Valentin Le

Pierre Aumond

Valentin Le Bescond

Arnaud Can

Pascal Gastineau

Open-source modeling chain

Keywords: noise exposure, activity-based model, multi-agent simulation, MATSim, NoiseModelling

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Introduction

The significant health impacts from noise are universally acknowledged, with road traffic being rated as the primary contribution. Exposure-response relationships have been proposed, and the corresponding burden of disease in terms of disability-adjusted lifeyears has been quantified, in indicating that at least one million healthy life years are lost every year due to traffic-related noise in western Europe (WHO, 2011). Consequently, the World Health Organization strongly recommends reducing noise levels produced by road traffic to below 53 decibels Lden and 45 dB Lnight given that road traffic above these levels is associated with adverse effects on health and on sleep, respectively (WHO, 2018). Recent studies suggest that these recommendations may even be lowered [START_REF] Vienneau | Association between Transportation Noise and Cardio-Metabolic Diseases: An Update of the WHO Meta-Analysis[END_REF][START_REF] Van Kamp | Evidence Relating to Environmental Noise Exposure and Annoyance, Sleep Disturbance, Cardio-Vascular and Metabolic Health Outcomes in the Context of IGCB (N): A Scoping Review of New Evidence[END_REF]. Since they concentrate both the highest noise levels and population densities, metropolitan areas are subject to the highest exposures. Quantifying and reducing noise pollution exposure is thus a crucial issue for cities in terms of health, quality of life and attractiveness. In order to move towards cities in which all socioeconomic groups benefit from a quieter environment, it is necessary to improve understanding of the complex interactions taking place between transport policies and impacts in terms of distribution of noise exposure among the population. In this paper, we propose an open source modeling framework that enables both characterizing the individual exposure to road traffic-related noise and investigating noise exposure inequality problems.

The stakes involved in noise reduction have been assessed through both economic calculations correlating noise levels with property prices (Andersson et al., 2010) and contingent evaluation methods that estimate people's willingness to pay for a quieter environment [START_REF] Lera-López | Willingness to pay to reduce environmental impacts from road transportation: a case study from the Spanish Pyrenees[END_REF][START_REF] Barreiro | How much are people willing to pay for silence[END_REF]. The correlation between noise exposure levels and socioeconomic groups has also been the subject of analyses; these studies most often conclude that disadvantaged populations are exposed to higher levels in their residential environment than affluent populations [START_REF] Braubach | Social inequities in environmental risks associated with housing and residential location-a review of evidence[END_REF]. However, a recent study has shown that in Paris, France (excluding the suburbs), residents of more affluent neighborhoods were actually exposed to higher levels of road traffic noise compared to their less privileged counterparts [START_REF] Havard | Social inequalities in residential exposure to road traffic noise: an environmental justice analysis based on the RECORD Cohort Study[END_REF]. Although instructive, these quantified effects and environmental justice analyses are limited in their scope by virtue of being based on estimates of exposure levels in the place of residence. The actual exposure of individuals lies at an intersection between a field of noise pollution and individuals, both of which vary in time and space. In relying on concomitant noise and time-activity pattern measurements, [START_REF] Chaix | Mobile Sensing in Environmental Health and Neighborhood Research[END_REF] and El [START_REF] El Aarbaoui | Short-term association between personal exposure to noise and heart rate variability: The RECORD MultiSensor Study[END_REF] demonstrated the strong influence of mobility on noise exposure.

From a decision-making perspective, traffic models are needed to assess the impact of mobility management policies on noise exposure. Some couplings between microscopic traffic models and noise prediction tools are already operational. These couplings make use of various traffic (SYMUVIA in [START_REF] Can | Dynamic traffic modeling for noise impact assessment of traffic strategies[END_REF], Chevallier et al. (2009) and [START_REF] Can | Dynamic approach for the study of the spatial impact of road traffic noise at peak hours[END_REF]; PARAMICS in De [START_REF] De Coensel | Combined assessment of noise and air pollution caused by road traffic[END_REF][START_REF] De Coensel | Effect of traffic signal coordination on noise and air pollutant emissions[END_REF] and [START_REF] Luo | Dynamic modeling of road traffic noise around buildings in an urban area[END_REF]; VISSIM in Estevez-Mauriz and Forssen (2019); HUTSIM in [START_REF] Heltimo | A microlevel method for road traffic noise prediction[END_REF]) and acoustic models (NMPB in [START_REF] Can | Dynamic traffic modeling for noise impact assessment of traffic strategies[END_REF]; FHWA in Chevallier et al. (2019);HARMONOISE in De Coensel et al. (2010);IMAGINE in De Coensel et al. (2012); CNOSSOS in Estevez-Mauriz & Forssen (2019) and [START_REF] Can | Dynamic approach for the study of the spatial impact of road traffic noise at peak hours[END_REF]; NORD in [START_REF] Heltimo | A microlevel method for road traffic noise prediction[END_REF]). The underlying principle is that the traffic model captures the effects of a traffic control strategy on vehicle kinematics, while the acoustic model calculates the resulting impact on vehicle emissions and moreover the levels in a set of receivers. Tested traffic control strategies include: a comparison between roundabouts and traffic signal intersections [START_REF] Can | Dynamic traffic modeling for noise impact assessment of traffic strategies[END_REF]Chevallier et al., 2009;Estevez-Mauriz and Forssen, 2019), speed reduction measures [START_REF] Luo | Dynamic modeling of road traffic noise around buildings in an urban area[END_REF], introduction of a dedicated bus lane [START_REF] Can | Dynamic traffic modeling for noise impact assessment of traffic strategies[END_REF], green wave implementation [START_REF] Can | Dynamic traffic modeling for noise impact assessment of traffic strategies[END_REF][START_REF] De Coensel | Combined assessment of noise and air pollution caused by road traffic[END_REF], traffic signal optimization [START_REF] De Coensel | Effect of traffic signal coordination on noise and air pollutant emissions[END_REF], and demand management during morning rush hour [START_REF] Can | Dynamic approach for the study of the spatial impact of road traffic noise at peak hours[END_REF]. Although various models have been used, all studies converge towards the expected result that reduced speeds and flows, combined with smoother traffic, serve to lower noise levels. However, with the exception of [START_REF] Can | Dynamic approach for the study of the spatial impact of road traffic noise at peak hours[END_REF], which deals with a network spanning several km², these studies are limited to very small areas and do not consider the reassignment of vehicles onto the network due to the tested strategy. In addition, all these studies ignore both multimodal trips and time-activity patterns in their exposure calculations.

In order to refine noise exposure calculations and assess the impact of transport policies on exposure, the development of efficient city-scale modeling chains, including a traffic module and an acoustic module, has thus become a key issue for the scientific community.

Efficient city-scale acoustic models have been developed for noise mapping purposes. As regards traffic modeling, multi-agent traffic models are able to meet these requirements.

Each agent in the simulation is an inhabitant whose trips are defined by a daily activity schedule and constrained by network traffic conditions. In addition, each agent is described by its socioeconomic attributes, hence paving the way for an assessment of environmental inequalities. Numerous studies have been carried out combining traffic agent-based models and air pollution models (see [START_REF] Ramacher | Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas[END_REF] for a review), in emphasizing the need to account for population dynamics when estimating population exposure. These models have been applied to reveal the relationships between exposure levels and socioeconomic groups. Among these studies, [START_REF] Gurram | Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of highresolution data[END_REF] showed that people living in poverty travel longer distances, tend to be of working age or are a minority group facing disproportionately high exposure levels. Moreover, [START_REF] Lu | Activity-based air pollution exposure assessment: Differences between homemakers and cycling commuters[END_REF] found that exposure to NO2 is greater among commuters than among homemakers. These models were also used to conduct traffic strategy assessments. Various congestion and emission pricing schemes are, for instance, compared in Agarwal and Kickhöfer (2015).

Couplings between multi-agent traffic models and noise models are rarer. The first work was performed by [START_REF] Gerike | Mapping external noise costs to the transport users conceptual issues and empirical results[END_REF], who coupled MATSim with the noise mapping software IMMI, although no numerical simulation example was given therein. Subsequently, [START_REF] Houot | Les mobilites urbaines et leur impact sur l'exposition au bruit : simulation de scenarios prospectifs appliques à la ville de Besancon [Urban mobilities and their impact on noise exposure: simulation of prospective scenarios applied to the city of Besancon[END_REF] coupled the MobiSim model with the MITHRA-GIS noise mapping software on a case study in Besançon (France) in order to test various scenarios: modification of the public transit offering, desynchronization of individuals' activity planning. Scenarios were analyzed in terms of the spatial distribution of sound levels for different time slots. Their study suggested that a small increase in the proportion of public transit exerts only a limited impact on the sound environment, while desynchronizing individuals' activity planning significantly modifies the temporal profile of the sound environment throughout the day. [START_REF] Kaddoura | An activity-based and dynamic approach to calculate road traffic noise damages[END_REF] proposed a coupling between MATSim and the RLS90 acoustic model, revealing the need to take the spatial and temporal variations of populations into account in exposure calculations. This modeling framework was then used in [START_REF] Kaddoura | Simultaneous internalization of traffic congestion and noise exposure costs[END_REF] for a multi-criteria assessment involving traffic and noise indicators. This last study proved the benefit of policy-making that adopts a dynamic and differentiated approach whether or not the traffic is congested. The acoustic module of this modeling chain was updated in [START_REF] Kuehnel | Noise Shielding in an Agent-Based Transport Model Using Volunteered Geographic Data[END_REF] and [START_REF] Zwick | Agent-based simulation of city-wide autonomous ride-pooling and the impact on traffic noise[END_REF], updated noise prediction model in MATSim, that is RLS19. [START_REF] Kuehnel | Impact of simulation-based traffic noise on rent prices[END_REF] proposed a method to estimate the effect of noise on housing prices based on this same coupling. [START_REF] Cucurachi | Noise footprint from personal land-based mobility[END_REF] also relied on MATSim to evaluate the contribution of road transportation to the total noise footprint of land-based mobility. Lastly, from a decision-making perspective, [START_REF] Bean | A case study of off-hour delivery collaboration and costsharing between freight receivers and carriers[END_REF] proposed a modeling approach based on MATSim to investigate offhour deliveries in Cape Town (South Africa); their work highlighted the need to account for the induced increase in noise pollution.

All these studies have shown the value of using multi-agent modeling to analyze acoustic impacts. In this paper, a complementary approach is introduced, more dedicated to an audience of acousticians. The aim is for this community to grasp the potential of multiagent traffic modeling in order to rise new research questions on acoustic modeling, on associations with annoyance, or on the estimation of specific acoustic indicators. Based on this premise, a module for reading and integrating MATSim outputs has been integrated into the NoiseModelling noise prediction tool, which is a free and open source tool, developed since 2010, and designed to produce environmental noise maps over very large urban areas that implements the CNOSSOS method (Commission Directive, 2015;[START_REF] Bocher | NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps[END_REF]Aumond et al., 2020). Section 2 presents the resulting modeling chain. The potential of a modeling chain for use in exposure calculations and associated analysis is illustrated in Section 3 through examples from a case study in Nantes (France). Section 4 discusses the limitations and necessary further developments, while Section 5 offers a conclusion.

Methods

Description of the modeling framework

The method introduced herein couples a traffic simulation component with a noise modeling component. The first component is a multi-agent traffic simulation, MATSim (see Section 2.1.1 for a presentation), fed by inputs generated by the Eqasim component described in Section 2.1.2. The noise modeling component consists of the NoiseModelling software described in Section 2.1.3.

Transportation modeling component

The transport modeling component consists of the Multi-Agent Transport Simulation MATSim, as described in [START_REF] Horni | Introducing matsim[END_REF]. As is the case with other agent-based simulations, MATSim has been inspired by co-evolutionary principles. All agents seek to maximize their travel score while sharing the traffic network and its capacities with other agents. The map of all road segments is represented as a network graph, with roads being represented as links between two nodes and nodes serving as road intersections. The MATSim simulation cycle is depicted in Figure 1. The initial demand is derived from every agent's preferred schedule, i.e. where and when they wish to be within the simulated day.

The mobility simulator (mobsim) is responsible for modeling the traffic interaction on the network. Several mobsim solvers are available, but the most common ones are based on a FIFO (First In, First Out) queue model. The principle herein is that every link has an input FIFO queue and an output FIFO queue. A vehicle can leave a link and enter the next one if it meets the following conditions:

It is the first vehicle in the output queue of the link scheduled to be exited; It spends at least the minimum travel time allocated (max speed * length) on the link scheduled to be exited;

The link scheduled to be entered has not yet reached its maximum vehicle capacity.

The scoring part assigns a value to the agent's simulated trips. During the replanning phase, a given percentage of agents are allowed to alter their schedule, and mobsim is run again with these adjustments. The agents' ultimate purpose is to choose the trips with the best scores. After a desired number of iterations, MATSim outputs the agents' final schedule and traffic events for analysis.

Fig. 1: MATSim simulation cycle (Horni et al., 2016)

To run a MATSim simulation, several input files are required, as described in Table 1 below. The traffic network, a list of "links" with their geometry and a list of attributes (length, traffic capacity, etc.).

persons.xml.gz

The list of agents with their daily schedules.

activities.xml.gz

The list of all agents' "activities" and their locations, as defined in the agent's schedule.

transit_schedule.xml.gz

The list of public transit (bus, train, tram, etc.).

config.xml

The Simulation configuration file, setting the various parameters and locating the other files.

After running a MATSim simulation, a list of xml files containing the simulation output data is generated. The main output files used for coupling with NoiseModelling are listed in Table 2.

output_events.xml.gz

The list of traffic events.

MATSim events vary based on the mobsim solver, which is configured according to the type of transport used (e.g. public transit, car, bike). For example, events are generated by the simulation every time a vehicle moves from one road segment to another or every time an agent enters or exits a car or public transit vehicle. The list of events generated by a typical car trip is shown in Figure 2.

The Eqasim project

In order to prepare the MATSim simulation inputs, and more importantly a simplified microscopic representation of the actual population, the Eqasim project, as developed by [START_REF] Hörl | Open data travel demand synthesis for agent-based transport simulation: A case study for Paris and Île-de-France[END_REF], can be used. This project consists of a modeling pipeline of data consumption and processing steps, making the transition from available public census data and other open source databases to a fully functional MATSim simulation in the French context. This project was chosen for the case study presented in Section 3 given its highprofile use in generating a MATSim scenario for the entire Paris Region (Ile-de-France).

Since France's census format is standardized, this project provided the most straightforward way to generate a valid traffic model.

Let's mention here that the MATSim scenarios generated by this pipeline involve a nonstandard approach to simulating traffic. More precisely, in Eqasim, the replanning phase of MATSim is replaced by a Discrete Mode Choice model that more heavily emphasizes mode change than on-time departure changes; this emphasis is typically reversed in the default MATSim model.

Noise modeling component

The NoiseModelling is closely paired with spatial database H2GIS or PostGIS, given their large number of spatial functions and features. The calculation method, which relies heavily on GIS spatial data, is thus aligned with previous work that has demonstrated the contribution of GIS to calculating noise propagation via market software [START_REF] Tang | Influences of urban forms on traffic-induced noise and air pollution: Results from a modelling system[END_REF][START_REF] Gulliver | Development of an open-source road traffic noise model for exposure assessment[END_REF]Alesheikh and Omidvari, 2010;[START_REF] Reed | A GIS tool for modeling anthropogenic noise propagation in natural ecosystems[END_REF].

This calculation is split into three modules:

-A noise emission calculation module estimates the sound power level based on input data, such as the mean flow rates and mean speeds per road segment, along with the traffic composition (ratio of medium, heavy and two-wheel vehicles);

-A path-finding algorithm searches the paths between sources and receivers, in considering the distance propagation, the number of reflections and diffractions (illustrated in Figure 3); -The software computes the attenuation value along each of the propagation paths for a selected range of octave bands, using the CNOSSOS-EU model.

The sound level at a receiver is the sum of the contributions of all sources; its output consists of a grid of receivers with an attributed sound level value computed in dB.

Coupling

General remarks

The entire modeling framework is illustrated in Figure 4. The initial inputs are:

Census data of the population to model; Geographic data imported from the Open Street Map (OSM) project.

Fig. 4: Modeling framework

The Eqasim project uses these inputs in order to generate a synthetic population, directly usable in the MATSim multi-agent traffic simulation. More specifically, Eqasim is used to:

generate a traffic network for the various transport modes such as car, train, bus, bike, etc;

generate a list of agents, with characteristics such as age, gender, driver's license or household income;

generate a list of a geographically placed activities (or facilities); generate a schedule, i.e. the sequence of activities that the agents will try to follow during the day, for every agent. Applying a procedure inspired by statistical matching algorithms, trip chains from household travel survey data are attached to the synthetic population [START_REF] Hörl | Open data travel demand synthesis for agent-based transport simulation: A case study for Paris and Île-de-France[END_REF].

These inputs are then used to run a MATSim traffic simulation.

The MATSim simulation result is a list of events representing, among other things, vehicle movements. The elements in this list of benefit for noise calculations (time, position, vehicle, speed, etc.) are then extracted and used as noise sources in the noise modeling software.

Buildings are directly imported from the OSM database. Building height is taken from the OSM map and height is inferred based on available meta-data, such as the number of floors, when the height is not available. Noise receivers are determined from the agent's activity positions. The noise levels at the receiver positions can then be calculated. By importing the MATSim resulting agent schedules, the time and position of every agent is accessible.

Agent noise exposures at any given time and throughout the simulated day can then be calculated.

The coupling steps and their key parameters are discussed in this section. The way time is handled, during the simulated day, is described in Section 2.2.2. The road traffic emissions calculation is described in Section 2.2.3. Sections 2.2.4, 2.2.5 and 2.2.6 cover respectively the receiver placement technique, the attenuation matrix calculation and custom implementations added to the framework.

Time discretization

Standardized noise maps in the European Union are built in considering 3 time periods, namely day, evening and night, respectively 6 am -6 pm, 6 pm -10 pm and 6 pm -6 am for France. The input data for constructing these maps are given for each of these time periods; in most cases, they are typically given as annual averages. With MATSim, the state of the simulated traffic can be accessed at every time during the day, thus providing the opportunity to improve time resolution when generating traffic noise contribution maps.

Time periods can be managed in two ways. The first one is to average traffic data over the day, evening and night periods and then generate a noise map that is comparable to the European standard. The second way is to set a fixed time step, e.g. 1 hour, and generate a noise map for each one of these time steps, thus resulting in a finer time quantification for the traffic noise maps and leading to a new set of potential analyses requiring time variation.

In the work presented herein, the three following time discretization periods were implemented and can be chosen at runtime by the user: (i) day, evening, night; (ii) every hour; and (iii) every 15 minutes.

Calculating road traffic noise emissions

In CNOSSOS-EU, the source power level of a road segment is calculated based on the following parameters:

-Vehicle category;

-Number of vehicles per hour per category;

-Average speed per vehicle category.

Four vehicle categories serve to analyze road traffic, as defined in the CNOSSOS-EU model, i.e.:

-Category 1: Light-duty motor vehicles;

-Category 2: Medium heavy-duty vehicles;

-Category 3: Heavy-duty vehicles;

-Category 4: Powered two-wheelers.

In the NoiseModelling software, noise sources are modeled as road segments with a vehicle flow per hour and an average speed per category. To extract vehicle flow and average speed per road segment (or network link), the enterLink and leaveLink events are extracted from the events output file (see Figure 2).

For each road segment, each vehicle type and each time period, the number of vehicles passing and their speed are stored. The average speed is then calculated and the NoiseModelling libraries are used to calculate a sound power level per meter for every road segment and every time period.

Placing noise receivers

In NoiseModelling, noise levels are calculated at the listed receiver's positions. This software offers various options for placing receivers. A new option has been added; it is specific to the coupling presented here and detailed in Section 2.2.6.2. Agents' activities, found in the activities.xml output file, can be imported into NoiseModelling in order to directly place receivers at the activities position, which in turn makes it possible to calculate noise levels in the different locations the agents may visit during the simulated day.

Calculating the Source-Receiver noise attenuation matrix

Once the receivers have been placed and the road sections discretized onto point sound sources, the source-receiver attenuation matrix is computed using the NoiseModelling libraries. For each source-receiver couple, this matrix stores the noise attenuation, which encompasses all the various noise propagation paths between source and receiver.

More specifically, for each receiver, a path-finding algorithm locates the list of contributing noise sources, along with their respective attenuation. In practice, this attenuation represents the arithmetic difference between the sound power level of the source (Lw, in decibels) and the pressure level at the receiver (Lp, in decibels). This matrix is calculated once and stored in the NoiseModelling database.

As a result, several noise maps can be calculated just by modifying the power level of the sources, for instance in the event average traffic speeds are modified. For this step, the new contribution of a sound source at a given receiver is obtained by simply subtracting the source receiver attenuation from the new source power level.

The last stage entails using the attenuation matrix with the road segment noise power level to calculate, for each receiver and for each time period, a resulting sound pressure level. A noise map is thus obtained from a MATSim simulation output.

Integration of the coupling

Such a coupling was performed between MATSim version 11.0 and NoiseModelling version 3.3. It was decided to integrate the coupling inside NoiseModelling, in the form of a list of MATSim-related scripts. Since both MATSim and NoiseModelling are written in Java programming language, this coupling was accomplished using this same language. This common language was particularly useful for creating import scripts, by using the MATSim library as a dependency in NoiseModelling. The coupling code was released in version 3.3.2 of NoiseModelling, along with a specific tutorial (https://noisemodelling.readthedocs.io/en/latest/Matsim_Tutorial.html) and can be found on the project Gihub repository: https://github.com/Ifsttar/NoiseModelling.

An example of a script is shown in Figure 5, using the NoiseModelling GUI tool, WPSBuilder.

Fig. 5: A NoiseModelling script that imports traffic data from MATSim

Some of the specific developments achieved during the coupling are detailed in Sections 2.2.6.1 and 2.2.6.2, focusing respectively on the importation of roads and receiver placement.

Maintaining road segment geometries

One of the issues involved when coupling the two models pertains to road geometries. In MATSim, when importing an Open Street Map (OSM) file, the roads are simplified, as illustrated in Figure 6.

Fig. 6: Road simplification in MATSim

This simplification is carried out in MATSim to limit network size, and thus the computational cost of the simulation. Most OSM map import codes replace curved roads with straight lines between each network intersection, while saving the total length of the curved street as additional data. This simplified network of links is illustrated using red lines in Figure 6. An issue arises if these links are directly used as a noise source in NoiseModelling for two reasons affecting the sound propagation calculation step: 1) the simplified network can pass through buildings; and 2) the distance from the source to a fixed receiver can vary significantly. Both cases are illustrated in Figure 6.

To overcome this particular issue, a list of original geometries is stored in a CSV file when importing the network in MATSim, prior to link simplifications. This geometry file is then used, when importing the MATsim traffic output into NoiseModelling, to replace the simplified roads before the NoiseModelling propagation calculation .

Snapping Activity Receivers to Buildings

Map data © OpenStreetMap

Snapping activity receivers to buildings is a crucial step since source-receiver distances have an important impact on the resulting noise levels. Here one wants to comply as much as possible with the existing exposure calculation standard methods, so that comparing in a further work the obtained results under different modeling approaches or different mobility policies would be possible.

When generated by the Eqasim pipeline, version 1.1.0, agent activities are placed using postal addresses. The pipeline actually uses a publicly available database provided by The National Institute of Geographic and Forest Information (IGN), i.e. BD-TOPO, to assign activities to a proper address. For example, an "education" activity location is chosen amongst schools or universities addresses. The problem here is that the actual position of the activity is placed near the front door of every building. So, if these locations are used directly as receiver positions, the resulting source-receiver distances would not correspond to the equivalent for standardized noise maps. A second issue is that the receivers would almost always be the most exposed to the traffic noise.

As a consequence, to calculate noise level at the activities in a way comparable to that of the standardized noise maps, the agents' activity locations are "snapped" to buildings, according to the following method:

All noise receiver points that would be placed around buildings, should it be possible to evaluate the standard noise exposure, are generated with a meters from one another (Figure 7).

Next, for each activity, the closest building is identified and a random receiver is chosen amongst the receivers that surround the chosen building. The receiver's position is assigned to the activity. A maximum distance of 200 meters is set, beyond which the activity's position remains the same apart from height, which is imposed at 4 meters.

Fig. 7: Placement of the NoiseModelling receiver based on the agent's activity positions

Note that with this method for assigning agents to buildings, the capacity of buildings is not a used variable, at least not directly. The exact method was not analyzed here and is outside the scope of our work, so it is not impossible to have activities that locally exceed the capacity of buildings, even if this might be rare.

Case study

A MATSim simulation was set up for Nantes (France) and 10 surrounding cities as a proof of concept to illustrate how the proposed modeling framework provides a workable system for refining the assessment of individual exposure to road traffic noise. The NoiseModelling software was used to calculate the agent-based noise levels, according to the method described in Section CNOSSOS-EU emission and propagation standard.

Description of the study area

Initial Activity Position

Randomly Chosen

Standard Receivers

The study area is shown in Figure 8. Nantes is the France's sixth-largest city, with a population of 303,382. The entire study area contains 522,008 inhabitants (according to the latest census data) for a surface area of 317.5 km 2 (i.e. population density = 1.645 people/km²); it has been divided into 206 distinct IRIS (i.e. average population per IRIS = 2,534).1 Municipalities with over 10,000 population plus a large proportion of those with 5,000 to 10,000 population have been divided into several IRIS units and, by extension, all municipalities not divided into IRIS units do constitute IRIS units in themselves. The agents were ascribed activity plans generated by Eqasim, which corresponded to a total of 476,823 trips during the simulated day.

The resulting traffic was analyzed in 15-minute time periods. The NoiseModelling propagation was computed with the following parameters:

Maximum source distance: 750 meters;

Maximum reflection distance: 50 meters;

Reflection order: 1; Vertical diffraction: enabled;

Horizontal diffraction: enabled.

To have an approximate idea of the total computation load, the different steps and their corresponding computation time are presented below for a 14-core, 3.6GHz, 48Go of Ram Ubuntu virtual machine:

Eqasim synthetic population: 2 hours;

MATSim simulation: 4 hours;

NoiseModelling simulation: 5 hours. From an agent's perspective, in moving from activity to activity throughout the daily plan, a noise level can be assigned every 15 minutes. The noise levels received by an agent throughout the day are computed by summing all 15-minute noise levels depending on the daily activities. Similarly, the noise level at an agent's "home" activity can be computed.

Results

This "home" activity noise level is comparable to those that would have been obtained from a standardized static noise map. Indeed, the classical method for calculating noise exposure assigns city dwellers to their place of residence. Both noise level exposures are plotted in Figures 12 and13 for a "worker" and "student", respectively.

The dose level plotted in these figures represents the cumulative noise exposure level of the agent-based or home-based activity. This value can only increase and its final value, at the end of the day, equals the equivalent noise level over the course of this same day. indicates that this agent is exposed to approx. 73dB(A) daily, whereas a standard noise map would only consider the home exposure of 40dB(A). This discrepancy is accentuated by the energy averages used in acoustics: the daily noise dose is heavily influenced by the time slots during the day when the agent is exposed to high noise levels. The agent depicted in Figure 13 follows a typical student activity plan: at home until 8 am, at school or university between 8 am and 6 pm, back home between 6 pm and 8 pm, out of the house from 8 pm until 11 pm, and then home after that. Here, the two curves Agent_LAeq and Home_LAeq only differ between 6 am and 6 pm and between 8 pm and 11

pm. The agent is exposed to lower road traffic noise levels at the place of education than what would have been received by staying at home. This curve shape is typical of an agent living in a noisy environment (e.g. near the ring road), while working in a less noisy site, e.g. a university campus. In this example, the agent's leisure activity takes place in a very quiet location. Consequently, the agent-based noise exposure calculation indicates that this agent is exposed to around 52dB(A), whereas a standard noise map would only consider the home exposure of 59dB(A).

These two examples illustrate both the limitations of conventional methods for calculating noise exposure based solely on place of residence and the possibility of examining in greater detail the impact of mobility on noise exposure.

Discussion

We are proposing a multi-agent noise modeling framework that relies on open source tools and data. The modeling framework presented comprises two distinct modeling systems: a multi-agent traffic simulation component, MATSim, and a noise modeling component,

NoiseModelling. The application of this framework to a case study has illustrated its relevance when considering an assessment of the dynamic exposure of individuals to road traffic noise. However, outputs from both models are characterized by a variety of uncertainties, both of which are capable of affecting the exposure assessment. In addition, this approach encounters limitations that will need to be addressed in the future.

The following limitations related to the traffic simulation component for estimating noise exposure have been identified:

As regards the multi-agent traffic simulation component, Zughe et al. (2019) showed the population scaling factor to be the most influential parameter in MATSim simulations. Some authors [START_REF] Hörl | Open data travel demand synthesis for agent-based transport simulation: A case study for Paris and Île-de-France[END_REF][START_REF] Ben-Dor | Spatial implications of Population Downscaling: A MATSim Study of Sioux Falls Morning Peak Traffic[END_REF], 2021;Zughe et al., 2019) investigated the sampling rate at which reliable simulation results can be expected; however, studies of downscaling implications when assessing individuals' exposure to traffic noise are still lacking. Even if a downscaled population could be considered reasonable from the standpoint of traffic simulation, uncertainty remains regarding the reliability of the noise exposure calculation stemming from the simulation. Most studies previously conducted (e.g. [START_REF] Kaddoura | An activity-based and dynamic approach to calculate road traffic noise damages[END_REF] used a scaling factor of 0.1 and did not include a sensitivity analysis for this parameter. Without further information, a scaling factor of 0.25 has been chosen for this study; however, undertaking a study with a scaling factor of 1 (i.e. considering all agents) is feasible for a city the size of Nantes.

The modeling framework adopts a representation of sound sources in the form of a traffic flow described per road segment by means of a flow rate and average speed.

This approach can lead to discrepancies near intersections, where vehicle speeds are not uniform and where acceleration phases affect emissions [START_REF] Can | Estimation of road traffic noise emissions: the influence of speed and acceleration[END_REF]. Errors can reach 4 dB(A) for highly congested road segments yet remain limited under free-flow conditions [START_REF] Can | Towards traffic situation noise emission models[END_REF]. It is quite difficult however to assess the benefit of representing sound sources in the form of vehicle trajectories, thus providing access to the distributions of speeds and accelerations practiced by the vehicles on the network. The flow model integrated into MATSim is not in fact intended to finely replicate the vehicle trajectories.

Since MATSim is not a microscopic modeling framework, it simplifies the vehicle kinematics. Lane changes, acceleration phases, driver decisions, overtaking or adaptative signal controls are just some of the many traffic phenomena not modeled herein. This shortcoming could be mitigated to some degree by stochastically adding dynamics to the traffic flow between the MATSim and NoiseModelling simulations. Acceleration profiles, speed randomization or overtaking can, to some extent, be added to the coupling method in order to better describe the kinematic parameters with an impact on noise emissions. Let's note that integrating these dynamic phenomena would not be compatible with the CNOSSOS-EU model but instead more similar to the approach followed within previous couplings that have relied on microscopic traffic software. These improvements could build upon the probabilistic approach proposed by Aumond et Lastly, a few limitations are specific to the Eqasim Project when using it to compute noise emissions, namely:

-Freight traffic is not generated. Moreover, freight vehicles mainly belong to the 2 nd and 3 rd CNOSSOS vehicle categories, which implies that only public transit buses are present in these categories.

-No motorbikes, i.e. the 4 th CNOSSOS vehicles category, are present; hence, they cannot be used in NoiseModelling.

These limitations are anticipated to be overcome in future work.

The following limitations related to the noise simulation component for estimating noise exposure have been identified:

As regards the noise component, the way receivers are placed in the NoiseModelling simulation exerts a tremendous impact on results. If an activity receiver is placed on the street side of a building, it will have a much higher noise exposure value than if the same activity receiver is placed on its shady side. Relying on how the Eqasim Project generates activity positions, i.e. randomly inside city districts, and then snapping this position onto buildings tends to randomize these differences. Yet a more elaborate way of placing activity receivers may be worth implementing. At the cost of increased computation times, a set of receivers could be created around the building and averaged in order to provide a more building-centered approach to activities-based noise exposure.

The modeling framework considers exposures with an exclusive focus on road transport noise, whereby impacts are expressed through energy-averaged indicators.

Although recommended by calculation standards, this approach does not fully reflect the acoustic impacts potentially observed in urban areas. [START_REF] Gille | Noise annoyance to urban road traffic with powered-two-wheelers: quiet periods, order and number of vehicles[END_REF] highlighted that two-wheeled vehicles and the strong temporal variations in noise levels they induced were perceived very negatively. Moreover, the effects are nonlinear even with aggregated indicators. The relationships built to link noise annoyance to long-term noise levels have a polynomial form [START_REF] Miedema | Exposure-response relationships for transportation noise[END_REF]. In addition, research on soundscape has reached a consensus regarding the importance of preserving quiet urban neighborhoods, with methods for their identification and preservation described in EEA ( 2014). The context is also of great importance. Rey Gozalo and Barrigon Morillas (2017) showed that sound environments in quiet neighborhoods are generally perceived differently (more positive) for passers-by than for residents.

Lastly, as regards the exposure calculation, the current implementation ignores agents' exposure during trips since it focuses on activities. To fully address agents' actual exposure, it will be necessary to rely on experimental campaign data to understand the factors between levels calculated on the street and actual exposure by type of transport or activity.

Conclusion

When assessing the impact of traffic-generated noise on populations, an examination of population activity throughout the day, instead of considering just their home building exposure, can lead to very different conclusions. Agent-based traffic and noise modeling software can be used to evaluate such differences. The proposed framework opens new perspectives in road traffic noise analysis. For instance, it is now possible to conduct an evaluation of the traffic noise impact on certain categories of agents, based for example on their social attributes or locations of residence and activity, with the symmetrical advantage of being able to evaluate the impact of a certain category of agents on global traffic noise emissions. The benefits of the tools and methods from NoiseModelling also enable the acoustics enhance the sound environment characterization through for instance specific noise indicators calculation.

Acknowledgements

This work is part of the MUTANDIS project (Innovative solutions for sustainable mobility in periurban areas), funded by the I-sSite Future.

Agarwal

Fig. 2 :

 2 Fig. 2: MATSim events (Horni et al., 2016)

  Noise modeling component of the modeling chain consists of NoiseModelling, which is a free and open source software developed primarily by the DECIDE team of the Lab-STICC (CNRS) and the Joint Research Unit in Environmental Acoustics UMRAE (Gustave Eiffel University)[START_REF] Bocher | NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps[END_REF] Aumond et al., 2020). The noise calculation method implemented within NoiseModelling is based on the standard European method CNOSSOS, which serves as a reference method to use for the Directive 2015/996 relative to environmental noise assessment and management(Commission Directive, 2015). The CNOSSOS model specifies, for different vehicle categories, the noise emission formulas to use to estimate the acoustic power relative to a given road traffic flow. The CNOSSOS model also specifies the noise propagation calculation method , describing the main physical phenomena affecting noise propagation: it includes methods for searching the acoustic rays between sources and receivers, reflections, diffractions, etc. The reader could refer to the Directive 2015/996 (Commission Directive, 2015) for a complete description of the model and its associated physical principles.

Fig. 3 :

 3 Fig. 3 : NoiseModelling ray tracing propagation implementation of the CNOSSOS method (source: noise-planet.org)

  NoiseModelling script. In following Directive 2002/49/EC of the European Parliament and European Council of the 25 June 2002 Standard (EC 2002), receivers are placed around buildings 4 meters above ground, 2 meters from the facade and 5

Fig. 8 :

 8 Fig. 8: The municipalities chosen for the Nantes case study, and their IRIS blocs

Fig. 9 :

 9 Fig. 9: Nantes area vehicles colored according to their speed (red: fast, blue: slow)

Fig. 10 :

 10 Fig. 10: Noise levels, at agent activities positions, between 9 am and 9:15 am

Fig. 12 :

 12 Fig. 12: Evolution in exposure of a "worker" agent during the day

Fig. 13 :

 13 Fig. 13: Evolution in exposure of a "student" agent during the day

  al. (2018) to assess sound level distribution, in which vehicle positions are randomly chosen based on road traffic characteristics. Another possibility would be to locally complement the modeling with the outputs of a microscopic road traffic model, as it is done in Chevallier et al. (2019) and Estevez-Mauriz et al. (2019). This would enable estimating dynamic noise indicators but entails calibration and downscaling issues.

  The multi-agent noise modeling framework presented in this paper has been assembled by coupling open source tools, namely the Eqasim Project, the MATSim multi-agent transport simulator and the NoiseModelling software implementing the CNOSSOS-EU emission and propagation standard, thus taking full advantage of the development work carried out within the two communities and ensuring a reproducibility and extensibility. The coupling was integrated within NoiseModelling, in the form of a list of MATSim-related scripts.

Table 1 : MATSim inputs File name Contents

 1

network.xml.gz

Table 2 : MATSim outputs File name Content

 2

output_plans.xml.gz

The resulting daily schedules of all agents.

  , A., Kickhöfer, B., 2015. Agent-based Simultaneous Optimization of Congestion and Air Pollution: A Real-World Case Study. Procedia Computer Science 52, 914-919. Alesheikh, A.A., Omidvari, M., 2010. Application of GIS in Urban Traffic Noise Pollution. International Journal of Occupational Hygiene 2(2), 79-84. Andersson, H., Jonsson, L., Ögren, M., 2010. Property Prices and Exposure to Multiple Noise Sources: Hedonic Regression with Road and Railway Noise. Environmental and Aumond, P., Fortin, N., Can, A., 2020. Overview of the NoiseModelling open-source software version 3 and its applications, Proceedings of the 49th International Congress and Exhibition on Noise Control Engineering e-congress, 20-23 Aout 2020, Seoul, South Korea.

	Resource Economics 45(1), 73-89.
	Aumond, P., Jacquesson, L., Can, A., 2018. Probabilistic modeling framework for
	multisource	sound	mapping.	Appl.	Acoust.	139,	34-43.	doi:
	10.1016/j.apacoust.2018.04.017.

In French, IRIS is an acronym of "aggregated units for statistical information".


Xu Lin

Marc Jolivet
email: marc.jolivet@univ-rennes1.fr

Jing Liu-Zeng
email: liu_zeng@tju.edu.cn

Feng Cheng
email: cfcf.chengfeng@gmail.com

Yuntao Tian
email: tianyuntao@mail.sysu.edu.cn

Chang An Li

Chang'an Li

Mesozoic-Cenozoic cooling history of the Eastern Qinghai Nan Shan (NW China): Apatite low-temperature thermochronology constraints

Keywords: PALAEO Tibetan Plateau, Longyang Gorge, Yellow River, Fission track, U-Th/

Understanding the formation of the Qilian Shan in the NE Tibetan Plateau provides insights into the growth mechanisms of the northern portion of the plateau through time. The onset time of exhumation in the Qilian Shan is still debated. The Qinghai Nan Shan subrange, located in the southeastern Qilian Shan, is cut by the Yellow River that forms Longyang Gorge, providing a good vertical profile for thermochronological investigation of exhumation. In this paper, we reconstruct the J o u r n a l P r e -p r o o f Journal Pre-proof cooling and exhumation history of the Eastern Qinghai Nan Shan using apatite fission-track(AFT) and apatite (U-Th)/He(AHe) thermochronological dating on basement rocks. The fission-track data display a marked age-elevation trend from Early Cretaceous (114-122 Ma) ages at the bottom of the canyon to Early-Middle Jurassic ages (165-180 Ma) at the top. Mean (U-Th)/He ages are mainly Early Cretaceous (97-128 Ma) with one Eocene outlier (42 Ma). Combined with fission track lengths, D par values and thermal modeling results, these data indicate that the study area resided above the closure temperature of AFT and AHe until Early Cretaceous. Rapid cooling occurred during the Early Cretaceous and early Cenozoic likely driven by the Lhasa and Indian blocks collision with the southern margin of the Asian continent. Nonetheless, since then exhumation was limited to less than a few kilometers in the study area. Combined with regional geological studies, the thermochronological analysis demonstrates that, since the Mesozoic, intense exhumation occurred simultaneously along the northern and southern margins of the Qilian Shan. Hence, we suggest that the geomorphic framework of the northeast margin of the Tibetan Plateau was established during the Early Cretaceous-early Cenozoic.

 et al., 2016a;Cheng et al., 2019b;[START_REF] Fang | Cenozoic magnetostratigraphy of the Xining Basin, NE Tibetan Plateau, and its constraints on paleontological, sedimentological and tectonomorphological evolution[END_REF][START_REF] Zuza | Underthrusting and duplexing beneath the northern Tibetan Plateau and the evolution of the Himalayan-Tibetan orogen[END_REF]. However, others suggested that uplift initiated much more recently, in the late Cenozoic [START_REF] Tapponnier | Oblique Stepwise Rise and Growth of the Tibet Plateau[END_REF][START_REF] Lease | J o u r n a l P r e -p r o o f Journal Pre-proof Middle Miocene reorganization of deformation along the northeastern Tibetan Plateau[END_REF][START_REF] Zhang | Magnetostratigraphy of the Neogene Chaka basin and its implications for mountain building processes in the north-eastern Tibetan Plateau[END_REF]Wang et al., 2016b;[START_REF] Hu | Sedimentary evolution of the foreland basin in the NE Tibetan Plateau and the growth of the Qilian Shan since 7 Ma[END_REF].

Similarly, the models proposed for the uplift pattern of the Qilian Shan imply either stepwise uplift from south to north [START_REF] Zheng | Progressive northward growth of the northern Qilian Shan-Hexi Corridor (northeastern Tibet) during the Cenozoic[END_REF][START_REF] Zhuang | Understanding the geologic evolution of Northern Tibetan Plateau with multiple thermochronometers[END_REF]Cheng et al., 2019b) or synchronous uplift from both north and south [START_REF] Jolivet | Mesozoic and Cenozoic tectonics of the northern edge of the Tibetan plateau: fission-track constraints[END_REF][START_REF] He | Cenozoic exhumation in the Qilian Shan, northeastern Tibetan Plateau: Evidence from detrital fission-track thermochronology in the Jiuquan Basin[END_REF][START_REF] He | Cenozoic deformation history of the Qilian Shan (northeastern Tibetan Plateau) constrained by detrital apatite fission-track thermochronology in the northeastern Qaidam Basin[END_REF][START_REF] Zuza | Tectonic evolution of the Qilian Shan: an early Paleozoic orogen reactivated in the Cenozoic[END_REF]Cheng et al., 2019c).

The eastern part of the Qilian Shan is drained by the Yellow River, which is the largest exorheic river in the northeastern Tibetan Plateau. It flows out of the eastern section of the Qilian Shan, cutting a series of deep canyons such as the Longyang Gorge located in the Eastern Qinghai Nan Shan [START_REF] Perrineau | Incision rate of the Yellow River in Northeastern Tibet constrained by 10 Be and 26 Al cosmogenic isotope dating of fluvial terraces: implications for catchment evolution and plateau building[END_REF][START_REF] Nie | Loess Plateau storage of Northeastern Tibetan Plateau-derived Yellow River sediment[END_REF][START_REF] Su | Chronology of the Yellow River terraces at Qingtong Gorge (NE Tibet): Insights into evolution of the Yellow River since the Middle Pleistocene[END_REF].

There, the Yellow River has incised more than 2 km below a low-relief regional surface [START_REF] Liu-Zeng | Quantifying landscape differences across the Tibetan Plateau: Implications for topographic relief evolution[END_REF]. Low-temperature thermochronological studies on vertical profiles along deeply incised river valleys have proven an effective approach to decipher the exhumation time and rates in orogenic belts such as the Longmen Shan, along the eastern margin of the Tibetan Plateau [START_REF] Kirby | Late Cenozoic evolution of the eastern margin of the Tibetan Plateau: Inferences from Ar40/Ar39 and (U-Th)/He thermochronology[END_REF][START_REF] Clark | Late Cenozoic uplift of southeastern Tibet[END_REF][START_REF] Jolivet | Mesozoic-Cenozoic evolution of the Danba dome (Songpan Garzê, East Tibet) as inferred from LA-ICPMS U-Pb and fission-track data[END_REF][START_REF] Tian | Synchronous fluvial response to surface uplift in the eastern Tibetan Plateau: implications for crustal dynamics[END_REF][START_REF] Zhang | Pulsed exhumation of interior eastern Tibet: implications for relief generation mechanisms and the origin of high-elevation planation surfaces[END_REF][START_REF] Liu-Zeng | Multiple episodes of fast exhumation since Cretaceous in southeast Tibet, revealed by low-temperature thermochronology[END_REF]. Apatite (U-Th)/He (AHe) (application range of 40 °C to 80 °C, representing the helium partial retention zone (HePRZ)) [START_REF] Ehlers | Apatite (U-Th)/He thermochronometry: methods and applications to problems in tectonic and surface processes[END_REF] and apatite fission track (AFT) (application range of ~110±10 °C to 60 °C, defining the Partial Annealing Zone (PAZ)) [START_REF] Donelick | Apatite Fission-Track Analysis[END_REF] thermochronology has already been used to describe the evolution of the Qilian Shan [START_REF] Jolivet | Mesozoic and Cenozoic tectonics of the northern edge of the Tibetan plateau: fission-track constraints[END_REF][START_REF] Wang | Onset timing of significant unroofing around Qaidam basin, northern Tibet, China: constraints from 40Ar/39Ar and FT thermochronology on granitoids[END_REF][START_REF] Lin | The uplift history of the Haiyuan-Liupan Shan region northeast of the present Tibetan Plateau: integrated constraint from stratigraphy and thermochronology[END_REF]Pan et al., 2013;[START_REF] Li | Cenozoic cooling history of the North Qilian Shan, northern Tibetan Plateau, and the initiation of the Haiyuan fault: Constraints from apatite-and zircon-fission track thermochronology[END_REF] J o u r n a l P r e -p r o o f Journal Pre-proof [START_REF] Wang | Cenozoic exhumation of the Qilian Shan in the northeastern Tibetan Plateau: Evidence from low-temperature thermochronology[END_REF]. The scale of incision along the Longyang Gorge is large enough for low-temperature thermochronology to be used to constrain the long-term rock cooling histories. However, none of these above studies addressed the timing of exhumation using the vertical profiles provided by deeply incised river valleys along the Qilian Shan.

Therefore, in this study, we use AFT and AHe data from a sub-vertical profile within the Longyang Gorge to date the exhumation of the range (Fig. 1c). Combining these data with regional geological studies, we discuss the Mesozoic and Cenozoic exhumation history of the Qilian Shan. The results improve our understanding of the tectonic evolution model of the northeast margin of the Tibetan Plateau.

Geological and geomorphic settings

The northeastern Tibetan Plateau is bounded by the West Qinling and East Kunlun Shan to the south, the Qilian Shan to the northwest, and the Liupan Shan to the east (Fig. 1a). Among these ranges, the Qilian Shan forms a major NW-trending thrust-fold belt overprinting a system that originally formed during the early Paleozoic and was later reactivated during the Mesozoic and Cenozoic [START_REF] Xiao | Early Paleozoic to Devonian multiple-accretionary model for the Qilian Shan, NW China[END_REF][START_REF] Song | Tectonics of the north Qilian orogen, NW China[END_REF][START_REF] Zuza | Tectonic evolution of the Qilian Shan: an early Paleozoic orogen reactivated in the Cenozoic[END_REF]. The ~300-km-wide and 3000-to 4500-m-high range is one of the most active thrust belts in the India-Asia collision zone [START_REF] Cheng | Lateral J o u r n a l P r e -p r o o f Journal Pre-proof extrusion along the Altyn Tagh Fault, Qilian Shan (NE Tibet): insight from a 3D crustal budget[END_REF][START_REF] Zuza | Testing models of Tibetan Plateau formation with Cenozoic shortening estimates across the Qilian Shan-Nan Shan thrust belt[END_REF], flanked by basins with a mean elevation of 1500-3000 m. It can be subdivided, from north to south into the North Qilian Shan, the Tuole Shan, the Danghe Nan Shan, the Shule Nan Shan, the Central Qilian Shan, the South Qilian Shan and Zongwulong Shan, the Qinghai Nan Shan and finally the Laji Shan (Xiao et al., 2009, Fig. 1b).

J o u r n a l P r e -p r o o f

Journal Pre-proof

The lithospheric deformation of the northeastern Tibetan Plateau has been governed by convergence and collision of plates in the southern part of the Asian continent [START_REF] Yin | Geologic evolution of the Himalayan-Tibetan orogen[END_REF][START_REF] Zuza | Tectonic evolution of the Qilian Shan: an early Paleozoic orogen reactivated in the Cenozoic[END_REF]Guillot et al., 2019, Fig. 1a). During the Jurassic and Cretaceous, the northeastern Tibetan Plateau has been affected by compressive tectonic deformation (Qinghai Geologic Bureau, 1989;[START_REF] Ritts | Magnitude of post-Middle Jurassic (Bajocian) displacement on the central Altyn Tagh fault system, northwest China[END_REF], as suggested by several angular unconformities in the Jurassic-Cretaceous coarse-grained clastic deposits of the Qaidam Basin to the south, the Hexi Corridor to the north and the Xining Basin to the east (Qinghai Geologic Bureau, 1989;[START_REF] Vincent | Evolution of the Minle and Chaoshui Basins, China: Implications for Mesozoic strike-slip basin formation in Central Asia[END_REF][START_REF] Horton | Mesozoic-Cenozoic evolution of the Xining-Minhe and Dangchang basins, northeastern Tibetan Plateau: magnetostratigraphic and biostratigraphic results[END_REF]. The Cenozoic tectonic evolution of the Qilian Shan was characterized by a renewed and intense episode of tectonic uplift driven by the ongoing India-Eurasia convergence and the associated rise, thickening, shortening and lateral extrusion of the Tibetan Plateau [START_REF] Jolivet | Exhumation history of the Altun Shan with evidence for the timing of the subduction of the Tarim block beneath the Altyn Tagh system, North Tibet[END_REF][START_REF] Yin | Geologic evolution of the Himalayan-Tibetan orogen[END_REF][START_REF] Wu | Two-stage evolution of the Altyn Tagh Fault during the Cenozoic: new insight from provenance analysis of a geological section in NW Qaidam Basin, NW China[END_REF]Cheng et al., 2016;[START_REF] Zuza | Tectonic evolution of the Qilian Shan: an early Paleozoic orogen reactivated in the Cenozoic[END_REF].

The Qinghai Nan Shan is separating the Gonghe Basin to the south from the Qinghai Lake to the north [START_REF] Chang | Sedimentary evidence of the uplift of the Qinghai Nanshan (the Mountains south to Qinghai Lake) and its implication for structural evolution of the lake Qinghai-Gonghe Basin[END_REF]Fu et al., 2013, Fig. 2). The sub-range connects with the Zongwulong Shan to the west and the Laji Shan to the east, forming the southern rim of the Qilian Shan. Paleozoic and Mesozoic basic volcanic rocks, coarse-grained granodiorite and granites were intruded into the basement of the Qinghai Nan Shan (Qinghai Geologic Bureau, 1989, Fig. 2). Resting unconformably on the basement, Triassic, Jurassic and Neogene sedimentary rocks are widely distributed.

The Yellow River flows out of the eastern part of the Gonghe Basin and cuts through the Qinghai Nan Shan to form the Longyang Gorge. It is a 33 km long, 9 km wide and J o u r n a l P r e -p r o o f erosion in the Guide Basin formed the deep Longyang Gorge during the period 1.2-0.012 Ma [START_REF] Craddock | Structural and geomorphic evolution of the Gonghe Basin complex, northeastern Tibet: implications for the timing of plateau growth[END_REF][START_REF] Zhang | Pleistocene drainage reorganization driven by the isostatic response to deep incision into the northeastern Tibetan Plateau[END_REF][START_REF] Guo | Assessing tectonic and climatic controls for Late Quaternary fluvial terraces in Guide, Jianzha, and Xunhua Basins along the Yellow River on the northeastern Tibetan Plateau[END_REF]. A series of stepped erosion surfaces are exposed north and south of the gorges, the upper and most extended one being covered by flat lying Neogene to Quaternary sediments (Fang et al., 2005, Fig. 3).

To the north, the base of the sediment cover is represented by Early Miocene alluvial fans of the Garang and Guide formations resting unconformably on the major surface worn through a basement formed by early Mesozoic granites and Triassic sediments (Fang et al., 2005). These deposits are overlain by the Late Miocene-Early Pliocene alluvial and lacustrine Ashigong and Herjia formations [START_REF] Fang | Late Cenozoic deformation and uplift of the NE Tibetan Plateau: evidence from high-resolution magnetostratigraphy of the Guide Basin, Qinghai Province, China[END_REF]. The whole series are capped by Quaternary lacustrine and aeolian deposits [START_REF] Guo | Assessing tectonic and climatic controls for Late Quaternary fluvial terraces in Guide, Jianzha, and Xunhua Basins along the Yellow River on the northeastern Tibetan Plateau[END_REF]. To the south, the Early Miocene deposits seem to be lacking and Late Miocene sediments directly cover the erosion surface.

Sampling and method

Sampling strategy

The vertical profile approach uses samples collected over significant relief and a short horizontal distance in order to reconstruct the exhumation history of the region [START_REF] Fitzgerald | Asymmetric exhumation across the Pyrenean orogen: implications for the tectonic evolution of a collisional orogen[END_REF][START_REF] Braun | Estimating exhumation rate and relief evolution by spectral analysis of age-elevation datasets[END_REF]. We collected seven granite samples from an altitudinal transect (36°17′25″N-36°07'30"N, 100°51′16″E-100°59'30"E) that maximizes relief by spanning the high elevation (3270 m), low-relief surfaces (2350 m), to near the Longyang Gorge bottom on the southern flanks of the Eastern Qinghai Nan Shan (Fig. 3;

J o u r n a l P r e -p r o o f

Journal Pre-proof Table 1). One additional bedrock sample (HR-9) was collected from bank of the Langma River (a tributary of the Yellow River, 36°08′29″N, 101°02′19″E, 2537 m) on the northern piedmont of the studied area. Samples cover an elevation range from 2350 m to 3270 m, measured using a hand-held Garmin GPS receiver. An age-elevation profile where AFT and/or AHe ages are plotted versus sample elevation allows for the evaluation of variations in the apparent exhumation rate [START_REF] Fitzgerald | Asymmetric exhumation across the Pyrenean orogen: implications for the tectonic evolution of a collisional orogen[END_REF][START_REF] Braun | Estimating exhumation rate and relief evolution by spectral analysis of age-elevation datasets[END_REF]. In this case, the elevation difference between the two samples (uppermost sample:h 1 and lowermost sample:h 2 ) is divided by the corresponding sample ages difference (uppermost sample:t 1 and lowermost sample:t 2 ) to obtain the exhumation rate, E: E=(t 1 -t 2 )/(h 1 /h 2 ) [START_REF] Blythe | J o u r n a l P r e -p r o o f Journal Pre-proof Plio-Quaternary exhumation history of the central Nepalese Himalaya: 1. Apatite and zircon fission track and apatite [U-Th]/He analyses[END_REF][START_REF] Malusà | Application of thermochronology to geologic problems: bedrock and detrital approaches[END_REF].

Analytical methods

Basement rock samples were crushed mechanically. Apatite grains were concentrated following standard procedures using heavy liquid and magnetic separation techniques. AFT analyses were performed at the Chronus Camp Research, Brazil. Apatite grains were mounted in epoxy resin, cut to expose internal grain surfaces. Sample mounts were etched in 5.5 M HNO 3 at 21 °C for 20 s to reveal the spontaneous fission tracks (Carslon et al., 1999). Spontaneous fission tracks were counted using a Zeiss microscope with a magnification of 1000. The lengths of horizontal confined tracks were measured in order to construct an AFT length-frequency distribution and model the thermal history of the samples. AFT ages were obtained using direct measurements of uranium concentrations with a Neptune high-resolution multi-collector ICP-MS coupled with a J o u r n a l P r e -p r o o f [START_REF] Soares | Characterization of apatites as potential trace element reference materials for fission-track dating by LAICP-MS[END_REF]. The uranium concentration of each counted grain was determined using a laser ablation spot size covering the maximum area over which etched tracks had been previously counted. The size of the area for fission-track measurement and consequently the LA-ICP-MS laser ablation depends on the grain size and the occurrence of inclusions or fractures with the standard analyzes beam size between 30-40 μm. The NIST SRM 610 standard was used as an independent check on LA-ICP-MS system performance. AFT ages were calculated using the scheme described by [START_REF] Donelick | Apatite Fission-Track Analysis[END_REF] based on an adaptation of the zeta calibration approach of [START_REF] Hurford | The zeta age calibration of fission-track dating[END_REF] and Hasabe et al. (2004). The chemical composition of the apatite affects the characteristics of the annealing process, and the D par parameter was used as a proxy for the annealing kinematics [START_REF] Donelick | Apatite Fission-Track Analysis[END_REF][START_REF] Sobel | Influence of etching conditions on apatite fission-track etch pit diameter[END_REF]. The horizontal confined fission tracks were measured on surfaces parallel to the crystal C-axis (Green et al., 1986). For samples that passed the χ 2 test with age dispersion<10%, the pooled ages are used. However, due to the large spread of single grain ages [START_REF] Gleadow | Fission track lengths in the apatite annealing zone and the interpretation of mixed ages[END_REF][START_REF] An | An immediate response to the Indian-Eurasian collision along the northeastern Tibetan Plateau: Evidence from apatite fission track analysis in the Kuantan Shan-Hei Shan[END_REF][START_REF] Li | Cenozoic multi-phase deformation in the Qilian Shan and out-of-sequence development of the northern Tibetan Plateau[END_REF], or chemical composition variation of apatite grains in a single sample [START_REF] Galbraith | Statistical models for mixed fission track ages[END_REF][START_REF] O'sullivan | The importance of apatite composition and single grain ages when interpreting fission track data from plutonic rocks: a case study from the Coast Ranges, British Columbia[END_REF], passing the χ 2 test with age dispersion>20%, the central ages are addressed.

The first step for (U-Th)/He dating was to make suitable grains selection. In general, the grains are selected based on their morphology, size, optical clarity, and compositional uniformity. Thereafter, the euhedral crystals or grains with morphologies that can be geometrically approximated are used as they allow the correction for alpha-ejection

J o u r n a l P r e -p r o o f

Journal Pre-proof [START_REF] Flowers | Exploiting radiation damage control on apatite (U-Th)/He dates in cratonic regions[END_REF]. The AHe analysis was also conducted at Chronus Camp Research, Brazil. Helium concentration was measured using an Alphachron (Australian Scientific Instrument Pty Limited-ASI) mass spectrometer with an extraction temperature of 850-900 °C obtained using a 970 nm diode laser and a heating duration of 5 min.

Extraction values of >99% of 4 He in the heated crystal were obtained. This was verified through a replicate heating procedure checking that secondary extraction did not account for more than a hot blank. For apatite, 25 μL spike solution (diluted in 7 Mol/L HNO 3 ) containing about 15 ng/mL U and 5 ng/mL Th ( 235 U/ 238 U = 838±7, 230 Th/ 232 Th = 10.45±0.05) was added into each vial. The time for full dissolution was about 4h at room temperature. 325 μL reagent grade Milli-Q water was then added to the vial, reaching a total volume of 350 μL. Durango apatite was used as an age standard to verify the quality of the analytical procedure.

The weighted mean AHe ages were calculated using Isoplot/Ex_ver3 [START_REF] Ludwig | Isoplot 3.00: A geochronological toolkit for Microsoft Excel[END_REF]. Accurate interpretation of AHe ages must incorporate the effect of crystal size (radius, Rs) and effective U concentration (eU=U+0.235×Th) as both parameters have a non-negligible effect on the He diffusion [START_REF] Reiners | Influence of crystal size on apatite (U-Th)/He thermochronology: An example from the Bighorn Mountains, Wyoming[END_REF][START_REF] Farley | U-Th)/He dating: Techniques, calibrations, and applications[END_REF][START_REF] Shuster | The influence of natural radiation damage on helium diffusion kinetics in apatite[END_REF].

Thermal modeling

Forward and inverse thermal modeling can be performed simultaneously using the HeFTy software (version 1.9.1) [START_REF] Ketcham | Forward and inverse modeling of low-temperature thermochronometry data[END_REF]. Forward modeling is the process of predicting what thermochronometer measurements one would expect for a sample that We assumed an average annual surface temperature of 20 °C. The Kolmogorov-Smirnov test was employed to assess the fit between the modeled and measured track-length distributions, with merit values of 0.5 and 0.05 for good and acceptable fits respectively. Ten thousand paths were calculated for each sample, producing several hundreds of acceptable and good time-temperature paths. To better constrain the low-temperature thermal history of the eastern Qinghai Nan Shan, the time-temperature histories were further modeled using the AHe data and the diffusion model proposed by [START_REF] Farley | Helium diffusion from apatite: General behavior as illustrated by J o u r n a l P r e -p r o o f Journal Pre-proof Durango fluorapatite[END_REF]. The following parameters were used as input: the weighted mean of all measured ages, the grain radius, and the content of uranium, thorium and samarium. Because of intrasample age variability, AHe data had to be entered selectively into the models to achieve reasonable results [START_REF] Ketcham | Reproducibility of thermal history reconstruction from apatite fission-track and (U-Th)/He data[END_REF]. Thermal history modeling was carried out for samples with similar or consistent AHe ages.

Otherwise, if the AHe ages in the sample were too dispersed, it could not be selected.

Results

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Journal Pre-proof Six samples from the hanging wall of the Eastern Qinghai Nan Shan thrust fault provided AFT ages and seven samples yielded AHe ages. AFT ages range between 180±11 Ma and 114.3±7.9 Ma. These samples yield fairly consistent mean AHe ages from the vertical section clustering in the Early Cretaceous (128±5.9-97.7±1.6 Ma) and Eocene (42.5±0.6 Ma). Geographic locations and AFT ages with 1 standard errors are reported in Figure 3 and 5 and Table 2. U, Th, and He concentrations, crystal dimensions, AHe raw and corrected ages are summarized in Table 3.

The AFT age data display a marked correlation with sample elevation (r 2 =0.67) allowing to calculate a mean exhumation rate of 15.0 m/Ma ranging from 119 Ma to 180 Ma (Fig. 5). AFT age-elevation profiles from both this and earlier studies in the central part of Qinghai Nan Shan [START_REF] Craddock | Structural and geomorphic evolution of the Gonghe Basin complex, northeastern Tibet: implications for the timing of plateau growth[END_REF] are presented in Figure 5. These AFT ages are clearly younger than the Indosinian crystallization age(~270Ma) of the granitic intrusions they belong to [START_REF] Zeng | Mid-Permian rifting in Central China: Record of geochronology, geochemistry and Sr-Nd-Hf isotopes of bimodal magmatism on NE Qinghai-Tibetan Plateau[END_REF], suggesting post-crystallization cooling through the apatite fission track PAZ during regional exhumation events. The mean track lengths are within 11.9-13.2 μm (Table 2), with standard deviations of 1.53 μm to 2.55 μm. The track length distributions are of "undisturbed basement" type [START_REF] Gleadow | Fission track lengths in the apatite annealing zone and the interpretation of mixed ages[END_REF]. The D par values vary from 1.0 μm to 4.2 μm and do not show clear relationships with the corresponding AFT ages and fission track lengths implying that chemical heterogeneity of the samples has little effect on the annealing kinetics (Fig. 6).

Three to five single-grain AHe age analyses were performed for each sample from the Longyang Gorge and Langma River transects (Fig. 5). The samples yield single AHe ages ranging from 159.1±7.3 Ma to 36.6±0.5 Ma (Table 3). Most AHe ages are J o u r n a l P r e -p r o o f significantly younger than the AFT ages on the same sample, especially the higher elevation samples (HR-6, HR-7, Fig. 5). The lower elevation samples (LY-1, 2, 3), however, have several single-grain AHe ages that overlap with the AFT ages within uncertainty. In addition, there are some single-grain AHe ages in each of the three lower samples older than their corresponding AFT ages. The comparisons between single-grain AHe ages and eU and Rs do not show clear relationship, suggesting that radiation damages or grain size are not the main factors explaining the abnormally older AHe ages (Shuster et al., 2006, Fig. 7). In contrast, it is likely linked to other magnified factors such as a protracted residence in the apatite (U-Th)/He PRZ [START_REF] Fitzgerald | Interpretation of (U-Th)/He single grain ages from slowly cooled crustal terranes: a case study from the Transantarctic Mountains of southern Victoria Land[END_REF][START_REF] Biswas | Exhumation and uplift of the Shillong plateau and its influence on the eastern Himalayas: New constraints from apatite and zircon (U-Th-[Sm])/He and apatite fission track analyses[END_REF][START_REF] Clinkscales | Exhumation history of the north-central Shanxi Rift, North China, revealed by low-temperature thermochronology[END_REF], U, and Th zoning [START_REF] Flowers | Interpreting data dispersion and " inverted " dates in apatite (U-Th)/He and fission-track datasets: An example from the US midcontinent[END_REF] or other elements zoning [START_REF] Jolivet | Neogene extension and volcanism in the Kunlun Fault Zone, northern Tibet: new constraints on the age of the Kunlun Fault[END_REF][START_REF] Djimbi | Impact of apatite chemical composition on (U-Th)/He thermochronometry: An atomistic point of view[END_REF].

Modeled t-T paths obtained from AFT data are shown in Figure 8. Samples HR-6, and HR-7 cooled relatively rapidly during the Middle Triassic to the Early-Middle Jurassic, before a phase of slow cooling through the PAZ, which the samples exited during the Early Cretaceous (HR-7) to Late Cretaceous (HR-6). Samples LY-1, LY-2 and LY-3 are characterized by a poorly defined initial cooling during the Triassic to Jurassic up to the base of the PAZ followed by a rapid cooling that initiated during the Early Cretaceous. Modeling of AHe data confirm the occurrence of a Cretaceous-early Cenozoic exhumation event (Fig. 9).

Discussion

Exhumation history of the Eastern Qinghai Nan Shan and implications for the

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Journal Pre-proof formation of the Qilian Shan AFT ages of samples HR-6 and HR-7 at the top of the Eastern Qinghai Nan Shan cluster at 180±11-165±9.5 Ma, cooling below the AFT PAZ occurred during the Early-Middle Jurassic. AFT and AHe thermal history modeling results and AHe ages from the lower samples LY-1 and LY-2 also support a Jurassic cooling in the Eastern Qinghai Nan Shan. Regionally, Jurassic cooling has been reported from the North Qilian Shan [START_REF] George | Miocene cooling in the northern Qilian Shan, northeastern margin of the Tibetan Plateau, revealed by apatite fission-track and vitrinite-reflectance analysis[END_REF][START_REF] Jolivet | Mesozoic and Cenozoic tectonics of the northern edge of the Tibetan plateau: fission-track constraints[END_REF][START_REF] Li | Cenozoic cooling history of the North Qilian Shan, northern Tibetan Plateau, and the initiation of the Haiyuan fault: Constraints from apatite-and zircon-fission track thermochronology[END_REF], the Altun Shan [START_REF] Sobel | Jurassic to Cenozoic exhumation history of the Altyn Tagh range, northwest China, constrained by 40Ar/39Ar and apatite fission track thermochronology[END_REF], the East Kunlun Shan [START_REF] Tian | Multi-stage tectonic events of the Eastern Kunlun Mountains, Northern Tibetan Plateau constrained by fission track thermochronology[END_REF], the Liupan Shan [START_REF] Lin | The uplift history of the Haiyuan-Liupan Shan region northeast of the present Tibetan Plateau: integrated constraint from stratigraphy and thermochronology[END_REF][START_REF] Peng | Thermochronological constraints on the Meso-Cenozoic tectonic evolution of the Haiyuan-Liupanshan region, northeastern Tibetan Plateau[END_REF], the West Qinling Shan [START_REF] Zattin | Exhumation of the western Qinling mountain range and the building of the northeastern margin of the Tibetan Plateau[END_REF] or the Longmen Shan [START_REF] Roger | The tectonic evolution of the Songpan-Garzê (North Tibet) and adjacent areas from Proterozoic to Present: A synthesis[END_REF][START_REF] Jolivet | Mesozoic-Cenozoic evolution of the Danba dome (Songpan Garzê, East Tibet) as inferred from LA-ICPMS U-Pb and fission-track data[END_REF]. Jurassic cooling event may be the response to the collision between the Qiangtang and Kunlun blocks [START_REF] Jolivet | Mesozoic and Cenozoic tectonics of the northern edge of the Tibetan plateau: fission-track constraints[END_REF][START_REF] Karplus | Injection of Tibetan crust beneath the south Qaidam Basin: Evidence from INDEPTH IV wide-angle seismic data[END_REF][START_REF] Tian | Multi-stage tectonic events of the Eastern Kunlun Mountains, Northern Tibetan Plateau constrained by fission track thermochronology[END_REF]Zhang et al., 2020a).

However, on the age-elevation profile, the bottom three samples (LY-1, LY-2, and LY-3) show overlapping mean AFT and AHe ages concentrated in the Early Cretaceous (Fig. 5), which suggests the Eastern Qinghai Nan Shan experienced rapid exhumation during that time. This timing is further confirmed by the AFT and AHe thermal history modeling results. Similar events have been suggested based on AFT and AHe ages in the central part of Qinghai Nan Shan (Craddock, 2011, Fig. 5). In addition, Craddock (2011)'s AHe results support a rapid exhumation during the early Cenozoic (39-51 Ma), consistent with the AHe age in sample HR-9 (42 Ma) obtained in this study. Such an outlier AHe age (HR-9) is likely to be controlled by regional fault activity. Therefore, it indicates that exhumation again appeared in the whole Qinghai Nan Shan during the early

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Journal Pre-proof Cenozoic. However, that amount of Cenozoic exhumation was probably not large enough to completely erase the Mesozoic low temperature signal still preserved in the range.

Moreover, tectonic exhumation events can also be found in the Central and North Qilian Shan during the Early Cretaceous and Early Cenozoic. For example, bedrock apatite FT age and thermal history modeling results show that the Central Qilian Shan were exhumed rapidly at 120-68 Ma and 60-40 Ma [START_REF] Qi | Apatite fission track evidence for the Cretaceous-Cenozoic cooling history of the Qilian Shan (NW China) and for stepwise northeastward growth of the northeastern Tibetan J o u r n a l P r e -p r o o f Journal Pre-proof Plateau since early Eocene[END_REF]He et al., 2020, Fig. 10), which is consistent with the detrital AFT ages from the adjacent sedimentary strata [START_REF] Li | Cenozoic multi-phase deformation in the Qilian Shan and out-of-sequence development of the northern Tibetan Plateau[END_REF]. Similarly, bedrock and detrital AFT ages indicate that intense exhumation occurred in the North Qilian Shan at 115-80 Ma [START_REF] George | Miocene cooling in the northern Qilian Shan, northeastern margin of the Tibetan Plateau, revealed by apatite fission-track and vitrinite-reflectance analysis[END_REF][START_REF] Jolivet | Mesozoic and Cenozoic tectonics of the northern edge of the Tibetan plateau: fission-track constraints[END_REF][START_REF] Duvall | Low-temperature thermochronometry along the Kunlun and Haiyuan Faults, NE Tibetan Plateau: Evidence for kinematic change during late-stage orogenesis[END_REF]Pan et al., 2013;[START_REF] Lin | J o u r n a l P r e -p r o o f Journal Pre-proof Mesozoic and Cenozoic tectonics of the northeastern edge of the Tibetan plateau: Evidence from modern river detrital apatite fission-track age constraints[END_REF] and 59-38 Ma [START_REF] He | Cenozoic exhumation in the Qilian Shan, northeastern Tibetan Plateau: Evidence from detrital fission-track thermochronology in the Jiuquan Basin[END_REF]Li et al., 2019, Fig. 10). Sediments characteristics in the northeastern Qaidam Basin and Hexi Corridor provide evidence of simultaneous sustained exhumation in the South and North Qilian Shan during the Early Cretaceous and early Cenozoic [START_REF] Vincent | Evolution of the Minle and Chaoshui Basins, China: Implications for Mesozoic strike-slip basin formation in Central Asia[END_REF][START_REF] Ritts | Magnitude of post-Middle Jurassic (Bajocian) displacement on the central Altyn Tagh fault system, northwest China[END_REF][START_REF] Dai | Early tectonic uplift of the northern Tibetan Plateau[END_REF][START_REF] Fang | Late Cenozoic deformation and uplift of the NE Tibetan Plateau: evidence from high-resolution magnetostratigraphy of the Guide Basin, Qinghai Province, China[END_REF]Yin et al., 2008a,b;Cheng et al., 2019b,d;Zhang et al., 2020b). It is therefore that both the Early Cretaceous and the early Cenozoic are the most important periods for the formation of the Qilian Shan.

Implications for intracontinental deformation of the northeastern Tibetan

Plateau

Combined with existing low-temperature thermochronology datasets and thermal history modeling results, we infer that the intense exhumation occurred in the whole

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Journal Pre-proof Qinghai Nan Shan during the Early Cretaceous and early Cenozoic. However, thick Early Miocene sandstone and conglomerate deposits (Garang and Guidemen fms.) are widely distributed in the piedmonts of the Eastern Qinghai Nan Shan, unconformably overlying the crystalline basement (Fig. 3). Moreover, well-developed Late Miocene initiated growth strata along the western and central parts of Qinghai Nan Shan, revealed that strong topographic gradients occurred in the Late Miocene-Pliocene [START_REF] Chang | Sedimentary evidence of the uplift of the Qinghai Nanshan (the Mountains south to Qinghai Lake) and its implication for structural evolution of the lake Qinghai-Gonghe Basin[END_REF][START_REF] Zhang | Magnetostratigraphy of the Neogene Chaka basin and its implications for mountain building processes in the north-eastern Tibetan Plateau[END_REF]. Our low temperature thermochronology data and thermal modeling results, however, do not show any Neogene cooling event. Considering the mean exhumation rate derived from our models (15.0 m/Ma), the total exhumation of the Eastern Qinghai Nan Shan at 120 Ma can reach 1800 m while the subsequent exhumation is very limited. Regional exhumation in the study area and central parts of Qinghai Nan Shan during the Neogene [START_REF] Craddock | Structural and geomorphic evolution of the Gonghe Basin complex, northeastern Tibet: implications for the timing of plateau growth[END_REF] was not enough to completely obliterate the Mesozoic signal. Although the Qinghai Nan Shan underwent rapid exhumation during the Early Cretaceous and early Cenozoic, the subsequent exhumation was limited and the Miocene intense exhumation occurred [START_REF] Chang | Sedimentary evidence of the uplift of the Qinghai Nanshan (the Mountains south to Qinghai Lake) and its implication for structural evolution of the lake Qinghai-Gonghe Basin[END_REF][START_REF] Craddock | Structural and geomorphic evolution of the Gonghe Basin complex, northeastern Tibet: implications for the timing of plateau growth[END_REF][START_REF] Zhang | Magnetostratigraphy of the Neogene Chaka basin and its implications for mountain building processes in the north-eastern Tibetan Plateau[END_REF], most probably due to tectonic compression. A similar orogenic belt exhumation pattern was discussed in the West Qinling Shan [START_REF] Zattin | Exhumation of the western Qinling mountain range and the building of the northeastern margin of the Tibetan Plateau[END_REF] and eastern part of the North Qilian Shan (Pan et al., 2013;[START_REF] Li | Cenozoic cooling history of the North Qilian Shan, northern Tibetan Plateau, and the initiation of the Haiyuan fault: Constraints from apatite-and zircon-fission track thermochronology[END_REF].

Regionally, Early Cretaceous-early Cenozoic exhumation events are also recorded in the Liupan Shan [START_REF] Lin | The uplift history of the Haiyuan-Liupan Shan region northeast of the present Tibetan Plateau: integrated constraint from stratigraphy and thermochronology[END_REF][START_REF] Peng | Thermochronological constraints on the Meso-Cenozoic tectonic evolution of the Haiyuan-Liupanshan region, northeastern Tibetan Plateau[END_REF], the Altun Shan [START_REF] Jolivet | Mesozoic and Cenozoic tectonics of the northern edge of the Tibetan plateau: fission-track constraints[END_REF][START_REF] Sobel | Jurassic to Cenozoic exhumation history of the Altyn Tagh range, northwest China, constrained by 40Ar/39Ar and apatite fission track thermochronology[END_REF], the Qimantagh Shan [START_REF] Wang | Cenozoic exhumation of the Qilian Shan in the northeastern Tibetan Plateau: Evidence from low-temperature thermochronology[END_REF], the East Kunlun Shan [START_REF] Yuan | Apatite fission track evidence for Neogene uplift in the eastern Kunlun mountains, northern Qinghai-Tibet Plateau, China[END_REF][START_REF] Dai | Multi-stage tectono-magmatic events of the Eastern Kunlun Range, northern Tibet: insights from U-Pb geochronology and (U-Th)/He thermochronology[END_REF]Wang et al., 2017c), as well as in the Kuantan Shan and J o u r n a l P r e -p r o o f Journal Pre-proof Longshou Shan [START_REF] Zhang | Late Mesozoic-Cenozoic Exhumation of the Northern Hexi Corridor: Constrained by Apatite Fission Track Ages of the Longshoushan[END_REF][START_REF] An | An immediate response to the Indian-Eurasian collision along the northeastern Tibetan Plateau: Evidence from apatite fission track analysis in the Kuantan Shan-Hei Shan[END_REF]. We suggest that the geomorphic framework of the northeast margin of the Tibetan Plateau was initially established during the Early Cretaceous in response to the collision of the Qiangtang and Lhasa blocks (120 Ma, [START_REF] Kapp | Geological records of the Lhasa-Qiangtang and Indo-Asian collisions in the Nima area of central Tibet[END_REF][START_REF] Guillot | How and When Did the Tibetan Plateau Grow?[END_REF][START_REF] Zhang | A 6000-km-long Neo-Tethyan arc system with coherent magmatic flare-ups and lulls in South Asia[END_REF] and further enhanced during the early Cenozoic initial stage of the Indian collision [START_REF] Hu | The timing of India-Asia collision onset-Facts, theories, controversies[END_REF].

Conclusions

New AFT and AHe thromochronological data combined with inverse thermal modeling from the Eastern Qinghai Nan Shan record exhumation during the Early Cretaceous-early Cenozoic following a period of continuous but slow cooling during the Late Jurassic. We relate this first exhumation phase to the collision between the Qiangtang and the Kunlun blocks, and the second exhumation to the collisions of the Lhasa and Indian blocks with the southern margin of Eurasia. However, the Early Cretaceous-early Cenozoic exhumation was not sufficient to completely exhume the early Mesozoic apatite PAZ as Early Jurassic AFT ages are still preserved. Combined with previous regional geological studies, indicates intense exhumation occurred simultaneously along the northern and southern margins of the Qilian Shan since the Mesozoic. The geomorphic framework of the northeast margin of the Tibetan Plateau was initially established since the Early Cretaceous.

Figure captions

Fig. 1 (a) Sketch map of the major blocks and suture zones of Tibetan Plateau and adjacent regions (after [START_REF] Yin | Geologic evolution of the Himalayan-Tibetan orogen[END_REF][START_REF] Tapponnier | Oblique Stepwise Rise and Growth of the Tibet Plateau[END_REF]. The red arrows indicate where the plate is moving (after Wang et al., 2017b) [START_REF] Vermeesch | RadialPlotter: a Java application for fission track, luminescence and other radial plots[END_REF] for AFT ages from the Eastern Qinghai Nan Shan.

J o u r n a l P r e -p r o o f Fig. 5 AFT and AHe cooling ages vs. elevations for eastern (our data) and central [START_REF] Craddock | Structural and geomorphic evolution of the Gonghe Basin complex, northeastern Tibet: implications for the timing of plateau growth[END_REF] part of the Qinghai Nan Shan. Dashed lines outline age-elevation trend. Fig. 8 AFT thermal history models obtained for the Eastern Qinghai Nan Shan vertical transect samples produced using HeFTy [START_REF] Ketcham | Forward and inverse modeling of low-temperature thermochronometry data[END_REF].The pink and green envelope represents a good fit and an acceptable fit. The black line corresponds to the "best fit model" line. Black rectangles are the constraint boxes. T-t constraints imposed into the modeling were described in the text. PAZ: partial annealing zone of apatite fission track. Fig. 9 AHe thermal history modeling using HeFTy software [START_REF] Ketcham | Forward and inverse modeling of low-temperature thermochronometry data[END_REF] J o u r n a l P r e -p r o o f

  J o u r n a l P r e -p r o o f Journal Pre-proof has undergone a particular temperature history. A comparison between predicted results and measured values is possible. Inverse models have the distinct advantage over forward modeling by hand of permitting many thousands of forward models to be calculated for a sample, obtaining hundreds or even thousands of acceptable time-temperature histories. A combination of AFT age, confined track length data, and available geological background are considered in the inversion modeling with the following time-temperature path constraints. (a) The maximum time and maximum temperature were set at 270 Ma-160 Ma and 200 °C-160 °C respectively, a temperature above which fission tracks in apatite grains are totally annealed. (b)

  . (b) Topography and major faults in the northeastern Tibetan Plateau. NQS=North Qilian Shan, TLS=Tuole Shan, TLNS=Tuole Nan Shan, SLNS=Shule Nan Shan, CQS=Cengtral Qilian Shan, LJS=Laji Shan, DHNS= Danghe Nan Shan, ZWLS=Zongwulong Shan, QHNS=Qinghai Nan Shan, GHNS=Gonghe Nan Shan, WQL=West Qingling. (c) Topographic profile along the transect A-A'. Yellow dots denote the locations of samples from the Eastern Qinghai Nan Shan. See Fig. 1b for location of A-A'.

Fig. 2

 2 Fig. 2 Simplified geologic map of the Qinghai Nan Shan and adjacent areas (modified

Fig. 3

 3 Fig. 3 Detailed geologic map of the study area, with sample locations shown as white

Fig. 4

 4 Fig.4Radial plots constructed using RadialPlotter software[START_REF] Vermeesch | RadialPlotter: a Java application for fission track, luminescence and other radial plots[END_REF] for AFT ages

Fig. 6

 6 Fig. 6 Plots of (A) AFT age and (B) track length vs. D par . Dpar is the arithmetic mean

Fig. 7

 7 Fig. 7 Plots of AHe ages vs. eU (eU=effective U concentration) (A) and Rs (sphere

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10

 12345678910 Figure1

Table 2

 2 Measured apatite fission track data in this study

						Zeta	U-UNK U-STD P(χ 2 ) Dpar	Age	L±SD(n)
				×10E+6 ×10E+5 ×10E+3	µg/g	µg/g		µm	Ma (±1σ)	µm
	HR-6	37	457	0.56	1.63	1.79	8.5	9.22	0.35	2.99	180±11	11.93±2.55(63)
	HR-7	31	521	1.05	1.63	2.31	14.9	10.99	0.86	1.61 165.6±9.5 13.18±1.85(48)
	HR-9	24	373	0.99	1.63	1.75	15.5	9.04	0.00	2.63	169±18	12.12±2.17(64)
	LY-1	39	719	1.16	1.98	1.59	19.68	9.98	0.11	1.94 114.3±7.9 13.23±1.53(57)
	LY-2	38	607	1.02	1.98	1.59	13.49	9.98	0.46	2.42 122.7±6.6 12.64±1.98(56)
	LY-3	31	453	0.95	1.98	1.64	16.48	10.27	0.10	1.92 119.1±7.8 13.07±1.55(54)
		N(grains): number of apatite crystals analyzed; n(tracks): number of apatite fission track
		analyzed; ρs (Ns) and ρSTD: spontaneous fission-track density of apatite crystals analyzed of
		samples and standards; Zeta: LA-ICP-MS calibration factor determined against Dur-2
		standards; U-UNK and U-STD: uranium-238 concentration of samples and Dur-2 standards
		measured by LA-ICP-MS; P(χ 2 ): chi-squared probability; Dpar: mean diameter of
		fission-track etch images parallel to the crystallographic c-axis. L±SD: mean track
		length+ standard deviation, n, number of tracks measured.

Sample N(grains) n(tracks) ρs (Ns) ρSTD

Table 3

 3 Results of single grain AHe dating in this study

					J o u r n a l P r e -p r o o f
											Th/	Raw		Corr.	Error	Mean age
	Sample length width Mass	Rs	4 He	U	Th	Sm	eU	U	age	Ft	age	(±1σ)	(±1σ)
		μm	μm	mg	μm	nmol/g ppm	ppm	ppm	ppm		Ma		Ma	Ma	Ma
	HR-6
	G-1	298	111.3	6.55 64.45	8.232	12.54 31.11 30.44	19.8	2.48	75.3	0.77	97.5	1.3
	G-2	274.8	134.9	7.37 71.35 19.402 30.06 70.31 36.22	46.6	2.33	76.1	0.79	95.9	1.3	97.7±1.6

Acknowledgements

We wish to thank Xitao Zhao from the Institute of Geology and Geophysics at Chinese J o u r n a l P r e -p r o o f Academy of Sciences in Beijing for helping us so much with the field work for this study. This work was financially supported by the National Natural Science Foundation of China (Grant 41671011, 41702178, 41972212), and the Hunan Natural Science Foundation of China (Grant 2019JJ40198). Profound comments and suggestions from the Guest editor Dr. Ryan Leary, Reviewer Dr. Xiubin Lin, Dr. Devon Orme and an anonymous reviewer are appreciated, which help to improve the paper significantly.

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Highlights

• Low-temperature thermochronological study was carried out for Yellow River valley.

• Eastern Qinghai Nan Shan experienced rapid exhumation since the late Mesozoic.

• Geomorphic framework of the NE Tibetan Plateau was initially established since the late Mesozoic.

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