Samuel Challéat

Johan Milian

Matthieu Renaud

L Godet

Rémi Bénos

Magalie Franchomme

Marion Maisonobe

Igor Babou

Caroline Parvaty

The nocturnal social-ecological systems of Réunion Island: challenges of knowledge and perspectives for action. Pre-proposal for a project in response to the AAPG2024 of the ANR

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

The nocturnal social-ecological systems of Réunion Island: challenges in knowledge and perspectives for action I. Pre-proposal's context, positioning and objectives

A. Context

On Réunion Island, more than anywhere else, the challenges of combating light pollution are particularly acute, given the high level of friction between artificial light at night (ALAN) and areas of high naturalness (Guetté et al., 2018), where major ecological issues are at stake. Classified as one of the world's 36 biodiversity hotspots and considered a conservation priority due to its extreme biological diversity, the island has seen its urban population grow significantly. In direct correlation with the dynamics of urbanization, the light footprint of La Réunion has undergone a radical change in recent years: its range and its pressure have increased considerably from the coast to the upper reaches of the island, the core zone of La Réunion National Park (Babou, 2015). The ecological impact of artificial lighting has been of concern to environmental associations and scientists since the 1990s (Le Corre et al., 2002;Chevillon et al., 2022). Launched in 2008, the 'Nuits sans lumière' initiative mobilizes the Park and a number of associations to promote public campaigns against lighting, accompanied by awareness-raising campaigns on the consequences of ALAN on endemic avifauna -Barau and Mascarene Petrels. However, by focusing on these flagship species and being limited in time (to the period of juvenile dispersal), these actions may have paradoxically contributed to hamper the emergence of less ecocentric approaches to light pollution control that could enable a lasting change in lighting practices (Challéat, Milian et al., 2022). Without abandoning naturalistic expertise, we now need to place it within a unifying framework, at the interface between environment and society, that is better able to grasp the complex real-world problems and provide keys for prioritizing actions and informing decisions whenever a trade-off between antagonistic needs for light and darkness appears necessary.

B. Research objectives and hypotheses

In this context, the FENOIR project aims at producing situated knowledge that will enable public action to overcome the aporias of the ecocentric approach to the 'ALAN problem' and to respond to the challenge -particularly acute on Réunion Island -of territorializing a holistic preservation of the nocturnal environment (Challéat, Lapostolle et al., 2023). Achieving such an objective requires an effort to converge knowledge -vernacular, usage, scientific and technical knowledge -or, in other words, the conjugation of different systems and repertoires of knowledge about the nocturnal environment (Tengö et al., 2014). To this end, the FENOIR project mobilizes the framework of nocturnal social-ecological systems (Challéat, 2018;Challéat, Barré et al., 2021). This holistic approach to the nocturnal environment is particularly relevant for an island territory where public action is now obliged to promote transformations under strong ecological constraints (Nadaï & Wallenborn, 2019). This change in the frame of reference for public action -from an ecocentric one, based on the fight against light pollution and reflected in the 'Nuits sans lumière' action, to a holistic one, based on the preservation of the nocturnal environment and reflected in the 'Jours de la Nuit' action program -was initiated by La Réunion National Park in 2020, following initial work by our team, which has since supported the Park and several other territorial actors in this approach (Challéat, Milian et al., 2022;Challéat, Lapostolle et al., 2023). In order to take this initial work a step further, the FENOIR project will pursue two objectives, with the aim of overcoming the main obstacles related to the lack of a sufficiently broad and well-founded repertoire of knowledge to create the conditions for deliberation between the needs of darkness and the needs of ALAN, and hence committing in an effective public environmental action for the preservation of the nocturnal environment of La Réunion. 1. To study, at different socio-spatial scales, (i) the socio-cultural practices and representations of the Reunionese night, (ii) the attachment and sensitivity to the components of the island's nocturnal environment, and (iii) the modes of governance of the territorial system that establishes darkness as a resource. The research will be based on the intersection of approaches from the socio-anthropology of relations between nature, knowledge and society, ethnography, the geography of knowledge and understanding, linguistics and literary studies, environmental geography and economics, regional planning and political science. The work will be organized in order to put into perspective, at different socio-spatial scales, the changes in cultural and social representations linked to the transformation of the nocturnal landscapes of La Réunion by ALAN, in order to identify the determining factors. The aim is also to gain a better understanding of the values associated with these landscapes and with darkness, and to unravel and understand the networks and interactions of the stakeholders involved in the regulation of lighting and the preservation of the island's nocturnal environment. Three hypotheses will guide our work here. Our first hypothesis is that the relationship with the night, in terms of representations and social relations, has undergone a significant change since the recent past, when Reunionese society was essentially rural and darkness was very present. The penetration of artificial lighting infrastructures was also much faster than in metropolitan France, where the process took more than a century. Consequently, the aim here is to understand how people's nocturnal lifestyles may have been affected by these changes. Our second hypothesis is that this rapid transformation of the Reunionese night is profoundly reconfiguring relations with the fénoir (the 'obscure fact' in Creole culture, the darkness in which our project was born and from which it takes its name) -the living memory of the 'natural' night, accompanied by practices that testify to the importance of darkness in the lives, uses, and imaginations of Creole populations, as well as to a unique relationship with non-human beings. The goal here is to continue to explore the thickness of darkness, from its magico-religious dimension to its environmental dimension, while taking care to avoid the pitfalls of folklorization and environmental declinism (Banos et al., 2021). Our third hypothesis is that, for the territorialization of the preservation of the nocturnal environment to function properly, the tools of public action in this field must become vehicles for the socialization of the fight against light pollution, and not vehicles for a cognitive confiscation through the technicization of the problem. The aim here is to bridge the gap between scientific knowledge and action, and to deepen the links between multilevel governance and participatory processes.

2.

To study, at different spatio-temporal scales, (i) the structures and dynamics of light pressure, (ii) the vulnerability of natural environments (biodiversity patterns and processes) to ALAN and (iii) the uses of darkness by wildlife and how these uses are affected by artificial light. Research here will be based on the intersection of environmental metrology, spatial modeling, biogeography, and nocturnal ecology, supported by environmental physics, scientific computing, bioacoustics, and artificial intelligence. The work will be organized to initiate the construction of a repertoire of knowledge on different spatio-temporal scales about ALAN and its fine interactions with meteorological and orographic conditions, about interactions between light pressure and other anthropogenic pressures on the environment, and about the effects of light pressure on the activity patterns of birds. Two hypotheses will guide our work here. Our first hypothesis is that Réunion Island is a prime area for a better understanding, based on ground observations, of the multiscalar mechanisms that control the diffusion of artificial light and directly influence changes in the shape and intensity of the light footprint. Approaching the 'ALAN problem' on La Réunion exclusively through satellite imagery ignores the atypical nature of this island, which covers an area of 2,512 km 2 and has a relief that is both high and extremely fragmented, with extreme contrasts in meteorological conditions. Based on ground observations (network of photometric sensors), the goal is to model the light pressure in detail, taking into account the meteorological and orographic conditions. Our second hypothesis is that the lack of fundamental knowledge of nocturnal biogeography and ecology, generated in situ and focusing on the effects of light pressure on natural environments and on the activity rhythms of wildlife in all its diversity, prevents public authorities from prioritizing their actions in time and space. This calls for a pragmatic approach that takes into account the vulnerability of ecosystems to light pressure and its interactions with other forms of anthropogenic pressures. However, to further answer the question of 'what, where and when to put on the political agenda', it is necessary to meet the ecological stakes of preserving the nocturnal environment on the one hand, and the situated socio-technical possibilities for planning and action on the other (Challéat & Lapostolle, 2014). In other words, the aim is to install, through our interdisciplinary experimental set-up, the conditions for a socio-ecosystemic negotiation of the modalities of intervention on the island's lighting points network following the needs for both darkness and light.

C. Work packages and methodology

In order to achieve these objectives, we adopt a broad interdisciplinary approach, combining knowledge from social and territorial sciences, natural sciences, universe sciences, engineering and environmental physics. By combining these disciplines and different research methods (fundamental research, action research, participatory research), and by creating 'bridging objects' (BO) to promote the circulation of diverse knowledge, the FENOIR project will enable an unprecedented socio-ecosystemic approach to the nocturnal environment. The project is divided into five work packages, which will be further detailed into sub-packages in phase 2: WP1. Practicing & inhabiting darkness: an ethno-geography of the Reunionese nights. This WP focuses on investigating nocturnal territorialities in order to characterise nocturnal social-ecological systems. The aim is to understand how people live with the darkness and what specific features the fénoir can reveal in the construction of these territorialities (Franchomme et al., 2023). Operative actions therefore focus on dwellers and use methodologies to study in depth the processes that feed these territorialities, through two fundamental components. (i) Frequenting the nocturnal places. The first part of the research will be to explore and map Réunion's nightlife (the history and development of nighttime recreational activities) and to link it to the different types of nocturnal practices. The second part will mobilize ethnographic methods to describe the places of the fénoir and the relationships between inhabitants, darkness and 'nightscapes'. (ii) Frequenting the nocturnal rhythms and temporalities. We will study the place occupied by nightlife in island cultural practices through the analysis of a literary corpus and music listening data from users of the music streaming platform Deezer, which reveals the local and territorial specificities of Reunion Island (Cura & Maisonobe, 2023). BO produced for WP2: detailed knowledge of nocturnal territorialities, elements on the meaning of places and relationships to lived spaces. For WP4: implementation of researcher-inhabitant workshops around nocturnal soundscapes (whose methodology was designed during the CNRS-funded OUTRENOIR program -2022-2023).

WP2. Governing & territorializing darkness: public action to preserve the nocturnal environment of La Réunion.

From the island scale to the micro-local scale, this WP will focus on the existing arenas of territorial governance around the issue of artificial lighting, and will seek to produce elements that will allow their grammar to be adapted in order to promote their widening. (i) Understanding the interplay of actors involved in the preservation of the nocturnal environment. A survey will be carried out to pay close attention to the discourses, arenas and places where decisions are made, and to question the political inequalities of participation in the development of public policies and decision making. (ii) The multiple values of darkness and its effects on framing public action. By comparing stated preferences (Glenk et al., 2020) the aim here consists to assess the effects of a change in public action frame of reference on the preferences and different values expressed by residents in relation to the issues of preserving the nocturnal environment. (iii) Issues and challenges for territorializing nocturnal environment. The construction of grassroots innovations towards transition in lighting practices and the setup of associated local action communities will both be surveyed through a few case studies. This work will seek to implement collective intelligence based on the plurality of viewpoints, perspectives, scientific and vernacular knowledge, and life experiences. BO produced for WP3: installation of local action communities related to the implementation of educational projects on the nocturnal environment. For WP4: installation of local workshops to build socio-ecosystemic scenarios for mitigating light pressure with inhabitants and public lighting managers.

WP3. Modelling & mapping darkness: the Light Pressure Monitoring Network on Réunion Island (LPMN-Run).

This WP will focus on continuing the deployment of TESS-W photometers (Zamorano et al., 2016) across the island, then implementing the organization, FAIRization and exploitation of the collected data, up to the fine modeling of the light pressure on Réunion. (i) Completion of the LPMN-Run, already under construction since 2021: the aim is to increase the density of this network by installing about 100 photometers, making La Réunion the most densely equipped territory in the world. We will also work to make some of the sensors autonomous, as several of the proposed sites are located in isolated areas. (ii) Light pressure modeling and mapping. The natural brightness of the night sky will be modeled (Masana et al., 2022) to characterize its evolution at photometer installation sites and thus extract the artificial component from ground-based measurements. Enriched with satellite and ground-based data (DTM, GIS database of lighting points), the LPMN-Run will allow us to produce the first high-resolution mapping of Réunion's light footprint, integrating meteorological and orographic variability (Aubé et al., 2020). (iii) Use the LPMN-Run as a vehicle for local projects to educate the public about the nocturnal environment. The development of participatory science projects (Kyba et al., 2023) to build local action communities around the public data produced by LPMN-Run will be pursued, with a particular emphasis on school audiences (Barré et al., 2023). BO produced for WP2: multimodal dashboard (under development) for use by naturalist and astronomer experts, decision makers, and inhabitants-citizens. For WP4: mapping of light pollution, helping to define areas of ecological concern with respect to this pollution. WP4. Monitoring & understanding wildlife in darkness: a nighttime biogeography of Réunion Island. This WP aims to investigate the impact of light pressure on wildlife activity rhythms, particularly on the island's passerines, using bird calls and songs as indicators. (i) A comprehensive island-wide comparison of light pressure with other anthropogenic factors. The goal is to identify spatial correlations and discrepancies between light pressure and other anthropogenic pressures across the island, using cartographic methods developed by Guetté A. and the IUCN in metropolitan France to map landscape anthropization (Guetté et al., 2021). (ii) Assessment of bird vocal activity across the island, examining how light pressure affects temporal changes in bird vocalizations. Birdsong extraction based on acoustic sensors next to photometers, and using AI (Moummad et al., 2023) combined with manual listening and annotation, will allow us to understand the impact of light and sound pressures along with other anthropogenic indicators on birdsong chronology. (iii) Focusing on sites with varying light and anthropogenic pressures, the WP will focus on the temporal behavioral parameters of an endemic species in decline (Mascarene Paradise-Flycatcher) and an invasive species (Red-whiskered bulbul) using AI. Different vocalizations such as songs, contact calls, and alarm calls will be analyzed to disentangle the influence of light and other anthropogenic pressures on the temporal behavior of these species. BO produced for WP1: acoustic recordings enabling the installation of workshops around nocturnal soundscapes. For WP2: inputs for workshops with stakeholders of lighting governance, aiming at the construction of socio-ecosystemic ALAN-mitigating scenarios. WP5. Coordinating & disseminating research and its results. This transversal WP will organise the interdisciplinary seminars structuring the project. It will be in charge of the partnership coordination and logistics with the 4 research partner teams. Finally, it will focus on the transfer of results in two ways: the scientific publications, and the production of (i) a photographic exhibition, (ii) an illustrated book for the general public, and (iii) a film showing an interdisciplinary approach 'in effect'. Led by the coordinator, this WP will involve all project members and two service providers.

D. State-of-the-art positioning and scientific innovation

This project is in line with the perspectives opened up by studies on the night (Galinier, 2010;Edensor, 2013;Shaw, 2018) and, more precisely, on the nocturnal environment (Challéat, 2018;Milian et al., 2023). Through the case of La Réunion, the FENOIR project will fill a gap in the rich field of Night Studies (Gwiazdzinski et al., 2020), where few works have managed to move away from the Northern countries, to decentralize their focus in the nights of tropical spaces (Fouquet, 2011;Kouadio & Kouakou, 2021). These latter, however, have been studied by anthropology and cultural geography -more specifically, from the point of view of festive practices and economic activities (Morelle & Fournet-Guérin, 2006;Fouquet, 2016) -or by history, from the point of view of the recomposition of meanings, perceptions and atmospheres that they generate (Nativel, 2016). Regarding the monitoring of light pressure, it is now necessary to "gather more information from measurement data [...], for example through long-term observations of night sky brightness" (Kocifaj et al., 2023). Thus, the LPMN-Run will allow the FENOIR project to feed several scientific fronts in this field: analysis of long-term trends in light pressure (Fiorentin et al., 2022), of the links between the night sky brightness and atmospheric aerosols (Kocifaj & Bara, 2021), and discussions on the production of holistic indicators to harmonize light pressure measurement methods across disciplinary boundaries (Hölker et al., 2021). From the point of view of the natural sciences, the FENOIR project will generate knowledge that will enable our team to play a major role in constructing answers to several of the "11 pressing research questions on how light pollution affects biodiversity" proposed by Franz Hölker et alii (2021). This project claims to be innovative and original on several levels. i) For the proposed combination of disciplines and research methods, and for the interdisciplinary framework, which both make it possible to bring together a variety of complementary points of view on 'the obscure fact'. ii) For the central place given to the focal object 'nocturnal environment', both as a framework for the integration and mesological analysis (Berque, 2014) of disciplinary contributions, and as a mediating tool. iii) For its pioneering role in Réunion Island, where the Night Studies have yet to take root. The FENOIR project will provide the scientific community with a valuable point of comparison, at the scale of the Indian Ocean and beyond. iv) Finally, from the point of view of the methods and bridging objects deployed to circulate and translate the results obtained -from one WP to another, but also in terms of public action -and to ensure the dissemination of research far beyond the academic sphere alone.

II. Partnership (consortium or team)

The scientific coordinator of the project, Samuel Challéat, Research Fellow in Environmental Geography at the UMR GÉODE (CNRS-InÉE), will dedicate 70% of his working time to the project -i.e. 33.6 months of the project's 48 months. He has experience in scientific management and coordination of several interdisciplinary research programs, as well as of the Nocturnal Environment Observatory (NEO, CNRS). The consortium is strongly consolidated by the working habits acquired within this multidisciplinary structure, as well as by the joint participation of several of its members in various programs, including two concerning La Réunion. The mobilization of skills present on the island, already in

place within the NEO since 2022, has been reinforced. This criterion will also be a priority for the proposed recruitment of 3 temporary contracts. The team consists of researchers in Geography and Land Use Planning (Challéat S., Milian, J., Maisonobe M., Franchomme M.), Anthropology, Ethno-Geography and Linguistics (Babou I., Bénos R., Parvaty Marimoutou C.), Environmental Economics (Tardieu L.), Ecology and Biogeography (Gaudet L., Froidevaux J., Le Corre M., Guetté A.), Environmental & Engineering Physics (Morvan S., Morel B., Brouat C., Delsaut M., Renaud M.) and Artificial Intelligence (Farrugia N.). The requested funding, to cover the whole team missions, support the purchase of equipment and recruit contractual researchers, is spread over four laboratories. The UMR GÉODE will support the project. It provides expertise in the analysis of complex human-environment interactions and socio-environmental dynamics, as well as in Physical Environmental Metrology. It hosts the NEO and makes its Environmental Metrology platform available to the project. The UMR LADYSS brings skills in understanding changes in contemporary societies and in analyzing processes of territorialization of individual and collective actions to preserve the environment. The Observatoire des Sciences de l'Univers de La Réunion (OSU-Réunion) provides expertise in universe and atmospheric sciences, engineering sciences, metrology and environmental physics. The UMR LETG provides expertise in conservation ecology, analysis of landscape anthropization processes and their impact on biodiversity, and bioacoustics.

III. References


Mohamed Anis Fekih
email: mohamed-anis.fekih@insa-lyon.fr

Walid Bechkit
email: walid.bechkit@insa-lyon.fr

Hervé Rivano
email: herve.rivano@insa-lyon.fr

Route Selection in Low-cost Participatory Mobile Sensing of Air Quality

Keywords: Route selection, participatory sensing, low-cost sensors, air quality monitoring

Mobile crowdsensing is a powerful paradigm that takes advantage of low-cost sensors and population density. It allows for large-scale deployments and collection of extensive data, offering a great advantage in multiple fields such as air pollution monitoring, which is a major concern worldwide. Given the mobile nature of the crowd, mobile crowdsensing platforms need to implement adequate route selection/planning solutions to better guide the crowd through the area of interest and maximize the quality of monitoring. In this paper, we propose two route selection algorithms that take into consideration the low accuracy of low-cost sensors in order to find the most informative routes. The similarity-based route selection algorithm aims to maximize spatial coverage by reducing overlaps between participant routes. The cluster-based route selection takes advantage of hierarchical clustering to build groups of similar points of the map according to explanatory variables. We compare the proposed solutions to baseline route selection algorithms, and the results show that our solutions allow for a better estimation while being efficient in terms of travel distance.

I. INTRODUCTION

Mobile crowdsensing [START_REF] Ganti | Mobile crowdsensing: current state and future challenges[END_REF] is an emerging and powerful paradigm which has recently received a great deal of attention, due to rapidly emerging sensing platforms and their growing needs in terms of both quantity and quality of data. This paradigm has become an appealing solution to collect data without making large investments, especially with the emergence and widespread use of low-cost wireless sensors. Mobile crowdsensing highly involves citizens in the sensing process and has been adopted in numerous solutions such as environmental monitoring [START_REF] Zappatore | Crowd-sensing our smart cities: A platform for noise monitoring and acoustic urban planning[END_REF], [START_REF] Fekih | Participatory air quality and urban heat islands monitoring system[END_REF], healthcare [START_REF] Kraft | Combining mobile crowdsensing and ecological momentary assessments in the healthcare domain[END_REF], [START_REF] Cecilia | Mobile crowdsensing approaches to address the covid-19 pandemic in spain[END_REF], transportation [START_REF] Hu | Smartroad: Smartphone-based crowd sensing for traffic regulator detection and identification[END_REF], [START_REF] Yan | Cloud-assisted mobile crowd sensing for traffic congestion control[END_REF], etc. Participants in mobile crowdsensing use sensors embedded on their smart devices (smartphones, smartwatches, etc.) or autonomous nodes to accomplish sensing tasks.

Given the mobile nature of the crowd, mobile crowdsensing platforms need to implement adequate route planning/selection solutions to better guide the crowd through the area of interest and maximize the quality of monitoring. Route planning and This work has been supported by the "LABEX IMU" (ANR-10-LABX-0088) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). It has also been supported by the ANR under the project ANR-21-CE25-0003 (DRON-MAP).

route selection are of great importance in mobile crowdsensing and smart cities applications [START_REF] Tao | Efficient path planning and truthful incentive mechanism design for mobile crowdsensing[END_REF]- [START_REF] Zhang | Shortest feasible paths with partial charging for battery-powered electric vehicles in smart cities[END_REF]. On one side, participants in route planning delegate the construction of their routes to the monitoring platform, which will drive their movements by leading them through specific points of interest. On the other side, in route selection, each participant can have multiple pre-computed routes, and the role of the system is to select the most appropriate one regarding the needs of the task. It is clear that the second method considerably limits the degree of freedom of sensing platforms as they are restricted to choose from already built candidate routes that may inevitably overlap, thus limiting coverage and increasing data redundancy.

One of the applications that greatly benefits from mobile crowdsensing is air quality monitoring, due to its major importance, the complexity of deploying expensive monitoring stations (especially in dense urban areas), the large number of potential participants, etc. In fact, several million deaths are attributable to air pollution each year [START_REF]Burden of disease from the joint effects of household and ambient air pollution for 2016[END_REF]. Air pollution consists of chemical and particles in the atmosphere mainly caused by human activities, such as energy production and fuel combustion. This human-generated pollution is prevalent in urban areas [START_REF] Cannistraro | Analysis of air pollution in the urban center of four cities sicilian[END_REF] due to high population and building densities, which result in an increasing risk of exposure to pollutants, leading to serious health problems such as cancer, respiratory distress, etc.

In our previous research work, we have designed a low-cost participatory air quality monitoring system that features small, portable sensing nodes equipped with three low-cost sensing probes for monitoring NO 2 , PM 1 , PM 2.5 , PM 10 , temperature, and relative humidity [START_REF] Fekih | Participatory air quality and urban heat islands monitoring system[END_REF]. The sensor nodes have been evaluated against reference sensors and have been involved in multiple mobile sensing campaigns in Lyon city since 2019 [START_REF] Fekih | Participatory air quality and urban heat islands monitoring system[END_REF]. Besides the mapping of air quality, the collected data also served to understand the relationship between the sampling rate, the energy consumption, and the mapping quality. The capacity of mapping approaches was also investigated, and the results revealed some heterogeneity in mapping quality between different areas, due to the fact that some of them were covered by multiple sensors, while others were not [START_REF] Fekih | On the data analysis of participatory air pollution monitoring using low-cost sensors[END_REF]. This led to questions related to the movement of the crowd and how it could be organized in order to maximize the overall monitoring quality while taking into consideration the constraints of the participants.

In this paper, we address the route selection problem in participatory sensing, while focusing on the application of air quality monitoring to validate our proposal. We present two route selection algorithms, where the first one computes the similarity between the different possible routes, while the second algorithm takes advantage of the similarity between the points of the map through clustering. We evaluate the performance of the proposed algorithms and compare them to other baseline route selection algorithms. Furthermore, we show through the results that our clustering-based routing improves the estimation quality while being efficient regarding the travel distance. The reminder of this paper is organized as follows, Section II discusses related works. Section III describes the scenario and the objective of this paper. Section IV presents the baseline route selection algorithms and explains the two proposed route selection solutions. The validation use case of our proposed algorithms is introduced in Section V. Section VI discusses the obtained results. Finally, Section VII concludes this paper.

II. RELATED WORKS

Mobile crowdsensing have raised questions regarding the movement of the crowd and how to maximize coverage while taking into consideration the constraints of the participants and the addressed application. This has opened the door for new research studies that harness citizen's mobility to improve the mapping of environmental phenomena.

Synthetic measurements were used in [START_REF] Bertero | Urbanscale no2 prediction with sensors aboard bicycles: A comparison of statistical methods using synthetic observations[END_REF] to predict a map of NO 2 concentrations in the city of Marseille, France, while considering up to 4500 bike-tracks randomly generated. The simulated concentrations were generated using a numerical model with a spatial resolution of 25 × 25km 2 . Fictive bike tracks were constructed using a cyclist route planner API. The resulting dataset served to train and compare the performance of three estimation models, namely ordinary kriging, multilayer perceptron neural network, and a generalized additive model (GAM).

In multiple crowdsensing applications, the system owner pays the participants for their collected data. In these use cases, the participants often perform sensing tasks at specific locations rather than continuous sampling. The price is generally related to the number of accomplished tasks, their priority, the quality of the data, etc. In this context, given a budget, the system owner optimizes the mobility of the crowd in order to maximize the number of completed tasks or the coverage of the study area, while satisfying the constraints of the participants.

The work in [START_REF] Chintakunta | Improving sensing coverage in vehicular crowdsensing using location diversity[END_REF] addresses the coverage problem in vehicular crowdsensing by incentivizing participants to deviate from their original path to improve the overall spatial coverage. The authors take advantage of game theory in which vehicles are the participants and their new routes are their strategies. In the proposed solution, all participants are assumed to start at the same time and the reward for a participant A depends on the number of deviations made to the original path and the diversity of the new route. This diversity metric is computed using the Fréchet distance between the path of A and the routes of all other participants. The goal then is to find the set of deviations to be made to participant routes, such that the coverage is maximized.

A three-phase routing algorithm is proposed in [START_REF] Tao | Efficient path planning and truthful incentive mechanism design for mobile crowdsensing[END_REF] in a task assignment context. A task is characterized by its location and its added value to the system. In the first phase, the algorithm iteratively constructs the route from the origin to the destination, by considering at each step the task that has the largest added value. The task is assigned to the user if his device's energy is enough to travel from the current location to the target task, then to the destination point. The second phase of the algorithm performs the construction backwards (i.e., from the destination point to the starting point of the participant). The final part of the solution is the selection of the route with the highest added value. This solution is appropriate if we consider specific sensing locations.

Instead of suggesting a whole route to a participant, the work in [START_REF] Chen | Trajectory segment selection with limited budget in mobile crowd sensing[END_REF] takes a different approach to maximize the coverage quality while avoiding redundant data. The study proposes a reverse greedy algorithm that selects only a subset of segments from the participant's route, based on the cost of the task and the available budget. The selected segments are those along which the participant performs the task. Thus, the participant will be rewarded according to the selected segments instead of the whole journey. To achieve such result, the algorithm eliminates redundant segments. Two segments are considered redundant if the distance separating their respective endpoints is less than a predefined distance. After each round of trimming, if the total cost is greater than the available budget, a new round starts with a larger threshold distance to further reduce the number of selected segments.

Gong et al. [START_REF] Gong | Location-based online task assignment and path planning for mobile crowdsensing[END_REF] addressed the path planning problem to maximize the total task quality in a scenario where users and tasks arrive dynamically. Each user has a limited distance budget and has to register his starting and destination points upon arrival. One of the proposed algorithms in this work selects the tasks that lead to the largest gain-cost ratio, one by one in a greedy manner, as long as they satisfy the travel distance budget of the user. An alternative solution tends to guide users to task-dense areas to maximize the cost-gain ratio. In addition to that, the authors designed an algorithm that takes into account the impact of a candidate task on the travel distance budget, in order to leverage tasks with low impact on the available budget. Given a candidate task, this algorithm evaluates the possibilities of the next step by computing the distance from this task to the others. The task that does not distract the participant from the rest of the tasks is selected. This algorithm yielded better performance compared to the other two solutions.

In [START_REF] Chen | Prosc+: Profit-driven online participant selection in compressive mobile crowdsensing[END_REF], the goal is to maximize the sub-profit in each time slot to approximately approach the maximum profit of all slots for a given task. A task is represented as a number of sensing locations during a certain period, the area of interest is divided into N cells, and the task duration into M time slots with the same length. Each participant in this scenario is guided to move along the shortest path. However, to avoid similar routes and maintain a stable dispersed distribution, the authors use 2D entropy to guide participant distribution. The more decentralized the participants, the higher the entropy value. The solution first randomly selects a number of cells and compute the entropy, then repeats the selection process for multiple iterations. At last, the participant distribution with the largest entropy is selected as an approximately optimal solution.

Although the aforementioned works explore mobile crowdsensing capabilities to accomplish sensing tasks, the majority of them focus more on specific sensing locations rather than sensing the entire zone. This prevents performing continuous sampling which considerably limits the potential of the crowdsensing and significantly reduces the spatial resolution, especially in air quality monitoring applications where there is no sensing range as explained in [START_REF] Boubrima | On the deployment of wireless sensor networks for air quality mapping: Optimization models and algorithms[END_REF]. Furthermore, most air quality monitoring platforms are based on low-cost sensors, as in [START_REF] Fekih | Participatory air quality and urban heat islands monitoring system[END_REF] [20] [START_REF] Anjomshoaa | City scanner: Building and scheduling a mobile sensing platform for smart city services[END_REF]. However, the majority do not take into consideration the inaccuracies of these sensors, which is one of the main challenges when it comes to predicting air pollution using low-cost environmental sensors. In addition, in participatory sensing, participants may use embedded sensors on their smartphones, adding a new challenge which is sensor heterogeneity. Therefore, taking into consideration sensing errors during the participant recruitment phase and the route planning process should be of utmost importance.

III. PROBLEM STATEMENT

In this section, we describe the scenario we focus on as well as the global objective we aim at. Afterwards, we give a mathematical formulation of the problem to solve it.

A. Scenario Description

In this work, we focus on a scenario in which multiple participants are equipped with heterogeneous low-cost environmental sensors to measure a specific phenomenon in a delimited area. Without loss of generality, we consider air quality mapping as use case. Each participant has a starting point and wants to get a path to reach his destination using a routing service. The participant is also willing to take a path that contributes to the knowledge of the studied phenomenon, without deviating too much from the optimal path.

B. Objective

Our global mission, in this context, is to suggest to each participant a route that might not necessarily be the optimal (shortest) path in terms of distance/duration, but does improve the estimation while being acceptable in terms of journey distance/duration. This implies taking into consideration not only the length of the participant's routes and their relationships with other participant routes, but also the accuracy level of his sensor. In other words, our goal is to find the best combination of routes that allows the system to reduce the overall mapping estimation error while still satisfying participants' constraints in terms of trip distance/duration. The assumptions upon which we build our solution are as follows:

• The duration of the sampling is considerably short (negligible) • Each route can be divided into smaller segments and routes may overlap along a certain distance (i.e., have segments in common)

C. Mathematical notation

Let U = [u 1 , u 2 , ..., u n ] be the set of participants, S = [s 1 , s 2 , ..., s n ] the set of their respective sensors, σ k the standard error of the k-th sensor, and P k = [p k1 , p k2 , ..., p kq ] the set of possible paths for the k-th participant. The goal is to select for each participant u k a path p kj from his q possible routes, knowing that he is equipped with a sensor s k which has a standard error σ k .

A brute force solution would be to test all the possible combinations (i.e., q n combinations). However, as the space of solutions grows exponentially with the number of participants, the implementation of such solution in a real life scenario is impractical. To cope with that, we rely on heuristics that are not optimal, but have smaller solution spaces, hence running much faster than the exhaustive search.

IV. ROUTE SELECTION SOLUTIONS

Through this contribution, we address route selection in the context of participatory air quality sensing. The objective is to maximize the quality of the prediction where there are no measurements, using a spatial interpolation method and an efficient route selection algorithm. First, we briefly review three traditional solutions that that could be utilized for comparison purposes. We then introduce two algorithms that take into consideration sensing errors and the relationship between participant routes.

A. Traditional route selection algorithms

Generally, route planning services always offer either the shortest path or q possible paths, while taking into consideration multiple parameters (e.g., traffic condition, waypoints, type of path, etc.). Route selection consists of choosing for each participant a path among the q proposed ones. This is a key element as it determines the geographical zones that will be sampled, which highly impacts the estimation quality. We consider in this part three traditional routing approaches as baseline:

• Shortest-path-based routing (SPR): This algorithm prioritizes participant comfort by reducing the travel distance for all participants, through suggesting the shortest route among the q paths. In SPR, all route selections are performed independently for each participant. • Longest-path-based routing (LPR) : In contrast to SPR, this approach aims at maximizing the set of points to visit.

It suggests to all participants the longest route possible among the q suggested routes. It is to be noted that in this contribution, we consider the longest path within a predefined stretch factor with respect to the shortest path. Suggesting the longest-path for all participants will intuitively expand the set of collected measures, and hence, improve the estimation quality. However, the correlation between paths is not considered, which means that this method does not always maximize the quality of the spatial mapping. • Random routing (RR): For each participant, randomly select a route among the q possible ones without taking into consideration its length, the already selected routes for previous participants, or the accuracy level of the sensors.

All three algorithms cited above are easy to implement, but their downside is that they do not take into consideration the correlations between participant routes or the accuracy levels of the sensors. In fact, the routes can pass through similar points or even different points that provide redundant information (points with similar characteristics). These observations feed the need for more sophisticated techniques, that will efficiently attribute routes to the participants that are not necessarily the longest or the shortest ones, but bring as much diversity in the dataset as possible. This can be achieved by introducing metrics and techniques that help to choose the most distinct routes possible and thus bring more information.

B. First proposition: Similarity-based route selection

The level of accuracy differs from a sensor to another, even between sensors of the same type. Therefore, to offer a good estimation, platforms should have an efficient routing approach that also takes into consideration the heterogeneity of sensing quality. In this regard, given a pool of participants sorted according to their sensors' accuracy, the similaritybased algorithm operates in a greedy manner by considering the q possible routes of the first participant (q routes satisfying a given distance threshold between the shortest and the longest path) and selects the longest one. After that, the algorithm iterates over the next participants in the pool and chooses for each one the route that has the lowest similarity with the already selected routes from the previous participants. Every time a route is selected for a participant, the latter is removed from the pool and the algorithm moves to the participant with the next best sensor, in a greedy manner, until the pool is empty. This process is illustrated by Algorithm 1.

The calculation of the similarity percentage between routes has a major role in finding the most distinct routes possible, and can be computed in multiple ways. Without loss of generality, we have mainly explored a commonly used metric in image segmentation, namely the Sørensen-Dice coefficient (also known as Dice Similarity Coefficient or DSC) [START_REF] Mathai | Fast vessel segmentation and tracking in ultra high-frequency ultrasound images[END_REF]. Considering two participant routes A and B, the formula for this coefficient is given as follows:

DSC A,B = 2 × |A ∩ B| |A| + |B| (1)
Algorithm 1 Similarity-based route selection Input: U {the set of users} Output: P {the set of selected paths} 1: P ← ∅ {initialize the set of selected paths} 2: U ← order(U, descending accuracy) {order the set of users based on the accuracy of their sensors} 3: P ← longest path(u 0 ) {select the longest path for the first user in U} 4: for u ∈ U with u ̸ = u 0 do

C. Second proposition: Cluster-based route selection

Instead of computing the similarity between participant routes in terms of overlapping, this approach focuses on the similarity between the points of the map. The main idea is to regroup the points of the map, not based on the spatial distance separating them, but on explanatory (also called independent) variables related to surrounding conditions (such as distance to routes, meteorology, land-use data, etc.). Indeed, this design is guided by the fact that air quality mapping often make use of explanatory (independent) variables to predict response (dependent) variable in unmeasured points (pollution concentrations in our case) based on available samples of the same variables in measured points. To achieve our goal, we have opted for the agglomerative hierarchical clustering, which is a widely used technique of hierarchical clustering [START_REF] Everitt | Hierarchical clustering[END_REF], in order to create groups of similar points that might be spatially far from each other, but present similar properties. First, each point of the map is assigned to an individual cluster, and we calculate the distance between the clusters based on the independent variables. Then, clusters are merged successively while minimizing the sum of squared differences between the clusters being merged. As a result, all points of the map are classified into c clusters. Then, for each route, we calculate the number of clusters it traverses. The main idea of this route selection approach is to choose for each participant the route that passes through the largest number of clusters (see Algorithm 2). It is worth mentioning that the performance of this approach depends on the set of explanatory variables collected, and the preprocessing applied to filter out those with low correlation (with the dependent variable) or redundant information.

V. VALIDATION

In order to validate our proposal, we followed the methodology presented in Fig 1 . We give here an overview of the P ← P ∪ s path 8: end for 9: return P validation approach while more details are given in next subsections. As shown in Fig 1, we consider simulated pollution concentrations as a reference map of the studied area. We first start with a pool of participants, each with a starting and destination points. Each participant has a pollution sensor with its own standard error σ k . Instead of constructing the different routes for each user, we rely on a routing service that provides us with several alternative paths whose length is within a predefined stretch factor of the shortest path. After that comes the route selection phase, during which the algorithm suggests a path for each participant based on the reliability of the sensors and the relationship between the routes. Following that, for each location l visited by a participant's route, a synthetic measurement is generated using a normal distribution of mean y l and variance σ 2 k , with y l being the reference concentration at the location l. Finally, the generated synthetic observations are passed to spatial interpolation methods in order to produce a predicted pollution concentrations map. The latter is then compared against the reference map to evaluate the impact and performance of the route selection approaches.

A. Interpolation methods

The use of spatial interpolation methods is a common practice in air quality monitoring field, since there are often fewer sensors to measure every single point of a map, even with a mobile crowd. These techniques give an estimation at unmeasured points based on available samples of the same studied variable [START_REF] Xie | A review of urban air pollution monitoring and exposure assessment methods[END_REF]. In addition, to better explain the phenomenon, estimation models often need extra information about the surrounding environment. These characteristics may include, but are not limited to, meteorological parameters (e.g., temperature, wind speed, etc.), land-use data (e.g., number of buildings, etc.). Without loss of generality, we have selected three models: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN) Regression, and eXtreme Gradient Boosting (Xgboost).

Multiple linear regression is an extended version of the simple linear regression that uses just one explanatory variable. MLR aims to model a linear relationship between the studied phenomenon and a number of explanatory variables to predict the concentration values at points where no data was collected [START_REF] Xie | A review of urban air pollution monitoring and exposure assessment methods[END_REF], [START_REF] Larkin | Global land use regression model for nitrogen dioxide air pollution[END_REF].

K-Nearest Neighbors is a popular algorithm usually used for classification. The general idea behind it consists of considering the K nearest samples or observations to the point to be predicted based on a distance metric. In regression, the algorithm uses "feature similarity" to identify the k closest points and then assigns the average of their observations to the point of interest. The similarity between the target point and its neighbors is obtained by applying a distance metric to the explanatory variables. The size of the neighborhood (i.e., the value of k) is an important part of this algorithm. On one hand, a small neighborhood reduces the number of points used in the regression and leads to overfitting problems. On the other hand, a large value of k means including more points in the estimation causing high sensibility to noise [START_REF] Ren | Comparison of machine learning and land use regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous united states[END_REF], [START_REF] Fekih | On the regression and assimilation for air quality mapping using dense low-cost wsn[END_REF].

Xgboost is a fast and powerful ensemble learning method that assumes taking lots of small steps in the right direction results in a better prediction [START_REF] Ren | Comparison of machine learning and land use regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous united states[END_REF]. It implements the gradient boosting decision tree algorithm in which new decision trees are iteratively built to correct the errors of the previous ones, indeed each new tree boosts the attributes that led to estimation errors of the previous trees of the model [START_REF] Chen | Xgboost: A scalable tree boosting system[END_REF]. Xgboost has shown a good performance in estimating air pollution concentrations in [START_REF] Fekih | On the regression and assimilation for air quality mapping using dense low-cost wsn[END_REF].

B. Study area and reference map

In this paper, we consider the agglomeration of Lyon, which is located in the region of "Auvergne-Rhône-Alpes" in the southeast of France. Our work mainly focuses on a 5 × 5 km 2 area, corresponding to the center of the Lyon city and its immediate vicinity (see Fig 2).

In order to build a reference map of pollution concentrations, we consider NO 2 pollutant and simulations generated by a numerical model called SIRANE [29] [30] [31]. This model is designed for urban areas and is widely adopted by certified air quality monitoring agencies in France. The simulated data

C. Generation of participant routes and sensor measurements

In our use case, a participant is represented as a triple consisting of his starting point, his destination, and the accuracy level of the sensor he carries. First, we generate 200 random participants with a distance between the starting and destination ranging from 1km to 5km. To match a realistic scenario in which participants have heterogeneous sensors, the accuracy of participant sensors varied between 1µg/m 3 and 20µg/m 3 . Then, we use an existing routing service to generate a minimum of 5 alternative routes for each participant to meet a realistic scenario and to have a substantial search space. Furthermore, to avoid ending up with very long alternative routes, we only consider routes that are at most 30% longer than the shortest path.

In the second step, synthetic sensor measurements are generated using the standard errors of the sensors and a normal distribution at each point of the map that a participant's route passes through.

D. Computing the similarity

In order to implement the similarity-based algorithm, we need to compute the similarity metric between the different routes. This metric should consider segments that do not necessarily overlap but are very close to each other. This may happen when two participants pass by the same road but in opposite directions or parallel sidewalks. For this reason, we build a buffer around each segment of a route, and then compute the similarity between the buffers. The size of the buffer highly influences the similarity metric. Indeed, the larger the buffer is, the higher the similarity value. This size should also be adapted to the spatial resolution of the available data. For the following validation tests, we choose a buffer size of 60 meters around the segment (i.e., 30 meters from each side of the segment).

E. Clusters construction

The construction of clusters is a crucial phase in the clusterbased route selection approach. It represents the foundation upon which the whole algorithm is built. Therefore, the choice of the optimal number of clusters to construct has a great importance. On one hand, choosing few clusters lowers the similarity threshold. As a result, more points are clustered together without showing much resemblance. On the other hand, a large number of clusters seeks very similar points. Hence, the clusters may gather very few points.

In order to choose an adequate number of clusters in our context, we have evaluated the MAE of the estimation while varying the number of clusters from 250 to 1500 with a step of 250 clusters and 20 iterations for each step. Results depicted in Table I show that the lowest MAE value is reached with 1250 clusters.

VI. PERFORMANCE EVALUATION AND DISCUSSION

To evaluate the performance of our proposal, we first compare multiple spatial interpolation models using the same route selection strategy in order to get insights on which model performs better in estimating NO 2 concentrations. The best model is then used in the second part, which compares A. Comparison of the performance of MLR, KNN, and Xgboost

In order to get an insight on which statistical model gives better results, we conducted multiple simulations with the MLR, KNN, and XGBoost, using the similarity-based route selection approach, while increasing the number of participants from 15 to 40 and randomly generating sensor errors between 1µg/m 3 to 10µg/m 3 . The number of selected neighbors did not show a significant impact on the performance of KNN. Therefore, we chose k = 5 in our simulations. Fig. 4 depicts the results of the average MAE in function of the number of participants, obtained after 20 iterations. We observe that as the number of participants increases, the prediction error decreases for all three models. XGBoost outperforms KNN by around 30% and MLR by nearly 42% in terms of MAE. Moreover, we present in Table II the average execution time of the three models for one iteration. The table shows that MLR is the fastest model, while KNN is the slowest among the three. The reason that KNN is much slower is that the dataset used to perform the prediction is large in this study, which makes the prediction step computationally intensive for KNN, because it needs to loop over the entire dataset to find the closest points to each unsampled one. The observations from Fig. 4 along with those of Table II give hints about which model is more suitable for a real life scenario. In our case, we chose XGBoost for the upcoming evaluations.

B. Comparison of route selection algorithms

With the aim of evaluating the impact of route selection strategies on the estimation of NO 2 concentrations, we carried out multiple tests considering the shortest-path-based routing, the longest-path-based routing, and the two proposed route selection algorithms, i.e., the similarity-based routing, and the cluster-based routing. The two proposed solutions have the same goal, that is, offering the most informative routes possible, while taking into account the correlations between them. However, the difference lies in the evaluation of the relationship between the routes. The similarity-based routing computes the similarity between participant routes and tries to reduce the overlapping between the selected routes, while the cluster-based routing takes advantage of hierarchical clustering to form groups of similar points and then tries to maximize the set of visited groups.

The performance of the four route selection algorithms are evaluated using XGBoost, based on the observations from the previous test. Fig. 5 depicts the MAE of estimation in function of the number of participants. The experiments are performed with a number of participants increasing from 15 to 40 and repeated 20 times. At each iteration, we randomly select a new set of participants with sensor errors between 1µg/m 3 to 20µg/m 3 . Results clearly show that increasing the number of users helps decreasing the estimation error due to the presence of more routes and hence a larger area being covered. An interesting observation is that while the shortest-path-based routing has the worst performance in terms of MAE of the estimation, the similarity-based and the cluster-based solutions outperforms the longest-path-based routing. This means that choosing the longest path is not always a good decision to improve the estimation. Indeed, the similarity-based route selection performs up to 15.42% better than the shortestpath-based algorithm and 3.49% better than the longest-pathbased solution, while the cluster-based solution surpasses the shortest-path-based and the longest-path-based by 16.84% and 5.22%, respectively. In addition, we investigate the impact of the different route selection approaches on the travel distance of participants. For this purpose, we calculate the total traveling distance for each route selection solution. Fig 6 shows the total travel distance accumulated for all participants. As expected, the shortestpath-based algorithm offers the shortest distance by definition and hence outperforms all the other solutions. Nonetheless, results reveal that our proposed solutions are also more efficient than the longest-path selection in terms of traveled distance. Indeed, the similarity-based algorithm improves the travel distance by nearly 15.27% compared to the longestpath-based algorithm and 10.18% compared to the clusterbased solution. These promising results state that our proposed solutions give the lowest error while offering shorter routes compared to the best of baseline algorithms.

VII. CONCLUSION

Air pollution has become a major threat to human health in recent years. Public and government authorities are making considerable efforts to help reduce air pollution around the world. Today, traditional air pollution monitoring stations are clearly not sufficient to assess local exposure to pollutants, which is why mobile crowdsensing, powered by recent developments in sensing probes and communication protocols, has gained a lot of attention. In this work, we address the problem of route selection in participatory sensing with lowcost sensors. We propose two route selection algorithms that take into consideration the characteristics of low-cost sensors in the decision process. The similarity-based routing algorithm aims to give spatially dispersed routes to maximize coverage using the Sørensen-Dice coefficient, a commonly used metric in image segmentation. The cluster-based routing algorithm makes use of hierarchical clustering to build groups of similar points, then tries to visit as many groups as possible to increase the diversity of the collected information. Both algorithms perform in a greedy manner by suggesting at each iteration a route for the user with the most accurate sensor. We compare our solutions to two baseline route selection algorithms, namely the longest-path-based and the shortest-path-based algorithms. We also show through multiple simulations that our route selection approaches can obtain comparable results to the longest-pathbased approach or even outperform it, while being efficient regarding the travel distance. Our algorithms are adapted to a scenario in which participants do not necessarily arrive at the same time. Moreover, the idea behind the clustering-based approach can be adapted to other use cases, such as reducing the individual exposure of citizens to air pollution, by reducing the number of highly polluted clusters to be visited.

P

  ← P ∪ s path 7: end for 8: return P Where |A| and |B| are the areas of route A and route B, respectively, and |A ∩ B| the area of the intersection between both routes (i.e., area of overlapping).

Algorithm 2

 2 Cluster-based route selection Input: U {the set of users} Output: P {the set of selected paths} 1: P ← ∅ {initialize the set of selected paths} 2: C ← ∅ {the set of visited clusters} 3: U ← order(U, descending accuracy) {order the set of users based on the accuracy of their sensors} 4: for u ∈ U do 5: {select the path visiting more new clusters} s path ← arg max p∈P aths(u) N bClusters(p, C) 6: C ← C ∪ Clusters(s path) 7:

Fig. 1 .

 1 Fig. 1. The general methodology used in validation

Fig. 2 .

 2 Fig. 2. Map view of the area of interest

Fig. 3 .

 3 Fig. 3. Reference heatmap of NO 2 concentrations (simulated data)

Fig. 4 .

 4 Fig. 4. MAE vs number of participants

Fig. 5 .

 5 Fig. 5. MAE vs number of participants

Fig. 6 .

 6 Fig. 6. Total travel distance for the different route selection algorithms Finally, to give an idea of the final result, we perform simulations to estimate NO 2 concentrations with two configurations of sensing errors. For the first simulation, sensing errors are randomly generated between 1µg/m 3 and 5µg/m 3 , while in the second one, sensing errors vary between 1µg/m 3 and 20µg/m 3 . Both simulations are executed 20 times using 40 participants and the similarity-based route selection algorithm. The results are depicted in Fig 7 and Fig 8, respectively. It is shown that although capturing most of the highly polluted zones, the use of sensors with large errors leads to an overall noisy and poor estimation.

Fig. 7 .

 7 Fig. 7. Heatmap of estimated NO 2 concentrations using XGBoost, achieved with 40 participants and sensor errors varying between 1µg/m 3 and 5µg/m 3

Fig. 8 .

 8 Fig. 8. Heatmap of estimated NO 2 concentrations using XGBoost, achieved with 40 participants and sensor errors varying between 1µg/m 3 and 20µg/m 3

TABLE I MAE

 I VS THE NUMBER OF CLUSTERS WITH 40 PARTICIPANTS

	Number of clusters	250	500	750	1000	1250	1500
	MAE (µg/m 3 )	5.19	5.08	5.13	5.11	4.97	5.16