Relevant bibliographies by topics / Mobile air pollution monitoring

Academic literature on the topic 'Mobile air pollution monitoring'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Mobile air pollution monitoring"

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Lihui Lv, Lihui Lv, Wenqing Liu Wenqing Liu, Guangqiang Fan Guangqiang Fan, Tianshu Zhang Tianshu Zhang, Yunsheng Dong Yunsheng Dong, Zhenyi Chen Zhenyi Chen, Yang Liu Yang Liu, Haoyun Huang Haoyun Huang, and and Yang Zhou and Yang Zhou. "Application of mobile vehicle lidar for urban air pollution monitoring." Chinese Optics Letters 14, no. 6 (2016): 060101–60106. http://dx.doi.org/10.3788/col201614.060101.

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Adams, Matthew, and Denis Corr. "A Mobile Air Pollution Monitoring Data Set." Data 4, no. 1 (December 22, 2018): 2. http://dx.doi.org/10.3390/data4010002.

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Air pollution was observed in Hamilton, Ontario, Canada using monitors installed in a mobile platform from November 2005 up to November 2016. The dataset is an aggregation of several project specific monitoring days, which attempted to quantify air pollution spatial variation under varying conditions or in specific regions. Pollutants observed included carbon monoxide, nitric oxide, nitrogen dioxide, total nitrogen oxides, ground-level ozone, particulate matter concentrations for size cuts of 10 µm, 2.5 µm and 1 µm, and sulfur dioxide. Observations were collected over 114 days, which occurred in varying seasons and months. During sampling, the mobile platform travelled at an average speed of 27 km/h. The samples were collected as one-minute integrated samples and are prepared as line-segments, which include an offset for instrument response time. Sampling occurred on major freeways, highways, arterial and residential roads. This dataset is shared in hopes of supporting research on how to best utilize air pollution observations obtained with mobile air pollution platforms, which is a growing technique in the field of urban air pollution monitoring. We conclude with limitations in the data capture technique and recommendations for future mobile monitoring studies.
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Talib, Aya Mazin, and Mahdi Nsaif Jasim. "Geolocation based air pollution mobile monitoring system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 162. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp162-170.

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Air pollution is conducted to harmful substances like solid particles, gases or liquid droplets. More pollutants CO, SO2, NOx, CO2.This research is proposed the design and implementation of mobile, low cost and accurate air pollution monitoring system using Arduino microcontroller and gas sensor like MQ2, MQ131, MQ135, MQ136, DHT22, measuring materials mentioned above, smoke, Acetone, Alcohol, LPG, Toluene, temperature, humidity and GPS sensor”NEO-6M” that track the location of air pollution data, and display the analysis result on ESRI maps. The system also save the results on SQL server DB. The data is classified using data mining algorithms, presenting the result on a map helps governmental organizations, nature guards, and ecologists to analyze data in real time to simplify the decision making process. The proposed system uses J48 pruning tree classifier generated using cross validation of fold (10) with highest accuracy 100%, while IBK ≈99.67, Naïve bays ≈90.89, and SVM ≈81.4. It’s found that the common air quality for Baghdad (study area) is between (“Good”, “Satisfactory”, and “Moderately”) for 1835 records of air samples during (January and February 2021) time period.
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Nance, Earthea. "Monitoring Air Pollution Variability during Disasters." Atmosphere 12, no. 4 (March 25, 2021): 420. http://dx.doi.org/10.3390/atmos12040420.

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National environmental regulations lack short-term standards for variability in fine particulate matter (PM2.5); they depend solely on concentration-based standards. Twenty-five years of research has linked short-term PM2.5, that is, increases of at least 10 μg/m3 that can occur in-between regulatory readings, to increased mortality. Even as new technologies have emerged that could readily monitor short-term PM2.5, such as real-time monitoring and mobile monitoring, their primary application has been for research, not for air quality management. The Gulf oil spill offers a strategic setting in which regulatory monitoring, computer modeling, and stationary monitoring could be directly compared to mobile monitoring. Mobile monitoring was found to best capture the variability of PM2.5 during the disaster. The research also found that each short-term increase (≥10 μg/m3) in fine particulate matter was associated with a statistically significant increase of 0.105 deaths (p < 0.001) in people aged 65 and over, which represents a 0.32% increase. This research contributes to understanding the effects of PM2.5 on mortality during a disaster and provides justification for environmental managers to monitor PM2.5 variability, not only hourly averages of PM2.5 concentration.
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Coutrier, P. I., Saut M. Lubis, and Noegroho Hadi. "Air Quality Monitoring and Strategy in Indonesia." Scientific Contributions Oil and Gas 17, no. 1 (April 4, 2022): 2–11. http://dx.doi.org/10.29017/scog.17.1.884.

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BAPEDAL, The Environment Impact Management Agency is responsible for the air pollution control. In addressing the air pollution BAPEDAL launched the "Blue Sky Program". This program consist of two component, air pollution from the mobile sources and the air pollution from stationary sources.
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Khozouie, Nasim, and Faranak Fotouhi-Ghazvini. "Air pollution monitoring By sensors embedded on mobile phone." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 5 (October 30, 2013): 2628–33. http://dx.doi.org/10.24297/ijct.v11i5.1148.

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Mobile technology has been available for at least a decade and is increasingly being used in developing countries as away of contacting and connecting citizens and helping them to organize for a better life.Mobile phones are not just for phone calls, but they can also be used to collect data in several different formats and send them to a central server. There the data can be aggregated and analyzed, with tables and visualizations automatically generated. What is new is the sheer number of observation points that are potentially available by using mobile phones. With over 4 billion phones in use worldwide, the mobile phone network is emerging as a form of “global brain” with sensors everywhere. In addition, there are companies such as Fourier Systems that provide purpose-built mobile devices that are specifically designed for science experiments in school sand for data logging in any science project.
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Haus, R., K. Schäfer, W. Bautzer, J. Heland, H. Mosebach, H. Bittner, and T. Eisenmann. "Mobile Fourier-transform infrared spectroscopy monitoring of air pollution." Applied Optics 33, no. 24 (August 20, 1994): 5682. http://dx.doi.org/10.1364/ao.33.005682.

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Marć, Mariusz, Bożena Zabiegała, and Jacek Namieśnik. "Mobile Systems (Portable, Handheld, Transportable) for Monitoring Air Pollution." Critical Reviews in Analytical Chemistry 42, no. 1 (January 2012): 2–15. http://dx.doi.org/10.1080/10408347.2011.607079.

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Al-Ali, A. R., Imran Zualkernan, and Fadi Aloul. "A Mobile GPRS-Sensors Array for Air Pollution Monitoring." IEEE Sensors Journal 10, no. 10 (October 2010): 1666–71. http://dx.doi.org/10.1109/jsen.2010.2045890.

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Shakhov, Vladimir, Andrei Materukhin, Olga Sokolova, and Insoo Koo. "Optimizing Urban Air Pollution Detection Systems." Sensors 22, no. 13 (June 24, 2022): 4767. http://dx.doi.org/10.3390/s22134767.

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Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.
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Dissertations / Theses on the topic "Mobile air pollution monitoring"

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Alvear, Alvear Óscar Patricio. "Mobile Sensing Architecture for Air Pollution Monitoring." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/107928.

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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
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Meyer, Peter. "Air-pollution monitoring with a mobile CO₂-laser photoacoustic system /." Zürich, 1988. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=8651.

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Smith, Jeffrey Paul. "AirSniffer: A Smartphone-Based Sensor Module for Personal Micro-Climate Monitoring." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc849691/.

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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.
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Zamora, Mero Willian Jesús. "Crowdsensing solutions for urban pollution monitoring using smartphones." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/115483.

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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
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5

Wright, Monica Elizabeth. "An Investigation of Urban Mobile Source Aerosol Using Optical Properties Measured by CRDT/N: Diesel Particulate Matter and the Impact of Biodiesel." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/673.

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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.
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Rodriguez, Delphy. "Caractérisation de la pollution urbaine en Île-de-France par une synergie de mesures de surface et de modélisation fine échelle." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS341.

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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
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Поленкова, М. В. "Оцінка стану атмосферного повітря чернігівського регіону та заходи щодо його покращення." Thesis, Чернігів, 2020. http://ir.stu.cn.ua/123456789/21055.

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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.
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8

Lythe, Matthew Steven. "Spatial aspects of regional air pollution monitoring." Thesis, University of Surrey, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431117.

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江顯其 and Hin-kee Kong. "Air pollution impacts as indicated by roadside air quality monitoring stations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B3125424X.

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Lelas, Vedran. "Chance constrained models for air pollution monitoring and control /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Books on the topic "Mobile air pollution monitoring"

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Alberta. Air Issues and Monitoring Branch. Mobile monitoring survey of the Bow Corridor: March 2 and 12, 1994. [Edmonton]: Alberta Environmental Protection, 1995.

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Mulhern, Martin R. User's guide for the SERDP mobile meteorological monitoring system. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Environmental Technology Laboratory, 1998.

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Mulhern, Martin R. User's guide for the SERDP Mobile Meteorological Monitoring System. [Washington, D.C.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1998.

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G, Clarke Andrew, ed. Industrial air pollution monitoring. London: Chapman & Hall, 1998.

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Clarke, Andrew G., ed. Industrial Air Pollution Monitoring. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-1435-3.

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1948-, Sigrist Markus W., ed. Air monitoring by spectroscopic techniques. New York: Wiley, 1994.

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McCormick, John. Urban air pollution. Nairobi: UNEP, 1991.

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Queiroz, M. V. G. Air pollution monitoring in urban environment. Manchester: UMIST, 1993.

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John, Delaney. Air quality monitoring: Annual report. Johnstown Castle, Co. Wexford: Environmental Protection Agency, 2003.

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Office, General Accounting. Air pollution: Reliability of EPA's mobile source emission model could be improved : report to the Chairman, Subcommittee on Oversight and Investigations, Committee on Energy and Commerce, House of Representatives. Washington, D.C: The Office, 1990.

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Book chapters on the topic "Mobile air pollution monitoring"

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Kaur, Amritpal, and Jeff Kilby. "Wireless Sensor Networks (WSNs) in Air Pollution Monitoring: A Review." In Intelligent Communication Technologies and Virtual Mobile Networks, 745–58. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1844-5_59.

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Meyer, P. L., St Bernegger, and M. W. Sigrist. "Air Pollution Monitoring with a Mobile CO2 Laser Photoacoustic System." In Monitoring of Gaseous Pollutants by Tunable Diode Lasers, 46–50. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3991-2_6.

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Tiwary, Abhishek, and Ian Williams. "Mobile sources." In Air Pollution, 163–228. Fourth edition. | Boca Raton : CRC Press, 2018. | Earlier editions written by Jeremy Colls.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429469985-5.

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Knox, Andrew, Greg J. Evans, Colin J. Lee, and Jeffrey R. Brook. "Air Pollution air pollution Monitoring air pollution monitoring and Sustainability air pollution sustainability." In Encyclopedia of Sustainability Science and Technology, 167–203. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_373.

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Cape, J. N. "Air pollution." In Sensor Systems for Environmental Monitoring, 107–43. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0101-8_3.

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Denzer, R., G. Schimak, and H. Humer. "Air Pollution Monitoring." In Computer Techniques in Environmental Studies IV, 637–51. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1874-3_45.

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Girgždys, A., S. Trakumas, V. Ulevičius, and A. Juozaitis. "Urban Air Pollution Monitoring in Lithuania." In Urban Air Pollution, 391–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61120-9_31.

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Samaras, Zissis, and Spencer C. Sorensen. "Mobile Sources." In Urban Air Pollution — European Aspects, 63–91. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9080-8_5.

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Olsthoorn, A. F. M. "Monitoring of Root Growth." In Air Pollution and Ecosystems, 888–90. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-4003-1_115.

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Gomes, J. F. P. "Monitoring of Pollutant Emissions Using Stack Sampling Techniques." In Industrial Air Pollution, 51–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76051-8_7.

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Conference papers on the topic "Mobile air pollution monitoring"

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Hedgecock, W., P. Völgyesi, A. Ledeczi, X. Koutsoukos, A. Aldroubi, A. Szalay, and A. Terzis. "Mobile air pollution monitoring network." In the 2010 ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774253.

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Alhakbani, Noura, and Eiman Kanjo. "Zone based indoor mobile air pollution monitoring." In UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2494091.2496001.

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Ben-Aboud, Yassine, Mounir Ghogho, and Abdellatif Kobbane. "A research-oriented low-cost air pollution monitoring IoT platform." In 2020 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020. http://dx.doi.org/10.1109/iwcmc48107.2020.9148176.

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Kumar, Mandeep, S. Mini, and Trilochan Panigrahi. "A scalable approach to monitoring air pollution using IoT." In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2018. http://dx.doi.org/10.1109/i-smac.2018.8653653.

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Liu, Xinyu, Xinlei Chen, Xiangxiang Xu, Enhan Mai, Hae Young Noh, Pei Zhang, and Lin Zhang. "Delay Effect in Mobile Sensing System for Urban Air Pollution Monitoring." In SenSys '17: The 15th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3131672.3136997.

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Marjanovic, Martina, Sanja Grubesa, and Ivana Podnar Zarko. "Air and noise pollution monitoring in the city of Zagreb by using mobile crowdsensing." In 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2017. http://dx.doi.org/10.23919/softcom.2017.8115502.

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Bhardwaj, Ankit, Shiva Iyer, Yash Jalan, and Lakshminarayanan Subramanian. "Learning Pollution Maps from Mobile Phone Images." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/697.

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Air pollution monitoring and management is one of the key challenges for urban sectors, especially in developing countries. Measuring pollution levels requires significant investment in reliable and durable instrumentation and subsequent maintenance. On the other hand, there have been many attempts by researchers to develop image-based pollution measurement models which have shown significant results and established the feasibility of the idea. But, taking image-level models to a city-level system presents new challenges, which include scarcity of high-quality annotated data and a high amount of label noise. In this paper, we present a low-cost, end-to-end system for learning pollution maps using images captured through a mobile phone. We demonstrate our system for parts of New Delhi and Ghaziabad. We use transfer learning to overcome the problem of data scarcity. We investigate the effects of label noise in detail and introduce the metric of in-interval accuracy to evaluate our models in presence of noise. We use distributed averaging to learn pollution maps and mitigate the effects of noise to some extent. We also develop haze-based interpretable models which have comparable performance to mainstream models. With only 382 images from Delhi and Ghaziabad and single-scene dataset from Beijing and Shanghai, we are able to achieve a mean absolute error of 44 ug/m^3 in PM2.5 concentration on a test set of 267 images and an in-interval accuracy of 67% on predictions. Going further, we learn pollution maps with a mean absolute error as low as 35 ug/m^3 and in-interval accuracy as high as 74% significantly mitigating the image models' error. We also show that the noise in pollution labels emerging from unreliable sensing instrumentation forms a significant barrier to the realization of an ideal air pollution monitoring system. Our codebase can be found at https://github.com/ankitbha/pollution_with_images.
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Miyagawa, Yuta, Norihisa Segawa, Masato Yazawa, and Masa-yuki Yamamoto. "Development of a Low-cost Gas Sensor Unit for Wide Area Air Pollution Monitoring System (poster)." In MobiSys '19: The 17th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307334.3328632.

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Antonova, Zh A. "Comparative characteristic of atmospheric air quality on the right-bank and left-bank parts of Ulyanovsk." In VIII Vserossijskaja konferencija s mezhdunarodnym uchastiem «Mediko-fiziologicheskie problemy jekologii cheloveka». Publishing center of Ulyanovsk State University, 2021. http://dx.doi.org/10.34014/mpphe.2021-11-14.

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The investigation was carried out on the basis of analysis of data from stationary monitoring stations for atmospheric pollution during two-year period (2020-2021) and the city's zoning scheme. As a result of the investigation priority pollutants were identified for the right-bank and left-bank parts of Ulyanovsk. According to the list of priority pollutants, a number of prospective sources of these emissions were identified. Key words: stationary observation posts, priority pollutants, pollution sources, mobile posts, environmental standards.
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Papayannis, Alexandros D., Giorgos Tsaknakis, Giorgos Chourdakis, and Alexander A. Serafetinides. "Compact mobile lidar system based on the LabVIEW code: applications in urban air pollution monitoring in Athens, Greece." In Industrial Lasers and Inspection (EUROPTO Series), edited by Michel R. Carleer, Moira Hilton, Torsten Lamp, Rainer Reuter, George M. Russwurm, Klaus Schaefer, Konradin Weber, Klaus C. H. Weitkamp, Jean-Pierre Wolf, and Ljuba Woppowa. SPIE, 1999. http://dx.doi.org/10.1117/12.364163.

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Reports on the topic "Mobile air pollution monitoring"

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McEvers, J. A., M. S. Hileman, and N. T. Edwards. Air pollution effects field research facility: 3. UV-B exposure and monitoring system. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/10151009.

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Pekney, Natalie J., Matthew Reeder, Garret A. Veloski, and J. Rodney Diehl. Data Report for Monitoring at Six West Virginia Marcellus Shale Development Sites using NETL’s Mobile Air Monitoring Laboratory (July–November 2012). Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1330216.

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Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, August 2021. http://dx.doi.org/10.31979/mti.2021.2010.

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To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc.
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Bedoya-Maya, Felipe, Agustina Calatayud, and Vileydy Gonzalez-Mejia. Estimating the effect of urban road congestion on air quality in Latin America. Inter-American Development Bank, October 2022. http://dx.doi.org/10.18235/0004512.

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Road congestion and air pollution are key challenges for quality of life in urban settings. This research leverages highly disaggregated crowdsourced data from Latin America to study the effect of road congestion on levels of carbon monoxide, nitrogen dioxide, and particulate matter in four of the most congested cities in developing countries: Bogota, Buenos Aires, Mexico City, and Santiago. Based on a panel data econometric approach with over 4.4 billion records from Waze and hourly data from 54 air monitoring stations for 2019, our two-stage least square model shows a cumulative increase of 0.6% in response to a 1% of road congestion on the three air pollutants. Moreover, we find a nonlinear relationship between road congestion and air quality and estimate the threshold above which the effect decays. This study provides evidence that supports public policies designed to make urban mobility more sustainable by implementing measures to reduce road congestion in developing contexts.
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Coulson, Saskia, Melanie Woods, Drew Hemment, and Michelle Scott. Report and Assessment of Impact and Policy Outcomes Using Community Level Indicators: H2020 Making Sense Report. University of Dundee, 2017. http://dx.doi.org/10.20933/100001192.

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Making Sense is a European Commission H2020 funded project which aims at supporting participatory sensing initiatives that address environmental challenges in areas such as noise and air pollution. The development of Making Sense was informed by previous research on a crowdfunded open source platform for environmental sensing, SmartCitizen.me, developed at the Fab Lab Barcelona. Insights from this research identified several deterrents for a wider uptake of participatory sensing initiatives due to social and technical matters. For example, the participants struggled with the lack of social interactions, a lack of consensus and shared purpose amongst the group, and a limited understanding of the relevance the data had in their daily lives (Balestrini et al., 2014; Balestrini et al., 2015). As such, Making Sense seeks to explore if open source hardware, open source software and and open design can be used to enhance data literacy and maker practices in participatory sensing. Further to this, Making Sense tests methodologies aimed at empowering individuals and communities through developing a greater understanding of their environments and by supporting a culture of grassroot initiatives for action and change. To do this, Making Sense identified a need to underpin sensing with community building activities and develop strategies to inform and enable those participating in data collection with appropriate tools and skills. As Fetterman, Kaftarian and Wanderman (1996) state, citizens are empowered when they understand evaluation and connect it in a way that it has relevance to their lives. Therefore, this report examines the role that these activities have in participatory sensing. Specifically, we discuss the opportunities and challenges in using the concept of Community Level Indicators (CLIs), which are measurable and objective sources of information gathered to complement sensor data. We describe how CLIs are used to develop a more indepth understanding of the environmental problem at hand, and to record, monitor and evaluate the progress of change during initiatives. We propose that CLIs provide one way to move participatory sensing beyond a primarily technological practice and towards a social and environmental practice. This is achieved through an increased focus in the participants’ interests and concerns, and with an emphasis on collective problem solving and action. We position our claims against the following four challenge areas in participatory sensing: 1) generating and communicating information and understanding (c.f. Loreto, 2017), 2) analysing and finding relevance in data (c.f. Becker et al., 2013), 3) building community around participatory sensing (c.f. Fraser et al., 2005), and 4) achieving or monitoring change and impact (c.f. Cheadle et al., 2000). We discuss how the use of CLIs can tend to these challenges. Furthermore, we report and assess six ways in which CLIs can address these challenges and thereby support participatory sensing initiatives: i. Accountability ii. Community assessment iii. Short-term evaluation iv. Long-term evaluation v. Policy change vi. Capability The report then returns to the challenge areas and reflects on the learnings and recommendations that are gleaned from three Making Sense case studies. Afterwhich, there is an exposition of approaches and tools developed by Making Sense for the purposes of advancing participatory sensing in this way. Lastly, the authors speak to some of the policy outcomes that have been realised as a result of this research.
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