studies by X-ray and solution-state NMR, and highlight that the differences are mainly located at the dimer interfaces. This assignment of the assembly domain forms the first step towards investigation of full-length Cp183 particles, either filled with RNA, or mostly devoid of RNA upon extensive phosphorylation of the C-terminal domain.

Materials and methods

Protein expression and purification; sample preparation

Cp149 was expressed and purified as described previously [START_REF] Heger-Stevic | Assembly-Directed Antivirals Differentially Bind Quasiequivalent Pockets to Modify Hepatitis B Virus Capsid Tertiary and Quaternary Structure[END_REF]. Briefly, the pET-28a2-Cp149opt plasmid was transformed into E.coli BL21* Codon + (DE3) cells and grown at 37 °C overnight on a LB-agar plate containing 100 μg/ml ampicillin and 34 μg/ml chloramphenicol. Then, a single colony was inoculated into 3 ml LB medium and grown at 37 °C for 5 h. It was then transferred into 50 ml of standard M9 minimal medium containing 15 NH4Cl and 13 C-glucose as the sole nitrogen and carbon sources. The culture was incubated for 4 hours at 37 °C and transferred into 100 ml fresh 13 C-15 N M9 medium for an overnight incubation at 25 °C. 850 ml of 13 C-15 N M9 medium were inoculated with the 150 ml overnight 13 C-15 N M9 culture. When OD600 reached 0.7, the bacterial cells were grown for another 6 h at 25 °C after induction with 1 mM IPTG. Bacterial cells were pelleted by centrifugation (6,000 g, 20 min) and re-suspended in 15 ml of lysis buffer (50 mM Tris pH 7.4, 300 mM NaCl, 5 mM DTT). 15 mg of chicken lysozyme, 300 μl of protease inhibitor cocktail solution 50X and 750 µl of 10% Triton-X-100 was added to the cell suspension and incubated on ice for 45 min. Benzonase nuclease was added to the suspension and mixed for 30 min at room temperature. The cells were lysed by sonication and then centrifuged at 8000 g for 1 h to remove cell debris. The supernatant was loaded onto a step gradient from 10% to 70% (m/v) sucrose (buffered in 50 mM Tris pH 7.5, 300 mM NaCl, 5 mM DTT) and centrifuged in SW-32Ti Beckman Coulter rotor at 28,800 g for 3 h at 4 °C.

The gradient fractions containing Cp149 capsids were identified by SDS-PAGE (using Tris/Glycine/SDS buffer from BioRad and 15 % acrylamide) and precipitated by slow addition of (NH4)2SO4 to 20% saturation. After incubation on ice for 1 h and centrifugation at 25,000 g for 1 h, pellets were resuspended in purification buffer (50 mM Tris pH 7.5, 5 mM EDTA, 5 mM DTT, 5% sucrose). The protein solution was centrifuged again for 15 min to remove insoluble pellets and the supernatant containing soluble capsids was dialyzed overnight in purification buffer at 4 °C.

Electron microscopy

5 μL of sample were loaded on a carbon-coated grid and incubated for 2 minutes. The grid was then layered on top of 50 μL of 2 % phosphotungstic acid (m/v) pH 7 and incubated at room temperature for 2 minutes for negative staining. The grids were examined with a JEM-1400 transmission electron microscope operating at 100 kV.

Solid-state NMR spectroscopy

For NMR sample preparation, we started from 40 mg of soluble capsids which were concentrated to a final volume of l ml and sedimented into a thin-wall 3.2 mm zirconium rotor by centrifugation (180,000 g, 14 h, 4 °C) using a home-made filling tool [START_REF] Böckmann | Characterization of different water pools in solid-state NMR protein samples[END_REF]. All NMR experiments were conducted using a 3.2 mm triple-resonance ( 1 H, 13 C, 15 N) probe head at static magnetic field of 18.8 T corresponding to 800 MHz proton resonance frequency (Bruker Avance II). Assignments were obtained at a MAS frequency of 17.5 kHz using a combination of 2D and 3D correlation experiments, including: 2D DARR, 3D NCACB, NCACX, NCOCX, CANCO and CCC [START_REF] Schuetz | Protocols for the Sequential Solid-State NMR Spectroscopic Assignment of a Uniformly Labeled 25 kDa Protein: HET-s(1-227)[END_REF][START_REF] Habenstein | Extensive de novo solid-state NMR assignments of the 33 kDa C-terminal domain of the Ure2 prion[END_REF]).

The 3D CCC was performed with the probe in double-resonance ( 1 H, 13 C) mode allowing to increase the signal to noise ratio by a factor of 1.4. Experimental details are given in Table 1.

All spectra were referenced using DSS and recorded at a sample temperature of 4 °C, as determined by the resonance frequency of the supernatant water [START_REF] Böckmann | Characterization of different water pools in solid-state NMR protein samples[END_REF]. Two-dimensional DARR, NCA and NCO spectra were recorded on several other preparations of the same protein assembly, and resulted in reproducible spectra. Two-dimensional DARR and NCA spectra of Cp149 capsids were also recorded at 37 °C to confirm that under physiological temperatures, the protein conformation remained identical. Three-dimensional spectra were subsequently recorded for 2-6 days each on a single sample of Cp149. Sidechain assignments were achieved using NCACB, NCACX, NCOCX and CCC spectra [START_REF] Schuetz | Protocols for the Sequential Solid-State NMR Spectroscopic Assignment of a Uniformly Labeled 25 kDa Protein: HET-s(1-227)[END_REF][START_REF] Habenstein | Extensive de novo solid-state NMR assignments of the 33 kDa C-terminal domain of the Ure2 prion[END_REF]. All spectra were processed using TopSpin 3.2 (Bruker Biospin) by zero filling to no more than double the number of acquired points, the time domain signals were apodized with a squared cosine function (SSB 2.5-3 depending on spectrum and dimension). Spectra analysis and assignment was done with the CcpNmr Analysis package [START_REF] Vranken | The CCPN data model for NMR spectroscopy: development of a software pipeline[END_REF][START_REF] Stevens | A software framework for analysing solid-state MAS NMR data[END_REF].

Assignments and data deposition

Sequential assignments and secondary chemical shifts 13 C and 15 N backbone and side-chain chemical shifts of the Cp149 capsid protein have been deposited in the BioMagResBank (http://www.bmrb.wisc.edu) under the accession number 27317. Capsids expressed with high yield of minimum 50 mg per liter culture, and final preparations showed a high purity level as shown in Figure 1A. Purified proteins were analyzed under the electron microscope. As described previously [START_REF] Birnbaum | Hepatitis B virus nucleocapsid assembly: primary structure requirements in the core protein[END_REF], capsids were assembled directly during bacterial expression, as numerous HBV core protein capsids were observed under the electron microscope (see Figure 1B for negativestain EM pictures). Capsid preparations were homogenous and showed mainly T=4 capsids with a typical diameter of 30  3 nm. The 2D 13 C-15 N NCA spectrum of assigned Cp149 capsids is displayed in Figure 2A and shows narrow peaks with full widths at half maximum (FWHM) between 70 and 100 Hz in 13 C, which is comparable to line widths obtained previously in viral capsid proteins such as the HIV-1 virion [START_REF] Suiter | MAS NMR of HIV-1 protein assemblies[END_REF] and is indicative for homogeneous preparations of uniformly labeled proteins. Representative planes of the 3D NCACB, NCACO, CANCO and NCOCA experiments are shown in Figure 2B.

Assignments of Cp149 were completed at 87 % for all backbone atoms, with missing assignments predominantly found at the protein C-terminal end including residues 140 to 149 for which no cross peaks were detected in 3D spectra. In detail, 95.6 % of N, 97.1 % of Cα, 97.8 % of C', and 75.3 % of side chain non-H (including 96.2 % of Cβ) were unambiguously assigned for residues 1-139 of Cp149. A secondary chemical-shift analysis (Figure 3) reveals a backbone fold similar to that of the X-ray capsid structure as described in [START_REF] Wynne | The crystal structure of the human hepatitis B virus capsid[END_REF]. Helices α1 (residues 13-17) and α2 (residues 27-43) appear to be the same length in both capsids, furthermore the smaller values of secondary chemical shifts obtained for residues 37 and 38 are representative of the 90°-kink observed in the X-ray structure. The two short helical stretches between residues 7-9 and 21-23 described in the x-ray structure file can be identified as one-turn helices spanning 4 residues by NMR. NMR also detects a short -strand from residues 1 to 4, a stretch which takes an extended conformation in the x-ray structure. Helix α3 (residues 50-76 in solid-state NMR) is predicted to be longer by three residues, while helix α5 is predicted to be tworesidues shorter (residues 112-125). Another difference is observed in helix α4 that is not divided into two smaller helices in the NMR predictions but instead would form a continuous 30-amino acids helix, shorter by one residue compared to the X-ray structure. While these differences may be real structural differences between the two capsids analyzed, it is more likely that they are due to the more or less stringent conditions the structure analysis programs put on dihedral angles to identify helical segments. Also, NMR secondary chemical shifts might slightly vary depending on which average chemical shifts are taken as a basis. In both cases small variations can lead to values disregarded, or defined, as helices. For glycine residues (shown as grey bar), the deviation of the Cα shift from the random coil value is plotted. The -helices found in the X-ray structure of Cp149 capsid as described in [START_REF] Wynne | The crystal structure of the human hepatitis B virus capsid[END_REF]

Comparison to Cp149 dimer chemical shifts from solution-state NMR

Chemical shifts of the dimeric core protein have previously been determined using solutionstate NMR on a protein comprising a slightly different amino-acid sequence [START_REF] Freund | Moving towards High-Resolution Descriptions of the Molecular Interactions and Structural Rearrangements of the Human Hepatitis B Core Protein[END_REF]; furthermore, to prevent the dimers from assembling, solution spectra were taken at pH 9.5 and at low salt concentrations. The backbone assignments here presented for the capsid form of Cp149 are slightly more complete than the ones determined for the Cp149 dimer, as conformational exchange likely induced the loss of several signals in the solution-state study. Also, the high pH used in that study might have been responsible for solvent-saturation transfer effects. A comparison between the dimer and capsid chemical shifts is shown in Figure 4A for Cα and Cβ resonances. Chemical shifts of the six residues which were different between the two Cp149 constructs used were disregarded (A35, D40, D64, T74, V93 and L116 in the solution study). While overall chemical shifts are rather similar between the dimer and capsid form, interesting chemical shift differences, which we interpret as chemicalshift perturbations (CSPs) upon assembly, are observed in three distinct regions identified in Figure 4B. The first region includes residues 18 to 23, which are involved in contacts with two different surrounding dimers in the capsid structure. The residues with which they are in interaction on the neighboring dimers comprise amino acids 136-138, which however have not been assigned for the dimer in solution. Other significant CSPs are observed in a second region from residues 55 to 62, which could be due to the different oxidation state of C61, or alternatively to allosteric changes induced by the interaction between the dimers in the capsid form. These changes could be transmitted via a third region showing CSPs comprising residues 96 and 97, which is located in between regions 1 and 2. Met1 also shows an isolated CSP, probably due to the close proximity with the second region affected by capsid assembly.

CSPs observed for residues neighboring residues with different amino acids in the two constructs in the sequence, as is the case for residues N75 and L76, were disregarded in our analysis as they rather stem from sequence differences than to differences caused by assembly. Our results reveal the three major regions undergoing a conformational change between the core protein in solution and the core protein assembled as a capsid. or the solid-state NMR study (grey circles). Secondary structure elements extracted from the X-ray structure are shown above the graph as pink boxes. Chemical shift differences below -1.5 ppm or above 1.5 ppm (grey dotted lines) are considered as significant. Three regions were identified and are colored in green, red, and blue. Isolated Met1 is highlighted in grey. B) Sphere representation of residues with significant chemical-shift differences between Cp149 dimer and Cp149 capsid. It includes residues M1, F18, L19, S21, D22, F23, L55, A58, L60, C61, W62, K96 and I97, which are labeled on subunit B and colored as in panel A. Residues N75 and L76 are not included, as the V74 to T74 substitution in the dimer in solution is likely to be at the origin of the observed CSPs. Helices are labeled on subunit A. PDB: 1QGT. The figures involving structures were all prepared using the PyMol software (Warren Delano, http://www.pymol.org).

Experiment

Conclusion

The contribution of MAS-NMR spectroscopy to understanding structural and dynamical features of virus-like particles has become significant over the past years. Here we have shown that assembled hepatitis B virus capsids can be studied by solid-state NMR. This is of particular importance to further analyze the conformational dynamics the core protein is thought to undergo upon its multiple interactions with viral and cellular factors, both protein and nucleic acids, that eventually yield a mature infectious virion. Labeled Cp149 protein can be produced in pure form and in large quantity in E. coli and the protein yields high-quality spectra with narrow lines and a dispersion typical for an α-helical protein. Sequential assignments are to 87 % complete for Cp149 backbone, and yield secondary chemical shifts matching available X-ray structures of the capsid. Signals for residues downstream residue 140 are not detectable by either technique, owing to the dynamic behavior of the C-terminus, except for some core assembly mutants that are partially ordered down to residue 156 [START_REF] Klumpp | High-resolution crystal structure of a hepatitis B virus replication inhibitor bound to the viral core protein[END_REF]. However, a comparison with the solution-NMR chemical shifts reported for the Cp149 dimer [START_REF] Freund | Moving towards High-Resolution Descriptions of the Molecular Interactions and Structural Rearrangements of the Human Hepatitis B Core Protein[END_REF]) highlights conformational differences in three distinct regions of the protein, suggesting they are structurally affected by the transition from the dimer state to the capsid state. In summary, the work presented here sets the stage for future high resolution NMR studies of the HBV core protein which is central in HBV infection and thus is also becoming a highly attractive target for novel anti-HBV therapeutics [START_REF] Zlotnick | Core protein: A pleiotropic keystone in the HBV lifecycle[END_REF][START_REF] Durantel | New antiviral targets for innovative treatment concepts for hepatitis B virus and hepatitis delta virus[END_REF][START_REF] Pei | Past, Current, and Future Developments of Therapeutic Agents for Treatment of Chronic Hepatitis B Virus Infection[END_REF]).

Fig. 1

 1 Fig. 1 Sample characterization. A. SDS-PAGE 15% acrylamide of a molecular weight marker (Precision Plus Protein TM Unstained Standards from BioRad) and 20 µg of purified Cp149 expressed in E. coli. B. Negativestain EM picture of Cp149 concentrated at 1 mg/ml.

Fig.

  Fig. 2D and 3D spectra used in sequential assignments: A) 2D 13 C-15 N NCA spectrum of Cp149 capsids. All assigned N-Cα intra-residue correlations are labeled with the residue number as well the Cα-Cδ intra-residue correlations for proline residues. The middle of the spectrum is copied inside the black rectangle for more clarity. B) Representative plane for residue Leu65 of 3D CANCO (red), NCACX (dark blue), NCACB (green for positive signals and orange for negative signals) and NCOCX (cyan) spectra. For experimental details see

Fig. 3

 3 Fig. 3 Differences between secondary chemical shifts of Cα and Cβ resonances relative to their random coil shift (Wishart et al. 1995) as a function of Cp149 residue number. Missing assignments are represented as grey circles and include residues L119, V120, I126, R133, P134, as well as the C-terminal end from 140 to 149.

  are shown as pink boxes at the top of the graph and the ones predicted from solid-state NMR chemical shifts are shown as grey boxes. Four residues in a row with a positive value are indicative of an -helix and three residues in a row with a negative value indicate a -strand.

Fig. 4

 4 Fig. 4 Chemical shift perturbations between dimeric Cp149 and Cp149 capsids. CSP from Cα resonances are shown in blue and Cβ in red. Yellow diamonds stand for residues which were different between the two Cp149 constructs. Missing data points are due to missing assignments in either the solution-state (white circles)

Table 1 .

 1

Table 1 .

 1 NMR experimental details. sw stands for spectral width. Experiments were recorded on an 800 MHz spectrometer at a 17.5 kHz MAS frequency and at a sample temperature of 5 °C. The 15 N-insert was removed from the probe for the CCC experiment to increase the signal to noise ratio.

	DARR	NCA	NCO	NCACB

Acknowledgements

This work was supported by the French ANR (ANR-14-CE09-0024B), the LABEX ECOFECT (ANR-11-LABX-0048) within the Université de Lyon program Investissements d'Avenir (ANR-11-IDEX-0007) (AB), the Swiss National Science Foundation (Grant 200020_159707) (BHM), the DFG grant NA154/9-4 (MN), a doctoral stipend from the Chinese Scientific Council (SW) and by the Marie Skłodowska-Curie program (H2020-MSCA-IF-2016 748516) (LL). We thank the Centre d'Imagerie Quantitative Lyon-Est (CIQLE) for the access and training on the electron microscopy platform.