Relevant bibliographies by topics / Air – Pollution – Remote sensing

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  2. Dissertations / Theses
  3. Books
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  6. Reports

Academic literature on the topic 'Air – Pollution – Remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Air – Pollution – Remote sensing"

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Dubey, Bhawna. "Application of air pollution models and remote sensing in Air Quality Management." Indian Journal of Applied Research 4, no. 5 (October 1, 2011): 266–68. http://dx.doi.org/10.15373/2249555x/may2014/78.

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Benarie, Michel. "Optical remote sensing of air pollution." Science of The Total Environment 44, no. 3 (September 1985): 303–4. http://dx.doi.org/10.1016/0048-9697(85)90107-x.

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WOLF, J. P. "REMOTE SENSING OF AIR POLLUTION BY LIDAR." Le Journal de Physique IV 01, no. C7 (December 1991): C7–13—C7–16. http://dx.doi.org/10.1051/jp4:1991703.

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Liu, Yun, Yuqin Jing, and Yinan Lu. "Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence." Journal of Chemistry 2020 (March 4, 2020): 1–7. http://dx.doi.org/10.1155/2020/7390545.

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When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.
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Trifonova, T. A., N. V. Mishchenko, and Ye P. Grishina. "A REMOTE SENSING-BASED METHOD FOR DETERMINING INDUSTRIAL AIR POLLUTION." Mapping Sciences and Remote Sensing 35, no. 1 (January 1998): 22–30. http://dx.doi.org/10.1080/07493878.1998.10642074.

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Xiong, Xiaozhen, Jane Liu, Liangfu Chen, Weimin Ju, and Fred Moshary. "Special Issue “Remote Sensing of Greenhouse Gases and Air Pollution”." Remote Sensing 13, no. 11 (May 23, 2021): 2057. http://dx.doi.org/10.3390/rs13112057.

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Zelinger, Z., M. Střižı́k, P. Kubát, Z. Jaňour, P. Berger, A. Černý, and P. Engst. "Laser remote sensing and photoacoustic spectrometry applied in air pollution investigation." Optics and Lasers in Engineering 42, no. 4 (October 2004): 403–12. http://dx.doi.org/10.1016/j.optlaseng.2004.03.005.

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Vollmar, H. P., K. Bobey, and M. List. "Compact remote sensing system DIM-measurement of traffic-induced air pollution." Infrared Physics & Technology 37, no. 1 (February 1996): 45–50. http://dx.doi.org/10.1016/1350-4495(95)00110-7.

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Putrenko, V. V., and N. M. Pashynska. "THE USE OF REMOTE SENSING DATA FOR MODELING AIR QUALITY IN THE CITIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5/W1 (December 13, 2017): 57–62. http://dx.doi.org/10.5194/isprs-annals-iv-5-w1-57-2017.

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Monitoring of environmental pollution in the cities by the methods of remote sensing of the Earth is actual area of research for sustainable development. Ukraine has a poorly developed network of monitoring stations for air quality, the technical condition of which is deteriorating in recent years. Therefore, the possibility of obtaining data about the condition of air by remote sensing methods is of great importance. The paper considers the possibility of using the data about condition of atmosphere of the project AERONET to assess the air quality in Ukraine. The main pollution indicators were used data on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) content in the atmosphere. The main indicator of air quality in Ukraine is the air pollution index (API). We have built regression models the relationship between indicators of NO2, which are measured by remote sensing methods and ground-based measurements of indicators. There have also been built regression models, the relationship between the data given to the land of NO2 and API. To simulate the relationship between the API and PM2.5 were used geographically weighted regression model, which allows to take into account the territorial differentiation between these indicators. As a result, the maps that show the distribution of the main types of pollution in the territory of Ukraine, were constructed. PM2.5 data modeling is complicated with using existing indicators, which requires a separate organization observation network for PM2.5 content in the atmosphere for sustainable development in cities of Ukraine.
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Misra, P., and W. Takeuchi. "ASSESSING POPULATION SENSITIVITY TO URBAN AIR POLLUTION USING GOOGLE TRENDS AND REMOTE SENSING DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 93–100. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-93-2020.

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Abstract. This study demonstrates relationship between remote sensing satellite retrieved fien aerosol concentration and web-based search volumes of air quality related keywords. People’s perception of urban air pollution can verify policy effectiveness and gauge acceptability of policies. As a serious health issue in Asian cities, population may express concern or uncertainty for air pollution risk by performing search on the web to seek answers. A ‘social sensing’ approach that monitors such search queries, may assess people’ perception about air pollution as a risk. We hypothesize that trend and volume of searches show impact of air pollution on general population. The objectives of this research are to identify those atmospheric conditions under which relative search volume (RSV) obtained from Google Trends shows correlation with measured fine aerosol concentration, and to compare search volume sensitivity to rise in aerosol concentration in seven Asian megacities. We considered weekly relative search volumes from Google Trends (GT) for a four year period from January, 2015 to December, 2018 representing diverse PM2.5 concentrations. Search volumes for keywords corresponding to perception of air quality (‘air pollution’) and health effects (‘cough’ and ‘asthma’) were considered. To represent PM2.5 we used fine aerosol indicator developed in an earlier research. The results suggest that tendency to search for ‘air pollution’ and ‘cough’ occurs when AirRGB R is in excess and temperature is below the baseline values. Consistent with this, in cities with high baseline concentrations, sensitivity to rise in AirRGB R is also comparatively lower. The result of this study can used as an indirect measure of awareness in the form of perception and sensitivity of population to air quality. Such an analysis could be useful for forecasting health risks specially in cities lacking dedicated services.
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Dissertations / Theses on the topic "Air – Pollution – Remote sensing"

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Groves, Michael Anthony. "Remote sensing of air pollution related damage to forested areas." Thesis, Aston University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.253753.

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Diem, Jeremy Everett 1972. "A geographical analysis of air pollution in the Tucson region." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/289152.

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This dissertation presents a geographical analysis of air pollution in the Tucson region. Image processing, geographic information system (GIS), climatological, and statistical tools are used to develop and analyze air pollution-related databases. These databases are then used in conjunction with a limited number of spatial measurements of ozone concentrations to create accurate and theoretically sound ground-level ozone maps. High spatial resolution, gridded, multi-temporal, atmospheric emissions inventories (EIs) of ozone precursor chemical (i.e. volatile organic compounds (VOCs) and nitrogen oxides (NOₓ)) emissions are initially developed. GIS-driven "top-down" and "bottom-up" methods are employed to create anthropogenic VOC and NOx emissions inventories while satellite imagery and field surveys are employed to create biogenic VOC (BVOC) emissions inventories. Accounting for approximately 50% of the anthropogenic emissions, on-road vehicles are the dominant anthropogenic source. The forest and desert lands emit nearly all of the BVOCs within the entire Tucson region while exotic trees such as eucalyptus, pine, and palm emit most of the BVOCs within the City of Tucson. Relationships between VOC and NOₓ emissions, atmospheric conditions, and ambient ozone levels are determined by examining spatio-temporal variations in ozone levels, temporal variations in VOC and NOₓ emissions and atmospheric conditions, atmospheric conditions which are conducive to elevated ozone levels. In addition, the likelihood of ozone transport from Phoenix to Tucson is assessed. The highest ozone levels occur at "rural," downwind monitors, occur in August, and occur during the early afternoon hours. Atmospheric conditions conducive to elevated concentrations differ between the months while inter-city ozone transport is most likely to occur in June. Pooled, cross-sectional, times series, regression models are developed with the aid of cluster analysis and principal components analysis to spatially predict daily maximum 1-hr and 8-hr average ozone concentrations. Gridded, multi-temporal estimates of VOCs and NOₓ emissions are the primary predictor variables in the regression models. The pooled models are reasonably accurate with overall R² values from 0.90 to 0.92, 6 to 7% error, and predicted concentrations that are typically within 0.003 to 0.004 ppm of the observed concentrations. The predicted highest ozone concentrations occur in a monitorless area on the eastern edge of the City of Tucson.
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Bauduin, Sophie. "Remote sensing of atmospheric boundary layer composition using infrared satellite observations." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/239053.

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Measuring the composition of the planetary boundary layer is essential for monitoring pollutants and for understanding their impact on environment and health. Nadir satellite remote sensing is particularly appealing to sound this part of the atmosphere, but is however challenging because pollutants concentrations are generally weak and confined in a small part of the atmospheric column. Among the sounders currently in orbit, those operating in the thermal infrared have usually their maximum sensitivity in the mid-troposphere, and are thought to be inadequate to measure the near-surface atmospheric composition. Their sensitivity to this part of the atmosphere is indeed generally limited by low temperature contrast (called thermal contrast) between the ground and the air above it. Shortly before the beginning of this PhD, this has however been challenged with different studies, which have shown the possibility to measure air pollution with thermal infrared sounders in case of high thermal contrast conditions. This was especially demonstrated with the measurement of ammonia global distribution using the Infrared Atmospheric Sounding Interferometer (IASI). This work aims at fully exploring the capabilities of thermal infrared sounders to sound the near-surface atmospheric composition. It mainly focuses on the observations of the IASI instrument, and addresses the following questions: where and when is IASI sensitive to the near-surface atmosphere? How large and how variable is the sensitivity to near-surface pollutants? What are the parameters that drive this variability? The answers to these questions are looked at for two pollutants: sulphur dioxide (SO2) and carbon monoxide (CO), and are obtained through a series of different analyses. SO2 is the first constituent on which this work focuses. The retrieval of its near-surface concentration is first of all attempted in an area surrounding the industrial area of Norilsk. This region, well-known for the extraction of heavy metals and its extremely high levels of pollution, encounters large temperature inversions in winter, which trap SO2 close to the ground. By exploiting these (corresponding to high negative thermal contrast), we show that it is possible to retrieve the surface SO2 concentrations in the region. This is done using a simplified version of the optimal estimation method, based on the use of a total measurement error covariance matrix. Further, we show that the surface SO2 concentration retrieval using the ν3 band is limited, in addition to thermal contrast, by the strong water (H2O) absorption, which renders the lowest atmosphere opaque in this spectral range in case of large humidity. Two conditions are therefore shown to be required to monitor near-surface SO2 in the ν3 band: large thermal contrast and low surface humidity. These findings are confirmed with the retrieval of SO2 at global scale, performed using a newly developed retrieval scheme based on the conversion of radiance indexes into SO2 columns using look-up-tables. It is composed of two successive steps: 1) the determination of the altitude of SO2 and the selection of low plumes (below 4 km), 2) for the selected observations, the conversion of radiance indexes into integrated SO2 0–4 km columns. The distributions and time series so obtained are used to better characterise the variability of IASI sensitivity to surface SO2 in the ν3 band at the global scale, and more particularly, in terms of thermal contrast strength and total column of H2O.The characterisation of IASI sensitivity to CO is realised in a second part of the work. Radiative transfer simulations are conducted first to determine the possibility to detect enhancement in CO near-surface concentrations with IASI. The framework of the optimal estimation is then used to investigate the capability of IASI to decorrelate, as a function of thermal contrast, the CO concentration in the low troposphere from that in the high troposphere. Finally, comparisons of IASI CO observations with co-located aircraft and ground-based measurements are shown to confirm with real data how IASI sensitivity to near-surface CO varies in terms of thermal contrast conditions, and to which extent it allows determining the CO abundance in case of high pollution.
Mesurer la composition de la couche limite atmosphérique depuis les satellites est essentiel pour comprendre l’impact des polluants sur l’environnement global et sur la santé. Parmi les sondeurs actuellement en orbite, ceux opérant dans l’infrarouge thermique sont souvent considérés comme inadéquats pour cet objectif, leur sensibilité à l’atmosphère de surface étant généralement limitée par de faibles contrastes de température (appelés contrastes thermiques, TC) entre le sol et l’air au-dessus. Différentes études récentes ont cependant montré la possibilité de mesurer la pollution de l’air avec ce type de sondeur dans des conditions de TC élevé.Ce travail a pour objectif de redéfinir la capacité des sondeurs opérant dans l’infrarouge thermique à mesurer la composition de l’atmosphère de surface. Il se focalise sur les observations de l’instrument IASI (Interféromètre Atmosphérique de Sondage Infrarouge), et tente de répondre, pour les deux polluants que sont le dioxyde de soufre (SO2) et le monoxyde de carbone (CO), aux questions suivantes :Où et quand IASI est-il sensible à l’atmosphère de surface ?Quels sont les paramètres qui influencent cette sensibilité et dans quelle mesure?Dans une première partie, la thèse se focalise sur l’ajustement des concentrations de SO2 de surface pour la région industrielle de Norilsk, connue pour son niveau de pollution élevé. Nous montrons qu’il y est possible de restituer les concentrations de surface de SO2 en hiver, en exploitant les larges inversions de température (TC négatifs) qui s’y développent. Les restitutions reposent sur une version simplifiée de la méthode de l’estimation optimale, utilisant une matrice complète de l’erreur de mesure. En plus du TC, nous montrons que l’ajustement dans la bande ν3 du SO2 est également limité par la forte absorption de la vapeur d’eau (H2O), qui rend les basses couches de l’atmosphère opaques. La nécessité de combiner des TCs élevés et une faible humidité pour permettre la mesure du SO2 en surface est confirmée par une analyse à l’échelle globale, utilisant une méthode basée sur la mesure d’indices de radiance et leur conversion en colonnes de SO2 à l’aide de tables pré-calculées. Composée de 2 étapes, cette méthode identifie et sélectionne d’abord les panaches situés sous 4 km ;elle convertit ensuite les indices de radiance en colonnes de SO2 intégrées entre 0 et 4 km. Les distributions et séries temporelles obtenues sont utilisées pour caractériser, en termes de valeurs de TC et de colonnes totales d’H2O, la variabilité de la sensibilité de IASI au SO2 de surface dans la bande ν3.Dans la seconde partie du travail, des simulations de transfert radiatif sont entreprises pour déterminer la possibilité de détecter avec IASI des augmentations de la concentration de CO dans l’atmosphère de surface. Le formalisme de l’estimation optimale est aussi utilisé pour analyser l’influence du TC sur la capacité de IASI à décorreler les concentrations du CO dans la basse et la haute troposphère. Finalement, des comparaisons entre les concentrations de CO restituées des mesures IASI sous différentes conditions de TC et de pollution et celles mesurées par avions et par des stations au sol complètent la caractérisation.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Kaynak, Burcak. "Assimilation of trace gas retrievals obtained from satellite (SCIAMACHY), aircraft and ground observations into a regional scale air quality model (CMAQ-DDM/3D)." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37134.

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A major opportunity for using satellite observations of tropospheric chemical concentrations is to improve our scientific understanding of atmospheric processes by integrated analysis of satellite, aircraft, and ground-based observations with global and regional scale models. One endpoint of such efforts is to reduce modeling biases and uncertainties. The idea of coupling these observations with a regional scale air quality model was the starting point of this research. The overall objective of this research was to improve the NOₓ emission inventories by integrating observations from different platforms and regional air quality modeling. Specific objectives were: 1) Comparison of satellite NO₂ retrievals with simulated NO₂ by the regional air quality model. Comparison of simulated tropospheric gas concentrations simulated by the regional air quality model, with aircraft and ground-based observations; 3) Assessment of the uncertainties in comparing satellite NO₂ retrievals with NOₓ emissions estimates and model simulations; 4) Identification of biases in emission inventories by data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations with an iterative inverse method using the regional air quality model coupled with sensitivity calculations; 5) Improvement of our understanding of NOₓ emissions, and the interaction between regional and global air pollution by an integrated analysis of satellite NO₂ retrievals with the regional air quality model. Along with these objectives, a lightning NOₓ emission inventory was prepared for two months of summer 2004 to account for a significant upper level NOₓ source. Spatially-resolved weekly NO₂ variations from satellite retrievals were compared with estimated NOₓ emissions for different region types. Data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations were performed to evaluate the NOₓ emission inventory. This research contributes to a better understanding of the use of satellite NO₂ retrievals in air quality modeling, and improvements in the NOₓ emission inventories by correcting some of the inconsistencies that were found in the inventories. Therefore, it may provide groups that develop emissions estimates guidance on areas for improvement. In addition, this research indicates the weaknesses and the strengths of the satellite NO₂ retrievals and offers suggestions to improve the quality of the retrievals for further use in the tropospheric air pollution research.
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Rhudy, Scott Alan. "A remote sensing evaluation of the effectiveness of oxygenated fuels in the Raleigh, NC MSA." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/25872.

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Dohanich, Francis Albert. "On-Road Remote Sensing of Motor Vehicle Emissions: Associations between Exhaust Pollutant Levels and Vehicle Parameters for Arizona, California, Colorado, Illinois, Texas, and Utah." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc5524/.

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On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest with Texas and Arizona, lowest.
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Furtado, Clarice Miranda Fiorese. "Análise quantitativa das propriedades ópticas de aerossol urbano e de queimadas na Amazônia." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-14092016-232905/.

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Este trabalho tem como foco o entendimento das propriedades ópticas do aerossol na Amazônia utilizando várias técnicas: sensoriamento remoto em solo, sensoriamento remoto por satélites e medidas in situ. Propriedades ópticas medidas continuamente ao longo de mais de 15 anos pela rede AERONET na Amazônia foram analisadas buscando compreender como os eventos anuais de queima de biomassa e as emissões urbanas de Manaus afetam as propriedades das partículas. Medidas de longo prazo do sensor MODIS utilizando o sistema Giovanni da NASA foram úteis para caracterizar o impacto da pluma urbana de Manaus nas propriedades de aerossol e nuvens na Amazônia Central. A partir de medições in situ realizadas nas várias estações amostradoras do experimento GoAmazon2014/5, analisou-se em detalhes propriedades de absorção e espalhamento de aerossol antes e depois do impacto da pluma urbana de Manaus. Foi observada uma alta variabilidade na profundidade óptica do aerossol (AOD) bem como em outras propriedades, tais como absorção, espalhamento e distribuição de tamanho. Valores muito elevados de AOD foram observados em todos os sítios durante a estação seca, em particular na região do arco do desflorestamento. Análises dos expoentes Ångström de espalhamento e absorção separam as diferentes componentes absorvedoras do aerossol, entre eles o carbono elementar (EC), carbono orgânico (OC), poeira mineral e partículas biogênicas. Uma análise da forçante radiativa no topo da atmosfera em conjunto com a Matriz Ångström auxiliou no entendimento do papel da componente orgânica de espalhamento (OC) e da componente de absorção (BC) no aerossol de queimadas e urbano na forçante radiativa. A região menos afetada por queimadas na Amazônia foi caracterizada pela presença de um aerossol altamente espalhador durante a estação seca, com valores de albedo de espalhamento simples (SSA) na faixa de 0,91-0,94. Por outro lado, constatou-se valores médios de 0,85 a 0,89 em regiões fortemente impactadas por queimadas. Através de medidas in situ e medidas obtidas a partir da rede AERONET, foi possível observar significativo impacto da pluma de Manaus vento abaixo da cidade, especialmente na componente de absorção. O efeito da absorção do aerossol urbano na forçante radiativa é significativo, indo de uma forçante no sítio da EMBRAPA, antes da pluma, de -24 W/m² para cerca de -18 W/m² em Manacapuru, com o efeito da pluma de Manaus. A partir da análise de 12 anos de medidas dos sensores MODIS e MISR, observou-se alterações nas propriedades de nuvens e na carga atmosférica de aerossol. Foram analisadas a AOD, a temperatura do topo da nuvem e o raio efeito de gotículas de nuvens. Ficou clara a presença de nuvens mais altas e com raio efetivo menor em regiões com maior carga de aerossol, vento abaixo de Manaus.
This work focuses on understanding the optical properties of aerosol in the Amazon, using various techniques: remote sensing from the ground, remote sensing from satellites and in situ measurements. Aerosol optical properties continuously measured over more than 15 years carried out by the AERONET network in the Amazon were analyzed seeking to understand how the annual biomass burning emissions and urban emissions of Manaus affect particle properties. Long-term measurements of MODIS using the Giovanni NASA system were useful to characterize the impact of the urban plume of Manaus in the properties of aerosol and clouds in the central Amazon. Based on in situ measurements in the various sampling sites of the GoAmazon2014/5 experiment, the absorption and scattering properties of aerosol before and after the impact of Manaus urban plume were analyzed in detail. It was observed a large spatial and temporal variability in the aerosol optical depth (AOD) as well as in various properties such as absorption and scattering coefficients and size distribution. Very high levels of AOD were observed at all sites during the dry season, particularly in the deforestation arc region. Analysis of scattering and absorption Ångström exponents helps to identify different absorber components of the aerosol, including elemental carbon (EC), organic carbon (OC), mineral dust and biogenic particles. An analysis of the radiative forcing at the top of the atmosphere together with the Ångström Matrix helps in understanding the role of the scattering organic component (OC) versus the absorption component (BC) in biomass burning and urban aerosol particles in the radiative forcing. The region least affected by fires in the Amazon was characterized by a highly scattering aerosol during the dry season with Single Scattering Albedo (SSA) values in the range of 0.91-0.94, while we found mean values of 0.85 to 0.89 in heavily affected biomass burning regions. Through in situ measurements and AERONET observations it was possible to measure the significant impact of Manaus plume downwind of the urban area, especially in the absorption component. The effect of absorption of urban aerosol in the radiative forcing is significant, with measurements at the EMBRAPA site, before the impact of the plume, at -24 W/m² going to -18 W/m² in Manacapuru that is effect of the Manaus plume. It was also analyzed the changes in the properties of clouds and atmospheric aerosol loading over the last 12 years of MODIS and MISR measurements. The analysis of AOD, cloud top temperature and the radius of cloud droplets show a clear signal of Manaus plume. It was observed clouds with smaller effective radius in regions with higher aerosol load, downwind of Manaus.
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RODRIGUES, PATRICIA F. "Avaliação da higroscopicidade de aerossóis urbanos pela técnica LIDAR Raman." reponame:Repositório Institucional do IPEN, 2014. http://repositorio.ipen.br:8080/xmlui/handle/123456789/23498.

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Submitted by Maria Eneide de Souza Araujo (mearaujo@ipen.br) on 2015-02-24T19:21:41Z No. of bitstreams: 0
Made available in DSpace on 2015-02-24T19:21:41Z (GMT). No. of bitstreams: 0
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Tese (Doutorado em Tecnologia Nuclear)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
FAPESP:09/14758-7
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Riddell, Kevin Donald Alexander. "Design, testing and demonstration of a small unmanned aircraft system (SUAS) and payload for measuring wind speed and particulate matter in the atmospheric boundary layer." Thesis, Arts and Science, 2014. http://hdl.handle.net/10133/3416.

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The atmospheric boundary layer (ABL) is the layer of air directly influenced by the Earth’s surface and is the layer of the atmosphere most important to humans as this is the air we live in. Methods for measuring the properties of the ABL include three general approaches: satellite-based, ground- based and airborne. A major research challenge is that many contemporary methods provide a restricted spatial resolution or coverage of variations of ABL properties such as how wind speed varies across a landscape with complex topography. To enhance our capacity to measure the properties of the ABL, this thesis presents a new technique that involves a small unmanned aircraft system (sUAS) equipped with a customized payload for measuring wind speed and particulate matter. The research presented herein outlines two key phases in establishing the proof-of-concept of the payload and its integration on the sUAS: (1) design and testing and (2) field demonstration. The first project focuses on measuring wind speed, which has been measured with fixed wing sUASs in previous research, but not with a helicopter sUAS. The second project focuses on the measurement of particulate matter, which is a major air pollutant typically measured with ground- based sensors. Results from both proof-of-concept projects suggest that ABL research could benefit from the proposed techniques.
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De, Smedt Isabelle. "Long-term global observations of tropospheric formaldehyde retrieved from spaceborne nadir UV sensors." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209919.

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

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

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

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

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Books on the topic "Air – Pollution – Remote sensing"

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Parenteau, Kary-Anne. Remote sensing techniques for the detection and monitoring of air pollution. Sudbury, Ont: Laurentian University, Department of Earth Sciences, 1998.

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D'Antoni, Héctor L. Remote sensing and holocene vegetation: History of global change. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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Deutsche Forschungs- und Versuchsanstalt für Luft- und Raumfahrt., ed. Remote sensing of environmental pollution: Course held at CNIE, Buenos Aires, Argentina. Oberpfaffenhofen: DFVLR, Institut für Physik der Atmosphäre, 1987.

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Stoll, Peter J. Residential lit fireplace detection and density measurement using airborne mult-spectral [sic] sensors. Monterey, Calif: Naval Postgraduate School, 1997.

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Swayne, Kerry L. Infrared remote sensing of on-road motor vehicle emissions in Washington State. Bellevue, WA: Air Quality Program, Washington State Dept. of Ecology, Northwest Regional Office, 1999.

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Harig, Roland. Infrarot-Fernerkundungssystem für die chemische Gefahrenabwehr. Bonn: Bundesamt für Bevölkerungsschutz und Katastrophenhilfe, 2006.

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Scorer, R. S. The satellite as microscope. Chichester, England: Ellis Horwood, 1989.

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Satellite as microscope. New York: Ellis Horwood, 1990.

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Swayne, Kerry L. Infrared remote sensing of on-road motor vehicle emissions in Washington State: Kerry L. Swayne. Bellevue, WA: Air Quality Program, Washington State Dept. of Ecology, Northwest Regional Office, 1999.

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Agnès, Perrin, Ben Sari-Zizi Najate, Demaison J, and NATO Programme for Security through Science., eds. Remote sensing of the atmosphere for environmental security: [proceedings of the NATO Advanced Research Workshop on Remote Sensing of the Atmosphere for Environmental Security, Rabat, Morocco, 16-19 November 2005]. Dordrecht: Springer, published in cooperation with NATO Public Diplomacy Division, 2006.

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Book chapters on the topic "Air – Pollution – Remote sensing"

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Eldering, Annmarie. "Air Pollution." In Encyclopedia of Remote Sensing, 32–35. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_7.

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Singal, S. P., B. S. Gera, and Neeraj Saxena. "Sodar: A tool to characterize hazardous situations in air pollution and communication." In Acoustic Remote Sensing Applications, 325–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0009573.

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Kumari, Maya, Shivangi S. Somvanshi, and Syed Zubair. "Estimation of Air Pollution Using Regression Modelling Approach for Mumbai Region, Maharashtra, India." In Remote Sensing and GIScience, 229–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55092-9_13.

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Somvanshi, Shivangi Saxena, Aditi Vashisht, Umesh Chandra, and Geetanjali Kaushik. "Delhi Air Pollution Modeling Using Remote Sensing Technique." In Handbook of Environmental Materials Management, 1–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-58538-3_174-1.

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Erener, Arzu, Gülcan Sarp, and Özge Yıldırım. "Seasonal Air Pollution Investigation and Relation Analysis of Air Pollution Parameters to Meteorological Data (Kocaeli/Turkey)." In Advances in Remote Sensing and Geo Informatics Applications, 355–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01440-7_78.

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Hayasaka, Hiroshi, and Alpon Sepriando. "Severe Air Pollution Due to Peat Fires During 2015 Super El Niño in Central Kalimantan, Indonesia." In Springer Remote Sensing/Photogrammetry, 129–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67474-2_7.

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Bonino, G., D. Anfossi, P. Bacci, and A. Longhetto. "Remote Sensing of Stability Conditions during Severe Fog Episodes." In Air Pollution Modeling and Its Application IV, 601–19. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2455-3_31.

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Omasa, Kenji. "Diagnosis of Stomatal Response and Gas Exchange of Trees by Thermal Remote Sensing." In Air Pollution and Plant Biotechnology, 343–59. Tokyo: Springer Japan, 2002. http://dx.doi.org/10.1007/978-4-431-68388-9_18.

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Nichol, Janet E., Muhammad Bilal, Majid Nazeer, and Man Sing Wong. "Urban Pollution." In Urban Informatics, 243–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_16.

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AbstractThis chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB), and the merged DT/DB algorithms are evaluated. For urban heat island and urban climatic analysis using coarse- and medium- resolution thermal sensors, MODIS, Landsat, and ASTER are evaluated. For water-quality monitoring, medium spatial resolution sensors including Landsat, HJ1A/B, and Sentinel 2, are evaluated as potential replacements for expensive routine ship-borne monitoring.
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Clerici, G., and S. Sandroni. "A Wind-Field Model for Interpretation of Remote-Sensing Data in a Complex Area." In Air Pollution Modeling and Its Application V, 383–400. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4757-9125-9_25.

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Conference papers on the topic "Air – Pollution – Remote sensing"

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Aguilar, A., V. Garibay, and I. Cruz-Jimate. "Remote sensing study of motor vehicles’ emissions in Mexican Cities." In AIR POLLUTION 2009. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/air090181.

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José, R. San, J. L. Pérez, J. L. Morant, and R. M. González. "Remote sensing data assimilation in WRF-UCM mesoscale model: Madrid case study." In AIR POLLUTION 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/air100021.

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Beaulant, Anne-Lise, and Lucien Wald. "Aerosols detection for urban air pollution monitoring." In Remote Sensing, edited by James R. Slusser, Klaus Schäfer, and Adolfo Comerón. SPIE, 2006. http://dx.doi.org/10.1117/12.689946.

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Harbula, Jan, and Veronika Kopacková. "Air pollution detection using MODIS data." In SPIE Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2011. http://dx.doi.org/10.1117/12.898107.

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Belegante, L., D. Zisu, I. Ionel, and A. Nemuc. "Air pollution monitoring using the open path technique." In Remote Sensing, edited by Adolfo Comerón, Klaus Schäfer, James R. Slusser, Richard H. Picard, and Aldo Amodeo. SPIE, 2007. http://dx.doi.org/10.1117/12.737930.

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Mukai, Sonoyo, Itaru Sano, Makiko Nakata, and Masayoshi Yasumoto. "Detection of severe air pollution from multidirectional perspectives." In SPIE Remote Sensing, edited by Adolfo Comerón, Evgueni I. Kassianov, and Klaus Schäfer. SPIE, 2015. http://dx.doi.org/10.1117/12.2194677.

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Mukai, Sonoyo, Itaru Sano, and Makiko Nakata. "Improvement of retrieval algorithms for severe air pollution." In SPIE Remote Sensing, edited by Thilo Erbertseder, Thomas Esch, and Nektarios Chrysoulakis. SPIE, 2016. http://dx.doi.org/10.1117/12.2240485.

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Yong, Chen, Yang Ze-hou, Fan Dong, Chen Chun-li, Ren Peng, Li Xiao-feng, Feng Zhen-zhong, Zhao Pi-e, Zhang Guo-juan, and Yu Chen. "Laser remote sensing of air pollution clouds." In Optical Spectroscopy and Imaging, edited by Jin Yu, Zhe Wang, Mengxia Xie, Yuegang Fu, and Vincenzo Palleschi. SPIE, 2019. http://dx.doi.org/10.1117/12.2542541.

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Hohl, C. M., G. Marotz, D. Lane, R. Carter, A. Ensz, S. Guerra, and J. Anderson. "A comparison of vehicle air pollution emissions between different geodemographic groups in the Kansas City metro area (USA) using remote sensing." In AIR POLLUTION 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/air06032.

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Schäfer, Klaus, Patrick Wagner, Stefan Emeis, Carsten Jahn, Christoph Muenkel, and Peter Suppan. "Mixing layer height and air pollution levels in urban area." In SPIE Remote Sensing, edited by Evgueni I. Kassianov, Adolfo Comeron, Richard H. Picard, Klaus Schäfer, Upendra N. Singh, and Gelsomina Pappalardo. SPIE, 2012. http://dx.doi.org/10.1117/12.974328.

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Reports on the topic "Air – Pollution – Remote sensing"

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Jessup, Andrew T. FAIRS Experiment: Fluxes, Air-Sea Interaction, and Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada624665.

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Saltzman, Eric S., Mark Donelan, and Warren De Bruyn. Laboratory Investigation of Air-Sea Interfacial Properties in Relation to Gas Exchange and Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada631381.

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Drescher, Anushka Christina. Computed tomography and optical remote sensing: Development for the study of indoor air pollutant transport and dispersion. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/95330.

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Babson, David W., Michael L. Hargrave, Thomas L. Sever, John S. Isaacson, and James A. Zeidler. Archaeological, Geophysical, and Remote Sensing Investigations of the 1910 Wright Brothers' Hangar, Wright-Patterson Air Force Base, Ohio. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada352192.

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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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