Julie Bulteau
email: julie.bulteau@uvsq.fr

Thierry Feuillet
email: thierry.feuillet@univ-paris8.fr

Sophie Dantan
email: sophie@gmail.com

Carpooling and carsharing for commuting in the Paris region: A comprehensive exploration of the individual and contextual correlates of their uses

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

Introduction

Transport represents almost a quarter of Europe's greenhouse gas (GHG) emissions and is the main cause of air pollution in cities. Within this sector, road transport is by far the biggest emitter accounting for more than 70% of all GHG emissions from transport in 2014 (European Commission, https://ec.europa.eu/clima/policies/transport_en). In 2014 in France, the transportation sector (fossil fuel combustion) was the main source of GHGs (29.2%) and was responsible for almost 40% of CO 2 emissions (French Government, 2017, http://www.statistiques.developpementdurable.gouv.fr/indicateurs-indices/f/2082/0/emissions-gaz-effet-serre-secteur-1.html). In 2016, levels of nitrogen dioxide (NO 2 ) and particulate matter (PM 10 ) concentrations in Île-de-France (Paris region) remained problematic, significantly exceeding the limit values. In total, in 2016, more than 200,000 inhabitants were living in the agglomeration in the vicinity of major traffic arteries and were potentially affected by exceeding the daily limit value for PM 10 particles (35 days greater than 50 μg/m 3 allowed) [START_REF] Airparif | Airparif, Bilan de la qualité de l'air[END_REF].

The collaborative economy is booming and tending to change the current socio-economic model [START_REF] Botsman | What's mine is yours. The Rise of Collaborative Consumption[END_REF][START_REF] Frenken | Putting the sharing economy into perspective[END_REF]. This relative reconsideration of individual property by the collaborative economy is seen as a model that meets the criteria of sustainability. Shared mobility is an illustration. It corresponds to a form of sustainable mobility insofar as it reconciles (i) environmental benefits (e.g. decreasing negative externalities, congestion, GHG emissions and noise); (ii) economic benefits (e.g. sharing the costs of ownership, maintenance costs) and (iii) socio-ethics (better accessibility, increased well-being and quality of life, social inclusion) compared to traditional mobility and transport.

According to a European Commission report (2014), carsharing and carpooling schemes are part of the solutions to be implemented by cities and local authorities to reduce congestion and pollution. Therefore, a better understanding of the determinants of people's adherence to these shared mobility systems is needed in order to promote them and achieve sustainable mobility.

Research on the benefits of and barriers to carpooling has developed considerably, first as a result of the oil crisis in the 1970s and 1980s [START_REF] Margolin | INCENTIVES AND DISINCENTIVES OF RIDE SHARING[END_REF][START_REF] Stefen | Attitudes toward and Evaluation of Carpooling[END_REF] and in recent years with the awareness of developing sustainable cities and therefore sustainable mobility [START_REF] Abrahamse | Effectiveness of a web-based intervention to encourage carpooling to work: A case study of Wellington, New Zealand[END_REF][START_REF] Delhomme | Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling?[END_REF][START_REF] Shaheen | Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations[END_REF]. There is a significant literature on carpooling and congestion pricing as a tool (or transport policy) to promote the reduction of congestion and pollution. This type of policy is very common in North America with high occupancy vehicle (HOV) lanes and high occupancy vehicle and toll (HOT) lanes. It has been studied extensively from the point of view of congestion management [START_REF] Burris | The impact of HOT lanes on carpools[END_REF][START_REF] Konishi | Carpooling and congestion pricing: HOV and HOT lanes[END_REF][START_REF] Li | Who Chooses to Carpool and Why?: Examination of Texas Carpoolers[END_REF][START_REF] Small | Economics and urban transportation policy in the United States[END_REF][START_REF] Small | Differentiated Road Pricing, Express Lanes, and Carpools: Exploiting Heterogeneous Preferences in Policy Design [with Comments[END_REF]. Recently, there have been studies on the environmental effects of these management tools for congestion and pollution [START_REF] Javid | Quantifying the environmental impacts of increasing high occupancy vehicle lanes in the United States[END_REF]. While studies on carpooling systems have been common since the 1970s, there are fewer on carsharing systems, despite an increase over the last decade [START_REF] Namazu | Is carsharing for everyone? Understanding the diffusion of carsharing services[END_REF][START_REF] Shaheen | Growth in Worldwide Carsharing: An International Comparison[END_REF]. There is still very little research on carsharing in France and particularly in the Paris region. [START_REF] Hildermeier | Two ways of defining sustainable mobility: Autolib' and BeMobility[END_REF] compared carsharing systems in Paris and Berlin. There are recent studies on carsharing abroad, for example in Lisbon, where [START_REF] Baptista | Energy, Environmental and Mobility Impacts of Car-sharing Systems. Empirical Results from Lisbon, Portugal[END_REF] analyzed the energy, environmental and mobility impacts of carsharing systems. In Toronto, [START_REF] Costain | Synopsis of users' behaviour of a carsharing program: A case study in Toronto[END_REF] investigated the behavior of carsharing members. The environmental benefits of carsharing have also been studied in Rome [START_REF] Musso | Car Sharing in Rome: a Case Study to Support Sustainable Mobility[END_REF] while the propensity to join a carsharing service has been explored in Greece [START_REF] Efthymiou | Factors affecting the adoption of vehicle sharing systems by young drivers[END_REF][START_REF] Efthymiou | Modeling the propensity to join carsharing using hybrid choice models and mixed survey data[END_REF].

Most of the literature has aimed at identifying patterns of shared mobility users (carpoolers and carsharing users). However, these works have usually focused on one of these systems independently of the other (except [START_REF] Carroll | Examining the potential for car-shedding in the Greater Dublin Area[END_REF] and analyzed very specific issues (for example, [START_REF] Wilhelms | To earn is not enough: A means-end analysis to uncover peer-providers' participation motives in peer-to-peer carsharing[END_REF] analyzed peer-to-peer carsharing). Compared to the existing literature, the originality of our study, focusing on a sample of 2002 individuals living in the Paris region, is threefold: (i) it compares the determinants of carpoolers and non-carpoolers with those of the users/non-users of carsharing; (ii) it considers the spatial effect in addition to the usual factors determining the demand for transport, i.e. those relating to the price effect, the income effect, and the quantity/quality effect of transport. This spatial effect is considered by including spatial variables, which are rarely taken into account in existing studies. iii) it investigates the perception of the entourage (family members and colleagues) as potential determinants of the use of shared mobility, which is, to the best of our knowledge, unprecedented in this field.

The first section presents a review of the literature focusing on the two systems (carpooling and carsharing) in order to have an overview of the characteristics of the users worldwide. The second section focuses on our empirical research: Fisher's exact tests and Wilcoxon tests were used for qualitative and quantitative variables, respectively, and multivariate logistic models were used to characterize the differences between carpoolers and carsharing users and to identify the correlates of mode adoption in our sample. The third section discusses the findings and their implications for policy makers in Île-de-France in particular.

Literature review

Although carpooling and carsharing are the two major shared mobility modes, their purpose -and therefore their determinants -are assumed to be different. In the literature review below, the determinants of carpooling and carsharing are listed, based initially on the usual transport demand determinants defined in the transportation economics literature.

Carpooling and demand determinants

Transport demand determinants can be categorized in many ways. The traditional classification used in transportation economics is based on price, income, quantity and, sometimes, spatial effects [START_REF] Small | The Economics of Urban Transportation[END_REF]. The demand for carpooling and carsharing can follow this traditional classification, although it is now common to include supplementary effects (e.g. attitudinal variables) [START_REF] Neoh | How commuters' motivations to drive relate to propensity to carpool: Evidence from the United Kingdom and the United States[END_REF][START_REF] Neoh | What encourages people to carpool? An evaluation of factors with meta-analysis[END_REF]. The first factor affecting transportation demand is the price [START_REF] Mcfadden | The measurement of urban travel demand[END_REF]. In the case of the private car, the demand increases when the cost of the car use is reduced. One of the benefits promoted by carpooling is the reduction in the car use cost (e.g. cost sharing). According to [START_REF] Abrahamse | Effectiveness of a web-based intervention to encourage carpooling to work: A case study of Wellington, New Zealand[END_REF], when the costs of driving alone are relatively high, carpooling is more likely to be considered as an alternative, and in their study they noted that saving money was one of the most liked aspects of carpooling. According to [START_REF] Tischer | An empirical analysis of behavioral intentions of single-occupant auto drivers to shift to high occupancy vehicles[END_REF], the cost is an important factor associated with the intention to start carpooling. The results of the study of [START_REF] Van Der Waerden | Investigation of Factors that Stimulate Car Drivers to Change from Car to Carpooling in City Center Oriented Work Trips[END_REF] and [START_REF] Shaheen | Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations[END_REF] for casual carpooling followed the same direction. The fuel price elasticity of demand can deepen this price effect. Indeed, rising fuel prices limit the mobility by car [START_REF] Goodwin | A Review of New Demand Elasticities with Special Reference to Short and Long Run Effects of Price Changes[END_REF]. In the same vein, [START_REF] Bento | Carpooling and driver responses to fuel price changes: Evidence from traffic flows in Los Angeles[END_REF] highlighted the impact of the price changes of gasoline on the carpooling demand in Los Angeles (United States). In transportation economics, the second determinant of transportation demand is income. [START_REF] Dargay | The Demand for Local Bus Services in England[END_REF] and [START_REF] Bresson | Economic and structural determinants of the demand for public transport: an analysis on a panel of French urban areas using shrinkage estimators[END_REF] showed a positive relationship between income and car use and highlighted a negative relationship between the number of bus trips and the level of income. Carpooling is more common among commuters with lower incomes (e.g. [START_REF] Baldassare | Suburban attitudes toward policies aimed at reducing solo driving[END_REF][START_REF] Teal | Carpooling: Who, how and why[END_REF]. More generally, concerning the socio-demographic variables, some studies have found that women and younger people are more likely to switch to carpooling (e.g. [START_REF] Baldassare | Suburban attitudes toward policies aimed at reducing solo driving[END_REF][START_REF] Koppelman | MARKET RESEARCH EVALUATION OF ACTIONS TO REDUCE SUBURBAN TRAFFIC CONGESTION: COMMUTER TRAVEL BEHAVIOR AND RESPONSE TO DEMAND REDUCTION ACTIONS[END_REF]. The third determinant of transport demand is the quantity of goods or services available [START_REF] Mogridge | The Prediction of Car Ownership and Use Revisited: The Beginning of the End?[END_REF][START_REF] Mogridge | The Prediction of Car Ownership[END_REF] that can be related to a quality analysis (e.g. frequency for public transport). The quantity factor influencing carpooling demand can be measured by the motorization level, i.e. the number of vehicles per household, as well as whether the employer offers a carpooling service to employees. The quality and quantity of public transport can also be a determining factor in not carpooling [START_REF] Vanoutrive | What determines carpooling to workplaces in Belgium: location, organisation, or promotion?[END_REF]. The fourth determinant of demand considered in transport studies is the spatial effect. According to [START_REF] Small | The Economics of Urban Transportation[END_REF], the type and density of buildings and the type of activity are factors that influence travel decisions. A number of studies have analyzed the relationship between the built environment and transport-related behaviors [START_REF] Cao | The Influences of the Built Environment and Residential Self-Selection on Pedestrian Behavior: Evidence from Austin, TX[END_REF][START_REF] Ewing | Travel and the Built Environment: A Synthesis[END_REF][START_REF] Feuillet | Spatial heterogeneity of the relationships between environmental characteristics and active commuting: towards a locally varying social ecological model[END_REF][START_REF] Limanond | Accessibility and Mode-Destination Choice Decisions: Exploring Travel in Three Neighborhoods in Puget Sound[END_REF][START_REF] Van Acker | Car availability explained by the structural relationships between lifestyles, residential location, and underlying residential and travel attitudes[END_REF][START_REF] Van Acker | Commuting trips within tours: how is commuting related to land use?[END_REF]. For instance, a study conducted near Amsterdam found evidence that solo-driver attitudes about carpooling were negatively influenced by the construction of a new carpool lane [START_REF] Van Vugt | How a Structural Solution to a Real-World Social Dilemma Failed: A Field Experiment on the First Carpool Lane in Europe[END_REF]. However, to our knowledge, in relation to the carpooler's decision, these spatial effects are very rarely taken into account except in the papers of [START_REF] Vanoutrive | What determines carpooling to workplaces in Belgium: location, organisation, or promotion?[END_REF] and [START_REF] Wang | Attitudes, mode switching behavior, and the built environment: A longitudinal study in the Puget Sound Region[END_REF]. In carpooling studies, another determinant arises: attitudinal effects (personal effects) such as "time flexibility" or environmental awareness. To commute by carpooling, individuals need to communicate, coordinate and in most cases they need to adapt their schedule to enable cooperation [START_REF] Hussain | Negotiation and coordination in carpooling: Agent-based simulation model[END_REF]. In this study, the authors showed that when the flexibility time is larger, the chances for carpooling are greater than when using the smaller time window. For environmental awareness, [START_REF] Delhomme | Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling?[END_REF] found that compared to non-carpoolers, carpoolers are more environmentally aware. The results of [START_REF] Shaheen | Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations[END_REF] indicated that the environmental motivations for casual carpooling participation are ranked low (compared to convenience, time saving, and monetary saving). According to [START_REF] Canning | Reasons for participating in formal employer-led carpool schemes as perceived by their users[END_REF], intentions to reduce carbon footprints are important to carpooling drivers. A last determinant related to the interpersonal and societal effects can be taken into account. It includes the influence of the media, the entourage or nudges on the decision about the travel mode. [START_REF] Picard | Mobility decisions within couples[END_REF] notably illustrate how spouses' preferences and bargaining power within couples influence intra-household carpooling. However, these effects are rarely considered in the literature.

Carsharing and demand determinants

The growth in the costs of owning and using a car (vehicle purchase, gasoline, insurance and parking) is a reason for the development of carsharing in big cities [START_REF] Clewlow | Carsharing and sustainable travel behavior: Results from the San Francisco Bay Area[END_REF][START_REF] Costain | Synopsis of users' behaviour of a carsharing program: A case study in Toronto[END_REF][START_REF] Efthymiou | Factors affecting the adoption of vehicle sharing systems by young drivers[END_REF]. Depending on the carsharing system, fuel, maintenance, insurance, parking and sometimes congestion charges (e.g. in the case of London's Zipcar) are included in the price. According to [START_REF] Morency | Object-Oriented Analysis of Carsharing System[END_REF], [START_REF] Shaheen | Growth in Worldwide Carsharing: An International Comparison[END_REF] and [START_REF] Shaheen | North American Carsharing: 10-Year Retrospective[END_REF], car users usually underestimate car costs. Therefore, increasing the cost of car ownership and use would encourage carsharing.

Concerning the income and socio-demographic effects, [START_REF] Burkhardt | Who Is Attracted to Carsharing?[END_REF] found that in North America, only 50% of the members of carsharing services have a relatively high income (>60,000$). Similarly, [START_REF] Efthymiou | Modeling the propensity to join carsharing using hybrid choice models and mixed survey data[END_REF] highlighted that people with a medium to low income are more willing to join a hypothetical carsharing scheme. [START_REF] Zhou | Opportunities for and Impacts of Carsharing: A Survey of the Austin, Texas Market[END_REF] showed that car owners with a high income are not interested in a carsharing service. However, in the work of [START_REF] Kopp | Do sharing people behave differently? An empirical evaluation of the distinctive mobility patterns of free-floating car-sharing members[END_REF], the members of free-floating carsharing have a significantly higher educational and income level compared to non-members. Le [START_REF] Vine | The impact of free-floating carsharing on car ownership: Early-stage findings from London[END_REF] showed that the average income and education level of users of free-floating carsharing in London are both higher than for the general population. [START_REF] Douma | Carsharing in twin city: measuring impacts on travel behaviour and automobile ownership[END_REF] found that carsharing is popular among middle-income population groups. [START_REF] Cervero | City CarShare: Longer-Term Travel Demand and Car Ownership Impacts[END_REF] also found that income is inversely proportional to carsharing activities, meaning that carsharing is popular among middle-and lowerincome groups.

The quantity factor influencing carsharing demand can be measured by the motorization level, i.e. the number of vehicles per household, as well as the number of carsharing services available around the home. The latter is also a spatial variable. In the literature, Le [START_REF] Vine | The impact of free-floating carsharing on car ownership: Early-stage findings from London[END_REF] focused on the impact of free-floating carsharing on car ownership in London. Other results relating carsharing to car ownership are interesting. For example, [START_REF] Clewlow | Carsharing and sustainable travel behavior: Results from the San Francisco Bay Area[END_REF] showed that the average number of vehicles is significantly lower among carsharing member households than among non-member households while several studies have found that households tend to maintain or reduce their vehicle holdings after becoming carshare members [START_REF] Cervero | City CarShare: Longer-Term Travel Demand and Car Ownership Impacts[END_REF][START_REF] Martin | Impact of Carsharing on Household Vehicle Holdings: Results from North American Shared-Use Vehicle Survey[END_REF]. Regarding spatial effects (i.e. built and social environment variables), [START_REF] Costain | Synopsis of users' behaviour of a carsharing program: A case study in Toronto[END_REF] found that the majority of the members live in dense neighborhoods near downtown Toronto. Similar findings have been reported elsewhere [START_REF] Burkhardt | Who Is Attracted to Carsharing?[END_REF][START_REF] Cervero | City CarShare: First-Year Travel Demand Impacts[END_REF][START_REF] Shaheen | Travel Effects of a Suburban Commuter Carsharing Service: CarLink Case Study[END_REF][START_REF] Stillwater | Carsharing and the Built Environment[END_REF]. [START_REF] Costain | Synopsis of users' behaviour of a carsharing program: A case study in Toronto[END_REF] used the median income of neighborhoods where carsharing members live and found that those living in lower-income neighborhoods are highfrequency carsharing users. Finally, spatial effects in carsharing studies are rarely taken into account, except for the population density.

Attitudinal effects (personal effects), such as lifestyle or environmental awareness, are often taken into account in carsharing studies. For example, [START_REF] Costain | Synopsis of users' behaviour of a carsharing program: A case study in Toronto[END_REF] pointed out that carsharing members are, in general, environmentally-conscious people who are willing to pay for carbon offsetting if given the option. [START_REF] Burkhardt | Who Is Attracted to Carsharing?[END_REF] highlighted that carsharing appeals to individuals who can be considered social activists and environmental protectors. [START_REF] Schaefers | Exploring carsharing usage motives: A hierarchical means-end chain analysis[END_REF] found that environmental awareness appears to be an important psychosocial consequence for carsharing users. The results of [START_REF] Efthymiou | Factors affecting the adoption of vehicle sharing systems by young drivers[END_REF] and [START_REF] Efthymiou | Modeling the propensity to join carsharing using hybrid choice models and mixed survey data[END_REF] are similar. Finally, we have found no study concerning the relationship between interpersonal effects (e.g. an entourage effect) and carsharing.

In our study, we decided to consider carpoolers and carsharing users separately, since we hypothesized that the demand determinants could differ in each system. One objective was therefore to explore the differences between the correlates of the two modes.

Data and Methods

Study design and sampling

In order to understand carpooling and carsharing practices better and to identify the obstacles to using these shared modes for commuting, a specific questionnaire was designed within the VEDECOM Institute and the University of Paris-Saclay. It was distributed by the BVA survey institute to a sample of 2002 workers in the Paris region in September 2016. The sample was selected to be representative of the workers who travel within the Paris region, in terms of gender, age, sociooccupational category and department of residence. In order to correct possible bias, the respondents were given weights (using a calibration technique on margins) that indicated their representativeness in the population. In our questionnaire, carpooling and carsharing definitions were clarified in order to avoid any confusion between the two systems. The carpool definition given was: "Carpooling is the joint and organized use of a car by several individuals to travel", whereas the carsharing definition was: "Carsharing is the use of a car provided by an individual, a community or a specialized company". Although they are both shared modes, their purposes and uses are different so their determinants are assumed to be different too.

Individual, socio-demographic and transport-related variables

The questionnaire was divided into three parts. The first part included questions about general and socio-demographic characteristics (home address, work address, gender, age, income, educational level, household size) and participants' current commuting travel, such as the modes they use to commute, travel time, monthly transport budget, having a car driving license, inquiring about traffic status and mode availability. The second part concerned their interest in carpooling for commuting. This section began by a definition of carpooling and questions about their carpooling practices, such as whether their employer has a carpooling service, whether they use a carpool to commute, the frequency of carpooling, the conditions of carpooling (with whom, payed or not, etc.). Then, there were some questions about their reasons for carpooling or not to commute, their knowledge about carpooling services (companies) and whether their entourage (family and colleagues) practices carpooling in order to measure interpersonal effects. The third part related to their interest in carsharing for commuting. Like the second section, this began by a description of carsharing and questions about their carsharing practices such as if they use a peer-to-peer (defined as a system where a facilitating company connects car owners to car renters, i.e. between private individuals) or a free-floating (defined as system allowing users to start and end vehicle-rentals at any point in a city) carsharing service and how frequently. Then there were some questions about their reasons for carsharing or not to commute, their awareness of both peer-to-peer and free-floating carsharing services, and whether their entourage (family and colleagues) uses a carsharing service in order to measure interpersonal effects.

Contextual variables

Contextual variables potentially associated with shared mobility behaviors were assessed at the IRIS Census unit scale (data from 2014) or through individual buffers around the home address. The IRIS areas (acronym for "Aggregated Units for Statistical Information") are provided by the French National Institute of Statistics and Economic Studies (INSEE, www.insee.fr); they represent the smallest unit for dissemination of French infra-municipal data. Five variables related to the socioeconomic and built environments were specifically targeted: (i) the population density (inhabitants/km 2 , see figure 1); (ii) a social mix index. This is a measure of the evenness of distribution of the percentages of six main INSEE-based socio-professional classes (farmers, artisans, managers and higher intellectual professions, intermediate occupations, low-grade white collars, blue collars) in each IRIS. It therefore quantifies the social heterogeneity in each unit. It is inspired by the Shannon's diversity index and was computed as follows:

Social mix index = -ln / ln(6)

Where i corresponds to socio-professional classes (n = 6) and p i is the proportion of a specific socioprofessional class. The index ranges from 0 to 1. A value of 1 indicates an equal distribution of the six classes in the IRIS (see figure 1), that is a perfect social mix. A value of 0 indicates the occurrence of a single social class in the unit;

(iii) a deprivation index. This French deprivation index was calculated following the protocol described elsewhere [START_REF] Rey | Ecological association between a deprivation index and mortality in France over the period 1997 -2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death[END_REF]. Theoretically, deprivation has been initially defined by Townsend as a "state of observable and demonstrable disadvantage relative to the local community or the wider society to which an individual, family or group belongs" [START_REF] Townsend | Deprivation[END_REF][START_REF] Rey | Ecological association between a deprivation index and mortality in France over the period 1997 -2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death[END_REF]. Empirically, it has been here quantified as the first component of a principal component analysis including four socio-economic variables (the median household income, the percentage of high-school graduates in the population aged 15 years and older, the percentage of blue-collar workers in the active population, and the unemployment rate) (see figure 1);

(iv) the number of Autolib' stations in a 500-m radius buffer (i.e. approximately 10 minutes' walk) around each home address. Autolib' was the main carsharing company in the Paris area in 2016 (at the time of the questionnaire), although it has since been removed. (v) the train/RER (acronym for regional express network) station accessibility, assessed as a dummy variable (1 if there was a station in the 500-m radius individual buffer, 0 otherwise).

Figure 1. Maps of the three contextual variables included in the final models: population density, deprivation index and social mix index.

Statistical analyses

Bivariate statistics

First, descriptive statistics were carried out to characterize the subsamples of (i) carpoolers and noncarpoolers and (ii) carsharers and non-carsharers, according to individual and contextual variables.

Based on the literature and the data available from the questionnaire, we retained as individual variables: the monthly transport budget in €, the household income and socio-demographic variables such as gender, age, educational level and the number of people in the household. The quantity and quality effects were captured by including the number of cars in the household, the travel time to commute and the mode of transport used to commute. The awareness of carpooling between carpoolers and non-carpoolers and the awareness of both peer-to-peer and free-floating carsharing services for carsharing users and non-users were analyzed. Then, bivariate statistics were computed to explore the relationships between the use/non-use of the two modes and each of the putative explanatory variables (both individual and contextual).

Relations with the categorical explanatory variables has been explored through Fisher's exact tests (independence as null hypothesis), rather than chi² tests, because of some low frequencies in the contingency table. Relations with quantitative explanatory variables have been explored through Wilcoxon tests (independence as null hypothesis).

Multivariate econometric investigation

The variables that appeared significant in one of the two samples (carpoolers and carsharing users) in the bivariate analyses (Tables 1 and2) were kept as regressors in the subsequent multivariate econometric models. To model the first outcome (the use of carpooling), two models -logit and Poisson -were performed, according to the way we quantified it. First, the use of carpooling can be viewed as a binary decision; in this case, a logit model was used. Second, its use may be modeled as a frequency (here four frequencies: no use, once a week, twice a week, three or more times a week).

In this case, a Poisson regression was used, as a suitable method for modeling count data. The results of the two models were then compared to capture any nuances that would only be due to the regress and quantification choice (i.e. a methodological artefact). To model the second outcome (the use of carsharing), a logit model only was used, since the frequency of use of this mode was too weak to envisage something other than a dichotomous discretization.

In the logit model, the underlying assumption is that an individual gains a certain level of utility in making a decision. The utility is composed of two components: deterministic and random. The deterministic utility (V) is considered linear in the parameter function of variables (X) and corresponding parameters (β). Considering that the random utility component follows a logistic distribution, then the probability of choosing the binary decision can be written as:

Pr(binary decision) = = (∑ ) (∑ )
The parameters (β) of the model can be estimated using the maximum likelihood estimation process. The Hosmer-Lemeshow test was conducted as a goodness-of-fit test (p-values were not significant (>0.05), so the model appears well adapted to the data).

Poisson regression is typically used for the modeling of count data. The Poisson probability distribution of the number h of occurrences of an event (here the four different frequencies of carpooling) is expressed as follows [START_REF] Winkelmann | Econometric analysis of count data[END_REF]:

!(" = ℎ|%) = & '( % ) ℎ! +,! ℎ = 0,1,2,3 234 % > 0
where λ is the only Poisson parameter, as the distribution is equidispersed (i.e. the mean and the variance of Poisson distribution are both equal to λ). In the Poisson regression model, the expected value λ is the result of the exponential function of the linear combination of the explanatory variables and must be positive. Regression parameters are then estimated by maximizing the loglikelihood in an iterative manner. The deviance test was conducted as a goodness-of-fit test (p-values were not significant (>0.05), so the model is well adapted to the data).

Finally, the multicollinearity between the regressors in the three models was checked through the variance inflation factor (VIF). Values higher than 2 (implying a multiple coefficient of determination > 0.5) were removed.

All the statistical operations were run in R Studio (R Core Team, 2015).

Results and Discussion

In the three subsections below, the results are directly discussed in each subsection rather than in a separate discussion section, in order to ensure a clearer demonstration.

Overall characteristics of carpoolers and carsharers

The sample was composed of 2002 workers (56.4% women) living in the Paris area and with an average age of 41.8 years. The sample included 320 carpoolers (16%) and 185 carsharers (9%), which is in line with the existing literature (for example, carpoolers represent 12.2% of the sample in [START_REF] Abrahamse | Effectiveness of a web-based intervention to encourage carpooling to work: A case study of Wellington, New Zealand[END_REF], 9.3% in [START_REF] Habib | Modelling commuting mode choice with explicit consideration of carpool in the choice set formation[END_REF]. Tables 1 and2 present the descriptive and bivariate statistics of users and non-users of carpooling and carsharing, according to their individual and contextual characteristics, respectively.

Individual characteristics

Overall, we noted that the correlates of carpooling and carsharing are not the same. In terms of socio-demographics, gender, age and educational level are associated with carsharing but not with carpooling. Carsharing is mostly used by men (56%) and younger people (51.6% of carsharers are between 25 and 39 years old). Household income is associated with using both modes, unlike household size, which is independent.

In terms of transport-related variables, the main mode of transport for commuting is related to carpooling and carsharing, but not in the same way. For instance, there is an underrepresentation of public transportation users among carpoolers (19.4%) compared to non-carpoolers (32.7%), but an overrepresentation among carsharers (40.1%) compared to non-carsharers (27.8%). Transport budget and travel time are only associated with carpooling.

Regarding the interpersonal variables, we noted a strong influence of the behaviors of both family/friends and colleagues. Having carpoolers/carsharers among family/friends and colleagues seems to be clearly associated with engaging in these modes. For instance, 61% of carsharers reported having family members or friends who share the use of a car, compared to only 12.2% among non-carsharers.

Contextual characteristics

There is a relationship between the population density of the home environment and carsharing, but not carpooling. The carsharer residential context is much denser than that of non-carsharers (13,761 hab./km² vs. 8,737 hab./km²). The social mix index is significantly lower in the carsharer residential context than in that of non-carsharers (difference not significant regarding carpooling). In turn, the deprivation index is significantly higher in the carpooler residential context than in that of noncarpoolers. Finally, having a train/RER station around home is not related to carpooling or sharing the use of a car.

Multivariate modeling

Beyond the bivariate analysis presented above, a multivariate modeling was needed to ensure a ceteris paribus rationale, because of the likely collinearity between explanatory variables. The variables that were not significant in the bivariate analysis were not considered therein.

Results and interpretation of the logit and Poisson models for carpooling

Figures 2 and 3 present the odds ratios derived from the logit and Poisson models, respectively. As expected, some significant bivariate associations disappeared in the multivariate analyses. First, at the individual level, we found that socio-demographic variables are not associated with the likelihood of carpooling, which is in line with most studies (e.g. [START_REF] Buliung | Catching a ride on the information superhighway: toward an understanding of internet-based carpool formation and use[END_REF][START_REF] Canning | Reasons for participating in formal employer-led carpool schemes as perceived by their users[END_REF][START_REF] Ferguson | The rise and fall of the American carpool: 1970-1990[END_REF][START_REF] Teal | Carpooling: Who, how and why[END_REF]. Regarding socio-economic variables, only one income bracket appeared significant: individuals earning between 2.5 and 4 k€ are 0.41 times less likely to adopt carpooling than those earning less than 1 k€. Therefore, carpooling is more popular among low-income commuters, as outlined by other studies (e.g. [START_REF] Baldassare | Suburban attitudes toward policies aimed at reducing solo driving[END_REF][START_REF] Teal | Carpooling: Who, how and why[END_REF][START_REF] Vanoutrive | What determines carpooling to workplaces in Belgium: location, organisation, or promotion?[END_REF]. Most importantly, having family members/friends (OR = 1.69) or colleagues (OR = 4.65) who carpool is strongly associated with engaging in carpooling oneself. In the terminology of [START_REF] Manski | Identification of Endogenous Social Effects: The Reflection Problem[END_REF], this could result from a combination of "exogenous" peer-effects (the carpooling propensity varies with the exogenous characteristics of the entourage), "endogenous" effects (individuals carpool because the entourage carpools) and "correlated" effects (individuals carpool because they share similar characteristics with the carpooling entourage (e.g. they have the same sociodemographic profile).

In addition, individuals with a carpooling service at work are two times more likely to adopt carpooling than others. At the contextual level, only the deprivation index is significantly and positively associated with carpooling. The more deprived the neighborhood, the higher the likelihood of carpooling.

The results of the Poisson regression model exhibited the same associations, except that one travel bracket appeared significant: commuters with a travel time of between 16 and 30 minutes are 1.3 times more likely to engage in carpooling than those with a commuting time of less than 15 minutes.

Results and interpretation of the logit model for carsharing use

In the following model, the carsharing-based behaviors of colleagues were removed because of a too strong collinearity with family behaviors (VIF > 2). Regarding socio-demographic variables, two age groups were significant: aged between 40-49 (OR = 0.28) or 50-65 (OR = 0.24) years is associated with a lower likelihood of engaging in carsharing compared to the 18-24 years group. In other words, young people are the most susceptible to adopting carsharing, a finding already outlined elsewhere (e.g. [START_REF] Shaheen | Growth in Worldwide Carsharing: An International Comparison[END_REF]. In terms of income, only the group including those who did not want to answer the question is negatively associated with the likelihood of carsharing (OR = 0.13).

One may hypothesize that this category includes mainly high incomes. In this case, this finding would be in line with the study of [START_REF] Zhou | Opportunities for and Impacts of Carsharing: A Survey of the Austin, Texas Market[END_REF], who highlighted that high-income individuals are not interested in the carsharing solution. We also found that knowing two or more free-floating (OR = 4.18) or peer-to-peer (OR = 3.36) services is positively associated with using carsharing. Since the causality can theoretically be bidirectional in this relationship, two interpretations are possible: either adopting carsharing leads to learning about the available services, or -more interestinglyknowledge of the existing services encourages engaging in carsharing.

Regarding the interpersonal variables, as for carpooling, the behaviors of the entourage were found to be of prime importance. Individuals with family members and/or friends who share the use of a car are 10 times more likely to adopt carsharing themselves. Finally, the two contextual variables (population density and deprivation index) are also associated with the probability of carsharing. The denser the neighborhood, the higher the probability of carsharing. However, in contrast with carpooling, the more deprived the neighborhood, the lower the probability of carsharing. The effect of density can be explained by parking difficulties in very dense neighborhoods, which enhance carsharing by making car-ownership less relevant in those areas. The table 3 summarizes the main results. Table 3. Main findings and differences between carpooling and carsharing

Carpooling Carsharing

Socio-demographics and socio-economics variables < 0 -Individuals earning between 2.5 and 4 k€ are negatively associated than those earning less than 1 k€.

Contextual variables

> 0 -Deprivation index is positively associated with carpooling. The more deprived the neighborhood, the higher the likelihood of carpooling.

< 0 -Deprivation index: the more deprived the neighborhood, the lower the probability of carsharing -Population density: the denser the > 0 neighborhood, the higher the probability of carsharing.

Implications of the main findings

It is essential to realize that the determinants of carpooling and carsharing differ. Although the two solutions are shared modes, levers to encourage them have to be targeted differently. Regarding carpooling, the socio-demographic characteristics of individuals are mainly insignificant, whereas the availability of a carpooling service at work and social influences play an important role. These results imply that enhancing mobility management policies in the workplace would be effective. In France, since 1 January 2018, employer mobility plans are mandatory for companies with more than 100 employees. These plans propose a set of measures aimed at optimizing and increasing the efficiency of employees' journeys, as well as reducing the negative externalities of individual car use (pollutant emission and congestion). Our results suggest that offering a matchmaking service to favor the use of carpooling among employees would be a potentially relevant measure to include in mobility plans in the Paris region. In addition, we have revealed that the influence of the entourage (colleagues, family members) is important for carpooling. Once again, employers have a role to play in encouraging their employees to carpool for commuting.

Regarding carsharing, beyond the socio-demographic variables (age, income) that are positively associated with carsharing, our results highlight the importance of contextual variables. Densely populated and less deprived neighborhoods should be targeted as a priority for implementing carsharing services. Carsharing for commuting could be seen as an alternative to public transport for better-off households, for example at peak times in the Paris region. However, the use of a carsharing service is influenced by awareness of the service and by family members. Public policies regarding transport should consider this finding and reinforce information and communication about this service. Awareness and the entourage are thus relevant levers for tackling obstacles to using carsharing.

Conclusion

This study explored the potential determinants of the use of carpooling and carsharing for commuting among a comprehensive set of socio-demographic, socio-economic, interpersonal and contextual variables. The analyses were based on a representative sample of 2002 workers living in the Paris region. Four main findings are highlighted and offer some ways forward to encourage the use of shared modes such as carpooling and carsharing.

(i)

We show that the correlates differ between the two modes. For carpooling, our results suggest that socio-demographic characteristics are almost insignificant, except for income (negative association). In fact, carpooling is more popular among low-income commuters, while young people are the most prone to carsharing. (ii)

Our models highlight the importance of contextual variables, which have been little studied in the scientific literature of shared modes. We show that the use of carpooling mainly concerns people who live in rather deprived neighborhoods, while carsharing is overrepresented in well-to-do and denser neighborhoods. There is therefore a contextual rift between the use of these two shared modes. (iii)

We identify the importance of mobility management policies within the workplace.

Having a carpooling service within the company is positively associated with carpooling for commuting. Regarding carsharing, the awareness of existing services (free-floating and peer-to-peer) is positively associated with carsharing use. Public policies should therefore consider these results in order to implement information campaigns among target populations and to provide relevant guidelines for employers' mobility plans. (iv)

Finally, the main originality of this study is the identification of the key role of the entourage (colleagues and/or family members) in engaging in both carpooling and carsharing. Having family members and colleagues who carpool is strongly and positively associated with carpooling oneself for commuting. Regarding carsharing, only the influence of family member behaviors was studied, again showing a positive association. However, this result should be completed by further qualitative analyses.

In conclusion, this study suggests that a combination of individual, contextual and interpersonal (entourage) characteristics would probably be effective in favoring the use of carpooling and carsharing.

Figure 2 .

 2 Figure 2. Carpooling correlates: graphical illustration of the odds ratios estimated through the logit model in a sample of 2002 French workers (Paris region). Statistical significance of odds ratios is indicated by asterisks (* = p<0.05; ** = p<0.01; *** = p<0.001).

Figure 3 .

 3 Figure 3. Carpooling correlates: graphical illustration of the incidence rate ratios estimated through the Poisson model in a sample of 2002 French workers (Paris region). Statistical significance of incidence rate ratios is indicated by asterisks (* = p<0.05; ** = p<0.01; *** = p<0.001).

Figure 4 .

 4 Figure 4. Carsharing correlates: graphical illustration of the odds ratios estimated through the logit model in a sample of 2002 French workers (Paris region). Statistical significance of odds ratios is indicated by asterisks (* = p<0.05; ** = p<0.01; *** = p<0.001).

<0<0-

  Age groups: 40-49 and 50-65 are negatively associated compared to the 18-24 years group. family members/friends who share the use of a car are positively associated with adopting carsharing themselves.

Table 1 .

 1 Characteristics of users/non-users of carpooling and carsharing among a sample of 2002 French workers (Paris region) and bivariate relationships with individual variables. p-values < 0.05 (in bold) indicate whether the relationships are significant.

	Variables	Carpoolers	Non-carpoolers	Fishers's	Carsharers (%)	Non-carsharers	Fishers's
		(%)	(%)	exact test		(%)	exact test (p-
				(p-value)			value)
	Gender
	Women	53.61	57.01	0.27	43.96	56.87	<0.01
	Men	46.39	42.99		56.04	43.13
	Age group
	16-24	5.33	3.23	0.23	7.14	2.54	<0.01
	25-39	36.99	36.53		51.65	34.76
	40-49	36.68	40.30		29.67	41.22
	50-65	21.00	19.94		11.54	21.48
	Educational level
	< Bac	15.05	13.29	0.39	8.24	13.80	<0.05
	Bac	15.67	14.85		12.09	14.95
	Bac+2	19.75	24.25		23.08	23.38
	Bac+3/4	20.06	21.20		21.43	21.36
	Bac+5	16.30	15.99		17.58	16.28
	> Bac+5	13.17	10.42		17.58	10.22
	Household income per
	month in euros
	< 1000€	3.76	1.38	<0.05	3.30	1.44	<0.01
	1001 to 2500€	26.96	20.90		27.47	20.79
	2501 to 4000€	28.53	34.01		29.12	33.60
	4001 to 6000€	21.63	23.23		22.53	23.27
	6001 to 9000€	8.15	7.19		12.64	7.10
	> 9000€	1.57	2.34		2.75	2.25
	Decline to answer-I'd	9.40	10.96		2.20	11.55
	rather not say
	Household size
	1 17.87	17.84	0.22	17.58	17.61	0.27
	2 23.82	27.19		26.92	26.39

Table 2 .

 2 Characteristics of users/non-users of carpooling and carsharing among a sample of 2002 French workers (Paris region) and bivariate relationships with contextual variables. p-values < 0.05 (in bold) indicate whether the relationships are significant.

	Variables	Carpoolers		Non-carpoolers	Wilcoxon	Carsharers		Non-carsharers	Wilcoxon
						test (p-					test (p-
						value)					value)
		Mean	Sd	mean	Sd		Mean	Sd	Mean	Sd
	Population	9574	9531	9428	10368 0.07	13761	12332	8737	9629 <0.01
	density (hab./
	km²)
	Social mix index	-0.07	1.05	0.01	0.99	0.48	-0.14	1.01	0.02	1.00	<0.05
	Deprivation	0.11	1.07	-0.02	0.98	<0.05	-0.13	1.10	0.01	0.98	0.08
	index
	Density of	-	-	-	-	-	1.86	2.61	0.93	1.92	<0.01
	Autolib' stations
	(500-m buffer)
	Train/RER	%		%		Fishers's	%		%		Fishers's
	station					exact test					exact
	(500-m buffer)					(p-value)					test (p-
											value)
	No	79.94		83.17		0.17	80.22		83.08		0.35
	Yes	20.06		16.83			19.78		16.92

Acknowledgements

The study was funded by the project ACE-ICSEN (Adaptation aux Changements Environnementaux: une approche multi-échelle et transdisciplinaire -Institut des Changements Socio-Environnementaux). The questionnaire was specifically funded by VEDECOM Institute. The authors also whish to thank the anonymous reviewers for their constructive comments.


domain: chim phys

text:
S Vispoel

X Robert

Thomas

L Regalia

E Cousin

R R Gamache

B Vispoel

S Robert

X Thomas

Laboratory measurements and calculations of line shape parameters of the H2O-CO2 collision system

Keywords: high resolution infrared spectroscopy, line shape parameters, Modified Complex Robert-Bonamy calculations, planetary atmospheres, water vapor-carbon dioxide

Introduction

Water is a key molecule in the search for life on other planets. The origins of water on Earth are not yet well understood. In order to gain insights into this topic, the Solar system must be considered as a whole and the water contents of the different planetary and cometary bodies characterized. This study includes the understanding of the water cycle, the determination of the volume mixing ratio and the distribution with altitude of the different isotopologues of water. In particular, the ratio of deuterium to hydrogen (D/H) is a key diagnostic to characterizing the Solar system's evolution.

Several space instruments have flown or are flying around Mars and Venus. While these planets are our closest neighbors, they have a very different atmospheric structure and composition. Nadir viewing, provided that the spectral resolution and the SNR technique are adequate, and solar occultation viewing allow probing an atmosphere layer by layer to give vertical profile information. The solar occultation technique has the advantage of extremely localized sensitivity to the true atmospheric state (i.e. measured by Averaging Kernels), highly peaked on the impact tangent height. These profiles allow isotopic ratios, such as D/H, to be studied. Considering Mars, the ratio is an important evolutionary tracer for the atmosphere, surface, and their interactions in the climate system. The isotopic ratios are very useful because they are not as affected by modification related to the chemistry occurring in Mars' atmosphere as elemental abundances are [START_REF] De Bergh | Isotopic Ratios in Planetary Atmospheres[END_REF]. Investigations of the D/H ratio with altitude has led to interesting results [START_REF] Owen | Deuterium on Mars: the abundance of HDO and the value of D/H[END_REF][START_REF] Carr | D/H on Mars: effects of floods, volcanism, impacts, and polar processes[END_REF][START_REF] Encrenaz | The water vapor vertical distribution on mars from millimeter transitions of HDO andH2 18 O, Planet[END_REF][START_REF] Encrenaz | Infrared imaging spectroscopy of Mars: H2O mapping and determination of CO2 isotopic ratios[END_REF][START_REF] Bertaux | A warm layer in Venus' cryosphere and high-altitude measurements of HF, HCl, H2O and HDO[END_REF][START_REF] Fedorova | HDO and H2O vertical distributions and isotopic ratio in the Venus mesosphere by Solar Occultation at Infrared spectrometer on board Venus Express[END_REF]. Studying the spatial distribution of the D/H ratios can also generate interesting data. Villanueva et al. [START_REF] Villanueva | Strong water isotopic anomalies in the martian atmosphere: Probing current and ancient reservoirs[END_REF] used these 2D maps to study strong water isotopic anomalies in the Martian atmosphere; Good et al. [START_REF] Good | D/H isotope ratios in the global hydrologic cycle[END_REF] looked at D/H isotope ratios in the global hydrologic cycle; and Yang et al. [START_REF] Yang | The D/H ratio of water in the solar nebula during its formation and evolution[END_REF] modeled the D/H ratio of water in the solar nebula during its formation and evolution. These results could be refined if better line parameters were used, especially H2O broadened by CO2 line shape parameters. The current parameters used by the planetary community rely on empirical adaptation of other line lists or on scaling other broadening species data. In this study, we use our laboratory set-up and multispectrum procedure to provide measured line shape parameters and make state-ofthe-art Modified Complex Robert-Bonamy (MCRB) calculations of the half-width, line shift, and their temperature dependence for the H2O-CO2 collision system. Two spectral ranges of interest were identified around 2.7 and 6 microns and spectra recorded. The 6 micron band was analyzed previously by Brown et al. [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF] in 2007, while the 2.7 microns band was the target of a similar study, here in Reims, some twenty years ago [START_REF] Gamache | CO2-Broadening of Water-Vapor Lines[END_REF].

The spectrometer in Reims now has a better signal to noise ratio (~1000) than previously [START_REF] Gamache | CO2-Broadening of Water-Vapor Lines[END_REF] and the software developed to analyse the spectra in a multispectrum procedure [START_REF] Plateaux | Multispectrum fitting technique for data recorded by Fourier transform spectrometer: application to N2O and CH3D[END_REF] allows the retrieval of more consistent line shape parameters. For the deuterated species of water, HDO, measurements were made on transitions in the ν2. ν1, and ν3 bands [START_REF] Devi | Line parameters for CO2 broadening in the ν2 band of HD 16 O[END_REF][START_REF] Devi | Line parameters for CO2-and self-broadening in the ν3 band of HD 16 O[END_REF][START_REF] Devi | Line parameters for CO2-and self-broadening in the ν1 band of HD 16 O[END_REF] and MCRB calculations were made for the rotation, ν2. ν1, and ν3 bands [START_REF] Devi | Line parameters for CO2 broadening in the ν2 band of HD 16 O[END_REF][START_REF] Devi | Line parameters for CO2-and self-broadening in the ν3 band of HD 16 O[END_REF][START_REF] Devi | Line parameters for CO2-and self-broadening in the ν1 band of HD 16 O[END_REF][START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF]. In the present study, the newly measured spectra were analyzed in order to determine accurate line shape parameters of H2O broadened by CO2. We compared our measured results to the data available in the literature and with the MCRB calculations described below.

Section 1 describes the experimental conditions of the measured spectra and explains the line parameters retrieval procedure. Section 2 presents the theory and the calculations made. Finally, we will present our line shape measurements and calculations and compare them with literature values.

Experimental conditions:

1.1 Spectra recording Sixteen spectra were measured at room temperature with the Connes' type Fourier Transform Spectrometer (FTS) [START_REF] Connes | Astronomical Fourier spectrometer[END_REF][START_REF] Plateaux | Reims high resolution Fourier transform spectrometer. Data reduction for ozone[END_REF][START_REF] Régalia | FTS improvements and connection with a long White cell. Application: H216O intensity measurements around 1200cm-1[END_REF] built in the Group of Molecular and Atmospheric Spectrometry laboratory (GSMA) in Reims, France. This instrument had a 3-meter maximum path difference allowing a non-apodized resolution of 0.0017 cm -1 . Table 1 summarizes the recording conditions of the spectra in the two studied spectral regions. In the 2,7 µm region, all spectra were recorded with the following optical setup: a CaF2 beam splitter, some lenses and BaF2 windows and two InSb detectors, while in the 6 µm region we used two HgCdTe detectors. We always checked in spectral regions near 10 µm if the sample or the window emission could perturb the study but in the stated situation, we didn't observe anything. In addition to this, in the 6 µm spectral region to eliminate some waves on the 100 % of transmission of the spectra due to optical windows in the optical path, we had to divide by a spectrum recorded with an empty cell. We used two different optical spectral filters to select the recording spectral regions, the transmission windows are 1350 -2300 cm -1 and 3060 -4370 cm -1 respectively for the 6 µm and 2.7µm spectral region. During the experiment, the absorption path was maintained under vacuum and the pressure and the temperature were continuously monitored. Two absorption cells were used: a single-path cell with an optical path length of 13 cm and a 1-meter White cell [START_REF] White | Long optical paths of large aperture[END_REF] to achieve absorption path lengths of 4 and 8 meters. As shown in Table 1, for all spectra, the partial pressure of water vapor was kept very low in order to neglect as much as possible the influence of the self-broadening due to the H2O pressure in the determination of the line-shape parameters. In addition to the spectra listed in Table 1, we recorded a spectrum for each length of the cell, with only a low H2O pressure (1 Torr). These spectra allowed us to check the instrumental line shape and were used to calibrate the wavenumber scale of the spectra described in Table 1. For the spectral calibration, the line positions given in Loos et al. [START_REF] Loos | Measurement of positions, intensities and self-broadening line shape parameters of H2O lines in the spectral ranges 1850-2280cm-1 and 2390-4000cm-1[END_REF] were used as reference. This calibration is not crucial, as the multispectrum procedure allows discrimination of the different shifts for a given recording cell. Indeed, as the parameters are fitted simultaneously on several spectra in different experimental conditions, the shift due to the optical set-up is a parameter with the same value for all spectra, while the shift due to the pressure of the gas (absorbing or foreign) is depending on the studied spectrum.

The carbon dioxide buffer gas was supplied by Air Liquide company with a stated purity of 99.9999%. The pressure was measured with an uncertainty less than 0.3% using two MKS Baratron manometers with 10 and 1000 Torr full scale. The temperature was measured with a platinum probe with an uncertainty less than 0.5 K.

Table 1

The Figure 1 shows an example of the record spectra in the 2.7 µm spectral region

Line parameters retrieval

We used the multispectrum procedure developed at GSMA [START_REF] Plateaux | Multispectrum fitting technique for data recorded by Fourier transform spectrometer: application to N2O and CH3D[END_REF] to determine the line parameters. First, in order to minimize the effect of the apparatus function on the retrieved parameters, the experimental value of the Michelson's interferometer aperture was determined using spectra recorded at low pressures (not reported in the Table 1). The iris radius values of the FTS are indicated in Table 1. We did this verification even if the choice of the radius value is less critical than in the Doppler regime, since the H2O spectra were broadened by a pressure of CO2 from 100Torr to 500Torr. The verification was done on several lines taken in the whole spectral region allowing a check of the nominal iris radius that can be used. No specific signature appeared on the residuals of the fit during this verification. The nominal iris radius value was then used in the following analysis.

The multispectrum procedure enabled us to obtain simultaneously the position of the line, the line strength, the half-width and the line shift. An example of the multispectral fitting procedure is shown in Figure 2. The figure shows the simultaneous fit of three spectra with an absorption path length of 417,2 cm and different pressures (see table 1) in the upper panel and the residuals of the fit in the lower panel. Just over ten isolated lines were fit using two different values of self-broadening coefficients (broadening due to the pressure of H2O) in order to assess the impact of this parameter. A modification of 20 % of the fixed value of selfbroadening coefficient does not change the retrieved CO2-broadening parameter. Therefore, the self-broadening coefficients were fixed to the values of the HITRAN2016 database edition [START_REF] Gordon | The HITRAN2016 Molecular Spectroscopic Database[END_REF]. The impact of the self-shift parameter was also negligible.

Figure 2 2. Theory

For the collision system under consideration, H2O-CO2, and temperatures of the study, quantum dynamical methods are too slow to be practical even if adequate potential energy surfaces were available. Thus, the calculations of the half-width and line shift for the H2O-CO2 collision system were done using the semi-classical Modified Complex Robert-Bonamy formalism [START_REF] Robert | Short range force effects in semiclassical molecular line broadening calculations[END_REF][START_REF] Ma | Modification of the Robert-Bonamy formalism in calculating Lorentzian half-widths and shifts[END_REF] (MCRB). The half-width and the line shift are given by the real and imaginary parts, respectively, of the expression

γ + iδ ( ) f ←i = n 2 2π c v f v ( ) 0 ∞ ∫ dv 2π b 1-e -i S 1 + Im S 2 ( ) J 2 e -Re S 2 ( ) J 2       0 ∞ ∫ db (1).
In this expression, n2 is the number density of bath molecules, c is the speed of light, v is the relative velocity, f(v) is the Maxwell-Boltzmann distribution of velocity, < >J 2 is an average over the states of the bath molecules, and b is the impact parameter. S1(imaginary) and values. All other parameters are the recommended values from the literature: The dipole moment of water and its vibrational dependence are taken from Shostak and Muenter [START_REF] Shostak | The dipole moment of water.II. Analysis of the vibrational dependence of the dipole moment in terms of a dipole moment function[END_REF].

The quadrupole moments come from Flygare and Benson [START_REF] Flygare | The Molecular Zeeman Effect in Diamagnetic molecules and the determination of molecular magnetic moments (g values), magnetic susceptibilities, and molecular quadrupole moments[END_REF]. The polarizability and its vibrational dependence are given by Luo et al. [START_REF] Luo | Frequency-dependent polarizabilities and first hyperpolarizabilities of H2O[END_REF]. The ionization potential of water is taken from Ref. [START_REF] Lide | CRC Handbook of Physics and Chemistry[END_REF] and is a vibrationally-independent value. For CO2, the quadrupole moment is from the work of Graham et al. [START_REF] Graham | Measurements of the electric quadrupole moments of CO2 , CO and N2[END_REF], the value of the polarizability is from Bogaard and Orr [START_REF] Bogaard | MPT International Review of Science, Physical Chemistry, Series Two[END_REF], and the ionization potential is from Tanaka et al. [START_REF] Tanaka | Higher Ionization Potentials of Linear Triatomic Molecules. I. CO2[END_REF]. The numerical values are given in

Refs. [START_REF] Gamache | Halfwidths and Line Shifts of Water Vapor Broadened by CO2:Measurements and Complex Robert-Bonamy Formalism Calculations[END_REF] and [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF], for H2O and CO2, respectively.

The MCRB calculations included all real and imaginary components, atom-atom component expanded to high order and rank, trajectories determined solving Hamilton's equations, and explicit velocity averaging over the Boltzmann distribution.

MCRB calculations

Using HITRAN2016, all cold-band transitions for H2 16 O, H2 18 O, and H2 17 O in the range 1300-5000 cm -1 with J"≤22 and with full quantum assignments (note, in the UCL ab initio data [START_REF] Barber | A high-accuracy computed water line list[END_REF] on HITRAN only J and symmetry are good quantum numbers, so some transitions do not have all quantum numbers.) were extracted. The number of lines in each cold band is listed in Table 2. The MCRB formalism includes the vibrational dependence of the line shape parameters, thus calculations were made for each band listed in Table 2. The calculations of the half-width and line shift were made at 13 temperatures from 200 to 3000 K in order to determine the temperature dependence of these parameters. For each band, the unique rotational transitions were determined for the three principal isotopologues; then calculations were made for the H2 16 O isotopologue. These half-width and line shift values were used for the three isotopologues of this study since the line shape parameters are roughly equal for all three. The last step was to use the line shape parameter data at the 13 temperatures to determine the temperature dependence by the Gamache-Vispoel model [START_REF] Gamache | On the temperature dependence of half-widths and line shifts for molecular transitions in the microwave and infrared regions[END_REF].

These data were used to create an improved line file for applications for water vapor in CO2rich atmospheres. The previous H2O-CO2 line file [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] neglects vibrational dependence for H2 16 O, H2 18 O, and H2 17 O transitions and uses the rotation band half-widths for all transitions.

The line shifts, due to their strong vibrational dependence, were from a semi-empirical algorithm [START_REF] Jacquemart | Semi-empirical calculation of air-broadened half-widths and air pressure-induced frequency shifts of water-vapor absorption lines[END_REF] or from scaling.

Table 2 4. Results and comparisons

Spectral region around 6 µm

In this spectral region, line half-widths and shifts of H2O broadened by CO2 were compared to previous laboratory measurements of Brown et al. [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF] for the ν2 fundamental band and to the MCRB calculations done here. The data sets are shown in Figs. 3 and4 respectively for the half-widths and the pressure-induced line shift. In total 105 lines were analyzed. Shown in Fig. 3 and τ" as τ = Ka + Kc -J for use in their analysis. We observed similar behaviors as that seen in Table 8 of Brown et al. [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF]. Examples are given in Table 3, where pair transitions are shown for the measured and calculated half-widths and line shifts. Table 3 shows that as a function of Jm and Km the half-widths are nearly equal and the line shifts approximately equal but opposite in sign. A good example is the sequence for Jm = 4 and Km = 2. The exception is when the line shift is very small, for which the measurements and calculations are difficult to make.

Table 3

The good agreement with the previous measurements of Brown et al. [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF] validates the choice of the experimental conditions for the measured spectra, as well as the intermolecular potential used in the MCRB calculations.

Spectral region around 2.7 µm

As indicated in Table 1, for this spectral region, we recorded nine spectra with 2 different cells. We fitted simultaneously the spectra with the same cell length, it allows us to obtain lines parameters for transitions in a line intensity range from 3.2 x 10 -22 to 9.6 x 10 -20 cm/molec.: 60 transitions belong to the ν3 band, 23 to the 2ν2 band and 15 to the ν1 band. We did not find recent laboratory measurements for this spectral region in the literature. We compared our measurement and MCRB calculation results to the data from the Mars database [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF]. Figure 5 shows the half-widths in cm -1 atm -1 from our measurements with error bars 4. The MCRB calculations made here include the vibrational dependence that was neglected in Ref. [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] and the intermolecular potential was optimized for the H2O-CO2 collision system as discussed above. The result is that the calculations made here agree well with measurements with an APD of -1.1, -1.8, and -3.1 for the ν1, 2ν2, and ν3 bands, respectively. The calculations that neglect the vibrational dependence have -13.8, -12.0, and -11.8 APDs for the same data. The standard deviations show similar features. Given the relatively large value of the quadrupole moment of CO2, it could be argued that the vibrational dependence of the half-widths should not be too large. However, looking at the calculations to adjust the potential, the average difference between the potential used in Ref.

[17] and the results from the final potential of this work (pot 23) is roughly 9%. This result suggests that the vibrational dependence for these bands is ~3-4%. We will study the vibrational dependence of the half-width in a future work. approximation; and the red asterisk symbols correspond to scaling the air-induced pressure shift. The statistics for the comparison of the line shifts are in the bottom part of Table 4.

The average deviation between the measured and calculated line shifts goes between -0.001 to -0.004 cm -1 atm -1 with standard deviations around 0.006 cm -1 atm -1 for the 3 vibrational bands studied. The comparison with the approximate schemes used in Ref. [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] shows much larger average deviations, -0.07 to -0.08 cm -1 atm -1 , and standard deviations, ~0.065 cm -1 atm -1 . This figure and the statistics demonstrate the need for primary measurement or calculation to obtain good line shifts. It has been shown before [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF][START_REF] Gamache | Half-widths, their temperature dependence, and line shifts for the HDO-CO2 system for applications to planetary atmospheres[END_REF][START_REF] Vispoel | CO2-broadening coefficients in the ν3 fundamental band of methane[END_REF] that scaling techniques lead to very large errors, and it was stated [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] that the error from the semi-empirical line shift prediction routine can be large.

The line lists for the two previous spectral regions are given in the supplementary data.

Comparisons with data around 0.95 µm

Borkov et al. [START_REF] Borkov | Measurements of the broadening and shift parameters of the water vapor spectral lines in the 10,100-10,800 cm-1 region induced by pressure of carbon dioxide[END_REF] have measured the broadening and shift parameters of the water vapor spectral lines in the 10,100 -10,800 cm -1 region induced by pressure of carbon dioxide.

We compared the measurements and calculations made here to their results for the half-width.

Note, because the line shift is strongly dependent on vibration, a comparison of our line shifts to their data was not done. Borkov et al. made measurements for transitions in the ν1 + 2 ν2 + ν3, 3ν1, and 2ν1 + ν3 bands. These data are compared with our measurements and calculations in the 2.7 µm region (ν1, 2ν2, and ν3 bands). cm -1 , there is good agreement among the data sets. This result implies the vibrational dependence is of order ~3%, in agreement to the results from the previous section.

Conclusion

New laboratory measurements of line-shape parameters of H2O broadened by CO2 were performed. Two spectral regions were probed, i.e. 2.7 and 6 µm, as they are of interest for the planetary atmospheres remote sensing. MCRB calculations of the half-width, line shift, and their temperature dependence were made for the cold bands of H2O in the spectral range 1300-5000 cm -1 . The results were compared with the literature values [START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF][START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF][START_REF] Borkov | Measurements of the broadening and shift parameters of the water vapor spectral lines in the 10,100-10,800 cm-1 region induced by pressure of carbon dioxide[END_REF]. There is good agreement between the measured and calculated half-widths and line shifts with the data of Brown et al. in the 6 µm region. In the 2.7 µm region, our measurements and MCRB calculations agree well for both the half-widths and line shifts. Comparison with the halfwidths and line shifts from Ref. [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] are not as good, showing the effects of neglecting vibrational dependence for the half-widths and using approximate prediction routines for the line shifts. Comparisons of our measured and calculated half-widths with the data of Borkov et al. [START_REF] Borkov | Measurements of the broadening and shift parameters of the water vapor spectral lines in the 10,100-10,800 cm-1 region induced by pressure of carbon dioxide[END_REF] on the 0.9 µm region show good agreement. The measurements are available as supplementary files. The calculations in the range 1300-5000 cm -1 are available as a supplementary file. In the future, the MARS database [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] will be updated with the new parameters from this work and will be made publicly available, with a read_me.txt file, as a file from one of the authors website (faculty.uml.edu/robert_gamache). Note, using the improved model of temperature dependence [START_REF] Gamache | On the temperature dependence of half-widths and line shifts for molecular transitions in the microwave and infrared regions[END_REF], the same database can be used for Mars, Venus, or any other planetary atmosphere rich in CO2.

Table captions

Tables

1
Experimental conditions of recorded spectra with the Connes' type FTS ("DDM max" is the maximum of path difference for the interferogram measurement)

2
The Number of lines in each cold band from 1300 to 5000 cm -1 for the three principal isotopologues of H2O 3 Example of measured CO2 broadened H2O coefficients for "pair' transitions in the spectral region around 6 µm.

4

Comparison of the measured half-widths and line shifts with the MCRB calculations of this work and with the calculations of Gamache et al. [START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF] in the spectral region around 2.7 µm.. Given are the average percent difference (APD) and standard deviation (SD) in percent for the half-widths and the average deviation (Adev) and standard deviation in cm -1 atm -1 for the line shifts.

Figure captions

Figure 1: Example of recorded spectra in the 2.7 µm spectral region around 3956 cm -1 , with an absorption path length of 817,8 cm (see table 1).

Figure 2: Example of the fit with the multispectrum procedure of a H2O transition broadened by CO2 in the 2.7 µm spectral region. The three spectra have an absorption path length of 417,2 cm (see table 1).

Figure 1

 1 Figure 1

  S2(complex) are the first and second order terms in the successive expansion of the Liouville scattering matrix, which depend on the ro-vibrational states in the optical transition, the associated collisionally induced jumps from these states, on the intermolecular potential, and the characteristics of the collision dynamics. Note, semi-classical refers to the fact that the internal structure of the colliding molecules is treated quantum mechanically and that the dynamics of the collision process are treated by classical mechanics. Thus, in an optical transition from f i, the active molecule will undergo collisions with the bath molecules, for which the trajectories are determined by classical mechanics. In these collisions, the initial and final states of the radiating molecule, i and f, will undergo collisionally induced transitions to states i' and f', interrupting the radiation and causing collisional broadening and shifting. The states i' and f' are called collisionally connected states and are given by selection rules determined by the wavefunctions and intermolecular potential. Given the large number of terms in the intermolecular potential, there are many states, i' or f', that are possible. The quantum mechanical components of the calculation are the energy of the states involved in the collisionally induced transitions and the wavefunctions for the states, which are used to determine reduced matrix elements (RME), which give the probability of a collisionally induced transition. A recent study[START_REF] Gamache | Vibrational dependence, temperature dependence, and prediction of line shape parameters for the H2O-H2 collision system[END_REF] showed that MCRB calculations should use the ground state RMEs for all vibrational states.The intermolecular potential used in the calculations was comprised of electrostatic terms, the induction and London dispersion terms, and an atom-atom component expanded to 20 th order and rank 4. The atom-atom parameters were adjusted to bring the MCRB calculations in agreement with measurements of Tretyakov and Koshelev [27]. The final atom-atom parameters (called pot 23) are a change of εH-O, εH-C, εO-O, εO-C by -30%, a change of σH-O by -22.5% and a change of σH-C, σO-O, σO-C by -25% from the combination rule[START_REF] Hirschfelder | Molecular Theory of Gases and Liquids[END_REF]

  are the measured half-widths with error bars (blue solid triangle symbols), the MCRB calculated half-widths shifted slightly to the left (black open diamond symbols), and the measured half-widths from Brown et al. [11] with error bars shifted slightly to the right (red open square symbols) versus wavenumber of the transition. Comparing the current measurements and calculations for 105 transitions gives an average percent difference (APD) of -2.9% and a standard deviation (SD) of 4.9%. Comparing our measurements with those of Brown et al. for 100 transitions gives an APD of -3.2% and a SD of 3.0%. Comparing the measurements of Brown et al. with our MCRB calculations for 100 transitionsgives an APD of 0.17% and a SD of 5.5%. Thus, the half-width data of these two investigations and the calculations are in good agreement.In Fig.4, the measured line shifts with error bars (blue solid triangle symbols), the MCRB calculated line shifts shifted slightly to the left (black open diamond symbols), and the measured line shifts from Brown et al.[START_REF] Brown | CO2-broadened water in the pure rotation and ν2 fundamental regions[END_REF] with error bars shifted slightly to the right (red open square symbols) are plotted versus wavenumber of the transition. A comparison of our measurements and calculations for 105 transitions shows an average deviation of -0.0043 cm - 1 atm -1 and a standard deviation of 0.0048 cm -1 atm -1 . Comparing our shift measurements with those of Brown et al. for 100 transitions gives an average deviation of -0.0035 cm -1 atm -1 and a standard deviation of 0.0029 cm -1 atm -1 . Comparing the measurements of Brown et al. with our MCRB shift calculations for 100 transitions gives an average deviation of -0.00065 cm - 1 atm -1 and a standard deviation of 0.0040 cm -1 atm -1 . Again, showing excellent agreement between this study and that ofBrown et al.

Figure 3 Figure 4

 34 Figure 3

(

  blue solid triangle symbols), our MCRB calculations (black open diamond symbols), and the previous calculations of Gamache et al. [17] (red open square symbols) versus a line count. The data are arranged such that the first 16 points are ν1 band data, points 17-40 are 2ν2 band data, and points 41-105 are ν3 band data. The statistics for the agreement are given in top part of Table

Figure 7

 7 shows the half-widths in cm -1 atm -1 for the our measurements with error bars (blue solid triangle, open triangle, and open diamond symbols for our ν1, 2ν2, and ν3 band data, respectively), the MCRB calculations shifted slightly to the left (black solid star, open star, and star of David symbols for our ν1, 2ν2, and ν3 band data, respectively), and the Borkov et al. data with error bars shifted slightly to the right (red solid circle, open circle, and open circle with a cross symbols for their ν1+2ν2+ν3 ν1, 3ν1, and 2ν1+ν3 band data, respectively) versus line count. The data are arranged such that the first 13 points are ν1+2ν2+ν3 band data, points 14-19 are 3ν1 band data, and points 20-45 are 2ν1+ν3 band data (labeled at the bottom of the figure). In total 45 transitions were compared between the data sets. The statistics for the comparison of our measurements with the data of Borkov et al. are an APD of 1.5% with a SD of 7.3%; the comparison of our calculations to the data of Borkov et al. are an APD of -3.9% with a SD of 3.4%. The statistics imply a constant shift of our calculations, within 3-4%, from the Borkov et al. data. Even though the spectral regions of the two data sets are separated by about 6000

Figure 7

 7 Figure 7

Figure 3 :

 3 Figure3: CO2-broadened half-widths (cm -1 atm -1 ) of H216 O transitions in the 6 µm region

Figure 4 :

 4 Figure 4: CO2 pressure-induced line shifts (cm -1 atm -1 ) of H2 16 O transitions in the 6 µm

Figure 5 :

 5 Figure 5: CO2-broadened half-widths (cm -1 atm -1 ) of H2 16 O transitions in the 2.7 µm region

Figure 6 :

 6 Figure 6: CO2 pressure-induced line shifts (cm -1 atm -1 ) of H2 16 O transitions in the 2.7 µm

Figure 7 :

 7 Figure 7: Comparison of the half-widths (cm -1 atm -1 ) from the measurements (Exp) blue solid triangle, open triangle, and open diamond symbols for our ν1, 2ν2, and ν3

Table 4

 4 Figure 6 is a plot of the measured line shifts with error bars (blue solid triangle

symbols), our MCRB calculations (black open diamond symbols), and the data of Gamache et al.

[START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF]

. Because the study of Gamache et al.

[START_REF] Gamache | A spectral line list for water isotopologues in the 1100-4100 cm -1 region for application to CO2-rich planetary atmospheres[END_REF]

only made calculations for the rotation band, the values of the shifts in the database are from various approximate schemes from the work of Jacquemart et al.

[START_REF] Jacquemart | Semi-empirical calculation of air-broadened half-widths and air pressure-induced frequency shifts of water-vapor absorption lines[END_REF]

, or scaled directly from δair from HITRAN2012

[START_REF] Rothman | The HITRAN2012 molecular spectroscopic database[END_REF]

(δCO2= δair * 4.211). In the plot, the red open star symbols correspond to Jacquemart et al.'s section 4.2.3 approximation; the red open circle symbols correspond to Jacquemart et al.'s section 4.1 approximation; the red open plus symbols correspond to Jacquemart et al.'s section 4.2.1

Acknowledgements

This work was supported by the Programme National de Planétologie (PNP) of CNRS-INSU co-funded by CNES. Several authors (RRG, BV) are pleased to acknowledge support of this research by the National Science Foundation through Grant No. AGS-1622676. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors sincerely thank Dr H. Tran from LMD (Paris) for many useful discussions, her expertise and advice on the line profile.

Rotational Assignment

Jm Km τ' τ'' γExp.

cm -1 /atm γMCRB cm -1 /atm δExp.

cm -1 /atm δMCRB cm -1 /atm