Dissertations / Theses on the topic 'Mobile air pollution monitoring'

El crecimiento industrial ha acarreado grandes avances tecnológicos para nuestra sociedad. Lamentablemente, el precio a pagar por estos avances ha sido un aumento significativo de los niveles de contaminación del aire en todo el mundo, afectando tanto a zonas urbanas como a las zonas rurales. Por lo general, la monitorización de la calidad aire se realiza mediante estaciones de monitorización fijas. Sin embargo, este método es demasiado costoso, poco escalable y difícil de implementar en nuestras ciudades, las cuales están cada vez más pobladas. El uso de Mobile CrowdSensing, paradigma en el cual la monitorización la realizan los propios usuarios, permite realizar monitorización ambiental utilizando sensores móviles integrados en vehículos. Los posibles escenarios se pueden dividir en dos: entornos urbanos, donde hay un amplio conjunto de vehículos disponibles, y entornos rurales o industriales, donde el tráfico vehicular es escaso y está limitado a las principales arterias de transporte. Teniendo en cuenta estos dos escenarios, esta tesis propone una arquitectura, llamada EcoSensor, que permite monitorizar la contaminación del aire utilizando pequeños sensores de bajo coste instalados en diferentes tipos de vehículos, tales como bicicletas, automóviles o autobuses del sistema de transporte público, en el caso de entornos urbanos, y en drones o UAS en entornos rurales. La arquitectura propuesta está compuesta por tres componentes: un sensor de bajo coste para capturar datos de contaminación, un smartphone para realizar un preprocesamiento de la información y para transmitir los datos hacia un servidor central, y el servidor central, encargado de almacenar y procesar la información de contaminación ambiental. Para entornos urbanos, analizamos diferentes alternativas con respecto al diseño de una unidad de monitorización de bajo coste basada en plataformas de prototipado comerciales como RaspberryPi o Arduino, junto con sensores también de precio reducido. En la tesis realizamos un análisis, y proponemos un proceso, para llevar a cabo la monitorización ambiental utilizando la arquitectura propuesta. Este proceso abarca cuatro operaciones básicas: captura de datos, conversión de unidades, reducción de la variabilidad temporal, e interpolación espacial. Para entornos rurales, proponemos el uso de drones como unidades de sensorización móviles. Específicamente, equipamos el drone con capacidades de monitorización a través de un microordenador RaspberryPi y sensores de calidad del aire de bajo coste. Finalmente, se propone un algoritmo llamado PdUC para controlar el vuelo del UAV con el objetivo de realizar monitorización ambiental, identificando las áreas más contaminadas, y tratando de ese modo de mejorar la precisión general y la velocidad de monitorización. Además, proponemos una mejora a este algoritmo, denominada PdUC-D, basada en la discretización del área a monitorizar dividiéndola en pequeñas áreas (tiles), donde cada tile se monitoriza una sola vez, evitando así realizar muestreos redundantes. En general, verificamos que la monitorización móvil es una aproximación eficiente y fiable para monitorizar la contaminación del aire en cualquier entorno, ya sea usando vehículos o bicicletas en entornos urbanos, o UAVs en entornos rurales. Con respecto al proceso de monitorización ambiental, validamos nuestra propuesta comparando los valores obtenidos por nuestros sensores móviles de bajo coste con respecto a los valores típicos de referencia ofrecidos por las estaciones de monitorización fijas para el mismo período y ubicación, comprobando que los resultados son semejantes, y están acuerdo a lo esperado. Además, demostramos que PdUC-D, permite guiar autónomamente un UAV en tareas de monitorización del aire, ofreciendo un mejor rendimiento que los modelos de movilidad típicos, reduciendo tanto los errores de predicción como el tiempo para cubrir el área completa,
Industrial growth has brought unforeseen technological advances to our society. Unfortunately, the price to pay for these advances has been an increase of the air pollution levels worldwide, affecting both urban and countryside areas. Typically, air pollution monitoring relies on fixed monitoring stations to carry out the pollution control. However, this method is too expensive, not scalable, and hard to implement in any city. The Mobile Crowdsensing (MCS) approach, a novel paradigm whereby users are in charge of performing monitoring tasks, allows environment monitoring to be made using small sensors embedded in mobile vehicles. The possible scenarios can be divided into two: urban scenarios, where a wide set of vehicles are available, and rural and industrial areas, where vehicular traffic is scarce and limited to the main transportation arteries. Considering these two scenarios, in this thesis we propose an architecture, called EcoSensor, to monitor the air pollution using small sensors installed in vehicles, such as bicycles, private cars, or the public transportation system, applicable to urban scenarios, and the use of an Unmanned Aerial System (UAS) in rural scenarios. Three main components compose our architecture: a low-cost sensor to capture pollution data, a smartphone to preprocess the pollution information and transmit the data towards a central server, and the central server, to store and process pollution information. For urban scenarios, we analyze different alternatives regarding the design of a low-cost sensing unit based on commercial prototyping platforms such as Raspberry Pi or Arduino, and Commercial Off-the-shelf (COTS) air quality sensors. Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation. For rural scenarios, we propose the use of an Unmanned Aerial Vehicle (UAV) as a mobile sensor. Specifically, we equip the UAV with sensing capabilities through a Raspberry Pi microcomputer and low-cost air quality sensors. Finally, we propose an algorithm, called Pollution-driven UAV Control (PdUC), to control the UAV flight for monitoring tasks by focusing on the most polluted areas, and thereby attempting to improve the overall accuracy while minimizing flight time. We then propose an improvement to this algorithm, called Discretized Pollution-driven UAV Control (PdUC-D), where we discretize the target area by splitting it into small tiles, where each tile is monitored only once, thereby avoiding redundant sampling. Overall, we found that mobile sensing is a good approach for monitoring air pollution in any environment, either by using vehicles or bicycles in urban scenarios, or an UAVs in rural scenarios. We validate our proposal by comparing obtained values by our mobile sensors against typical values reported by monitoring stations at the same time and location, showing that the results are right, matching the expected values with a low error. Moreover, we proved that PdUC-D, our protocol for the autonomous guidance of UAVs performing air monitoring tasks, has better performance than typical mobility models in terms of reducing the prediction errors and reducing the time to cover the whole area.Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation.
El creixement industrial ha implicat grans avanços tecnològics per a la nostra societat. Lamentablement, el preu que cal pagar per aquests avanços ha sigut un augment significatiu dels nivells de contaminació de l'aire a tot el món, que afecta tant zones urbanes com zones rurals. En general, el monitoratge de la qualitat aire es fa mitjançant estacions de monitoratge fixes. No obstant això, aquest mètode és massa costós, poc escalable i difícil d'implementar a les nostres ciutats, les quals estan cada vegada més poblades. L'ús de Mobile CrowdSensing (MCS), paradigma en el qual el monitoratge el duen a terme els mateixos usuaris, permet realitzar monitorització ambiental tenint sensors mòbils integrats en vehicles. Els possibles escenaris es poden dividir en dos: entorns urbans, on hi ha un ampli conjunt de vehicles disponibles, i entorns rurals o industrials, on el trànsit vehicular és escàs i està limitat a les principals artèries de transport. Tenint en compte aquests dos escenaris, aquesta tesi proposa una arquitectura, anomenada EcoSensor, que permet monitorar la contaminació de l'aire utilitzant petits sensors de baix cost instal·lats en diferents tipus de vehicles, com ara bicicletes, automòbils o autobusos del sistema de transport públic, en el cas d'entorns urbans, i en UAVs (Unmanned Aerial Vehicles) en entorns rurals. L'arquitectura proposada està composta per tres components: un sensor de baix cost per a capturar dades de contaminació, un smartphone per a realitzar un preprocessament de la informació i per a transmetre les dades cap a un servidor central, i el servidor central, encarregat d'emmagatzemar i processar la informació de contaminació ambiental. Per a entorns urbans, analitzem diferents alternatives pel que fa al disseny d'una unitat de monitoratge (sensor mòbil) de baix cost basada en plataformes de prototipatge comercials com Raspberry Pi o Arduino, juntament amb sensors també de preu reduït. En la tesi fem una anàlisi, i proposem un procés, per a dur a terme el monitoratge ambiental utilitzant l'arquitectura proposada. Aquest procés abasta quatre operacions bàsiques: captura de dades, conversió d'unitats, reducció de la variabilitat temporal, i interpolació espacial. Per a entorns rurals, proposem l'ús de drons o Unmanned Aerial Vehicles (UAVs) com a unitats de sensorització mòbils. Específicament, equipem el dron amb capacitats de monitoratge a través d'un microordinador Raspberry Pi i sensors de qualitat de l'aire de baix cost. Finalment, es proposa un algorisme anomenat PdUC (Pollution-driven UAV Control) per a controlar el vol del UAV amb l'objectiu de realitzar monitoratge ambiental, que identifica les àrees més contaminades i que, d'aquesta manera, tracta de millorar la precisió general i la velocitat de monitoratge. A més, proposem una millora a aquest algorisme, denominada PdUC-D, basada en la discretització de l'àrea a monitorar dividint-la en xicotetes àrees (tiles), on cada tile es monitora una sola vegada, fet que evita dur a terme mostrejos redundants. En general, verifiquem que el monitoratge mòbil és una aproximació eficient i fiable per a monitorar la contaminació de l'aire en qualsevol entorn, ja siga usant vehicles o bicicletes en entorns urbans, o UAVs en entorns rurals. Pel que fa al procés de monitoratge ambiental, validem la nostra proposta comparant els valors obtinguts pels nostres sensors mòbils de baix cost pel que fa als valors típics de referència oferits per les estacions de monitoratge fixes per al mateix període i ubicació, i es comprova que els resultats són semblants, i estan d'acord amb el resultat esperat. A més, es demostra que PdUC-D permet guiar autònomament un UAV en tasques de monitoratge de l'aire, oferint un millor rendiment que els models de mobilitat típics, reduint tant els errors de predicció com el temps per a cobrir l'àrea completa, i aconseguint una major precisió dins de les àrees més
Alvear Alvear, ÓP. (2018). Mobile Sensing Architecture for Air Pollution Monitoring [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107928
TESIS

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.

La contaminación ambiental es uno de los principales problemas que afecta a nuestro planeta. El crecimiento industrial y los aglomerados urbanos, entre otros, están contribuyendo a que dicho problema se diversifique y se cronifique. La presencia de contaminantes ambientales en niveles elevados afecta la salud humana, siendo la calidad del aire y los niveles de ruido ejemplos de factores que pueden causar efectos negativos en las personas tanto psicológicamente como fisiológicamente. Sin embargo, la ubiquidad de los microcomputadores, y el aumento de los sensores incorporados en nuestros smartphones, han hecho posible la aparición de nuevas estrategias para medir dicha contaminación. Así, el Mobile Crowdsensing se ha convertido en un nuevo paradigma mediante el cual los teléfonos inteligentes emergen como tecnología habilitadora, y cuya adopción generalizada proporciona un enorme potencial para su crecimiento, permitiendo operar a gran escala, y con unos costes asumibles para la sociedad. A través del crowdsensing, los teléfonos inteligentes pueden convertirse en unidades de detección flexibles y multiuso que, a través de los sensores integrados en dichos dispositivos, o combinados con nuevos sensores, permiten monitorizar regiones de interés con una buena granularidad tanto espacial como temporal. En esta tesis nos centramos en el diseño de soluciones de crowdsensing usando smartphones donde abordamos problemas de contaminación ambiental, específicamente del ruido y de la contaminación del aire. Con este objetivo, se estudian, en primer lugar, las propuestas de crowdsensing que han surgido en los últimos años. Los resultados de nuestro estudio demuestran que todavía hay mucha heterogeneidad en términos de tecnologías utilizadas y métodos de implementación, aunque los diseños modulares en el cliente y en el servidor parecen ser dominantes. Con respecto a la contaminación del aire, proponemos una arquitectura que permita medir la contaminación del aire, concretamente del ozono, dentro de entornos urbanos. Nuestra propuesta utiliza smartphones como centro de la arquitectura, siendo estos dispositivos los encargados de leer los datos de un sensor móvil externo, y de luego enviar dichos datos a un servidor central para su procesamiento y tratamiento. Los resultados obtenidos demuestran que la orientación del sensor y el período de muestreo, dentro de ciertos límites, tienen muy poca influencia en los datos capturados. Con respecto a la contaminación acústica, proponemos una arquitectura para medir los niveles de ruido en entornos urbanos basada en crowdsensing, y cuya característica principal es que no requiere intervención del usuario. En esta tesis detallamos aspectos tales como la calibración de los smartphones, la calidad de las medidas obtenidas, el instante de muestreo, el diseño del servidor, y la interacción cliente-servidor. Además, hemos validado nuestra solución en escenarios reales para demostrar el potencial de la solución alcanzada. Los resultados experimentales muestran que, con nuestra propuesta, es posible medir niveles de ruido en diferentes zonas urbanas o rurales con un grado de precisión comparable al de los dispositivos profesionales, todo ello sin requerir intervención del usuario, y con un consumo reducido en cuanto a recursos del sistema. En general, las diferentes contribuciones de esta tesis doctoral ofrecen un punto de partida para nuevos desarrollos, ofreciendo estrategias de calibración y algoritmos eficientes de cara a realizar medidas representativas. Además, una importante ventaja de nuestra propuesta es que puede ser implementada de forma directa tanto en instituciones públicas como no gubernamentales en poco tiempo, ya que utiliza tecnología accesible y soluciones basadas en código abierto.
La contaminació ambiental és un dels principals problemes que afecten el nostre planeta. El creixement industrial i els aglomerats urbans, entre altres, estan contribuint al fet que aquest problema es diversifique i es cronifique. La presència de contaminants ambientals en nivells elevats afecta la salut humana, sent la qualitat de l'aire i els nivells de soroll exemples de factors que poden causar efectes negatius en les persones, tant psicològicament com fisiològicament. No obstant això, la ubiqüitat de les microcomputadores i l'augment dels sensors incorporats als nostres telèfons intel·ligents han fet possible l'aparició de noves estratègies per a mesurar aquesta contaminació. Així, el mobile crowdsensing s'ha convertit en un nou paradigma mitjançant el qual els telèfons intel·ligents emergeixen com a tecnologia habilitadora, i l'adopció generalitzada d'aquest proporciona un enorme potencial per al seu creixement, ja que permet operar a gran escala i amb uns costos assumibles per a la societat. A través del crowdsensing, els telèfons intel·ligents poden convertir-se en unitats de detecció flexibles i multiús que, a través dels sensors integrats en els esmentats dispositius, o combinats amb nous sensors, permeten monitoritzar regions d'interès amb una bona granularitat, tant espacial com temporal. En aquesta tesi ens centrem en el disseny de solucions de crowdsensing usant telèfons intel·ligents, on abordem problemes de contaminació ambiental, específicament del soroll i de la contaminació de l'aire. Amb aquest objectiu, s'estudien, en primer lloc, les propostes de crowdsensing que han sorgit en els últims anys. Els resultats del nostre estudi demostren que encara hi ha molta heterogeneïtat en termes de tecnologies utilitzades i mètodes d'implementació, encara que els dissenys modulars en el client i en el servidor semblen ser dominants. Pel que fa a la contaminació de l'aire, proposem una arquitectura que permeta mesurar la contaminació d'aquest, concretament de l'ozó, dins d'entorns urbans. La nostra proposta utilitza telèfons intel·ligents com a centre de l'arquitectura, sent aquests dispositius els encarregats de llegir les dades d'un sensor mòbil extern, i d'enviar després aquestes dades a un servidor central per al seu processament i tractament. Els resultats obtinguts demostren que l'orientació del sensor i el període de mostratge, dins de certs límits, tenen molt poca influència en les dades capturades. Pel que fa a la contaminació acústica, proposem una arquitectura per a mesurar els nivells de soroll en entorns urbans basada en crowdsensing, i la característica principal de la qual és que no requereix intervenció de la persona usuària. En aquesta tesi detallem aspectes com ara el calibratge dels telèfons intel·ligents, la qualitat de les mesures obtingudes, l'instant de mostratge, el disseny del servidor i la interacció client-servidor. A més, hem validat la nostra solució en escenaris reals per a demostrar el potencial de la solució assolida. Els resultats experimentals mostren que, amb la nostra proposta, és possible mesurar nivells de soroll en diferents zones urbanes o rurals amb un grau de precisió comparable al dels dispositius professionals, tot això sense requerir intervenció de l'usuari o usuària, i amb un consum reduït quant a recursos del sistema. En general, les diferents contribucions d'aquesta tesi doctoral ofereixen un punt de partida per a nous desenvolupaments, i ofereixen estratègies de calibratge i algorismes eficients amb vista a realitzar mesures representatives. A més, un important avantatge de la nostra proposta és que pot ser implementada de forma directa tant en institucions públiques com no governamentals en poc de temps, ja que utilitza tecnologia accessible i solucions basades en el codi obert.
Environmental pollution is one of the main problems that affect our planet. Industrial growth and urban agglomerations, among others, are contributing to the diversification and chronification of this problem. The presence of environmental pollutants at high levels affect human health, with air quality and noise levels being examples of factors that can cause negative effects on people both psychologically and physiologically. Traditionally, environmental pollution is measured through monitoring centers, which are usually fixed and have a high cost. However, the ubiquity of microcomputers and the increase in the number of sensors embedded in our smartphones, have paved the way for the appearance of new strategies to measure such pollution. Thus, Mobile Crowdsensing has become a new paradigm through which smartphones emerge as an enabling technology, and whose widespread adoption provides enormous potential for growth, allowing large-scale operations, and with costs acceptable to our society. Through crowdsensing, smartphones can become flexible and multipurpose detection units that, through the sensors integrated into these devices, or combined with new sensors, allow monitoring regions of interest with good spatial and temporal granularity. In this thesis, we focus on the design of crowdsensing solutions using smartphones. We deal with environmental pollution problems, specifically noise and air pollution. With this objective, the crowdsensing proposals that have emerged in recent years are studied in the first place. The results of our study show that there is still a lot of heterogeneity in terms of technologies used and implementation methods, although modular designs at both client and server seem to be dominant. Concerning air pollution, we propose an architecture that allows measuring air pollution, specifically ozone, in urban environments. Our proposal uses smartphones as the center of the architecture, being these devices responsible for reading the data obtained by an external mobile sensor, and then sending such data to a central server for processing and analysis. In this proposal, several problems have been analyzed with regard to the orientation of the external sensor and the sampling time, and the proposed solution has been validated in real scenarios. The results obtained show that the orientation of the sensor and the sampling period, within certain limits, have very little influence on the captured data. Also, by comparing the heat maps generated by our solution with the data from the existing monitoring stations in the city of Valencia, we demonstrate that our approach is capable of providing greater data granularity. Concerning noise pollution, we propose an architecture to measure noise levels in urban environments based on crowdsensing, and whose main characteristic is that it does not require user intervention. In this thesis, we detail aspects such as the calibration of smartphones, the quality of the measurements obtained, the sampling instant, the server design, and the client-server interaction. Besides, we have validated our solution in real scenarios to demonstrate the potential of the proposed solution. Experimental results show that, with our proposal, it is possible to measure noise levels in different urban or rural areas with a degree of precision comparable to that of professional devices, all without requiring the intervention of the user, and with reduced consumption of system resources. In general, the different contributions of this doctoral thesis provide a starting point for new developments, offering efficient calibration strategies and algorithms to make representative measurements. Besides, a significant advantage of our proposal is that it can be implemented straightforwardly by both public and non-governmental institutions in a short time, as it relies on accessible technology and open source software
Zamora Mero, WJ. (2018). Crowdsensing solutions for urban pollution monitoring using smartphones [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115483
TESIS

Mobile source emissions are a major contributor to global and local air pollution. Governments and regulatory agencies have been increasing the stringency of regulations in the transportation sector for the last ten years to help curb transportation sector air pollution. The need for regulations has been emphasized by scientific research on the impacts from ambient pollution, especially research on the effect of particulate matter on human health. The particulate emissions from diesel vehicles, diesel particulate matter (DPM) is considered a known or probable carcinogen in various countries and increased exposure to DPM is linked to increased cardiovascular health problems in humans. The toxicity of vehicle emissions and diesel particulate emissions in particular, in conjunction with an increased awareness of potential petroleum fuel shortages, international conflict over petroleum fuel sources and climate change science, have all contributed to the increase of biodiesel use as an additive to or replacement for petroleum fuel. The goal of this research is to determine how this increased use of biodiesel in the particular emission testing setup impacts urban air quality. To determine if biodiesel use contributes to a health or climate benefit, both the size range and general composition were investigated using a comprehensive comparison of the particulate component of the emissions in real time. The emissions from various biodiesel and diesel mixtures from a common diesel passenger vehicle were measured with a cavity ring-down transmissometer (CRDT) coupled with a condensation particle counter, a SMPS, a nephelometer, NOx, CO, CO2, and O3 measurements. From these data, key emission factors for several biodiesel and diesel fuel mixtures were developed. This approach reduces sampling artifacts and allows for the determination of optical properties, particle number concentration, and size distributions, along with several important gas phase species' concentrations. Findings indicate that biodiesel additions to diesel fuel do not necessarily have an air quality benefit for particulate emissions in this emission testing scenario. The often cited linear decrease in particulate emissions with increasing biodiesel content was not observed. Mixtures with half diesel and half biodiesel tended to have the highest particulate emissions in all size ranges. Mixtures with more than 50% biodiesel had slightly lower calculated mass for light absorbing carbon, but this reduction in mass is most likely a result of a shift in the size of the emission particles to a smaller size range, not a reduction in the total number of particles. Evaluation of the extensive optical properties from this experimental set-up indicates that biodiesel additions to diesel fuel has an impact on emission particle extinction in both visible and near-IR wavelengths. The B99 mixture had the smallest emission factor for extinction at 532 nm and at 1064 nm. For the extinction at 532 nm, the trend was not linear and the emission factor peaked at the B50 mixture. Results from intensive properties indicate that emissions from B5 and B25 mixtures have Ångström exponents close to 1, typical for black carbon emissions. The mixtures with a larger fraction of biodiesel have Ångström exponent values closer to 2, indicating more absorbing organic matter and/or smaller particle size in the emissions. Additional experimental testing should be completed to determine the application of these results and emission factors to other diesel vehicles or types of diesel and biodiesel fuel mixtures.

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

Поленкова, М. В. Оцінка стану атмосферного повітря чернігівського регіону та заходи щодо його покращення : магістерська робота: 05 Соціальні та поведінкові науки / М. В. Поленкова; керівник роботи Хоменко І. О. ; Національний університет «Чернігівська політехніка», кафедра теоретичної та прикладної економіки. – Чернігів, 2020. – 85 с.
Об’єктом дослідження є стан атмосферного повітря Чернігівської області. Предметом дослідження є моніторинг стану атмосферного повітря Чернігівської області. Мета дослідження – комплексна і цілісна оцінка фактичного стану забруднення атмосферного повітря в Чернігівській області. Дослідження спрямоване на обґрунтування практичних рекомендацій щодо обґрунтування механізму проведення моніторингу атмосферного повітря у Чернігівській області. Інформаційною базою дослідження слугували наукові праці вітчизняних та зарубіжних науковців, нормативні та законодавчі акти у сфері охорони та якості атмосферного повітря, Директиви Європейського Союзу, спрямовані на регулювання кількості викидів забруднювальних речовин в атмосферне повітря та його охорону, матеріали науково-практичних конференцій, збірники та видання Державної служби статистики України та Чернігівської області, Департаменту екології та природних ресурсів Чернігівської ОДА, матеріали з інформаційної мережі Internet.
The object of research is the state of the atmospheric air of Chernihiv region. The subject of the study is the monitoring of atmospheric air in the Chernihiv region. The purpose of the study is a comprehensive and holistic assessment of the actual state of air pollution in the Chernihiv region. The study is aimed at substantiating practical recommendations for substantiating the mechanism of air monitoring in Chernihiv region. The information base of the study was the scientific works of domestic and foreign scientists, regulations and legislation in the field of air protection and quality, EU Directives aimed at regulating the amount of pollutant emissions into the air and its protection, materials of scientific conferences, collections and publications State Statistics Service of Ukraine and Chernihiv region, Department of Ecology and Natural Resources of Chernihiv Regional State Administration, materials from the Internet.

Previous work on biological monitors for atmospheric lead has been reviewed. Experiments with hair-net and flat nylon-mesh envelope monitors failed to confirm a claim of reproducibility by previous workers, a percentage relative standard deviation of 24.5 for hair-net monitors and 13.4 to 56.7 for flat nylon-mesh envelopes was achieved. An homogenised acid-washed moss monitor in a diffusion tube produced a significant increase in lead deposition. Replicate diffusion tube monitors initially showed poor reproducibility. Homogenisation of the moss to < 1mm and maintained saturation produced a range of % RSD's of 8.6 to 12.8. Samples unwashed with acid showed more depostion than washed samples. Replicate monitors in which moss was replaced with various physical media displayed poor reproducibility. It seems unlikely that such passive monitors can replace established air filtration methods. At the commencement of this study in 1981, two types of dust gauge were in use in the North East Derbyshire area, they were the B.S. Standard Deposit Gauge and the B.S. Direction Dust Gauge. Sampling errors of the British Standard Deposit Gauge had been established by previous workers as being considerable. Proposed models to apply a wind direction correction to amounts of dust collected by Directional Gauges have inherent weaknesses. A new model was developed and its performance improved with successive refinements of input data. Wind data from a localised weather station was applied to the model and showed to improve the performance compared to using remote weather station data. Daily smoke and sulphur dioxide levels at 6 sites covering a 3 year period were used to develop a model which employs data collected at one monitoring station to predict levels in other parts of the area. Precipitation acidity has been measured within the area since 1950. Previous work on precipitation acidity data has been reviewed and an increase in precipitation acidity for the area was indicated. A rationalised air pollution monitoring network is proposed to fulfil the air quality management function for the N.E.D.D.C.

Air pollution is a global issue which has negative impacts not only on the environment but also on human health. Therefore, it is important to design and implement systems to allow cities and villages to monitor air quality so that they take the required actions to maintain a good air quality in the city/village. Since IoT facilitates implementing efficient monitoring systems, many IoT systems have been proposed to monitor air pollution. In this paper, we review different IoT-based systems to monitor air quality. In addition, we do an experiment where we propose and evaluate our system to monitor air pollution in a smart village, Veberöd, utilizing the LoRaWAN and the IoT platform, Yggio, which is already used in the village. Our proposed system is used to monitor temperature, humidity, pressure, PM1, PM2.5, PM10, CO2, and CO. As a result of our experiment, we found that the data received by Yggio was encoded, and Yggio did not provide the decoding functionality to decode the data sent from our devices. Therefore, another IoT platforms were used to decode, visualize, and analyse the data. The results of the experiments shows that as far as PM1, PM2.5, PM10, and CO are concerned, the air quality in the village is good. The results also showed that some LoRaWAN messages were lost and never received on Yggio.

Deterioration of air quality is a growing concern in the world. Air pollution causes serious health problems and also can sometimes result in death. In order to assess air quality, long term and continuous monitoring of pollutant levels in ambient air are needed, such monitoring is often expensive, cumbersome, and resource intensive and so the monitoring programs often fail to succeed. This research focused on designing an ambient air monitoring system by integrating (1) low-cost sensor with a battery, (2) repurposed materials to fabricate all-weather housing for air monitors, and (3) electronics needed to download the data to an on-site secure digital (SD) card, and to push the data wirelessly to the server. This monitoring system was tested at the selected locations in Harvey and Marrero Wastewater treatment plants (WWTPs) by monitoring hydrogen sulfide (H2S) levels. Preliminary analysis was done for few days and also, the results were analyzed.

Beginning in March 1999 at Georgia Institute of Technology, the ASACA (Assessment of Spatial Aerosol Composition in Atlanta) program has provided PM₂. ₅ concentration and speciation using particle concentration monitoring in and around metropolitan Atlanta. Since 1999, three of the ASACA sites have collected PM₂. ₅ in an urban setting: Fort McPherson (FT, SW), South Dekalb (SD, SE), and Tucker (TU, NE). In January 2007, TU was retired and Fire Station 8 (FS8, NE) was employed as the new urban site. Starting in 2002, PM₂. ₅ concentrations have also been characterized at a rural site, Fort Yargo (YG). Water-soluble ionic species and carbonaceous species concentrations are collected daily on filters using a three-channel particulate composition monitor (PCM). From 1999 to 2008, average PM₂. ₅ concentrations range from 12.9 µg/m3 at YG to 15.4 µg/m3 at TU. Sulfate and organic matter are the main components of Atlanta PM, contributing around 26% and 31% respectively to PM mass. Overall ASACA data quality increased from around 5 in 1999 to a value of 9 in 2005. Seasonal PM data quality appears to be significantly affected by volatility of secondary aerosol species during warm months because ionic data quality regularly decreases in the summer. PM is more sensitive to total sulfate concentration than nitrate and ammonia year-round.

Air Quality Monitoring Network (AQMN) is an essential tool to monitor and control atmospheric pollution. The use of mathematical models to predict spatial and temporal dispersion of pollutants within the atmosphere provides a general approach to determine the optimal number and location of monitoring sites in selected areas. The aim of this research work has been to develop an optimisation model to support the environmental decision-making process by optimally locating air pollution monitoring sites in urban areas with specific reference to the state of Kuwait. However, the design methodology has been based on generic concepts applicable to other areas. The experimental data collected for the year 2001 from the existing AQMN for the air pollutants, i.e. sulphur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3) and non-methane hydrocarbon (NMHC), are used to calculate their diurnal and seasonal variations. Distribution levels of the pollutants concentrations relative to: meteorological data, such as wind speed and wind direction with a special attention to, SO2, were also evaluated and analysed to establish the effects of weather conditions in the dispersion of the pollutants. As this research has revealed, for the first time, the levels of NOx and NMHC exceeded the allowable ambient limits most of the time over the residential areas of Kuwait. In addition, it has been found that the highest concentrations levels of SO2 at the monitoring sites were attributed to the fossil fuels used in power plants.

Les réseaux de capteurs sans fil (RCSF) sont largement utilisés dans les applications environnementales où l’objectif est de détecter un phénomène physique tel que la température, l’humidité, la pollution de l’air, etc. Dans ce contexte d’application, l’utilisation de RCSF permet de comprendre les variations du phénomène et donc être en mesure de prendre des décisions appropriées concernant son impact. En raison des limitations de ses méthodes de suivi traditionnelles et de sa grande variabilité spatiale et temporelle, la pollution de l'air est considérée comme l'un des principaux phénomènes physiques qui restent à étudier et à caractériser. Dans cette thèse, nous considérons trois applications concernant l’utilisation de RCSF pour le suivi de la pollution de l’air : la cartographie en temps réel de la qualité de l’air, la détection de dépassements de seuils des polluants et la correction de modèles physiques qui simulent le phénomène de dispersion de la pollution. Toutes ces applications nécessitent de déployer et d’ordonnancer minutieusement les capteurs afin de mieux comprendre la pollution atmosphérique tout en garantissant un coût de déploiement minimal et en maximisant la durée de vie du réseau. Notre objectif est de résoudre les problèmes de déploiement et d'ordonnancement tout en tenant compte des caractéristiques spécifiques du phénomène de la pollution de l’air. Nous proposons pour chaque cas d'application une approche efficace pour le déploiement de noeuds capteurs et puits. Nous proposons également une approche d’ordonnancement adaptée au cas de la correction de modèles physiques. Nos approches d'optimisation prennent en compte la nature physique de la pollution atmosphérique et intègrent les données réelles fournies par les plateformes existantes de suivi de la qualité de l’air. Dans chacune de nos approches d’optimisation, nous utilisons la programmation linéaire en nombres entiers pour concevoir des modèles d’optimisation adaptés à la résolution de petites et moyennes instances. Pour traiter les grandes instances, nous proposons des heuristiques en utilisant des techniques de relaxation linéaire. Outre nos travaux théoriques sur le suivi de la pollution atmosphérique, nous avons conçu et déployé dans la ville de Lyon un réseau de capteurs de pollution économe en énergie. Sur la base des caractéristiques de notre système et des jeux de données de la pollution atmosphérique, nous avons évalué l’efficacité de nos approches de déploiement et d’ordonnancement. Nous présentons et discutons dans cette thèse les résultats d'évaluation de performances ainsi que des lignes directrices pour la conception de systèmes de suivi de la pollution de l’air. Parmi nos principales conclusions, nous soulignons le fait que la taille optimale du réseau de capteurs dépend du degré de variation des concentrations de pollution dans la région de déploiement
Wireless sensor networks (WSN) are widely used in environmental applications where the aim is to sense a physical phenomenon such as temperature, humidity, air pollution, etc. In this context of application, the use of WSN allows to understand the variations of the phenomenon over the monitoring region and therefore be able to take adequate decisions regarding the impact of the phenomenon. Due to the limitations of its traditional costly monitoring methods in addition to its high spatial and temporal variability, air pollution is considered as one of the main physical phenomena that still need to be studied and characterized. In this thesis, we consider three main applications regarding the use of WSN for air pollution monitoring: 1) the construction of real time air quality maps using sensor measurements; 2) the detection of pollution threshold crossings; and 3) the correction of physical models that simulate the pollution dispersion phenomenon. All these applications need careful deployment and scheduling of sensors in order to get a better knowledge of air pollution while ensuring a minimal deployment cost and a maximal lifetime of the deployed sensor network. Our aim is to tackle the problems of WSN deployment and scheduling while considering the specific characteristics of the air pollution phenomenon. We propose for each application case a new efficient approach for the deployment of sensor and sink nodes. We also propose a WSN scheduling approach that is adapted to the case of physical models’ correction. Our optimization approaches take into account the physical nature of air pollution dispersion and incorporate real data provided by the existing pollution sensing platforms. As part of each approach, we use integer linear programming to derive optimization models that are well adapted to solving small and medium instances. To deal with large instances, we propose heuristic algorithms while using linear relaxation techniques. Besides our theoretical works on air pollution monitoring, we design from scratch and deploy in the Lyon city a cost-effective energy-efficient air pollution sensor network. Based on the characteristics of our monitoring system in addition to real world air pollution datasets, we evaluate the effectiveness of our deployment and scheduling approaches and provide engineering insights for the design of WSN-based air pollution monitoring systems. Among our conclusions, we highlight the fact that the size of the optimal sensor network depends on the degree of the variations of pollution concentrations within the monitoring region

A timed exposure diffusive sampler (TEDS) sensitive enough to measure hourly averages of nitrogen dioxide (NO2) at low parts per billion levels was developed for use in epidemiological studies. TEDS can be used for a variety of atmospheric pollutants. TEDS was tested in the laboratory against known concentrations of NO2 and against well established NO2 measurement systems. It was then tested in the field against the Environmental Protection Agency's reference method for measuring NO2. The TEDS method appears well suited to epidemiological investigations of air quality and extends previous work in this area by offering improved time resolution of changes in pollutant concentration at a cost sufficiently low to permit its use in large scale studies. The TEDS method also shows potential for miniaturization for use in personal sampling.

The need for a green clean living environment is increasing today, with the boom of the socioeconomic development, educational level. However, the environmental pollution becomes an alerted global issue due to the large amount of wastes discharged making this need to be not easily met at the moment. Greenhouse gas emission mainly from energy, transport and agricultural land use is causing climate change because of their long atmospheric lifetime and trapping the heat in the atmosphere. Harmful effects and damages caused by environment pollution and climate change are unpredictable. It was reported that every year millions of people die because of fine particles when exposing to air pollution and other millions die from water-born diseases. Management and monitoring of air and water pollution by using GIS technology is an effective method. The measured data can be obtained continuously, quickly and accurately at stations in any regions even with complex terrain. This helps reduce the required number of employees, manage automatically and continuously a large number of data
Ngày nay nhu cầu về một môi trường sống xanh, sạch đang gia tăng, với sự bùng nổ của phát triển kinh tế - xã hội và trình độ dân trí. Tuy nhiên, ô nhiễm môi trường đang trở thành một vấn đề cảnh báo toàn cầu do số lượng lớn các chất thải được xả ra môi trường làm cho nhu cầu này không dễ dàng được đáp ứng tại thời điểm này. Phát thải khí nhà kính chủ yếu là từ sử dụng năng lượng, giao thông vận tải và đất nông nghiệp đang gây ra biến đổi khí hậu vì thời gian tồn tại của cúng dài và giữ nhiệt trong khí quyển. Các ảnh hưởng xấu và thiệt hại gây ra bởi ô nhiễm môi trường và biến đổi khí hậu là không thể đoán trước. Thông tin báo cáo chỉ ra rằng mỗi năm có hàng triệu người chết vì hít các hạt bụi mịn khi tiếp xúc với ô nhiễm không khí; và hàng triệu người khác chết vì bệnh do nước sinh ra. Quản lý và giám sát ô nhiễm không khí và nước bằng cách sử dụng công nghệ GIS là một phương pháp hiệu quả. Các dữ liệu đo có thể được lấy liên tục, nhanh chóng và chính xác tại các trạm ở bất kể khu vực nào, ngay cả nơi có địa hình phức tạp. Điều này giúp làm giảm số lượng lao động cần thiết, quản lý tự động và liên tục một số lượng lớn dữ liệu

Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to effectively monitor urban air quality at reasonable cost and to develop long and short term air pollution prediction models. The aim of the research described was to investigate relationships between traffic characteristics and kerbside air pollution concentrations. Initially, the only pollution monitoring equipment available was basic and required constant supervision. The traffic data was made available from the demand-responsive traffic signal control systems in Leicestershire and Nottinghamshire. However, it was found that the surveys were too short to produce statistically significant results, and no useful conclusions could be drawn. Subsequently, an automatic, remote kerbside monitoring system was developed specifically for this research. The data collected was analysed using multiple regression techniques in an attempt to obtain an empirical relationship which could be used to predict roadside pollution concentrations from traffic and meteorological data. However, the residual series were found to be autocorrelated, which meant that the statistical tests were invalid. It was then found to be possible to fit an accurate model to the data using time series analysis, but that it could not predict levels even in the short-term. Finally, a semi-empirical model was developed by estimating the proportion of vehicles passing a point in each operating mode (cruising, accelerating, decelerating and idling) and using real data to derive the coefficients. Unfortunately, it was again not possible to define a reliable predictive relationship. However, suggestions have been made about how this research could be progressed to achieve its aim.

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.

Cache County and the Wasatch Front, Utah have persistently experienced some of the nation’s worst air quality over the past decade. Elevated PM2.5 concentrations during wintertime “red air day” episodes frequently exceed the National Ambient Air Quality Standards (NAAQS). We investigate the possible effects of two different economic policies in controlling these regional problems. Adapting a model originally developed to calculate the social investment necessary to control nationwide disease outbreaks, we estimate an optimal preventative capital stock (for example, investment in public transportation) of between $4.1 million and $14.1 million to control red air day episodes in Cache County, and $133 million to $1.6 billion dollars to control such episodes in the Wasatch Front. Further, we find that a seasonal gasoline tax rate of roughly $8 per gallon is necessary for policy makers in the Wasatch Front to impose at the pump if their goal is to maintain concentrations below the NAAQS on average during a typical winter-inversion season. This rate is roughly $2 more than the rate calculated for Cache County in a previously published study.

Thesis (M. S.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2006.
Mulholland, James, Committee Chair ; Russell, Armistead, Committee Member ; Tolbert, Paige, Committee Member.

Abstract Branches of adult Scots pine (Pinus sylvestris L.) trees were sampled from boreal dry pine forests to study needle longevity, its variation and its relation to other shoot characteristics. The stands studied were located along transects from two smelters and one city, e.g. along gradients of pollution impact. Constant needle age structures were assumed and static life-tables generated. Mean needle longevity was calculated as the sum of the proportions of living needle fascicles on the successive annual shoots. It fully incorporated the information of the static life-tables and was preferred to median or maximum ages because of its significantly lower variation. The first half of the shedding span, the duration of the period when the needle survival gradually dropped from 90 to 50 %, proved to validly reflect the changes in the needle age structure. Needle longevity decreased 15-40 % towards the pollution sources along the transects studied. Near the smelters, needle longevity decreased with the increasing needle Fe, or Fe, Ni and Cu concentration that represented the main constituents of the airborne particle emissions. Near the city, needle longevity decreased with the increasing needle N and P concentrations, annual needle mass and needle mass packing and decreasing needle area packing. In the urban forests, needle Mg, P and K concentrations decreased linearly with the decreasing needle survival from the second to the fourth needle age class. Concentrations in the living needles of the fourth age class stayed over 80 % of the average for all the age classes, though needle survival dropped below 50 %. A decreasing needle Mn concentration was detected towards all the emission sources. Leaching, especially from the soil, as a possible cause was discussed. Needle longevity had the lowest variation among the shoot characteristics, which increases its value as a tool in ecological monitoring. Low plasticity in needle longevity could be an acclimation to the ambient environmental conditions and length of the growing season and to maximise the carbon gain per time. Needle longevity decreased and annual needle mass and leaf mass per area increased upwards in the crowns of mature Scots pines, reflecting the acclimation to irradiance.

In the mid-1800s, monitoring networks were established to investigate atmospheric composition impacts, and the conditions giving rise to them. The development of these networks, in terms of coordination and standardisation between contributing sites, has resulted in large advances in knowledge of the nature of atmospheric composition. Currently thousands of sites collect high quality atmospheric composition measurements globally. This thesis contends that in order to maximise the information derived from these measurements, a further advancement in standardisation is required to encompass the interpretation of monitoring network data. Currently there are limited examples of a common interpretation of data applied across all sites in a monitoring network, especially in relation to specific atmospheric composition impacts. In this thesis, a ‘chemical climatology’ framework is outlined which provides a common basis for targeting analysis towards identifying the linkage between a specific atmospheric composition impact and its causal drivers. Case studies apply the chemical climatology framework to demonstrate its utility in deriving scientific and policy relevant conclusions using measurement data from the UK monitoring supersites located at Harwell and Auchencorth. Prior to this, the representativeness of each site is quantified through the application of cluster analysis to ozone data at 100 rural European sites to identify groupings of sites with similar ozone variation. Harwell was representative of rural locations within 120 km of London, while Auchencorth was representative of a larger, transboundary spatial domain including the remainder of the rural UK. The first case study links the impact of ozone on human health (quantified by SOMO10 and SOMO35 metrics) and vegetation (flux-based PODY) to meteorological and emissions drivers. Between 1990 and 2013 at Harwell, there was a significant decrease in the contribution of European ozone to determining the impacts. Improvement in the human health impact was heavily dependent on the choice of metric (SOMO35 decreased, no change in SOMO10), and the vegetation impacts had not improved as high ozone episodes frequently coincided with plant conditions which reduced ozone uptake. These chemical climates emphasise the need for ozone mitigation on larger (hemispheric) scales than currently implemented. Secondly, the impact of 27 measured VOCs on the extent of the regional ozone increment is assessed. The photochemical loss of VOCs is then linked to reported gridded VOC emissions using air mass back trajectory analysis. Ethene and m+p-xylene had the largest diurnal photochemical loss during maximum monthly regional ozone increment, but the key conclusion was the limitation introduced through the reporting of gridded VOC emissions in heavily aggregated source sectors. Finally, the conditions producing the long term health impact of particulate matter (quantified by annual average PM10 and PM2.5 concentrations) at each site are derived through integration of measurements of PM10 and PM2.5 with measurements of PM constituents. It is shown that the frequent, moderate PM10 and PM2.5 concentrations made a larger contribution to annual average values compared to the relatively infrequent high, episodic concentrations. The contribution of PM constituents and the contribution of local vs regional emissions to the range of PM concentrations is investigated. It was concluded that similar reductions in the contribution of secondary inorganic aerosol to the moderate PM10 and PM2.5 concentrations could be achieved from both the reduction of frequently traversed, smaller emissions sources, and less frequently traversed, larger emissions sources. The final chapter demonstrates the benefits from the extension of this framework to an entire monitoring network. It is envisioned that for each atmospheric composition impact, a standard set of statistics would be calculated which quantify the ‘impact’, ‘state’ and ‘drivers’ of that chemical climate. Calculation of ozone human health chemical climates across 100 European monitoring sites demonstrate this concept. This standardised interpretation of monitoring network data not only allows consistent comparison of an impact, but the common basis for determining how the impact is derived allows for the consideration of novel mitigation strategies and their spatial applicability.

The ceramic glazing techniques of vapour glazing, reduction lustre, Raku and fuming all require specialised firing conditions with the use of potentially hazardous kiln atmospheres and specialist kiln designs for their successful execution. The technique of reduction lustre is an ancient, highly decorative technique in which pigments or glazes containing reducible metal oxides such as copper, silver and bismuth are subjected to a reducing atmosphere which results in the formation of stable iridescent lustrous colour effects. Conventionally hydrocarbons are used to produce a reducing atmosphere, the combustion of which can lead to potentially high levels of CO. This research has concentrated on the technique of reduction lustre with the aim of producing a safe, environmentally friendly firing system. Reduction lustre effects were reproduced using a 100 litre down draft gas kiln designed and constructed for the purpose. A 40 litre electric kiln was modified for use with reducing atmospheres and a laboratory muffle kiln was also adapted to provide closely controlled firing conditions. Alternative reducing atmospheres were assessed, consisting of either 5% H2 in N2 or hydrocarbon vapour in N2 The former is reliable, safe, environmentally friendly and is recommended for studio pottery use. A theoretical design for a reduction lustre kiln incorporating a gas control system based on the use of a 5% H2 in N2 gas mixture and a gas tight outer casing was developed. Thermoanalytical methods were used to investigate the reduction behaviour of raw materials, glazes and frits under different atmospheric conditions. Diffusional and topochemical models of reduction reactions occurring in a typical lustre glaze have been evaluated. In-glaze lustres reduce with thecharacteristics of diffusion processes. Water diffusion out of the glaze may be rate determining. Studies carried out using energy dispersive x-ray analysis andphoto electron spectroscopy show that lustres are associated with the presence of metallic copper, the actual colour being dependent on the surface concentration of the metal. The aesthetic results of the various firing methods developed were assessed using a perceptual study based on the use of a semantic differential test developed from a multiple sorting survey carried out using lustred tiles. The survey used both lustred tiles to represent a 2-dimensional surface and lustred vases which represented a 3-dimensional lustre glazed surface. The survey showed that statistically significant differences were observed between lustred ceramics fired in different systems, allowing comparisons to be made in an objective manner.

Problems associated with instruments used to measure low levels of NO₂ include non-specificity, low sensitivity, and an uncharacterized dependence on environmental conditions. MDA Scientific has recently introduced a continuous colorimetric tape technique (chemcassette) to monitor for pollutant gases. Advantages of this technology include ease of use, fast-response alarm capability, and adaptability to measure different pollutants. This research characterized and compared chemcassette performance for NO₂ with other low-level NO₂ monitoring technologies including the luminox LMA-3 monitor and the EPA reference CSI chemiluminescnt monitor. The test protocol was based on EPA procedures. The chemcassette was found to be sensitive to temperature and relative humidity effects. The chemiluminescent monitor had the best overall performance. The luminox monitor displayed temperature dependence. Variation in stability of the chemcassette optical system, tape paper uniformity, and possible interference from ozone were characterized. (Abstract shortened with permission of author.)

According to World Health Organization, around 7 million people die every year due to diseases caused by air pollution. With the improvements in Internet of Things in the recent years, environmental sensing systems has started to gain importance. By using technologies like Cloud Computing, RFID, Wireless Sensor Networks, and open Application Programming Interfaces, it has become easier to collect data for visualization on different platforms. However, collected data need to be represented in an efficient way for better understanding and analysis, which requires design of data visualization tools. The GreenIoT initiative aims to provide open data with its infrastructure for sustainable city development in Uppsala. An environmental web application is presented within this thesis project, which visualizes the gathered environmental data to help municipality organizations to implement new policies for sustainable urban planning, and citizens to gain more knowledge to take sustainable decisions in their daily life. The application has been developed making use of the 4Dialog API, which is developed to provide data from a dedicated cloud storage for visualization purposes. According to the evaluation presented in this thesis, further development is needed to improve the performance to provide faster and more reliable service as well as the accessibility to promote openness and social inclusion.
Enligt World Health Organization dör 7 miljoner människor varje år på grund av sjukdomar orsakade av luftföroreningar. Med förbättringar inom Internet of Things under senare år, har betydelsen av system för miljösensorer. Genom att använda tekniker som molntjänster, RFID, trådlösa sensornätverk och öppna programmeringsgränssnitt, har det blivit enklare att samla in data för visualisering på olika plattformar. Men insamlad data behöver bli representerad på ett effektivt sätt för bättre förståelse och analys, vilket kräver utformande av verktyg för visualisering av data. Initiativet GreenIoT strävar mot att erbjuda öppen data med sin infrastruktur för hållbar stadsutveckling i Uppsala. I detta arbete presenteras en webb-tillämpning, som visualiserar den insamlade miljödatan för att hjälpa kommunen att implementera nya policies för hållbar stadsutveckling, och stimulera medborgare till att skaffa mer kunskap för att göra miljövänliga val i sin vardag. Tillämpningen har utvecklats med hjälp av 4Dialog API, som tillhandahåller data från lagring i molnet för visualiseringssyfte. Enligt den utvärdering som presenteras i denna rapport konstateras att vidare utveckling behövs för att förbättra dels prestanda för att erbjuda en snabbare och mer tillförlitlig service, och dels åtkomstmöjligheter för att främja öppenhet och social inkludering.

Some suburbs within North West (NW) metropolitan Adelaide have lung cancer mortality up to twice that expected from state averages. Previous international research investigating high lung cancer rates in similar shared industrial and residential areas have had inconsistent results. This case control study was conducted to determine whether residential exposure to industry is a risk factor for lung cancer in NW Adelaide. Contemporary ambient air monitoring was undertaken as an indicator of future respiratory health risk. 142 lung cancer patients and 415 age, gender matched population controls were interviewed utilising an event history calendar. Lifetime exposure indices were calculated for cigarette smoking, passive smoking, occupation, air pollution (residential proximity to industry) and hobbies. Data was analysed utilising chi- quared and conditional logistic regression. Ambient carcinogens and fine particulates with potential industrial sources in the region were monitored in five locations. In the final multivariate model leaving school early, pack- years of cigarettes and not living in close proximity to the power station or light industrial area were statistically significant risk factors for lung cancer. A composite score of residential exposure to all industries was not significant. However cautious interpretation is required as it was noted participating controls resided significantly closer to industry than non-participants. Average concentrations of ambient carcinogens were within guidelines; however diesel exhaust particulate and Polycyclic Aromatic Hydrocarbons were elevated at sites in proximity to heavy vehicle traffic. Diurnal variations in PM[subscript 2.5] included weather and traffic-related short term peaks, and other peaks potentially related to industrial activity. Cigarette smoking is likely to be the primary cause of elevated lung cancer mortality in suburbs of NW Adelaide. The negative effect of residential exposure to two industries may be due to participation bias. Whilst having more thorough exposure assessment than previous research, this study may have been limited by low participation rates in cases and controls. Air monitoring data suggests there is not a significant public health risk at present; however these results are unlikely to be indicative of historical exposures. Future public health initiatives to curb high lung cancer mortality in the NW should focus on smoking prevention and reduction strategies.
Thesis (Ph.D.)--Department of Medicine and Department of Public Health, 2004.

Denton, Texas consistently reaches ozone nonattainment levels. This has led to a large focus of air pollution monitoring efforts in the region, with long-range transport being explored as a key contributor. For this study, the University of North Texas Discovery Park campus was chosen as a prospective location for an extensive air quality monitoring station. Sixteen years of ozone and meteorological data for five state-run monitoring sites within a 25 mile radius, including the nearest Denton Airport site, was gathered from TCEQ online database for the month of April for the years 2000 to 2015. The data was analyzed to show a historical, regional perspective of ozone near the proposed site. The maximum ozone concentration measured at the Denton Airport location over the 16 year period was measured at 96 ppb in 2001. Experimental ozone and meteorological measurements were collected at the Discovery Park location from March 26 to April 3 and April 8 to April, 2016 and compared to the Denton Airport monitoring site. A time lag in ozone trends and an increase in peak ozone concentrations at the proposed location were observed at the proposed site in comparison to the Denton Airport site. Historical and experimental meteorological data agreed in indicating that southern winds that rarely exceed 20 miles per hour are the predominant wind pattern. Back trajectories, wind roses, pollution roses, and bivariate plots created for peak ozone days during experimental periods support long range transport as a considerable cause of high ozone levels in Denton. Furthermore, a study of the precursor characteristics at the Denton Airport site indicated the site was being affected by a local source of nitrogen dioxide that was not affecting the proposed location. The differences in the Denton Airport site and the proposed site indicate that further monitoring at Discovery Park would be insightful. An outline of an expansive mobile monitoring station and suggestions for effective utilization are provided to guide future studies in Denton and the surrounding North Texas region.

The fibrous aerosol monitor (FAM) was designed to count fibrous particulates as accurately as the standard method, P & CAM 239, now NIOSH 7400. The Hi-Vol (HV) method, a modification of the standard method, was developed to collect fibers over a shorter time interval (i.e., 10-minutes) with the same accuracy as the standard method. The current study sought to compare the HV method with the FAM to assess its validity in measuring airborne asbestos levels and to assess the importance of short-term variation in asbestos levels in determining appropriate respiratory protection. Correlation between the FAM and HV showed a poor linear relationship in all comparison studies. However, there was good correlation between the average of short-term HV samples and long-term time-weighted average (TWA) samples at reported low levels of airborne asbestos. All membrane filter techniques showed strong correlation with each other, but were weakly correlated with the FAM. (Abstract shortened with permission of author.)

Predmet istraživanja ove disertacije je monitoring i analiza ultraljubičastog zračenja, toplotnih indeksa i zagađujućih materija u vazduhu većih naselja na teritoriji Vojvodine. Cilj istraživanja je da se prostornom i vremenskom analizom posmatranih parametara dođe do što optimalnijih saznanja o stanju kvaliteta životne sredine na području Vojvodine kao i stvaranje jedinstvene baze podataka koja će služiti za buduća istraživanja. U radu je ispitan odnos između sunčevog globalnog i UV zračenja, izvedena i verifikovana empirijska jednačina za procenu dnevnih suma UV-B zračenja u osam naselja za period 1981-2008, analizirane koncentracije pet zagađujućih materija u sedam naselja za period 2001-2008. godina i analizirani toplotni indeksi u sedam naselja u periodu od 1992. do 2008. Analiza pokazuje da rekonstruisane vrednosti dnevnih suma UV-B zračenja i srednji godišnji toplotni indeksi u svim naseljima pokazuju tendenciju rasta i da dominantan uticaj na kvalitet vazduha u naseljima Vojvodine imaju čestice. Napravljena je jedinstvena baze podataka sa vrednostima koncentracija zagađivača vazduha, toplotnih indeksa, UV i globalnog zračenja.
This study analyzes UV radiation, heat indexes and air pollution in town in Vojvodina region. Objective of the thesis is to provide scientific facts about environmental quality in Vojvodina, as well as, to generate unique data base for the future research. The relationship between UV-B and global radiation has been studied and correlation equation for estimating UV-B from global radiation has been deduced. Equation was used for UV-B radiation calculation in eight towns in Vojvodina region during the period 1981-2008. This study also analyzes concentrations of five air pollutant in during the period 2001-2008, and heat index during the period 1992-2008. in seven towns in Vojvodina. The results from this study show growth UV-B radiation and heat index in all towns, and denote particulate maters as main air pollutants in Vojvodina towns.