Adriana Simona Mihăiţă

Laurent Dupont

Olivier Chery

Mauricio Camargo

Chen Cai

Adriana Simona Mihȃit ¸ȃ

Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

Keywords: air pollution, eco-neighbourhood, mobile sensing, decision trees, neural networks

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

Introduction

Addressing air pollution problems in growing urban cities has become a major problem due to ever increasing traffic in dense populated urban areas, extended industrialisation, high energy consumption, insufficient resources for monitoring and various issues in defining adapted policies [START_REF] Krupnick | Challenges to managing air pollution[END_REF][START_REF] Kumar | New directions: Can a "blue sky" return to indian megacities?[END_REF]. The challenge of managing air pollution becomes more difficult due to its dangerous effects on public health and the multitude of air pollution triggering factors. As a consequence, various studies in recent years have been concentrating on evaluating the impact of bad air quality on citizens, by moving away from traditional monitoring stations which are normally placed in highaltitude locations across cities, towards outdoor and easy-deployable air quality monitoring units, such as mobile sensors installed on cars, bikes or even carried by hand during daily travelling. This new type of collective approach for monitoring air quality brings numerous advantages in terms of real-time pollution measurement and hot-spot identification, but also comes with various challenges due to the amount of data generated and its accuracy. Therefore, there is a true challenge of not only switching towards a mobile air monitoring paradigm (and choosing the best adapted sensing units) but also in modelling efficiently the data generated by all these mobile sensing units. Data-driven modelling is an efficient way of extracting valuable information from generated data-sets, but is less effective when the data is sparse, incomplete or contains many inaccuracies.

There is currently a lack of research studies integrating both real-time air quality measurements through efficient mobile sensing units and afferent datadriven modelling. Appendix A presents a comparative state-of-art table in which we have gathered recent studies adopting new sensing technology available for outdoor air quality monitoring and/or efficient methods for data-driven air quality prediction. The findings have been analysed based on: a) the sensing technology which was used, b) whether the pollution stations were mobile or not, c) whether the units have been used near breathing level (close to human level), d) the time period of the experimentation and most importantly, e) the data analysis procedure which has been applied in order to obtain accurate predictions or meaningful insights. Our initial observation revealed that majority of studies have been using outdoor air quality sensors, but only half of them were used in a mobile setting, and were either fixed, attached to street lamps or inside monitoring boxes. For example, [START_REF] Castell | Localized real-time information on outdoor air quality at kindergartens in oslo, norway using low-cost sensor nodes[END_REF] installed 17 nodes monitoring N O 2 in kindergartens in Oslo at heights of 2.5-3 meters altitude, [START_REF] Ercilla-Montserrat | A study on air quality and heavy metals content of urban food produced in a mediterranean city (barcelona)[END_REF] installed outdoors sensors on rooftops of greenhouses or urban houses for detecting heavy mental contaminations due to atmospheric pollution, [START_REF] Heimann | Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors[END_REF] used 32 low-cost electrochemical sensor nodes in and around the city of Cambridge, UK, to extract underlying pollution levels (baselines) from air measurements, while [START_REF] Popoola | Use of networks of low cost air quality sensors to quantify air quality in urban settings[END_REF] used 17 low cost portable air quality devices which were installed at the London Heathrow airport in UK on lampposts at 3-meters hight altitude.

While outdoor fixed monitoring can provide a better information about the air quality, their high level of installation (often around 3 meters altitude) does not reflect a true impact of the pollution at human level. More recent studies have started to explore not only fixed outdoors sensing units, but also mobile sensors which can be carried by hand, installed on cars/etc. For example, a recent study published in [START_REF] Sm | Mobile monitoring of air pollution using low cost sensors to visualize spatio-temporal variation of pollutants at urban hotspots[END_REF] used a smart personal air quality system carried by pedestrians walking on predefined paths or by bus in different locations in India; the analysis revealed non-linear relations between the gaseous pollutant concentrations versus the resistance offered by different sensors; there wasn't however any data-driven modelling or predictions of the pollutants being measured. Similar direction was taken by [START_REF] Minet | Capturing the sensitivity of landuse regression models to short-term mobile monitoring campaigns using air pollution micro-sensors[END_REF] which used portable sensors for measuring N O 2 concentrations in Montreal carried by pedestrians walking or biking; the authors applied statistical and land-use regressions for data modelling which revealed sensitivity to the number of road segments and the number of visits per segment. Other studies presented in [START_REF] Suriano | Short term forecasting of the level of service on a motorway network, by using a microscopic simulation model[END_REF] and [START_REF] Zappi | The citisense air quality monitoring mobile sensor node[END_REF] deployed similar mobile sensors near to the breathing level, but there is no data-driven modelling attached to the results, except from visual data representation.

Recent advancements in machine learning showcased the power of using such models for any type of investigation where large volumes of data have been generated. For example, a recent study published in [START_REF] Zhou | Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts[END_REF] presents an artificial intelligence approach based on a Deep Multi-output LSTM (DM-LSTM) neural network model for predicting the air quality in the city of Taipei, Taiwan. The study is very promising but the data being used is generated by five fixed air quality monitoring stations in the city; these data-sets, once again, do not reflect pollution concentrations felt at human levels. Similarly, other powerful data-driven methods have been used for air quality prediction such as DEA (data envelopment analysis) in [START_REF] Zhou | Evaluating air quality in china based on daily data: Application of integer data envelopment analysis[END_REF], or Feed-Forward Neural Network and Random Forests in [START_REF] Borrego | Assessment of air quality microsensors versus reference methods: The eunetair joint exercise -part ii[END_REF]. But once again, the information being used comes from fixed data sources, not mobile sensing units carried by citizens for daily travel.

In an attempt to address these issues and research gap, this paper presents a research approach for: a) choosing the best mobile sensing units for outdoors air quality monitoring in an urban neighbourhood, b) constructing reliable experimenting protocols for evaluating the accuracy of such units with regards to fixed air pollution stations, and c) constructing a data-driven approach using decision trees and neural networks for predicting the air quality from mobilegenerated sensing data. We strongly believe that building such a data-driven modelling over mobile-generated pollution data would help to learn and detect patterns of air monitoring.

The paper is organized as follows. Section 2 presents the challenges faced by air quality monitoring in an international context and the need to use lowcost mobile pollution sensing units in an opportunistic citizen sensing. Section 3 introduces the case study of the Nancy Grand Coeur eco-neighbourhood and the air quality evaluation methods currently deployed in the city. Section 3.1 presents the stationary and mobile smart sensing units chosen to conduct the current study, followed by a description in Section 3.2 of three air quality experimenting protocols which we deployed in the most congested intersections. This paper is a further extension of our first two air pollution protocols published in [START_REF] Mihȃit ¸ȃ | Air quality monitoring using stationary versus mobile sensing units: a case study from lorraine, france[END_REF], by adding a third experimenting protocol and a data-driven prediction modelling on the mobile sensing generated data. The results of the three protocols are provided in Section 4 and concentrate around nitrogen dioxide and noise level evaluations, followed by conclusions and future perspectives of the current work.

Challenges in air quality monitoring

International Context

Currently air pollution is monitored at a regional level by networks of static and sparse stationary Air Quality Monitoring (AQM) stations, equipped with instruments for measuring various pollutants such as: carbon monoxide (CO), nitrogen oxides (N O x ), sulphur dioxide (SO 2 ), ozone (O 3 ) and particulate matter (P M ). The risk information is often provided as a concentration of pollutants or as an index of air quality (AQI) at a scale which can be easily interpreted by the public. These AQIs can vary in their approach for determining pollutant concentrations, as they follow different regional policies [START_REF] Plaia | Air quality indices: a review[END_REF] which can differ from one country to another. For example, Canada has adopted an Air Quality Health Index on an 11-point scale obtained from a nonlinear combination of particulate matter 2.5 microns (P M 2.5 ), nitrogen dioxide (N O 2 ) and ozone (O 3 ) [START_REF] Stieb | A new multipollutant, no-threshold air quality health index based on short-term associations observed in daily time-series analyses[END_REF]. On the other hand, in Europe all countries are required to comply with EU directives such as the Council Directive 96/62/EC on ambient air quality assessment and management, commonly referred as the Air Quality Framework Directive. Therefore the hourly and daily AQIs are calculated on a scale from 0 to 100 by taking into consideration P M 10 , N O 2 , O 3 , and where accessible, P M 2.5 , SO 2 and CO (van den [START_REF] Van Den Elshout | CAQI Air quality index, Comparing Urban Air Qualiy across borders[END_REF]. The EU directives recommend as well to install a specific number of monitoring stations for individual pollutant monitoring, based on the number of inhabitants and the geographic partitioning of that area/city (EU Air Quality Directive 2008/50/EC).

Although they offer high-precision results, the AQM stations are often highpriced and need a significant amount of resources to be routinely maintained and calibrated [START_REF] Chong | Sensor networks: Evolution, opportunities, and challenges[END_REF]. Often, the temporal and spatial resolution of a network of fixed AQM stations is far too sparse to incorporate the contribution of different sources of pollution without significant constraints and assumptions. The AQM stations would offer a global insight over large urban areas but they can not identify pollution hotspots inside the city centre or around large industrial areas for example. Often there are no real-time pollutant concentration maps available at high-resolution (< 1m) for large urban areas because they require a large amount of data, computing facilities and input details which are not available for many cities [START_REF] Kumar | The rise of low-cost sensing for managing air pollution in cities[END_REF]. These aspects led to the emergent idea of using more fine-grained monitoring units in an outdoor setting, which would come at a lower cost and maintenance. The motivation and benefits of using such units are described in the next section.

The need for low-cost sensing units

Recent improvement of low-cost sensor technology has lead to the development of a multitude of robust micro-sensing units (MSUs) with a lower power consumption which can be used for detailed air quality surveillance. These MSUs can be used either as individual nodes or in an interconnected distributed network, and would collect high-resolution spatial an temporal data when being mounted on cars, bicycles or carried daily by pedestrians. One of the main advantages of using low-cost sensing units is that they provide more input conditions, especially if they are used in significant numbers for detecting pollution hotspots. Their real-time information would allow a rapid assessment of the pollution problem and would lead to more efficient prevention strategies.

Due to a higher granularity provided, easy to handle functionalities and rapid access to real-time pollution concentrations, various research programmes have started to test both fixed and mobile monitoring sensors [START_REF] Mead | The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks[END_REF]. As well, including citizens in the testing and exploration of urban air pollution opens new opportunities for direct environmental awareness, debate and future prevention strategies. Some examples of such projects are: Air Quality Egg (AQE, 2016), Citizen Sense [START_REF] Gabrys | Citizen sense[END_REF] and the Smart Citizen Kit [START_REF] Sck | Smart citizen kit[END_REF], which offer a centralized collection of data, processing and real-time map visualisation through on-line platforms and mobile applications. These "citizen sensing" projects intend to expand citizen engagement in environmental issues, and help them making changes in their daily journey-to-work trips in order to avoid polluted urban areas. Currently there are various low-cost air quality sensors which are commercially available [START_REF] Alphasense | Alphasense air, sensors for air quality networks[END_REF] or prototype sensor networks (MIT, 2016). For a detailed state of the art regarding low-cost pollution sensors the user can refer to the works of [START_REF] Kumar | The rise of low-cost sensing for managing air pollution in cities[END_REF]. For our current study we have also conducted a state-of-art investigation on low-cost sensing units which will be further discussed in Section 3.1.

While this radical change in the air monitoring mentality promises a flexible pollution surveillance solution, the question around the accuracy of the generated data still remains an open research question. The main downfall of low-cost sensors remains their relatively low accuracy compared to official fixed AQM stations or other benchmark devices [START_REF] Williams | Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network[END_REF]. For example, [START_REF] Moltchanov | On the feasibility of measuring urban air pollution by wireless distributed sensor networks[END_REF] has observed a sensor-specific temporal variation of the calibration parameters, and proposed a periodical calibration of wireless sensors based on the nearby AQM stations which would capture the fine and dynamic spatial variability of pollutants at a high temporal resolution. Questions related to the battery power of the sensors and the life-expectancy of low-cost sensors can also be seen as a drawback for adopting MSUs, but their flexibility and remote-control possibility for data transmission and collection increased their popularity. Together with meteorological sensors for measuring humidity, temperature, wind speed and direction, they can form the basis for assessing pollution levels and can induce substantial behavioural changes at a larger scale amongst citizens. But these small monitoring units need to be used at a larger scale in order to provide a global and complete picture of the urban air pollu-tion; therefore the idea of citizens using them on a daily basis brought a higher popularity to this modular and dynamic approach for air pollution monitoring.

Towards opportunistic citizen sensing

The idea of using low-cost sensing for monitoring air quality has led to a shift in the air quality data collection, generating the notion of opportunistic citizen sensing, which implies that data collected for one specific purpose can be used for other purposes as well [START_REF] Campbell | The rise of peoplecentric sensing[END_REF]. Involving citizens in the data collection gave birth to the notion of anthropocentric opportunistic sensing, in which large volumes of sensing data are collected, stored and fused for further analysis and interpretation [START_REF] Kapadia | Opportunistic sensing: Security challenges for the new paradigm[END_REF]. Using data analytics for extracting meaningful insights from daily air pollution and noise exposure will provide unparalleled feedback to the citizens regarding their daily trips and route choice behaviour. By following the opportunistic aspect, the air pollution analytics can be coupled together with clinical research studies for analysing correlations between citizen movement and biological exposure (NIEHS, 2014). Emergency alerts could then be triggered when unusual air quality levels are signalised in specific areas of the city or when a significant number of citizens present clinical side-effects of air pollution exposure.

Our current work is driven by the idea of deploying an opportunistic citizen sensing coupled with an intelligent data-driven modelling in order to build future pollution pattern recognition and real-time anomaly detection. This would lead to the creation of a real-time situation awareness for pedestrian and travellers, helping them to customize their journey in order to improve their health condition. The air pollution data generated from such mobile and opportunistic sensing will be further integrated with existing traffic simulation models and improve the air quality prediction. The next section presents our case study and the steps we took for building a real-life mobile air pollution monitoring together with data-driven modelling techniques.

Case Study

The concept of eco-neighbourhood has emerged from a need to build an innovative place for technical, economic and social experimentation. Their role is quite complex as they must meet several principles of sustainable development (MCT, 2016): 1) involve all the city actors, 2) contribute to improving the daily life by developing a healthy and safe living environment for all residents, 3) participate in the economical and local dynamics, 4) promote a responsible resource management and adaptation to the climate change. The eco-neighbourhoods offer the opportunity to experience and anticipate the evolution of cities by guiding the decision makers. The latest changes in the development of digital tools and design practices (collaborative approach, usage integration directly from the design phase, citizen involvement in experimental smart city projects), offer new perspectives for quantifying the impact of the urban changes [START_REF] Dupont | Innovative public-private partnership to support smart city: the case of {Chaire REVES}[END_REF].

Motivation: With the urban project Nancy Grand Coeur (GN, 2012) the Grand Nancy Metropolis in France, wants to rehabilitate the 15-hectares area around the historical train station including its railway and industrial brown field. A visual representation of the train station hosting almost 9 million passengers each year is provided in Figure 1a). This ecological urban project is intended to be delivered by 2025, and the objectives for this central area are manifold: new fluid mobility, better traffic regulation, reconciliation between historical and modern neighbourhoods of the city, improved air quality, extended green spaces, reduced energy consumption, comfortable homes and offices. An important step to respond to this wide variety of problems is to analyse the air quality inside the neighbourhood, especially at a human level, when passing through the most circulated intersections of the Nancy Grand Coeur (NGC) neighbourhood: C129 and C201 (see selected area in sub-plots b) and c) from Figure 1)). Understanding how highly circulated streets impact the citizens on their daily journey-to-work trips is a true challenge which can give a clear insight on how the eco-neighbourhood needs to be reconfigured in order to protect its inhabitants.

Solution: The work presented in this paper is a continuation of our previous studies (Mihȃit ¸ȃ et al., 2014[START_REF] Mihȃit ¸ȃ | Integrating a mesoscopic traffic simulation model and a simplified no2 estimation model for predicting the impact of air pollution[END_REF] in which we proposed an integrated air pollution and traffic simulation model for building a simplified N O 2 estimation model which helped predicting and visualizing various environmental changes inside the NGC eco-neighbourhood. Our previous study has used reliable data sets provided by the Air Quality Monitoring Stations (AQM) belonging to the local air-quality management centre, as well as meteorological data. While these data sets are of high accuracy, they only represent global concentrations computed by the AQM station installed at high elevation from the ground (more than 10 meters altitude) in a single location in the city. The real and direct impact that pollution can have at the human level could be completely different than higher dispersed pollution concentrations. Therefore, our major objective is to build a research monitoring framework using mobile smart sensing units transmitting air quality information in real-time, coupled with a prediction engine and situation awareness for citizens travelling daily in NGC. Providing health risk information caused by air pollution is an important step for raising citizen awareness and triggering changes in their daily travelling behaviour.

The NGC project has the initiative to change the structural configuration of the C129 and C201 intersections represented in Figure 1c), in order to allow a higher inflow of vehicles to cross the neighbourhood every day. A large amount of vehicles in densely populated areas will contribute to an increasing deterioration of the air quality due to higher motor vehicle emissions. In 2012, the U.S. Environmental Protection Agency (US EPA) has shown that 61% of the total emissions of carbon monoxide (CO) and 35% of total emissions of nitrogen oxide were produced by highway vehicles (US-EPA, 2016). The complexity of the air pollution lies in its extent and the large amount of factors changing its behaviour, making it even more difficult to implement measures for protecting the citizens. According to the 2012 air quality assessment (MEDE, 2012), air pollution is caused by various industrial, commercial, domestic, agricultural activities, but the traffic congestion is the major cause. As 56% of the nitrogen dioxide in the air is caused by road transportation [START_REF] Mede | Technical Report. Paris: Ministére de l'écologie et du développement durable[END_REF], for this initial case study we mostly focus on N O 2 concentrations. The objectives of our study are manifold: 1) measuring air quality at a granular level in the city by using smart pollution sensors, 2) prepare the field for integrating citizens in a daily and global air quality data collection, 3) provide insights by comparing outputs of stationary and mobile smart pollution sensors, 4) derive data-driven predictions by using latest generated data from mobile units.

Choice of sensing units

In France, the State entrusts the monitoring of air quality to twenty approved AASQA associations (1901 Act) led by the ATMO Federation (ATMO, 2016). Air Lorraine [START_REF] Lorraine | Atmo grand est[END_REF] is one of the selected air monitoring associations which is responsible for continuously monitoring the air quality inside NGC and which has been our main reference source for testing the accuracy of the mobile sensor units deployed for this study.

As we are currently interested in analysing more granular air quality information, a series of mobile sensing units have been considered; a selection of these MSUs is provided in Appendix B, Table B.5. The choices have been selected after a thorough analysis of existent sensing units on the market at the time of the current project, their accuracy, the feasibility of being used outdoors on a daily basis, their costs and daily maintenance. For this paper, we only present the results obtained when investigating three units which are detailed in the following. Other comparative studies of smart pollution units are currently under testing and evaluation. In the following we give a brief discussion about the three MSUs which have been chosen for our experimenting protocols, the reason, scope as well as their advantages and disadvantages for the current study. The station can be mounted on cars, bicycles and can be carried by hand while its data is transmitted through GPRS, having a 48-hour autonomy.

The main advantages of this mobile sensing unit relies in its easy installation and utilisation, a two day autonomy and real-time data visualisation.

The station has been successfully used for building the open data portal MyGreenServices by INRIA [START_REF] Trousse | Co-creating environmental services based on pollution citizen sensing[END_REF] which offers real-time visualisation of environmental data collected by citizens, generates alert services and has a forum for sharing ideas and best practices in terms of eco-responsible behaviour. In an attempt to promote citizen awareness and trigger changes in the daily travelling behaviour of citizens, INRIA has provided for our project one Azimut station for testing, evaluation and comparison. The data analytics provided through the platform have been used for carrying out the second and third experimenting protocols, which are detailed in Sections 3.2.2-3.2.3. 3. Smart Citizen Kit (SCK)(Figure 2c)): is a crowd-funded product developed by Fab Lab Barcelona at the Institute for Advanced Architecture of Catalonia [START_REF] Sck | Smart citizen kit[END_REF]. This low cost mobile sensing unit can provide real-time data measuring for N O 2 and CO concentrations, noise, temperature, humidity and light. Its solar-panel and low power consumption, together with an ergonomic design make it attractive for daily usage. The device streams the data measured by its integrated sensors over Wi-Fi, using the FCC-certified wireless module on the data-processing board.

Results can be visualised through the on-line interface or through a dedicated mobile app. With disregard to is various advantages, the SCK is only produced on order and needs initial settings from the user. In Section 3.2.3 we present the results we have obtained by comparing the N O 2 concentrations provided by the Smart Citizen Kit and the Azimut station.

Experimenting protocols

This section describes the three experimenting protocols we have deployed during two weeks time period (29 th of April 2015 to 13 th of May 2015). The length of the experiments has been tied to project constraints for council approval, unit installation, data measuring and processing. For each experimenting protocol we provide insights regarding the purpose, the materials which have been used, the constrains as well as the data acquisition for interpretation.

First Experimenting Protocol

The first experimenting protocol aimed at determining a reliable data source for further comparison of N O 2 concentration with the regional AQM station which happens to be placed in the centre of NGC. For this study, 10 passive tubes provided by Air Lorraine have been installed at 3 meters altitude on street pillars inside two most circulated intersections of NGC (C129, C201), by using protection cases and fixing clamps.

The placement of the tubes has been chosen to be near some of the most congested streets, as represented in Figure 3. Seven tubes have been used to actively monitor these streets (Figure 3a)), two tubes have been placed near the location of the regional AQM station and one tube has been kept as a duplicate reference (Figure 3b)). While the tubes have been used successfully in other eco-neighbourhoods as previously detailed in (ATMO-Alsace, 2012), the placement of the two tubes near the AQM station has the role of double testing and verifying their accuracy during the experiment. In order for the measures to be accurate and non-saturated, certain constraints had to be addressed; tubes needed to be located: a) far from stopping areas such as traffic stops or parking slots in order to avoid over-saturation of the pollutant concentration, b) far away from blooming trees or high-ventilation areas c) at 2-3 meters altitude and d) far away from covering structures which would block air circulation.

At the end of the experimentation period, the tubes have been analysed by Air Lorraine and the results are presented in Section 4.1 of this article.

Second experimenting protocol

The second experimenting protocol used the Azimut mobile station which was carried in hand (at a human level, around 1.5 meters attitude) during the two weeks experimenting period by volunteers walking inside the NGC neighbourhood. In the first experimenting protocol the tubes have been installed at 3-meters altitude due to installation constrains, a height where the pollutant concentrations are starting to disperse in the air. Therefore, this installation altitude is not favourable for a direct evaluation of the "perceived" air pollution impact at a human level, which represents a major challenge and objective of this second experimenting protocol. The daily trajectory of the volunteers would pass near each of the 9 passive tube locations presented in the previous section, where the subject would wait for 5 minutes near each tube. The daily circuit is represented in Figure 4 a) andb).

The advantage of using the Azimut station relies in its high flexibility, mobility and real-time transmission of results through the MyGreenServices platform represented in Figure 5. The platform offers centralised results, personalised filtering, instant evaluation of concentrations based on European air quality monitoring indexes, as well as predefined alerts for raising real-time situation awareness. Having immediate access to results provides a higher awareness re- garding the exposure to pollutants, for both specialists and citizens travelling in the neighbourhood on a daily basis. Using the mobile Azimut station on this predefined trajectory allowed a consistent check of data transmission and quality, which was then compared and matched to the stationary units from the previous experimenting protocol. Despite the above advantages, the main limitations for applying this experimenting protocol were: a) the daily recharge of the Azimut station in order to prevent a discontinuity in the data collection and b) the lack of multiple Azimut stations which would have been tested in parallel on the same trajectory. The data profiling and results obtained during this protocol are further discussed in Section 4.2.

Third experimenting protocol

The third experimenting protocol used both the Azimut mobile station and the Smart Citizen Kit, in order to compare the accuracy and behaviour of both mobile sensing units in the same testing environment. The main purpose of this experiment was to investigate the usage of various mobile smart sensing units which had to monitor the same air pollutants. During 80 hours (13 th -15 th of May 2015), both units have been placed outside on an open private balcony facing one of the most circulated streets in C129, near the location of passive tube 5.

The main constraint for this experiment was the impossibility to use the SCK in the mobile circuit (previously presented in the second protocol), mostly due to the lack of Wi-Fi availability for transmitting the data to the online platform when walking in NGC. The SCK needs local Wi-Fi configuration for data transmission which was not available through mobile tethering, while local data storage was not possible with the received unit; therefore, we have configured the SCK for Wi-Fi usage on a private balcony near the closest passive tube. The data collected from the Azimut station followed the procedure explained in the previous protocol, while SCK provided real-time access to the collected data trough the Smart Citizen platform (SCK, 2016), as represented in Figure 6. The results and analysis of this third experimenting protocol are further discussed in Section 4.3.

Data profiling and results

First Protocol results

As previously mentioned in Section 3.2.1 the purpose of the first experimentation protocol was to establish an accurate and reliable source of information regarding the air pollution inside the eco-neighbourhood around main circulated areas (hotspots). The N O 2 levels collected from the passive tubes have been investigated in the air quality laboratory of Air Lorraine [START_REF] Lorraine | Atmo grand est[END_REF], and evaluated according to the ATMO indexes defined nationally by the French government: see decree on 22 nd of July 2004 related to air quality indices [START_REF] Medd | Arrêté du 22 juillet 2004 relatif aux indices de la qualité de l'air[END_REF] The evaluation scale is provided in Table 1 and uses indexes from 0 to 10 for different N O 2 concentration levels depending on their severity (0 and 10 standing for a very good, respectively very bad air quality index). The results for each tube are provided in Table 2 and are coloured accordingly to these standard indexes. We make the observation that Tube 10 has been kept as a duplicate for verification purposes, whereupon the low scored value. The results have been obtained during a time period which registered a mean temperature of 13.6 • C and a mean pressure of 1013.0 hPA. The investigation results indicate that overall the tubes have registered very good N O 2 concentrations corresponding to index 1 or 2 (according to Table 1). Tube 7 presented higher N O 2 levels which is explained by its position near a narrow but highly circulated road in the neighbourhood. . According to Table 2, Tubes 8 and 9 which have been placed near the AQM fixed station of Air Lorraine (located near the train station) presented an average N O 2 level or 23.15(µg/m 3 ). The official N O 2 concentration registered by the AQM station during the same period of time indicated a level of 24.11(µg/m 3 ) which translates in a 3.9% error between the tubes and the AQM station. The location of these tubes near the AQM station (see Figure 4b)) has been intentionally chosen for re-verifying the accuracy of passive tubes against the official reported N O 2 levels at the whole regional level.

Findings: The findings of the first protocol confirm a high accuracy of the fixed tubes which have been later used for the comparison analysis with the smart mobile stations. This protocol was the base set-up for comparing mobile sensing results and making sure their accuracy is validated.

Second Protocol results

N O 2 findings

The second experimentation protocol aimed at investigating the air pollution and noise levels as reported by the smart mobile unit Azimut. As previously detailed in Section 3.2.2, the experiments took place between the same time period when the fixed pollution tubes have been tested. For easing the experimental result interpretation, in this section we present the analysis results obtained during the evening peak hour (6pm-7pm). Figure 7 presents the N O 2 concentration levels registered for every day of the study period during PM traffic peak with the average values ranging from a minimum of 41.48(µg/m 3 ) at 7pm up to a maximum of 91.3(µg/m 3 ) at 6:45pm. Figure 7 contains as well markers on the X-axis of the time period that corresponds to the waiting time near each tube location along the trajectory shown in Figure 4 (for example between 6:00pm and 6:05pm the Azimut carers would be stopping near Tube 2 in order to record the concentration in this hotspot of the neighbourhood).

According to Figure 7, the lowest pollution scores have been obtained during Sunday 03/05/15 as traffic activity in the city center was low. The highest N O 2 levels reached 152(µg/m 3 ) during Monday 11/05/15 which corresponds to a "medium" towards "poor" pollution level according to Table 1. This can be explained by an increased traffic demand on Mondays when citizens returned to work after long weekend (8/05/15 was public holiday). An important observation is that the average peak of N O 2 concentrations over the whole study period has been registered between 6:45pm and 6:50pm, which corresponds to the waiting time near Tube 7. A possible explanation comes from the narrow street configuration and dense traffic that circulates in this area compared to other locations which have a wider exposure to air flow and multiple circulation lanes.

Once again, the finding confirms that pollution levels near Tube 7 (as registered using the mobile pollution sensor Azimut) are higher than those of other tube locations. Overall, the mobile station seems to register lower N O 2 concentration levels towards 7pm, at the location of Tube 8 and 9, which are placed near the AQM station of Air Lorraine. The findings confirm a similar trend between the N O 2 values registered around the tube locations by both stationary and mobile sensors with differences which will be discussed in the following.

To summarise, the real-life mobile sensing we have deployed revealed its high capability to detect correctly the hot-spots for N O 2 concentrations under the influence of severe traffic congestion.

Weather impact on N O 2 concentrations

An important aspect that we considered for this study is the weather impact on the N O 2 evolution. Our previous findings [START_REF] Mihȃit ¸ȃ | Integrating a mesoscopic traffic simulation model and a simplified no2 estimation model for predicting the impact of air pollution[END_REF] revealed that temperature, wind and humidity play a very important role in influencing the pollution dispersion or accumulation in the city and that predicting the air pollution levels is not only impacted by a specific day profile (day of the week/weekend/public holiday) or mobility patterns (peak/non-peak hours), but also by various exogenous factors which can highly affect the pollutant evolution in time. In order to understand the current emission levels, one needs to analyse not only the daily traffic patterns, but also previous weather conditions that have led to the current concentration levels. In the following we conduct a comparative analysis of different day profiles, weather conditions and traffic counts registered during the study period in order to understand how these factors can influence air pollution monitoring at both human level and stationary monitoring stations placed at higher heights.

Figure 8 presents the N O 2 evolution for Mondays, Tuesdays, Thursdays and Fridays, while Table 3 summarises the T emperature( • C), Humidity(%), P recipitations(mm/h) and W ind(km/h) registered during the study period. The N O 2 concentration levels for two typical Mondays are shown in Figure 8a) and although both days presented an average of 550 cars per hour passing the predefined trajectory shown in Figure 4, one can easily observe that the N O 2 concentration on 4/05/2015 was significantly lower than that of 11/05/2015. Although the weather parameters during these two days are almost similar according to Table 3 (temperature was around 22 -25 • C, wind around 11 -13km/h), by analysing the three previous days to our chosen dates, one can notice different weather conditions: prior to 4/05/2015 the humidity was higher, temperature lower and there was less sunshine, while prior to 11/05/2015 there were lower precipitations, a higher temperature and more sunshine. This aspect indicates that high humidity, low temperature and high precipitations can reduce the N O 2 accumulation in the city. The finding is also supported by the comparison between Thursdays as presented in Figure 8c); similarly, the highest N O 2 levels were registered during 07/05/2015, a day with higher temperature, lower humidity and low wind levels, when compared to 30/04/2015 which registered much lower temperatures and higher precipitations.

Day

T ( A special case is the comparison between Tuesdays (see Figure 8b) when humidity and wind conditions were almost similar during the observed time period; this translated in similar N O 2 levels, except from 6:00pm until 6:18pm on 12/05/2015 when the higher temperature (24.6 • C) registered at 6:00pm induced higher N O 2 levels. After the temperature decreased to 21 • C around 6:18pm, the N O 2 level presented similar evolutionary patterns as one week before. The comparison for Friday is shown in Figure 8d) and strengthens even more our previous findings, with the observation that the lower concentration levels registered on 01/05/2015 were also influenced by the reduced traffic flow as this day was a national public holiday. ference is not only caused by the increased number of cars during a regular weekend, but also by higher temperatures and lower air humidity. Findings: To resume, the weather impact investigation revealed a higher sensitivity of the mobile sensing units to external factors such as temperature, wind, humidity and atmospheric pressure. This could be caused by direct exposures of the units to pollution concentrations close to ground levels, but also to the influence of traffic congestion. The daily profiles of citizen trips coupled with weather information across extended time periods can be used to establish accurate daily patterns of pollution along specific urban routes in the city.

Noise pollution from mobile sensing

Besides N O 2 levels, the Azimut station continuously registered noise levels at the human level while following the proposed daily circuit. Figure 10 presents the mean and daily noise evolution registered during the study period with the associated European noise scale. The measurements indicate that noise levels ranged between 53.48 dB(A) and 89.76 dB(A), with an average reaching often 72.77 dB(A) which indicates a highly noisy/hazardous environment. In comparison to the N O 2 levels which have a dispersed behaviour and are harder to be analysed in time, noise levels seem to have a homogeneous evolution and follow almost similar trends from one day to another.

By undertaking a daily noise comparison similarly to the previous N O 2 analysis, one can easily identify almost similar evolutionary patterns of noise levels during a normal week day (as seen in Figure 11a) and b); lower noise levels were registered during public holidays, when traffic is heavily reduced in the city centre (see Figure 11c)). Overall, the current analysis revealed unexpected high noise levels inside the eco-neighbourhood NGC, which is the contrary objective of Grand Nancy Metropolis who wants to increase the liveability for its citizens, not only by offering good public transport services and multi-modal interconnection, but also good levels of air quality, reduced traffic jams and implicitly, noise levels. Findings: To resume, the second protocol revealed that:

• mobile sensing units are capable of accurately detecting hot-spots of N O 2 concentrations, especially under the influence of traffic congestion,

• mobiles sensing units are highly sensitive to the weather conditions but reflect an accurate impact of pollution at breathing level,

• mobile generated data can be accurately used for building daily patterns of pollution along specific urban routes; this could be further expanded to any data-driven modelling which could learn from historical patterns and generate situation awareness alerts whenever a real-time pollution concentration falls out of historical expected trends.

• noise can also be used as a data feature for any pollution prediction modelling due to their high relevance towards increased traffic congestion in affected areas.

Third protocol results

Adopting a smart mobile station for measuring the air quality at the human level can bring numerous benefits and additional insights to citizens, but one needs to verify the accuracy of the mobile station for calibration and validation purposes. As previously mentioned, the SCK was adopted not only for testing against fixed pollution units or large AQMs, but also to make the comparison between the robustness of various mobile devices for air quality monitoring. Although the SCK provided a bigger autonomy due to its integrated solar panel, the collected data couldn't be transmitted in real-time towards the on-line platform unless there was a continuous Wi-Fi availability in the area. The SCK used in this experiment offered a 80-hour continuous monitoring together with the Azimut Station, while being placed outside a balcony near the location of tube 5.

Figure 12a) presents the comparison between N O 2 levels registered by both Azimut and SCK; overall one could observe the higher variability of the Azimut station compared to the SCK, but also a long-term steadiness of results especially towards the end of the experimentation protocol when the SCK station registered an unexplained rise in N O 2 levels. Figure 12b) showcases the noise comparison between the two mobile devices, with a more steadier but higher noise levels registered by SCK when compared to Azimut. Although Azimut registered a lower noise range, one could observe its sensitivity to short-term variations when compared to SCK, which registered an overall noise value of 50dB with seldom higher peaks that have reached a maximum of 65dB. Overall, these results translate as medium to good noise levels when compared to standard scales, but the lack of noise fluctuations in the SCK behaviour on the long term can indicate less sensitivity to smaller noise variations in the surrounding environment.

Findings: The main and surprising finding of the 3rd protocol revealed that different mobile sensing units can report different concentrations of the same pollutant at the same moment in time, and can be very sensitive to different external factors. As previously mentioned, the main limitation of this protocol was mainly related to the impossibility of a long-term usage of the SCK in mobile outdoor circuits as the one in collection, analysis and interpretation in order to fully understand the cause in different behaviour and data variability. One would need to conduct several experimenting protocols, in different settings and on longer time periods before adopting one mobile unit over the other for outdoor and mobile air pollution monitoring.

Data driven models for air quality prediction

We have further performed a closer data-driven investigation over the factors that could influence the N O 2 concentration levels registered by mobile pollution units and the accuracy of the generated data sets for further air quality predictions. As Azimut was the sensing unit which provided continuous data collection (every minute) over the entire experimenting protocol, we have used this data to build the following features (variables): latitude, longitude, temperature, humidity and noise. We store these features in a matrix X t = [X(i, j)] j=1..5 i=1,..Np and consider the corresponding N O 2 vector as P t = [N i ] i=1,..Np , where N p is the total number of data record transmitted by Azimut during the second experimenting protocol which summed around 20160 records. We then consider the regression problem of predicting P t from X t , so as to determine the highly predictive features which influence the N O 2 concentration levels.

The data has been separated into a training set comprised of 75% of all records, a test data set of 15% and a validation data set of also 15%. We then fit a regression model on the training set and evaluate the model performance using the mean squared error (MSE). As a baseline we use a trivial model which predicts the mean N O 2 concentrations in the training set; any model that performs worse then this is typically useless.

The first underlying model is a decision tree using the CART algorithm. This intuitive model can fit the non-linearity in the data well, as it involves making splits in the data on some simple thresholds. Figure 13 shows the output of a decision tree with 3 levels of depth which indicates that the most predictive features are noise, humidity and location. The MSE of this model is 303.32 which is almost a 50.6% improvement from the baseline MSE of 598.62. The leafs of the tree represent a hard prediction of the target variable P t (in our case N O 2 concentrations), which is typically done by averaging the points that fall into a particular leaf node. The models seems to treat separately the cases when the registered noise is lower than 65dB or higher, but after a location investigation in both cases, humidity seems to be the most predictive feature of the model. Further on, we believe that data collected by mobile sensing unit can be used learn patterns of air pollution evolution, especially when being used in particular urban locations. When passing through a polluted area, if the pattern analysis detects anomalies and historical high pollution levels, the mobile unit could release alarms to the user to avoid the specific area. In order for this to happen, the data collected by the mobile unit needs to be accurate enough and has to contain sufficient information that could be used for predicting air pollution depending on location temperature, humidity, etc.

We have further used the above divided datasets from the Azimut mobile unit to train the second model: a neuronal network which is generally good at fitting specific practical functions. The neuronal network used for our predicting problem contains a hidden layer of 10 neurons and was trained first by using a Levenberg-Marquardt algorithm which typically requires more memory but less computational time; training automatically stops when the generalization stops improving, as indicated by an increase in the mean square error of the validation samples. The obtained MSE revolved around 150.59 which is a further 25% improvement from the decision tree algorithm and the overall R 2 value was 0.71. Despite fast running time (1 second) and 97 iterations, we have found that by using a Bayesian regularisation algorithm on the training set, the results improved significantly. Although the Bayesian regularisation typically requires more time for training on the dataset, it usually results in good generalisation for difficult, small or noisy datasets. The training stops according to adaptive weight minimization (regularization). The obtained MSE rounded up to 126.09 which is a further 4.09% in the prediction accuracy.

Figure 14 shows the R 2 values obtained for all datasets: training, testing and validation which reached an overall score of 0.81 when using the Bayesian Regularisation. While there is still place for improvement, the results are promising for further training on larger datasets when available. This is the best results obtained on the air pollution dataset generated by the Azimut which indicated that with a confidence of 81% one could predict in the future the N O 2 concentrations when using a mobile sensing unit in outdoors environment. We also make the observation that the current results apply to the specific mobile sensing unit and further tests and analysis could be imagined for a better performance evaluation of both air quality monitoring and prediction accuracy.

Findings: Machine learning modelling is an efficient tool for predicting accurately the mobile air quality. Decision-trees and neuronal network showed good capabilities for air quality prediction which could be further improved if more data would be available. A true challenge to extend this modelling would be to build collective data-driven predictions and anomaly detection algorithms for insuring a continuous real-time situation awareness.

Impact evaluation of fixed versus air mobile sensing

While the original purpose for this study was to propose a mobile sensing investigation coupled with data-driven modelling for air quality prediction, the biggest finding is mostly related to the difference and high impact of pollution concentrations registered at the human breathing level when compared to those reported by official stationary monitoring units. Figure 15 shows the summary of mean N O 2 levels registered during the study period by both the Azimut station and the passive tubes (experiments 1 and 2). Although the overall average concentration levels are in good evaluation scales (less than 90(µg/m 3 ), the difference between the two experimentation protocols reveal significant differences between monitoring techniques and an alarming direct citizen impact. From Figure 15 one can identify that most of the pollution levels registered by the mobile station carried at the human level near the locations of the passive tubes are almost three times higher than the stationary levels monitored at higher levels: tubes 8 and 9 registered almost 23.3(µg/m 3 ) from the passive tubes (placed at 3 meters altitude) and validated by the AQM station (placed at around 10 meters altitude) in comparison to 61.2(µg/m 3 ) recorded by the Azimut station carried at human level (1.5 meters altitude) near these tubes. The biggest difference between fixed and mobile air pollution monitoring is showcased by the passive tube 4, which recorded an N O 2 concentration of 14.9(µg/m 3 ) in comparison to 74.17(µg/m 3 ) registered by the mobile station Azimut; this translated in a human-level pollution score which is almost 5 times higher than official reported scores by the stationary monitoring devices. While various reasons and factors could be further taken into consideration for explaining the significant difference between the investigated emission levels, this finding brings a solid awareness towards the real impact that air and noise pollution can have on human health and the risk that citizens are facing when walking in extremely crowded and congested areas in the city. It is also a proof that mobile sensing units for air pollution monitoring are better at quantifying the exact exposure to air pollutants and bring a solid and real-time situation awareness.

Conclusions

This paper proposed a mobile air pollution monitoring framework coupled together with a data-driven modelling approach for predicting the air quality inside urban areas, at human breathing level. Three experimenting protocols have been implemented by using: a) fixed passive tubes for N O 2 monitoring which have been verified against a reliable AQM station (this represented the baseline verification for the accuracy of any future mobile sensing in this area), b) smart and mobile sensors with real-time data transmission and collection deployed through the use of citizens travelling daily in the neighbourhood and c) a static monitoring protocol for comparing the performance of two different mobile sensing units. The proposed experimentation protocols not only showed a significant higher impact of N O 2 concentrations when using smart sensors carried at human level and walking inside highly circulated urban areas, but also poor noise levels registered especially during evening peak hours. Weather conditions are also important factors to be used when analysing the pollution concentration due to their strong correlation and high temporal influence. Evenmore, the data-driven investigation revealed that data generated by the mobile sensing units when used outdoors can be accurately used to predict future N O 2 levels in urban areas.

Limitations: Besides the advantages and disadvantages of using each of the mobile units detailed in Section 3.1, the main limitations for monitoring, investigating and evaluating air quality by using a crowd-sensed initiative consists in the evaluation of the data accuracy. While multiple sensing units are available for testing and usage, one needs to verify the accuracy of the mobile stations for calibration and validation purposes, under different traffic and weather conditions. Despite important advantages of using low-cost sensing units for measuring air pollution at a very granular city scale, various questions about the use and large-scale utilisation of such devices remain challenging and unanswered. Important aspects which are currently under investigation relate to the regulated production and marketing of such units, the use and ownership of generated data, cost of maintenance and installation, etc. A very important question concerns the electronic waste and the impact on public health [START_REF] Grant | Health consequences of exposure to e-waste: a systematic review[END_REF], especially as many cities around the world are switching towards a sustainable and ecological paradigm [START_REF] Bayulken | Are lessons from eco-towns helping planners make more effective progress in transforming cities into sustainable urban systems: a literature review (part 2 of 2)[END_REF].

Future applications: To the best of our knowledge there isn't currently any research approach trying to apply advanced machine learning methods on mobile sensing-generated data for improving citizen's health. There is a lack of solutions proposing both real-life air quality monitoring at human level and data-driven prediction approaches for situation awareness and real-time alert generation. Data-driven modelling has a true potential for real-time operations as it can automatically detect non-linear spatial relationships between sensing units and could easily aggregate results for regional investigations as well.

A future real-life application of our study is to: a) extend the usage of mobile sensing units to more citizens driving/walking/cycling every day in the NGC eco-neighbourhood, b) improve the data driven modelling for air quality prediction and incident hot-spot identification (this would require extensive machine learning research investigations for addressing data sparsity and missing features), and c) build a situation awareness module which would learn from previous air pollution episodes and release pollution alerts to citizens in realtime.

A future data analysis and experimental investigation awaits for Metropolis research approval. The duration of the monitoring could be also extended to longer periods which can be a true challenge due to higher costs involving both human resources, material acquisition, data processing and interpretation. Seasonality could also be included in the analysis when more data would become available, as in our previous studies [START_REF] Mihȃit ¸ȃ | Integrating a mesoscopic traffic simulation model and a simplified no2 estimation model for predicting the impact of air pollution[END_REF].

One of the benefits of predicting air pollution hot-spots is to determine a change in the citizen driving behaviour which would change not only their routes to avoid polluted areas, but also their residential areas/recreational areas, etc. On the long-term this would lead to a reconfiguration of cities based on human health prioritisation.

The NGC project is further developing more studies on how to better integrate accurate air quality information with traffic congestion monitoring [START_REF] Mihȃit ¸ȃ | Predicting air quality by integrating a mesoscopic traffic simulation model and simplified air pollutant estimation methods[END_REF], but also how to involve citizens in an active crowd-source activity for raising awareness around pollution and traffic behaviour. Offering the correct monitoring tools will trigger more adapted urban actions which will improve on the long-term the life of inhabitants in such complex environments. based on the technology used (what sensors, what data), their mobility, their proximity near the breathing human level, and their innovative data-driven approaches. The results have been generated by using the following key words on major research platforms such as Science Direct, Web of Science, Elsevier and Google Scholar: "mobile sensors air quality pollution" and "air quality prediction". Please note that the list s not exhaustive and other research papers meeting our research criteria might be available. and accuracy of regional multi-stepahead air quality forecasts. -Provides real-time information.

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-Offers access to a centralised platform for data monitoring and pollution alert. -Azimut has a two days autonomy.

-Higher acquisition price than passive tubes. -Needs physical and fixed installation at 2-3 meters from the ground (see description in Section 3.1).

-The result analysis is done in a dedicated air quality laboratory. -Easy preparation, installation and result investigation.

-Low cost unit (under 10 Euro per analysis).

-Possibility to use the units at larger scale.

-Does not require any electrical charge. -Does not provide any real-time information.

-At least 1 or 2 weeks of waiting period for obtaining complete results.

-Fine-granular pollution peaks which are hourly-based can not be detected. -Mobile unit with both electrical recharge and integrated solar panel which can be used for both indoor and outdoor air pollution monitoring. -Low energy consumption and long autonomy.

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-Affordable cost unit price (averages around 200 Euro).

-The unit comes with an online platform access for datacollection and visualisation (smartcitizen.me). -Delivery time can be longer as each unit is made on-order.

-Unit needs permanent Wi-Fi connection for data transmission.

-Unit needs a special case for outdoor protection.

Pollux'nz city -The mobile unit contains P M 10 particle pollution sensors, temperature, sound and oxygen sensors.

-There are two main components to the system: a base station which collects the data from the array of sensors and a receiving module which measures and transmits the data (CKAB, 2017).

-Each sensor runs off of a battery, but features a PV solar panel which keeps the power source topped off. -Autonomous with a feature of direct measures and lecture from the online platform.

-It uses an Arduino to drive the system, and an XBee radio for communications. The base station also uses an XBee radio to poll the network, but it is not driven by an Arduino. -It does not measure the N O 2 concentrations directly.

-It needs high maintenance and configuration before the results can be analysed. -Comes with a platform for data collection and visualisation.

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-No N O 2 monitoring.

-The unit was hard to purchase and test. The project has been discontinued while waiting for the second round of financing.

Dylos Station -Indoor air quality monitoring for fine particles [START_REF] Coporation | Dylos air quality monitor[END_REF].

-Provides small and large particle counts with a direct reading on the unit.

-Has an increased lower sensitivity for detecting particles down to 0.5 micron.

-The units have a higher range of unit prices (199-425 Euro).

-No N O 2 or noise monitoring.

-The unit cannot be used for outdoor pollutant monitoring.

-It requires special software installation for historical data storage or visualisation. -The units are autonomous for months and have also solar panels integrated.

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-They have an embedded alert system for raising awareness. -The unit has a competitive price which makes it less affordable for daily citizen purchase and usage.

-It needs renewal after 500 cycles of utilisation. -The platform sends notifications to the users which can also use an API for data display.

-Price aligns with other mobile units for air quality monitoring (179 Euro). -The unit is mainly dedicated for indoor not outdoor air quality monitoring due to its Wi-Fi connectivity needs.

-No N O 2 or noise monitoring. -It has a compact optical dust sensor with a IRED infra-redemitting diode and a photo transistor, which are diagonally arranged in the device to measure the reflected light off of the airborne dust particles. -The unit can be used both indoors and outdoors and comes at an attractive price starting from 59$.

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-No N O 2 or noise are monitored.

-No indication regarding the autonomy is provided.

NetAtmo -NetAtmo provides a package of two stations for both indoor and outdoor air quality monitoring which register mainly temperature, humidity, atmospheric pressure, CO and noise (Ne-tAtmo, 2018).

-Results are being sent to a cloud storage for later analysis and smart-phone consultation. -Long lasting autonomy for up to years.

-N O 2 is not monitored.

-Needs permanent Wi-Fi connection at no more than meters.

  (a) Urban project for NGC by 2020, Source: Arep ville -J.M. Duthilleul. (b) Urban area in 2013. (c) Focus on most circulated intersections.

Figure 1 :

 1 Figure 1: Case study of the eco-neighbourhood Nancy Grand Coeur.

Figure 2

 2 Figure 2: a) Passive N O 2 tubes b) Azimut station c) Smart Citizen Kit.

1.

  Passive tubes (Figure2a)): The technique (passive sampling) is based on the passive transfer of pollutants by simple molecular diffusion of ambient air to an adsorbent which is specific to the targeted pollutants. The sampling module is in the form of a porous tube, called "passive tube" which is filled with adsorbent. The passive tubes are fixed in a protection box attached to a support near congested traffic areas. After the exposure time has elapsed, the tubes are sent to the Air Lorraine laboratory for analysis. The concentrations of pollutants obtained by this technique are concentrations averaged over the entire sampling period. This technique has been used for sampling of nitrogen dioxide (N O 2 ) and has the main advantage of being low cost and not requiring electrical recharge. The passive tubes have been successfully deployed in various project such as the (LigAir, 2009) project for characterizing ambient levels of formaldehyde around industrial sites, or for modelling air quality in the eco-neighbourhood Danube from Strasbourg (ATMO-Alsace, 2012), which was highly impacted by intense circulated areas. The main disadvantage of using the passive tubes is related to the fact that the results are analysed at the end of the experimentation period and cannot detect peaks of localized pollution concentrations during congested traffic hours. These tubes have been used for the implementation of the first experimenting protocol, which is detailed in Section 3.2.1. Nevertheless, they represent an accurate base for comparing N O 2 concentrations with official reported pollution levels from the AQM station during our testing period. 2. Azimut Station(Figure 2b)): is a product of Azimut Monitoring (Azimut, 2017) which uses electrochemical gas sensors for measuring the N O 2 emissions. Through a portable emission analyser it can provide continuous real-time monitoring of N O 2 , O 3 , noise, temperature and humidity.

Figure 3 :

 3 Figure 3: Locations of passive N O 2 tubes in NGC

Figure 4 :

 4 Figure 4: Daily trajectory using Azimut Station in NGC.

Figure 5 :

 5 Figure 5: MyGreenServices platform for visualising data collected by the Azimut station.

Figure 6 :

 6 Figure 6: Smartcitizen.me platform for visualising data collected by the Azimut station.

Figure 7 :

 7 Figure 7: Daily N O 2 concentrations registered by Azimut mobile station.

Figure 8 :

 8 Figure 8: Daily comparison of N O 2 concentrations.

Figure 9 :

 9 Figure 9: Weekend comparison of N O 2 concentrations.

Figure 10 :

 10 Figure 10: Daily and mean noise levels registered by the Azimut mobile station.

Figure 11 :

 11 Figure 11: Daily comparison of noise levels registered by the Azimut station.

Figure 4 .Figure 12 :

 412 Figure 12: Noise and N O 2 comparison between Azimut and SCK.

Figure 13 :

 13 Figure 13: Decision Tree outcome for Pt.

Figure 14 :

 14 Figure 14: Results for Neuronal Network training using Bayesian Inference.

Figure 15 :

 15 Figure 15: N O 2 pollution level registered by passive tubes places at 3 meters altitude versus the mobile station Azimuth carried at the human level.

  sensing unit can be fixed on cars, bikes, held in hand, etc.

  Passive tubes -Measures N O 2 by passive sampling (passive transfer of pollutants by molecular diffusion of ambient air to an adsorbent specific to the targeted pollutant).

  a product of the Green Watch Project[START_REF] Citypulse | Green watch city pulse[END_REF] which in 2009 used a prototype equipped with two environmental sensors (for measuring ozone and noise), a GPS chip and a Bluetooth chip. The device has the shape of a watch that his wearer takes with him to the city, capturing and storing measurements that are then published on the network.-The product measurements are sent to both the mobile phone and an open platform which can be used for monitoring, analysis and air quality modelling. -Autonomous, easy to carry and well connected.

Footbot

  has embedded software to send in real-time to the Could the recalibration results via Wi-Fi and rapidly process the data measured. -The data can be retrieved remotely from the Cloud service, and any updates or investigations can be easily followed.

Table 1 :

 1 . ATMO French National Index scale for N O 2 .[START_REF] Lorraine | Atmo grand est[END_REF].

	Value(µg/m 3 ) Index Qualifier
	0 -29	1	Very Good
	30 -54	2	Very Good
	55 -84	3	Good
	85 -109	4	Good
	110 -134	5	Medium
	135 -164	6	Medium
	165 -199	7	Poor
	200 -274	8	Poor
	275 -399	9	Bad
	≥ 400	10	Very Bad

Table 2 :

 2 N O 2 results of the passive tubes investigation

	Tube Number Registered Value(µg/m 3 ) Qualifier
	1	22.2	Very Good
	2	27.0	Very Good
	3	24.7	Very Good
	4	14.9	Very Good
	5	29.2	Very Good
	6	22.2	Very Good
	7	53.6	Very Good
	8	23.2	Very Good
	9	23.1	Very Good
	10	0.3	Very Good

Table 3 :

 3 Weather conditions during the study period.

Table A .

 A 4: State-of-art comparison.

	Data analysis
	Period
	Parameters measured	This study employed	hourly data of eight air	quality factors (PM2.5,	PM10, O3, NOx, NO2,	NO, SO2, CO) and five	meteorological factors	(rainfall, temperature,	wind speed, wind di-	and relative rection,	humidity).
	Near breath-	ing level	No
	Mobile	No
	Sensors technology	The proposed	DM-LSTM model	was evaluated by	three time series	of PM2.5, PM10,	and NOx simulta-	neously at five air	quality monitoring	stations in Taipei	City of Taiwan.
	Study objective	This study presents an	artificial intelligence ap-	proach based on a Deep	Multi-output LSTM (DM-	LSTM) neural network	model for predicting the	air quality in China.
	Ref.	(Zhou	et al.,	2019)

Table A .

 A 4 -State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. Table A.4 -State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. Table A.4 -State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page. State-of-art comparison of mobile sensing and data-driven air pollution modelling -continued from previous page.

	Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis Data analysis	Results demon-The work in-Linear regression The air quality Air and crop sam-A novel and flex-They proposed an Analysis revealed The individual sen-Based on these The authors ap-No data-driven Linear Regres-	strated that the cluded integer in addition to the modelling was ple analyses were ible method has extension of the that the correlation sor datasets were data, authors de-plied a statistical analysis was pro-sion in Pollution	FGM(1,1) model and zero-sum gain Feed-Forward Neu-done using the carried out by the been developed to street scale resolu-between temper-calibrated applying velop land-use analysis and vided, only a Measurements and	is significantly constraints into a ral Networks and EPISODE disper-Agency of Public determine sensor tion ADMS-Urban ature, RH and multi-linear regres-regression (LUR) land-use regres-graphical represen-a heat-map of	higher than that DEA approach for Random Forests sion model which Health of Barcelona baselines, i.e. un-dispersion model output voltage of sion (MLR) analy-models to create sion based on tation of data. Carbon Monoxide	of the traditional evaluating the air algorithms were is a 3-D Eule-with ENAC accred-derlying variation, for complex urban SPAM was linear. sis. pollution maps sub-segments, cate-Concentrations	GM(1,1) model. quality of 31 main employed, in an rian/Lagrangian itation (UNE-of measurements environments with The best fit line for with a high spa-gorized in terms of were generated.	The prediction cities in China by effort to improve dispersion model EN ISO/ IEC (that represent additional capabil-each parameter was tial resolution of the number of visits	results from 2017 using daily AQI sensor performance that provides urban 17025:2005). non-local emis-ity for the explicit used to get sensor 100mx100m. Au-per road segment.	to 2020 indicate data. There is no based solely on sen-and regional-scale sions) which is modelling of air-response function. thors compared Authors observed	that the concen-further inclusion sor data and local atmospheric pollu-suitable for appli-craft jet engine These response the accuracy of that LUR models	trations of PM2.5, of other pollution meteorological data tant concentrations cation to large data emissions as jet functions were later the derived mod-were highly sensi-	PM10, SO2, and data sources from from a reference such as N O 2 . The sets. sources. used in the sensor N/A. els across various tive to the number	NO2 will decrease, these cities. station. study mentions an system to mea-time scales and of road segments	whereas the 8-h air quality visual-sure the real-time observed a rapid and to the number	O3 and 24-h O3 isation portal and gaseous pollutant drop in accuracy of visits per road	concentrations will mapping using data concentrations. for maps with sub-segment.	increase. fusion techniques. weekly temporal	resolution.
	Period Period Period Period Period Period Period Period Period Period Period Period Period Period	2013-2014-Two From 1st-1st test: 2 months 5 weeks Pedestrians Between years Between between A car The pol-	2016. 2015. weeks in 31st of 40 days starting starting walking May and starting May and 2015-07-tour for lutant	October January starting from 28th from (random October from August 21 and each ex-data was	2014. 2016. from 4th of March 4th of 2015); 2017. April 2015. 2015-08-periment. collected	of April 2010. October bus travel 2012. 07. for 12 h.	2017; 2012. (Novem-	2nd test: ber 2015	34 days to Jan-	starting uary	from 2016).	22nd	of June	2017. Not an		exact	set-up ex-	perimen-	tation.
	Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured Parameters measured	The proposed model pre-The proposed model Both units utilised Al-Their battery driven sta-The following metals CO concentration mea-CO, N O, CO 2 , V OCs CO, N O 2 , O 3 , P M , Tem-O 3 N O , CO and O . N O 2 and O 3 concentra-N O , CO, O , CO , SO , They selected OXA and CO, N O 2 , and SO 2 .	dicted the average annual calculated the average phasense electrochemical tionary platforms mea-were analysed: N i, As, surements only were (total), Temperature, perature, Humidity. Field tions were recorded by VOCs, P M 1.0, P M 2.5, CLIMA modules to mea-	concentrations of PM2.5, monthly AQIs for each cell (ECC, model B4/BH) sured four gaseous com-Cd and P b on 4 sites: obtained from the Green-Relative Humidity, Wind measurements were per-cyclists and pedestrians. P M 10, Temperature, hu-sure carbon monoxide, hu-	PM10, SO2, NO2, 8-h O3, city. for measuring N O, N O 2 , ponents (CO, N O, N O 2 a peri-urban integrated house Gas Laboratory at Speed/Direction, Size formed by walking on Hourly temperature (T), midity. midity, temperature, am-	and 24-h O3. O 3 , CO, SO 2 and Total and O 3 ), particulate mat-rooftop greenhouse, a Royal Holloway, Univer-Specialised Particulates. both footpaths and trav-relative humidity (RH) bient light and barometric	V OC. Measurements of ter, temperature, relative peri-urban rooftop, an sity of London (RHUL), elling in the bus dur-and wind speed from the pressure.	CO 2 (SenseAir K30) and humidity and atmospheric urban courtyard and an 75 miles south-west of ing various times of a Montreal Pierre Elliott	particulate matter (Uni-pressure. The N O 2 sensor urban rooftop. Cambridge in Egham, day (morning, afternoon Trudeau International	versity of Hertfordshire employed in the AQMesh Surrey. Every sensor node and evening) and different Airport weather station	CAIR) were also under-v3.5 is an electrochem-was referenced to this one days in a week at selected were recorded and syn-	taken. The CAM10 and ical sensor provided by station. To remove local locations in Chennai city, chronized with the other	CAM11 boxes measured Alphasense (NO2-B42F) influences on the measure-India. measurements.	wind speed, wind direc-that incorporates a fil-ments, a meteorological N O 2 , CO and O 3 .		tion, temperature and hu-ter to eliminate cross-filter was applied.	midity, allowing compari-sensitivity issues with O 3 .	son of meteorological vari-	ables and source appor-	tionment.
	Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-Near breath-	ing level ing level ing level ing level ing level ing level ing level ing level ing level ing level ing level ing level ing level ing level	No No No N/A No No No (fixed on Yes N/A No (3 meters Yes (walk Yes Yes No	lampposts in high). and bike).	the airport).										N/A
	Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile Mobile	No No No No No No No Yes No Yes Yes Yes-Yes Yes	Airbox;	No-	Enea									Yes
	Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology Sensors technology	The indicators data All the data is Study used micro-The sensor nodes To collect air sam-The study mea-17 low cost portable Low-cost and light-A large array of The mobile mea-The study used Authors used a Authors used a) The Mobile-DAQ	are from the Air collected from sensor nodes which were located out-ples, high-volume sured the carbon air quality devices weight sensors 132 metal-oxide surement system N O 2 and O 3 sensor-system AIR-a Mobile Sensing unit integrates	Quality Assessment China National are low-cost devices doors, on the sensors (MCV monoxide (CO) by developed at the (SPAMS) were (Sensortech MICS consists of 10 Aeroqual Series BOX in Bari Italy, Box which can be a single-chip mi-	Report IV which Urban air quality with considerable playground of the CAV-A/mb) were deploying a net-Department of used for measuring 2614) and 11 sensor nodes in-500 portable sen-which is equipped mounted on vehi-crocontroller, air	depend on a new real-time publish-application po-kindergarten, at used, working at a work of 45 low-cost Chemistry in CO (TGS 2442), electrochemical stalled on top of sors (Aeroqual, with low-cost cles and contains pollution sensors	standard (GB 3095-ing platform. The tential, offset by heights of around 3 volume of 30m3/h electrochemical the University of N O 2 (MiCS 2714), (Alphasense) ozone public transport 2014a), two types electrochemi-a micro-controller, array, a General	2012) published in composition of important limita-meters. Addition-in 48-h periods, us-sensors, in and Cambridge, UK, O 3 (MiCS 2614), sensors, built into vehicles, which of GPS Garmin cal gas sensors, dust and carbon Packet Radio	2012. The PM2.5 different levels of tions when applied ally, 6 sensor nodes ing glass microfibre around the city of were installed P M , temperature 44 sensing devices, are equipped with Edge 800 as well optical particle monoxide sensors, Service Modem	values were manda-AQI appeared in a to urban air quality were installed in filters. Cambridge, UK at the London and humidity. was co-located at a semiconductor-as MapMyRide, detectors, NDIR GPS and a cellular (GPRS-Modem),	torily included in two-years data base monitoring. the streets in Oslo (out of which only Heathrow airport reference stations based O 3 sensor, a smartphone infra-red sensors, modem, and b) and a Global Po-	this new standard for 2014-2015. and 9 reference 32 were successfully (Alphasense B4, in Italy (4 stations) electrochemical-application. photo-ionisation personal sensing sitioning System	for the first time. stations were used reporting integral Sensair K33, Al-and Spain (5). based CO and detectors, and devices: a mobile Module (GPS-	Only a few sets of from Oslo. A com-data sets). phasense PID-AH, The proposed so-N O 2 sensors, and miniaturized sen-air quality sensor Module). The	annual data were mercial low-cost Pt1000, Honey-lution has an out-a compact device sors for tempera-and a smart phone Mobile-DAQ was	available. platform AQMesh well HIH4000, of-the-box central to measure UFP ture and relative to act as an in-mounted on a	v3.5 was used Gill WindSonic platform available concentrations. humidity. Another terface with the University bus that	for monitoring University of Hert-in the cloud which Additionally, the ENEA prototype central repository was driven around	results (provided fordshire). gathers available nodes monitor of portable sen-hosted on a cloud the campus of the	by Environmental IoT sensor data radio-frequency sor system which server. American Univer-	Instruments Ltd, (ThingSpeak) and electromagnetic connects to the sity of Sharjah	UK). (2) an online cus-fields, temperature smart-phone by (AUS) to collect	tom developed and humidity. Bluetooth has been pollutant data.	central platform. used inside cars	for air pollution	monitoring.
	Ref. Study objective Ref. Study objective Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective Table A.4 -Ref. Study objective	(Wu This study deployed grey (Zhou This work constructed a (Borrego This study presents part (Castell This study installed 17 (Ercilla-This study focuses on (Heimann This study shows a (Popoola This work demonstrates a (SM This paper deals with the (Ripoll In this work, the perfor-(Hasenfratz This work analyses one (Minet This study highlights (Suriano This work is a techni-(Devarakonda This work presents a (Al-This work proposed an on-	et al., prediction theory (which et al., set of data envelopment et al., of the results of the inter-et al., nodes monitoring N O 2 Montserrat the potential contamina-et al., purely measurement-et al., technique for source ap-et al., development of a smart et al., mance under field condi-et al., of the largest spatially et al., the challenges of short-et al., cal report on preliminary et al., vehicular-based mobile Ali line GPRS-Sensors Array	2018) can deal with the fore-2018) analysis (DEA) models for 2018) comparison campaign in 2018) in the playground kinder-et al., tion of heavy metals in hy-2015) based approach to extract 2018) portionment in complex 2019) personal air quality mon-2019) tions of two custom-made 2015) resolved UFP data set 2017) term mobile sampling 2015) real-world measurements 2013) approach for measuring et al., for air pollution monitor-	casting problem with lim-calculating air quality in Aveiro and is intended to gartens in Oslo and one 2018) droponic lettuce crops due underlying pollution environments, in this case itoring system (SPAMS) types of ozone sensing de-publicly available which campaigns in terms of by low-cost gas sensor-fine-grained air quality in 2010) ing which consists of a	ited samples), for predict-31 main cities in China by estimate the uncertainty node in the top of a refer-to atmospheric pollution levels (baselines) from a major international air-for real time air quality vices, based on metal-contains over 50 million the resulting exposure systems for air quality real-time. They proposed Mobile Data-Acquisition	ing the air quality in the using the daily Air Qual-of the measurements ence air quality monitor-in high-traffic areas. air measurements by ex-port, using a low-cost air monitoring, in order to oxide and electrochemical air measurements but also surfaces. A mobile mon-monitoring. two cost effective data Unit (Mobile-DAQ) and	Beijing-Tianjin-Hebei re-ity Index. according to the DQO ing station. The results ploiting different relative quality sensor network. measure the individual ex-sensors, was tested. proposed a mobile mea-itoring campaign was farming models: one that a fixed Internet-Enabled	gion between 2017 and of the European Air showed that indeed low-frequencies of local and posure to air pollution. surement system. Node conducted in 2015 in can be deployed on public Monitoring Pollution	2020. Quality Directive and cost sensors can provide background pollution Monitoring campaign was mobility trades off tempo-Montreal; N O 2 levels at transportation and the Server.	to improve their perfor-a good indication of the variations. designed for both pedes-ral resolution against spa-1395 road segments were second a personal sensing	mance with the aid of a air pollution levels, being trian exposure as well as tial resolution, enabling measured under repeated device.	computationally-oriented capable to reproduce the exposure while travelling a high spatial resolution visits.	methodology. trends during a high pol-in bus. Sensors were set-(Pereira This is a student project across large areas without	lution episode. Continued on next page up near to the breathing et al., which turned smart-the need for a huge num-	Continued on next page Continued on next page level throughout the mon-2018) phones into dynamic ber of fixed sensors.	itoring campaign to rep-sensor nodes which send	resent the actual personal data to a centralised	exposure to the air pollu-platform. Citizens play Continued on next page	Continued on next page Continued on next page Continued on next page tants. a significant and em-	Continued on next page powering role regarding Continued on next page	healthcare conditions.	Continued on next page	Continued on next page	Continued on next page	Continued on next page

N/A Table B.5: Air quality sensing unit comparison table.

Table B .

 B 5 -Air quality sensing unit comparison table -continued from previous page.

	Disadvantages
	Advantages
	Characteristics	-Measures N O
	Sensor	Smart Citizen	Kit	l

2 , O 3 , CO 2 , humidity, luminosity and noise.

Table B

 B

.5 -Air quality sensing unit comparison table -continued from previous page.

Table B .

 B 5 -Air quality sensing unit comparison table -continued from previous page.

	Disadvantages
	Advantages
	Characteristics	-The Geocube station measures	N O
	Sensor		Geocube

2 , O 3 , SO 2 , P M 10 , temperature, wind speed, wind direction and noise (IGN, 2006). -Contains three modules including GPS, data and radio management. -Outdoor station for monitoring landslides which can communicate together in a global network for transmitting data to a central node connected to the internet.

Table B .

 B 5 -Air quality sensing unit comparison table -continued from previous page.

	Disadvantages
	Advantages
	Characteristics	-Portable indoor station unit for	monitoring particulate matter	P M
	Sensor			Air.air!

2.5 and temperature which are then sent to a smartphone app by using a Low Energy Bluetooth (BLE) (AirAir, 2014).

Acknowledgements

The authors of this work are grateful for the support provided by various actors in this project: 1) Grand Nancy (GN) through the project Chaire REVES, 2) Brigitte Trousse, the president of France Living Labs for providing the Azimut station, 3) Air Lorraine (Alexandre Ockler and Gerard Pol) for providing the passive tubes, 4) Barcelona Fab Living Lab for providing the Smart Citizen Kit in time for the experiments, and 5) Aline Carier, Thibault Frère, Oussama Okasha and Ophélie Voirin for conducting the daily experimenting protocols. The experiments of this work have been done in the ERPI laboratory from Nancy France. The final writing and correction of this article has been done in Data61/CSIRO, Australia.

Appendix A.

The table A.4 in this Appendix section lists all the studies which have been working towards the analysis and prediction of air quality by either using fixed or mobile sensors, or by developing new data-driven methods. They are compared

Appendix B.

In Table B.5 we summarize the main air pollution sensors which have been initially considered for this study, their characteristics, advantages and disadvantages which correspond to our current purpose and needs for this study. While various comparative studies could be further undertaken, our main objective was to choose an appropriate mobile sensing unit which could be easily adopted at larger scale but which could also provide accurate results when compared to highly efficient AQMs.