Добірка наукової літератури з теми "Mobile air pollution monitoring"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Mobile air pollution monitoring".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Mobile air pollution monitoring"
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.
Повний текст джерелаAdams, 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.
Повний текст джерелаTalib, 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.
Повний текст джерелаNance, Earthea. "Monitoring Air Pollution Variability during Disasters." Atmosphere 12, no. 4 (March 25, 2021): 420. http://dx.doi.org/10.3390/atmos12040420.
Повний текст джерелаCoutrier, 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.
Повний текст джерелаKhozouie, 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.
Повний текст джерелаHaus, 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.
Повний текст джерелаMarć, 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.
Повний текст джерелаAl-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.
Повний текст джерелаShakhov, 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.
Повний текст джерелаДисертації з теми "Mobile air pollution monitoring"
Alvear, Alvear Óscar Patricio. "Mobile Sensing Architecture for Air Pollution Monitoring." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/107928.
Повний текст джерелаIndustrial 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
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.
Повний текст джерелаSmith, 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/.
Повний текст джерелаZamora, Mero Willian Jesús. "Crowdsensing solutions for urban pollution monitoring using smartphones." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/115483.
Повний текст джерелаLa 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
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.
Повний текст джерелаRodriguez, 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.
Повний текст джерелаThe 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". Thesis, Чернігів, 2020. http://ir.stu.cn.ua/123456789/21055.
Повний текст джерелаОб’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.
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.
Повний текст джерела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.
Повний текст джерелаLelas, Vedran. "Chance constrained models for air pollution monitoring and control /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
Повний текст джерелаКниги з теми "Mobile air pollution monitoring"
Alberta. Air Issues and Monitoring Branch. Mobile monitoring survey of the Bow Corridor: March 2 and 12, 1994. [Edmonton]: Alberta Environmental Protection, 1995.
Знайти повний текст джерелаMulhern, 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.
Знайти повний текст джерелаMulhern, 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.
Знайти повний текст джерелаG, Clarke Andrew, ed. Industrial air pollution monitoring. London: Chapman & Hall, 1998.
Знайти повний текст джерелаClarke, Andrew G., ed. Industrial Air Pollution Monitoring. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-1435-3.
Повний текст джерела1948-, Sigrist Markus W., ed. Air monitoring by spectroscopic techniques. New York: Wiley, 1994.
Знайти повний текст джерелаMcCormick, John. Urban air pollution. Nairobi: UNEP, 1991.
Знайти повний текст джерелаQueiroz, M. V. G. Air pollution monitoring in urban environment. Manchester: UMIST, 1993.
Знайти повний текст джерелаJohn, Delaney. Air quality monitoring: Annual report. Johnstown Castle, Co. Wexford: Environmental Protection Agency, 2003.
Знайти повний текст джерелаOffice, 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.
Знайти повний текст джерелаЧастини книг з теми "Mobile air pollution monitoring"
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.
Повний текст джерелаMeyer, 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.
Повний текст джерелаTiwary, 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.
Повний текст джерелаKnox, 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.
Повний текст джерелаCape, 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.
Повний текст джерелаDenzer, 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.
Повний текст джерелаGirgždys, 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.
Повний текст джерелаSamaras, 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.
Повний текст джерелаOlsthoorn, 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.
Повний текст джерелаGomes, 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.
Повний текст джерелаТези доповідей конференцій з теми "Mobile air pollution monitoring"
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.
Повний текст джерелаAlhakbani, 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.
Повний текст джерелаBen-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.
Повний текст джерелаKumar, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаMarjanovic, 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.
Повний текст джерелаBhardwaj, 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.
Повний текст джерелаMiyagawa, 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.
Повний текст джерелаAntonova, 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.
Повний текст джерелаPapayannis, 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.
Повний текст джерелаЗвіти організацій з теми "Mobile air pollution monitoring"
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.
Повний текст джерелаPekney, 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.
Повний текст джерелаKwon, 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.
Повний текст джерелаBedoya-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.
Повний текст джерелаCoulson, 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.
Повний текст джерела