Dissertationen zum Thema „Portable air pollution sensors“

La pollution de l’air contribue à dégrader la qualité de vie et à réduire l’espérance de vie des populations. L’organisation mondiale de la santé estime que la pollution de l’air est responsable de 7 millions de morts par an dans le monde. Elle participe à aggraver les maladies respiratoires, cause des cancers du poumon et des crises cardiaques. La pollution de l’air a donc des conséquences sanitaires importantes sur la vie humaine et la biodiversité. Ces dernières années, des progrès considérables ont été réalisés dans le domaine des microcontrôleurs et des modules de télécommunications. Ces derniers sont de plus efficients énergétiquement, performants, abordables, accessibles et sont responsables de l’émergence des objets connectés. Parallèlement, les récents développements des microsystèmes électromécaniques et des capteurs électrochimiques ont permis la miniaturisation des technologies permettant de mesurer de nombreux paramètres environnementaux dont la qualité de l’air. Ces avancées technologiques ont ainsi permis la conception et la production dans un cadre académique de capteurs de la qualité de l’air, portatifs, connectés, autonomes et en capacité de réaliser des acquisitions à une fréquence temporelle élevée. Jusqu’à récemment, l’un des majeurs freins à la compréhension de l’impact de la pollution de l’air sur la santé fut l’impossibilité de connaître l’exposition réelle des individus durant leur vie quotidienne ; la pollution de l’air est complexe et varie en fonction des habitudes, des activités et environnements empruntés par les individus. Ces capteurs portatifs de la qualité de l’air permettent donc de lever entièrement ce frein ainsi qu’un nombre important de contraintes. Ils sont conçus pour être utilisables en mobilité, sur de longues périodes et produisent des données granulaires, immédiatement disponibles, décrivant l’exposition à la pollution de l’air du porteur. Bien que les modules de mesure embarqués dans ces capteurs ne soient aujourd’hui pas aussi performants que les instruments de références ou la télédétection, lorsqu’il s’agit d’évaluer l’exposition individuelle à la pollution de l’air, parce qu'ils sont au plus proche des individus, ils permettent d’obtenir l’information la plus fidèle et constituent donc un outil indispensable pour l’avenir de la recherche épidémiologique. Dans ce contexte, nous avons participé au développement et à l’amélioration de deux capteurs de la qualité de l’air ; le CANARIN II et le CANARIN nano. Le CANARIN II est un capteur connecté communiquant par Wi-Fi, qui rapporte les concentrations de particules de diamètre 10, 2.5 et 1 micromètre, ainsi que les paramètres environnementaux de température, humidité et pression, chaque minute et les rend disponible en temps réel. Le CANARIN nano est, quant à lui, un capteur de plus petite taille, possédant les mêmes capacités que le CANARIN II, tout en faisant additionnellement l’acquisition des composés organiques volatils. Il est en capacité de fonctionner de manière autonome, puisque communiquant par réseau cellulaire. Deux types de résultats ont été obtenus avec les capteurs CANARIN ; d’une part, des résultats produits à partir de leur utilisation dans des conditions de vie réelle, d'autre part, des résultats en lien avec l'interprétation et la compréhension des mesures produites par les capteurs de particules dont les CANARINs sont équipés. Ces deux capteurs furent ainsi tous deux exploités par deux projets de recherche, au sein desquels nous avons accompagné le déploiement de plusieurs flottes de capteurs hétérogènes et réalisé l’analyse des données acquises. [...]
Air pollution contributes to the degradation of the quality of life and the reduction of life expectancy of the populations. The World Health Organization estimates that air pollution is responsible for 7 million deaths per year worldwide. It contributes to the aggravation of respiratory diseases, causes lung cancer and heart attacks. Air pollution has therefore significant health consequences on human life and biodiversity. Over the last few years, considerable progress has been made in the field of microcontrollers and telecommunications modules. These are more energy efficient, powerful, affordable, accessible, and are responsible for the growth of connected objects. In the meantime, the recent development of microelectromechanical systems and electrochemical sensors has allowed the miniaturization of technologies measuring many environmental parameters including air quality. These technological breakthroughs have enabled the design and production in an academic environment, of portable, connected, autonomous air quality sensors capable of performing acquisitions at a high temporal frequency. Until recently, one of the major obstacles to understanding the impact of air pollution on human health was the inability to track the real exposure of individuals during their daily lives; air pollution is complex, and varies according to the habits, activities and environments in which individuals spend their lives. Portable air quality sensors completely remove this obstacle as well as a number of other important constraints. These are designed to be used in mobility, over long periods of time, and produce immediately available granular data, which describes the exposure to air pollution of the person wearing it. Although the measurement modules embedded in these sensors are not currently as reliable as reference tools or remote sensing, when it comes to assessing individual exposure to air pollution, because they are as close as possible to the wearer, they provide the most accurate information, and are therefore an indispensable tool for the future of epidemiological research. In this context, we have been involved in the development and improvement of two air quality sensors; the CANARIN II and the CANARIN nano. The CANARIN II is a connected sensor communicating via Wi-Fi, which reports the concentration of 10, 2.5 and 1 micrometer diameter particles, as well as the environmental parameters of temperature, humidity, and pressure, every minute, making them available in real time. The CANARIN nano is a smaller sensor with the same capabilities of the CANARIN II, while additionally sensing volatile organic compounds levels. The CANARIN nano is able to operate autonomously, as it communicates through the cellular network. Two types of results have been obtained with the CANARIN sensors; on one hand, results produced from their use in real life conditions, and on the other hand, results related to the interpretation and understanding of the measurements produced by the particle sensors. These two sensors were both used in two research projects, in which we have helped deploy several heterogeneous sensor fleets and analyzed the acquired data. Firstly, in the POLLUSCOPE project funded by the French National Research Agency, where 86 volunteers from the general population wore a set of air pollution sensors for a total of 101 weeks, 35 of which the volunteers were also equipped with health sensors. Secondly, in the POLLAR project, where 43 subjects underwent polysomnography and then wore one CANARIN sensor for 10 days, thus allowing for the first time to explore the link between sleep apnea and particulate matter exposure. [...]

Although air pollution is one of the largest threats to human health, the data available to the public is often sparse and not very accurate nor updated. For example, there exists only about 5-10 air quality measuring points across the city of Stockholm. This means that the available data is good in close proximity of the sensing equipment but can differentiate much only a couple of blocks away. In order for individuals to receive up to date information around a larger city, stationary measurements are not sufficient enough to get a clear picture of how the current state of the air quality stands. Instead, other methods of collecting this data is needed, for instance by making the measurements mobile. GOEASY is a project financed by the European Commission where Galileo, Europe’s new navigational service, is used to enable more location-based service applications. As part of the GOEASY project is the evaluation of the potential of collaborative applications where users are engaged to help individuals affected by breathing-related diseases such as asthma. This thesis presents the choice of architecture and the implementation of a mobile platform serving this purpose. Using sensors mounted on a range of objects real time air quality data is collected and made available. The result is a mobile platform and connected Android application which by utilizing air quality sensors, reports pollution measurements together with positional coordinates to a central server. Thanks to the features of the underlying systems used, this provides a platform which is accurate and more resilient to exploits compared to traditional location-based services available today. The result allows individuals with respiratory conditions to receive much more accurate and up to date information in a larger resolution. It also serves the purpose of demonstrating the potential of the supporting technology as part of the GOEASY project.
Trots att föroreningar i luften är bland de största hoten mot mänsklig hälsa är den information som finns tillgänglig för allmänheten ofta både gles och inte tillräckligt noggrann eller uppdaterad. Till exempel finns det i hela Storstockholm endast mellan 5–10 luftkvalitetstationer som mäter föroreningar. Detta innebär att den data som finns tillgänglig är bra i närheten av mätutrustningen men kan skilja sig mycket enbart ett par kvarter bort. För att öka mängden information som är tillgänglig till allmänheten räcker inte längre enbart de stationära lösningarna som finns idag för att visa hur de rådande halterna av föroreningar står sig. Andra metoder måste införas, exempelvis genom att nyttja mobila mätningar från en plattform som kan röra sig fritt. GOEASY är ett projekt finansierat av den Europeiska Kommissionen, där Galileo, Europas nya navigationssystem används för att tillåta fler platsbaserade tjänster att äntra marknaden. Som en del av GOEASY projektet ingår evalueringen av potentialen i en applikation där användare samlar in data för att hjälpa individer med andningssvårigheter som astma. Denna avhandling presenterar valen till arkitekturen samt implementationen av en mobil plattform som en del av GOEASY. Lösningen använder sig av mobila luftkvalitetsensorer som kan monteras på en rad olika objekt som samlar data i realtid som görs tillgänglig för allmänheten. Resultatet är en mobil plattform och tillhörande Android applikation som med hjälp av luftkvalitetsensorer rapporterar halten av olika skadliga föroreningar tillsammans med platsinformation till en central server. Tack vare egenskaperna av de underliggande systemen som används, skapas en plattform som är mycket mer precis när det gäller positionering jämfört med liknande system som finns tillgängligt. Det resulterande systemet gör det möjligt för individer med andningssvårigheter att få tillgång till noggrannare samt mer uppdaterad information i större utsträckning än vad som för närvarande är tillgängligt. Systemet fyller även syftet med att demonstrera potentialen i den bakomliggande teknologin som en del av GOEASY.

Portable mass spectrometry has become an important analytical tool for chemical detection and identification outside of a lab setting. Many variations and applications have been developed to benefit various fields of science. Membrane inlet mass spectrometry is used to allow certain analytes to pass into the mass spectrometer without breaking vacuum or letting in large particulate matter. These two important analytical tools have been applied to the detection of harmful chemicals in the air. Earth-based separations and reverse gas stack modelling are useful mathematical tools that can be used to locate the source of a chemical release by back calculation. Earth-based separations studies the way different molecules will diffuse and separate through the air. Reverse gas stack modelling refers to the concentration differences of a chemical in relation to its distance from its source. These four analytical techniques can be combined to quickly and accurately locate various harmful chemical releases. The same system can be used for many applications and has been tested to detect harmful chemicals within and air-handling system. The monitoring of air-handling systems can greatly reduce the threat of harm to the building occupants by detecting hazardous chemicals and shutting off the air flow to minimize human exposure.

Atmospheric formaldehyde (H2CO) is an intermediate product common to the degradation of many volatile organic compounds and therefore it is a central component of the tropospheric chemistry. While the global formaldehyde background is due to methane oxidation, emissions of non-methane volatile organic compounds (NMVOCs) from biogenic, biomass burning and anthropogenic continental sources result in important and localised enhancements of the H2CO concentration. Recent spaceborne nadir sensors provide an opportunity to quantify the abundance of tropospheric formaldehyde at the global scale, and thereby to improve our knowledge of NMVOC emissions. This is essential for a better understanding of the processes that control the production and the evolution of tropospheric ozone, a key actor in air quality and climate change, but also of the hydroxyl radical OH, the main cleansing agent of our troposphere. For this reason, H2CO satellite observations are increasingly used in combination with tropospheric chemistry transport models to constrain NMVOC emission inventories in so-called top-down inversion approaches. Such inverse modelling applications require well characterised satellite data products consistently retrieved over long time periods.

This work reports on global observations of formaldehyde columns retrieved from the successive solar backscatter nadir sensors GOME, SCIAMACHY and GOME-2, respectively launched in 1995, 2002 and 2006. The retrieval procedure is based on the differential optical absorption spectroscopy technique (DOAS). Formaldehyde concentrations integrated along the mean atmospheric optical path are derived from the recorded spectra in the UV region, and further converted to vertical columns by means of calculated air mass factors. These are obtained from radiative transfer simulations, accounting for cloud coverage, surface properties and best-guess H2CO profiles, the latter being derived from the IMAGES chemistry transport model. A key task of the thesis has consisted in the optimisation of the H2CO retrieval settings from multiple sensors, taking into account the instrumental specificities of each sounder. As a result of these efforts, a homogeneous dataset of formaldehyde columns covering the period from 1996 to 2010 has been created. This comes with a comprehensive error budget that treats errors related to the spectral fit of the columns as well as those associated to the air mass factor evaluation. The time series of the GOME, SCIAMACHY and GOME-2 H2CO observations is shown to be consistent and stable over time. In addition, GOME-2 brings a significant reduction of the noise on spatiotemporally averaged observations, leading to a better identification of the emission sources. Our dataset is used to study the regional formaldehyde distribution, as well as its seasonal and interannual variations, principally related to temperature changes and fire events, but also to anthropogenic activities. Moreover, building on the quality of our 15-year time series, we present the first analysis of long-term changes in the H2CO columns. Positive trends, in the range of 1.5 to 4% yr-1, are found in Asia, more particularly in Eastern China and India, and are related to the known increase of anthropogenic NMVOC emissions in these regions. Finally, our dataset has been extensively used in several studies, in particular by the BIRA-IASB modelling team to constrain NMVOC emission fluxes. The results demonstrate the high potential of satellite data as top-down constraint for biogenic and biomass burning NMVOC emission inventories, especially in Tropical ecosystems, in Southeastern Asia, and in Southeastern US.

Le formaldéhyde (H2CO) joue un rôle central dans la chimie de la troposphère en tant que produit intermédiaire commun à la dégradation chimique de la plupart des composés organiques volatils dans l’atmosphère. L’oxydation du méthane est responsable de plus de la moitié de la concentration moyenne globale du formaldéhyde. Sur les continents en revanche, les hydrocarbures non-méthaniques (NMVOCs) émis par la végétation, les feux de biomasse et les activités humaines, augmentent de façon significative et localisée la concentration de H2CO. Les récents senseurs satellitaires à visée nadir offrent la possibilité de quantifier à l’échelle globale l’abondance du formaldéhyde dans la troposphère et de ce fait, d’améliorer notre connaissance des émissions de NMVOCs. Ceci est essentiel à la compréhension des mécanismes contrôlant la production et l’évolution de l’ozone troposphérique, élément clé pour la qualité de l’air et les changements climatiques, mais aussi du composé hydroxyle OH, le principal agent nettoyant de notre troposphère. C’est pourquoi, une méthode de plus en plus répandue pour améliorer les inventaires d’émissions des NMVOCs consiste en l’utilisation d’observations satellitaires de H2CO en combinaison avec un modèle de chimie et de transport troposphérique, dans une approche appelée modélisation inverse. Ce genre d’application demande des produits satellitaires bien caractérisés et dérivés de façon cohérente sur de longues périodes de temps.

Le travail présenté dans ce manuscrit porte sur l’inversion des colonnes de formaldéhyde à partir de spectres de la radiation solaire rétrodiffusée par l’atmosphère terrestre, mesurés par les senseurs GOME, SCIAMACHY et GOME-2, lancés successivement en 1995, 2002 et 2006. La méthode d’inversion est basée sur la spectroscopie d’absorption optique différentielle (DOAS). Les concentrations de formaldéhyde intégrées le long du chemin optique moyen dans l’atmosphère sont dérivées à partir des spectres mesurés, et ensuite transformées en colonnes verticales par le biais de facteurs de conversion appelés facteurs de masse d’air. Ces derniers sont calculés à l’aide d’un modèle de transfert radiatif, en tenant compte de la présence de nuages, des propriétés de la surface terrestre et la distribution verticale supposée du formaldéhyde, fournie par le modèle IMAGES. Un des objectifs principaux de la thèse a été d’optimiser les paramètres d’inversion pour H2CO, et ceci pour les trois senseurs, tout en tenant compte des spécificités de chaque instrument. Ces efforts ont conduit à la création d’un jeu de données homogène, couvrant la période de 1996 à 2010. Les colonnes sont fournies avec un bilan d’erreur complet, incluant les erreurs liées à l’inversion des concentrations dans les spectres, ainsi que celles provenant de l’évaluation des facteurs de masse d’air. La série temporelle des observations de GOME, SCIAMACHY et GOME-2 présente une bonne cohérence et stabilité sur toute la période. Nous montrons aussi que la meilleure couverture terrestre de GOME-2 entraîne une réduction significative du bruit sur les observations moyennées, permettant une meilleure identification des sources d’émission. Notre jeu de données est exploité pour étudier la distribution régionale du formaldéhyde, ainsi que ses variations saisonnières et interannuelles, principalement liées aux variations de température et aux feux de végétation, mais aussi aux activités anthropiques. De plus, en s’appuyant sur la qualité de la série temporelle de 15 ans, nous présentons la première analyse des variations à long terme des concentrations de H2CO. Des tendances positives, de l’ordre de 1.5 à 4% par an, sont observées en Asie, en particulier dans l’est de la Chine et en Inde, liées à l’augmentation des émissions anthropiques d’hydrocarbures dans ces régions. Finalement, nos données ont été largement exploitées par le groupe de modélisation de l’IASB pour faire des études de modélisation inverse des émissions de NMVOCs. Les résultats démontrent le haut potentiel des données satellitaires pour contraindre les inventaires d’émissions dues à la végétation et aux feux de biomasse, particulièrement dans les écosystèmes tropicaux, en Asie du sud-est, et dans le sud-est des Etats-Unis.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

Environmental factors can have a significant impact on an individual's health and well-being, and a primary characteristic of environments is air quality. Air sensing equipment is available to the public, but it is often expensive,stationary, or unusable for persons without technical expertise. The goal of this project is to develop an inexpensive and portable sensor module for public use. The system is capable of measuring temperature in Celsius and Fahrenheit, heat index, relative humidity, and carbon dioxide concentration. The sensor module, referred to as the "sniffer," consists of a printed circuit board that interconnects a carbon dioxide sensor, a temperature/humidity sensor, an Arduino microcontroller, and a Bluetooth module. The sniffer is small enough to be worn as a pendant or a belt attachment, and it is rugged enough to consistently collect and transmit data to a user's smartphone throughout their workday. The accompanying smartphone app uses Bluetooth and GPS hardware to collect data and affix samples with a time stamp and GPS coordinates. The accumulated sensor data is saved to a file on the user's phone, which is then examined on a standard computer.

Poor air quality has been projected to be the world’s top cause of environmental premature mortality by 2050 surpassing poor sanitation and dirty water (IGBP / IGAC press release, 2012 ). One of the major challenges of air quality management is how to adequately quantify both the spatial and temporal variations of pollutants for the purpose of implementing necessary mitigation measures. The work described in this thesis aims to address this problem using novel electrochemical based air quality (AQ) sensors. These instruments are shown to provide cost effective, portable, reliable, indicative measurements for urban air quality assessment as well as for personal exposure studies. Three principal pollutants CO, NO and NO2 are simultaneously measured in each unit of the AQ instrument including temperature / RH measurements as well as GPS (for time and position) and GPRS for data transmission. Laboratory studies showed that the electrochemical sensor nodes can be highly sensitive, showing linear response during calibration tests at ppb level (0-160 ppb). The instrumental detection limits were found to be < 4 ppb (CO and NO) and < 1 ppb for NO2 with fast response time equivalent to t90 < 20 s. Several field studies were carried out involving deployment of both the mobile and static electrochemical sensor nodes. Results from some short-term studies in four different cities including Cambridge (UK), London (UK), Valencia (Spain) and Lagos (Nigeria) are presented. The measurements in these cities represent snapshot of the pollution levels, the stark contrast between the pollution level especially CO (mean mixing ratio of 16 ppm over 3 hrs) in Lagos and the other three cities is a reflection of the poor air quality in that part of the world. Results from long-term AQ monitoring using network of 46 static AQ sensors were used to characterise pollution in different environments ranging from urban to semi-urban and rural locations. By coupling meteorological information (wind measurements) with pollution data, pollution sources, and phenomena like the street canyon effect can be studied. Results from the long-term study also revealed that siting of the current fixed monitoring stations can fail to represent the actual air quality distribution and may therefore be unrepresentative. This work has shown the capability of electrochemical based AQ sensors in complementing the existing fixed site monitors thus demonstrating an emerging measurement paradigm for air quality monitoring and regulation, source attribution and human exposure studies.

This thesis presents a research on quantitative assessment of temperature in urban residential settings and its influence on extreme temperature exposure to humans, energy usage and indoor air pollution in households, which is of significance at the time when climate change mitigation approaches are being considered. The work aimed to quantify indoor temperature profile; indoor-outdoor temperature relationship; indoor temperature occupant experience; and the association between temperature (indoor and outdoor) and air quality in houses. A temperature relationship model was developed applying advanced statistical methods to over one-year empirical temperature (indoor and outdoor) data sets of 90 houses in Brisbane, Australia.

La pollution atmosphérique urbaine, fléau mondial causant des millions de décès par an, rend les cartographies précises de ce phénomène non seulement pertinente mais vitale pour la santé publique.Actuellement, la surveillance de la qualité de l'air est assurée par des stations fixes de surveillance de la qualité de l'air. Ces stations de référence fournissent une mesure très précise de la qualité de l'air au prix d'une couverture spatiale limitée.L'idée d'utiliser de nouveaux capteurs à faible coût développés à partir de récentes avancées technologiques, plus petits, intégrant un système de positionnement global (GPS) a rapidement émergée. Les scientifiques disposent ainsi d'outils supplémentaires pour affiner les cartes spatio-temporelles de la pollution atmosphérique et créer de nouveaux ensembles de données fournissant des informations sur la qualité de l'air qui n'étaient pas disponibles auparavant.La génération de cartographies précises de la qualité de l'air à l'aide de ces capteurs bas coût présente plusieurs défis majeurs. Ces défis sont principalement liés à la nature du phénomène étudié, à la précision et au volume des données.Compte tenu de ces difficultés, il est essentiel de savoir comment combiner toutes ces sources de données floues pour obtenir une image claire de la pollution urbaine.C'est dans ce contexte que s'inscrit cette thèse dont le but est l'analyse et le développement de modèles statistiques qui exploitent les données acquises par des capteurs mobiles bas coût. Elle contribue à l'objectif de fournir des estimations et des prévisions spatiales et temporelles plus précises de la pollution de l'air urbain, en combinant modèles mathématiques et avancées technologiques
Urban air pollution, a global health crisis causing millions of deaths every year, makes accurate mapping of this phenomenon not only relevant, but vital to public health.Currently, air quality is measured by fixed air quality monitoring stations. These reference stations provide a highly accurate measure of air quality, at the cost of limited spatial coverage.The idea of using new low-cost sensors developed from recent technological advances, smaller in size and incorporating a global positioning system (GPS), quickly emerged. This gives scientists additional tools to refine spatio-temporal maps of air pollution and create new datasets providing information on air quality that was previously unavailable.Generating precise air quality maps using these low-cost sensors presents several major challenges. These challenges are mainly related to the nature of the phenomenon being studied, and to the accuracy and volume of the data.Given these difficulties, it is important to know how to combine all these fuzzy data sources to obtain a clear picture of urban pollution.The aim of this thesis is to analyze and develop statistical models that exploit data acquired by low-cost mobile sensors. It contributes to the objective of providing more accurate spatial and temporal estimates and forecasts of urban air pollution, by combining mathematical models and technological advances

La pollution atmosphérique résulte d’un mélange complexe de composés, des gaz et des particules, dont les effets sont notoirement néfastes. Les composés organiques volatils (COV) tiennent un rôle prépondérant dans la chimie atmosphérique et sont précurseurs d’ozone et d’aérosols organiques secondaires (AOS). En Île-de-France, l’exposition à la pollution est préoccupante ; or, des incertitudes significatives sont toujours associées aux sources de polluants, ainsi qu’à leur intensité et leurs variabilités à différentes échelles de temps et très peu d’investigations ont porté sur la quantification de l’exposition individuelle. Dans ce contexte, cette thèse a cherché à mieux caractériser les variabilités temporelle et spatiale de la pollution en Île-de-France.La fiabilité discutable des capteurs portables a été dépassée par l’élaboration d’un protocole de sélection et de qualification comprenant différents tests en mesures fixes, en chambre et en mobilité. Cette nouvelle méthodologie, basée notamment sur l’utilisation d’un outil combinant différents indicateurs statistiques, a été appliquée pour retenir l’AE51, le Cairclip et le Canarin, mesurant respectivement le carbone suie (BC), le dioxyde d’azote (NO2) et les particules (PM).Ces trois capteurs ont été déployés au cours de campagnes de mesures impliquant une trentaine de volontaires. L’exposition individuelle ainsi quantifiée est plus élevée à l’automne qu’au printemps et varie de manière substantielle en fonction des différents environnements fréquentés. La proximité de la circulation routière (pour le BC et le NO2) ainsi que les activités de cuisine et la fumée de tabac (pour les PM) présentent des contributions importantes à l’exposition totale (jusqu’à 34 %, 26 % et 44 % respectivement), alors même que le temps passé dans ces environnements est faible.En plus du trafic routier, le BC est traditionnellement imputé au feu de bois. Une campagne hivernale de mesures (3,5 mois) a permis d’imputer respectivement 22 % et 47 % des COV mesurés à ces deux sources. Certains composés ont été mesurés et associés au feu de bois pour la première fois en air ambiant comme le benzènediol et le méthylbutènone. Une comparaison avec l’inventaire régional des émissions a permis d’identifier des similitudes et des différences pour proposer des améliorations
Air pollution results from a complex mixture of compounds, gases and particulate matter, whose effects have proven to be harmful. Volatile organic compounds (VOCs) play a major role in atmospheric chemistry and are precursors of ozone and secondary organic aerosols (SOAs). In Île-de-France, exposure to pollution is a concern; however, significant uncertainties are still associated with the pollutants’ sources, as well as their intensity and variability at different time scales, and very few investigations have focused on quantifying personal exposure. In this context, this research sought to better characterize the temporal and spatial variabilities of pollution in Île-de-France.The questionable reliability of portable sensors has been addressed by the design of a selection and qualification protocol including various tests in static measurements, controlled chamber and mobility. This new methodology, based in particular on the use of a tool combining different statistical indicators, was applied to choose the AE51, Cairclip and Canarin, measuring black carbon (BC), nitrogen dioxide (NO2) and particulate matter (PM) respectively.These three sensors were deployed during measurement campaigns involving about thirty volunteers. The personal exposure thus quantified is higher in fall than in spring and varies substantially according to the different environments frequented. Proximity to road traffic (for BC and NO2) as well as cooking activities and tobacco smoke (for PM) make significant contributions to total exposure (up to 34 %, 26 % and 44 % respectively), even though the time spent in these environments is short.In addition to road traffic, BC is traditionally attributed to wood burning. A winter measurement campaign (3.5 months) attributed respectively 22 % and 47 % of the measured VOCs to these two sources. Compounds including benzenediol and methylbuteone were measured and associated with wood burning for the first time in ambient air. A comparison with the regional emissions inventory identified similarities as well as differences and suggested improvements

Outdoor air pollution is a major contributor to adverse health effects of citizens, in particular those living in urban environments. Air quality monitoring networks are set up to measure air quality in different environments in compliance with national and European legislation. Generally, only a few fixed monitoring sites are located within a city and thus cannot represent air pollutant concentrations in urban areas accurately enough to allow for a detailed human exposure assessment. Other approaches to derive detailed urban air pollutant concentration estimates exist, such as dispersion models and land-use regression (LUR) models. Low-cost portable air quality monitors are also emerging, which have the potential to add value to existing monitoring networks by providing measurements at greater spatial resolution and also to provide individual-level exposure assessment. The aim of this thesis is to demonstrate how measurements and modelling in combination allow detailed investigations of the variability of air pollutants in space and time in urban area, and in turn improve on the current exposure assessment methods. Three types of low-cost portable monitors measuring NO2, O3 (Aeroqual monitors) and PM2.5 (microPEM monitor) were evaluated against their respective reference instruments. The Aeroqual O3 monitor showed very good correlation (r2 > 0.9) with the respective reference instruments, but biases in the slope and intercept coefficients indicated that calibration of Aeroqual O3 monitor was needed. The Aeroqual NO2 monitor was subject to cross-sensitivity from O3, which, as demonstrated, can be effectively corrected by making O3 and NO2 measurements in tandem. Correlation between the microPEM monitor and its reference instrument was poor (r2 < 0.1) when PM2.5 concentrations were low (< 10 μg m-3), but significantly improved (r2 > 0.69) during periods with elevated PM2.5 concentrations. Relative humidity was not found to affect the raw results of PM2.5 measurements in a consistent manner. All three types of monitors cannot be used as equivalent or indicative methods instead of reference methods in studies that require quantification of absolute pollutant concentrations. However, the generally good correlations with reference instruments reassure their application in studies of relative trends of air pollution. Concentrations of PM2.5, ultrafine particles (UFP) and black carbon (BC) were quantified using portable monitors through a combination of mobile and static measurements in the city of Edinburgh, UK. The spatial variability of UFP and BC was large, of similar magnitude and about 3 times higher than the spatial variability of PM2.5. Elevated concentrations of UFP and BC were observed along streets with high traffic volumes whereas PM2.5 showed less variation between streets and a footpath without road traffic. Both BC and UFP significantly correlated with traffic counts, while no significant correlation between PM2.5 and traffic counts was observed. The relationships between UFP, NO2 and inorganic components of PM2.5 were further investigated through long-term measurements at roadside, urban background and rural sites. UFP moderately correlated with NOx (NO2 + NO) and showed varying relationships with NOx depending on the particle size distribution. Principal component analysis and air-mass back trajectory analysis revealed that PM2.5 concentrations were dominated by long-range transport of secondary inorganic aerosols, whereas UFP were mainly related to varying local emissions and meteorological conditions. These findings imply the need for different policies for managing human exposure to these different particle components: control of much BC and UFP appears to be manageable at local scale by restricting traffic emissions; however, abatement of PM2.5 requires a more strategic approach, in cooperation with other regions and countries on emissions control to curb long-range transport of PM2.5 precursors. A dispersion model (ADMS-Urban) was used to simulate high resolution NO2 and O3 concentrations in Edinburgh. The effects of different emission and meteorological input datasets on the resulting modelled NO2 concentrations were investigated. The modelled NO2 and O3 concentrations using the optimal model setup were validated against reference instrument and diffusion tube measurements. Temporal variability of NO2 was predicted well at locations that were not heavily influenced by local effects, such as road junctions and bus stops. Temporal variability of O3 was predicted better than for NO2. Long-term spatial variability of NO2 was found to correlate well with diffusion tube measurements, while modelled spatial variability of O3 in ADMS-Urban compared poorly with diffusion tube measurements. However, it was found that the O3 diffusion tube measurements may be subject to some unidentified biases affecting their accuracy. Land-use regression (LUR) models are widely used to estimate exposure to air pollution in urban areas. An appropriately sized and designed monitoring network is an important component for the development of a robust LUR model. Concentrations of NO2 were simulated by ADMS-Urban at ‘virtual’ monitoring sites in 54 different network designs of varying numbers and types of site, using a 25 km2 area including much of the Edinburgh city area. Separate LUR models were developed for each network. These LUR models were then used to estimate ambient NO2 concentrations at all residential addresses, which were evaluated against the ADMS-Urban modelled concentration at these addresses. The improvement in predictive capability of the LUR models was insignificant above ~30 monitoring sites, although more sites tended to yield more precise LUR models. Monitoring networks containing sites located within highly populated areas better estimated NO2 concentrations across all residential locations. LUR models constructed from networks containing more roadside sites better characterised the high end of residential NO2 concentrations but had increased errors when considering the whole range of concentrations. No particular composition of monitoring network resulted in good estimation simultaneously across all residential NO2 concentration and of the highest NO2 levels implying a lack of spatial contrast in LUR-modelled pollution surface compared with the dispersion model. Finally, the results from the measurement and modelling studies presented in thesis are synthesised in the context of current exposure assessment studies. Low-cost air-quality monitors currently do not possess and are unlikely in the near future to provide the robustness and accuracy to replace the existing routine monitoring network. Development of the low-cost air-quality should be aiming at upgrading them as the indicative method as defined in the data quality objective in the EU directive. The monitoring sites used to build LUR models should capture well the population distribution in the study area as opposed to capturing the greatest pollution contrast. The traditional methods of evaluating LUR models are also ineffective in characterising the models’ capability at estimating pollutant concentration at residential address. Given that the dispersion models are also subject to the availability and uncertainties in the input data, future air quality model development should endeavour to incorporate both dispersion and land-use regression models, where the uncertainty in the input data can be reduced by using LUR models built on actual measurements, and the limitation in the statistical modelling can be replaced by adopting the deterministic approach used in the dispersion model.

L’impact sanitaire lié à la pollution de l’air nécessite une estimation précise de celle-ci. Les réseaux de stations de mesures des agences de surveillance de la qualité de l’air (AIRPARIF en Île-de-France) ne sont pas suffisamment denses pour renseigner sur l’hétérogénéité de la pollution en ville. Et, les modèles haute résolution simulant les champs de concentration de polluants en 3D ont une large couverture spatiale mais sont limités par leurs incertitudes. Ces deux sources d’information exploitées indépendamment ne permettent pas d’évaluer finement l’exposition d’un individu. Nous proposons deux approches pour résoudre ce problème : (1) par la mesure directe des polluants avec des capteurs mobiles à bas coût et des instruments de référence. Des niveaux de pollution très variables ont été constatés entre les microenvironnements et dans une même pièce. Ces capteurs devraient être déployés en grand nombre pour palier à leurs contraintes techniques. Les instruments de référence, très coûteux et volumineux, ne peuvent être utilisés que ponctuellement. (2) en combinant les concentrations simulées par le modèle Parallel Micro-SWIFT-SPRAY (PMSS) à Paris avec une résolution horizontale de 3 mètres et les mesures des stations de surface AIRPARIF. Nous avons déterminé des « zones de représentativité » - zones géographiques où les concentrations sont très proches de celle de la station - uniquement à partir des sorties du modèle PMSS. Ensuite, nous avons développé un modèle bayésien pour propager la mesure des stations dans ces zones
The harmful effects of air pollution need a high-resolution concentration estimate. Ambient pollutant concentrations are routinely measured by surface monitoring sites of local agencies (AIRPARIF in Paris area, France). Such networks are not dense enough to represent the strong horizontal gradients of pollutant concentrations over urban areas. And, high-resolution models that simulate 3D pollutant concentration fields have a large spatial coverage but suffer from uncertainties. Those both information sources exploited independently are not able to accurately assess an individual’s exposure. We suggest two approaches to solve this problem : (1) direct pollution measurement by using low cost mobile sensors and reference instruments. A high variability across pollution levels is shown between microenvironments and also in the same room. Mobile sensors should be deployed on a large scale due to their technical constraints. Reference instruments are very expensive, cumbersome, and can only be used occasionally. (2) by combining concentration fields of the Parallel Micro-SWIFT-SPRAY (PMSS) model over Paris at a horizontal resolution of 3 meters with AIRPARIF local ground stations measurements. We determined “representativeness areas” - perimeter where concentrations are very close to the one of the station location – only from PMSS simulations. Next, we developed a Bayesian model to extend the stations measurements within these areas

La pollution atmosphérique résulte d’un mélange complexe de composés, des gaz et des particules, dont les effets sont notoirement néfastes. Les composés organiques volatils (COV) tiennent un rôle prépondérant dans la chimie atmosphérique et sont précurseurs d’ozone et d’aérosols organiques secondaires (AOS). En Île-de-France, l’exposition à la pollution est préoccupante ; or, des incertitudes significatives sont toujours associées aux sources de polluants, ainsi qu’à leur intensité et leurs variabilités à différentes échelles de temps et très peu d’investigations ont porté sur la quantification de l’exposition individuelle. Dans ce contexte, cette thèse a cherché à mieux caractériser les variabilités temporelle et spatiale de la pollution en Île-de-France.La fiabilité discutable des capteurs portables a été dépassée par l’élaboration d’un protocole de sélection et de qualification comprenant différents tests en mesures fixes, en chambre et en mobilité. Cette nouvelle méthodologie, basée notamment sur l’utilisation d’un outil combinant différents indicateurs statistiques, a été appliquée pour retenir l’AE51, le Cairclip et le Canarin, mesurant respectivement le carbone suie (BC), le dioxyde d’azote (NO2) et les particules (PM).Ces trois capteurs ont été déployés au cours de campagnes de mesures impliquant une trentaine de volontaires. L’exposition individuelle ainsi quantifiée est plus élevée à l’automne qu’au printemps et varie de manière substantielle en fonction des différents environnements fréquentés. La proximité de la circulation routière (pour le BC et le NO2) ainsi que les activités de cuisine et la fumée de tabac (pour les PM) présentent des contributions importantes à l’exposition totale (jusqu’à 34 %, 26 % et 44 % respectivement), alors même que le temps passé dans ces environnements est faible.En plus du trafic routier, le BC est traditionnellement imputé au feu de bois. Une campagne hivernale de mesures (3,5 mois) a permis d’imputer respectivement 22 % et 47 % des COV mesurés à ces deux sources. Certains composés ont été mesurés et associés au feu de bois pour la première fois en air ambiant comme le benzènediol et le méthylbutènone. Une comparaison avec l’inventaire régional des émissions a permis d’identifier des similitudes et des différences pour proposer des améliorations
Air pollution results from a complex mixture of compounds, gases and particulate matter, whose effects have proven to be harmful. Volatile organic compounds (VOCs) play a major role in atmospheric chemistry and are precursors of ozone and secondary organic aerosols (SOAs). In Île-de-France, exposure to pollution is a concern; however, significant uncertainties are still associated with the pollutants’ sources, as well as their intensity and variability at different time scales, and very few investigations have focused on quantifying personal exposure. In this context, this research sought to better characterize the temporal and spatial variabilities of pollution in Île-de-France.The questionable reliability of portable sensors has been addressed by the design of a selection and qualification protocol including various tests in static measurements, controlled chamber and mobility. This new methodology, based in particular on the use of a tool combining different statistical indicators, was applied to choose the AE51, Cairclip and Canarin, measuring black carbon (BC), nitrogen dioxide (NO2) and particulate matter (PM) respectively.These three sensors were deployed during measurement campaigns involving about thirty volunteers. The personal exposure thus quantified is higher in fall than in spring and varies substantially according to the different environments frequented. Proximity to road traffic (for BC and NO2) as well as cooking activities and tobacco smoke (for PM) make significant contributions to total exposure (up to 34 %, 26 % and 44 % respectively), even though the time spent in these environments is short.In addition to road traffic, BC is traditionally attributed to wood burning. A winter measurement campaign (3.5 months) attributed respectively 22 % and 47 % of the measured VOCs to these two sources. Compounds including benzenediol and methylbuteone were measured and associated with wood burning for the first time in ambient air. A comparison with the regional emissions inventory identified similarities as well as differences and suggested improvements

L’objectif de cette thèse est de concevoir un dispositif multi-capteurs de gaz à faible coût pour la surveillance des gaz polluants en environnement externe, et d’élaborer des méthodes de calibrage automatique et d’analyse de données adaptées à son exploitation. Ce dispositif vise à contribuer à la densification du réseau de surveillance de la pollution atmosphérique actuel afin de permettre une meilleure résolution spatiale et temporelle. Premièrement, nous avons sélectionné des micro-capteurs électrochimiques et des micro-capteurs à oxyde métallique capables de détecter et de quantifier des faibles concentrations de dioxyde d’azote et d’ozone dans l’atmosphère. Nous avons caractérisé ces micro-capteurs en laboratoire en termes de sensibilité et de reproductibilité, et les avons testés sur le terrain (à proximité d’un axe autoroutier) en suivant le protocole d’évaluation de micro-capteurs de gaz proposé par le Centre Commun de Recherche Européen (C.C.R.). L’analyse individuelle des données obtenues à partir de chaque capteur a mis en évidence la nécessité de fusionner les réponses de tous les capteurs ainsi que les valeurs de la température et de l’humidité de l’air, à l’aide des méthodes multi-variables de classification et de quantification. Les performances de plusieurs méthodes de classification, utilisées généralement dans le domaine des dispositifs multi-capteurs de gaz, ont été étudiées. Leur comparaison nous a conduit à choisir la régression SVM (Séparateur à Vaste Marge) grâce à sa précision et à sa robustesse. Les hyper-paramètres de cette méthode ont été optimisés à partir de l’algorithme de Recherche par Motifs Généralisés en raison de sa simplicité et de sa fiabilité. La méthode d’étalonnage proposée permet à notre dispositif de quantifier les deux polluants atmosphériques visés dans la gamme des précisions recommandées par les directives européennes concernant les mesures de type « indicative ». Ensuite, nous avons abordé le problème des dérives des capteurs qui engendrent des réétalonnages périodiques. Nous avons proposé une méthode de standardisation basée sur la régression SVM pour permettre de réduire le coût et l’effort d’un étalonnage complet. Cette même méthode de standardisation a été utilisée avec succès pour le transfert d’étalonnage entre plusieurs systèmes identiques afin d’éviter des étalonnages individuels. Nos travaux de recherche ont été valorisés par la publication de deux articles dans les journaux référencés JCR et deux communications dans des conférences internationales IEEE
The objective of this thesis is to design a low-cost gas sensor array for outdoor gas pollutants monitoring and to develop adapted standardization and data analysis methods. The device aims is to contribute to the densification of the current atmospheric pollution-monitoring network for better spatial and temporal resolution. First, we select electrochemical and metal oxide gas sensors capable to detecting and quantifying low concentrations of nitrogen dioxide and ozone in ambient air near a highway. We have characterized these micro-sensors in the laboratory using a diffusing controller system and tested them in the field by following procedures inspired from the evaluation protocol of low cost gas sensors proposed by the European Joint Research Center (JRC). The individual analysis of the data from each sensor highlights the need to merge the responses of all the sensors as well as the values of the temperature and the humidity of the atmosphere using multivariate methods of classification. The performance of several classification methods generally used in the field of multi-sensor gas systems was studied. This comparison leads us to choose Support Vector Machine regression (SVM) thanks to its performance in terms of precision and robustness. We also conduct a study on the optimization of the SVM hyper-parameters using optimization algorithms. The Generalized Pattern Search algorithm (GPS) was chosen among other algorithms due to its simplicity and reliability. Then, we highlight the problem of sensor drift over time and the need for periodic calibration. We proposed a standardization method based on SVM regression, thereby reducing the cost and the effort of full recalibration. This standardization method was also used for calibration transfer between several identical systems in order to avoid individual calibration of each system. The research work elaborated in this PhD was published in two journal papers and two IEEE conference papers

Ce manuscrit est consacré à l'étude et au développement de microsystèmes capteurs de gaz sélectifs au dioxyde d'azote, destinés au contrôle de la qualité de l'air atmosphérique. La stratégie que nous avons développée consiste à associer une structure sensible à base de matériaux semi-conducteurs partiellement sélectifs aux gaz oxydants et des filtres sélectifs à l'ozone. L'objectif premier est la mise en oeuvre et la caractérisation de matériaux chimiques strictement imperméables à l'ozone (O3) et non-réactifs vis-à-vis du dioxyde d'azote (NO2). Notre choix s'est focalisé sur un matériau moléculaire, l'indigo, connu pour sa réactivité vis-à-vis de O3, et plusieurs nanomatériaux carbonés. Pour ces derniers, la possibilité de conformer leurs textures, leurs morphologies et leurs chimies de surface par traitements thermiques, chimiques et mécaniques, permet d'étendre le panel de matériaux potentiels et d'identifier les facteurs d'influence de leur réactivité avec les espèces gazeuses. La caractérisation de l'ensemble de ces matériaux a nécessité l'utilisation de techniques adaptées et complémentaires (adsorption de N2 à 77 K, spectroscopies Raman, XPS, IR en mode ATR, RPE et NEXAFS). Les filtres chimiques les plus efficaces (hauts rendements de filtration et grande durabilité) ont été sélectionnés d'après des tests de soumission aux gaz selon une méthodologie adaptée. Enfin, l'association de ces meilleurs filtres et de la structure capteur a conduit à l'élaboration de prototypes microsystèmes capteurs de gaz optimisés. De plus, une contribution à la compréhension des mécanismes d'interaction de l'indigo et de certains nanocarbones avec O3 et NO2 a aussi permis d'améliorer le microsystème en développant des méthodologies pertinentes et innovantes mais également en réalisant la synthèse de nouveaux filtres indigo / nanocarbone.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
Air pollution is unquestionably a public health emergency, and the rates of pollution continue to rise at an alarming rate in cities all over the world. Nevertheless, the traditional monitoring equipment is very expensive, and the available measurements are not sufficient to precisely classify air quality in several locations in a city. Recent advancements in air quality measuring technology provide a potential opportunity to increase the air quality data, and to raise public awareness of health issues arising from air pollution. This study focuses on the development and evaluation of a new prototype for the monitoring of fine particulate matter (PM2.5). It describes the design approach and the evaluation methods, in which a series of field experiments were conducted to evaluate the performance of the prototype and of a commercial low-cost device in comparison with a reference monitor. The results showed that the prototype presented a good performance in environments with a high variation of particle concentrations (variations above 100μg/m³), such as cooking-environments and exposure to cigarette smoke, for most of the experiments (R² = 0.55-0.85). However, their agreement was very poor in environments without high variability of particle concentrations. The performance comparison between identical sensors purchased in the same year revealed a very high agreement (R² = 0.92), but prototypes which utilized sensors acquired in different years presented a very weak correlation in most of the experiments. The analysis of the commercial low-cost device’s performance revealed a moderate to strong linear correlation with the reference monitor in all the experiments (R= 0.51-0.93); this study also demonstrates that the maximum limit of detection of the device was much lower than the value given by the manufacturer (approximately 180μg/m³, in contrast to the value of 400μg/m³). For applications of real-time measurements, the prototype developed in this research may be especially utilized as indicative of PM2.5 hotspots and trends in ambient conditions, primarily in residences, monitoring the frequency and duration of high exposure events, such as cooking, smoking, and biomass burning. Nevertheless, this research demonstrates the necessity for individual sensor performance testing prior to field use, and that presumptions about the representativeness of measurements of PM2.5 carried out by low-cost sensors should be made with caution.

abstract: Air pollution has been linked to various health problems but how different air pollutants and exposure levels contribute to those diseases remain largely unknown. Researchers have mainly relied on data from government air monitoring stations to study the health effects of air pollution exposure. The limited information provided by sparse stations has low spatial and temporal resolution, which is not able to represent the actual exposure of individuals. A tool that can accurately monitor personal exposure provides valuable data for epidemiologists to understand the relationship between air pollution and certain diseases. It also allows individuals to be aware of any ambient air quality issues and prevent air pollution exposure. To build such a tool, sensors with features of fast response, small size, long lifetime, high sensitivity, high selectivity, and multi-analyte sensing are of great importance. In order to meet these requirements, three generations of novel colorimetric sensors have been developed. The first generation is mosaic colorimetric sensors based on tiny sensor blocks and by detecting absorbance change after each air sample injection, the target analyte concentration can be measured. The second generation is a gradient-based colorimetric sensor. Lateral transport of analytes across the colorimetric sensor surface creates a color gradient that shifts along the transport direction over time, and the sensor tracks the gradient shift and converts it into analyte concentration in real-time. The third generation is gradient-based colorimetric arrays fabricated by inkjet-printing method that integrates multiple sensors on a miniaturized sensor chip. Unlike traditional colorimetric sensors, such as detection tubes and optoelectronic nose, that are typically for one-time use, the presented three generations of colorimetric sensors aim to continuously monitor multiple air pollutants and the sensor lifetime and fabrication methods have been improved over each generation. Ozone, nitrogen dioxide, formaldehyde and carbon monoxide are chosen as analytes of interest. The performance of sensors has been validated in the lab and field tests, proving the capability of the sensors to be used for personal exposure monitoring.
Dissertation/Thesis
Doctoral Dissertation Chemistry 2019

Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
Nos dias de hoje, o tema da poluição ambiental, causa cada vez mais preocupação, à medida que as consequências causadas pelos efeitos nocivos da mesma são mais evidentes. Assim surge a crescente necessidade para a sensibilização da população, de modo a que sejam alterados hábitos prejudiciais ao meio ambiente e tomadas medidas com o intuito de o preservar. Para tal, é necessário desmascarar este problema, que passa muitas vezes despercebido, criando uma maneira de atribuir um rosto à poluição ambiental de modo a que as pessoas a possam visualizar com outros olhos, e corrigir a sua influência negativa para a qualidade do ar.O trabalho desenvolvido nesta dissertação procura fornecer uma ferramenta que permita monitorizar a poluição ambiental, e posteriormente disponibilizar a informação à população, sobre estado do ar nas zonas urbanas. Para tal, foi considerada a implementação de uma rede de sensores fixos e móveis, de baixo custo, que poderão ser espalhados por toda a área urbana, através da introdução de nós móveis em transportes públicos. O sistema implementado permite realizar medições georreferenciadas no espaço e no tempo, para os poluentes Monóxido de Carbono (CO), Dióxido de Carbono (CO2), Dióxido de Azoto (NO2), Ozono (O3) e Material Particulado (PM), bem como informação sobre a temperatura, humidade relativa do ar, pressão atmosférica e radiação solar. A informação recolhida é armazenada num servidor para posterior análise. Para a demonstração de resultados, foram construídos mapas de poluição sobre um mapa da cidade, utilizando dois métodos de estimação distintos: IDW e Kriging.Após testar o sistema, os resultados obtidos demonstraram-se coerentes com o esperado, atribuindo maiores níveis de poluição para o centro da cidade onde o tráfego rodoviário é mais intenso, e atribuindo menores concentrações de poluição nas áreas circundantes e zonas de maior área florestal.
Nowadays, the environmental pollution is causing more and more concern, as the harmful effects are increasingly evident. As a result, there is a growing need to raise awareness among the population to change harmful habits to the environment and take measures to preserve it. To do so, it’s necessary to unmask this issue, which often passes by invisible, creating some way to attribute a face to environmental pollution, so that people can see it with different eyes and correct its negative influence on air quality.The work developed in this dissertation seeks to provide a tool to monitoring environmental pollution and subsequently provide information to population, about the air quality in urban areas. Therefore, it was considered an implementation of a low-cost network, composed of fixed and mobile sensors, that could be spread throughout the urban area, with the introduction of mobile sensor nodes in public transports. The implemented system allows performing georeferenced measurements in space and time, for the pollutants Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3) and Particulate Matter (PM), as well as temperature, air relative humidity, atmospheric pressure, and solar radiation. The collected information is stored on a server for further analysis. For results demonstration, pollution maps are built on a city map, using two distinct estimation methods: IDW and Kriging.After testing the system, the obtained results prove to be consistent, with expected, assigning higher pollution levels in the center of the city, where road traffic is more intense, and assigning lower pollution concentrations in both surrounding and forest areas.