Spatial–Temporal Analysis of Urban Air Pollution

Koussoulakou, Alexandra

doi:10.1016/b978-0-08-042415-6.50020-x

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Spatiotemporal mapping is used in a variety of applications, for it provides a realistic visual representation of the variation of the natural process in the spatiotemporal domain, as well as a quantitative assessment of its uncertainty (e.g., Bilonick, 1985;Dimitrakopoulos and Luo, 1993;Koussoulakou, 1994;Vyas and Christakos, 1997).…”

Section: Introductionmentioning

confidence: 99%

Modern geostatistics: computational BME analysis in the light of uncertain physical knowledge - the Equus Beds study

Serre

Christakos

1999

Stochastic Environmental Research and Risk Assessment (SERRA)

106

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This paper is concerned with a computational formulation of the Bayesian maximum entropy (BME) mapping method, which can handle rigorously and ef®ciently spatiotemporal applications of considerable practical importance. BME is a method of modern geostatistics that can integrate and process physical knowledge that belongs to two major bases: general knowledge (i.e., obtained from general principles and laws, summary statistics and background information), and speci®catory knowledge (i.e., obtained through experience with the speci®c situation). BME allows considerable¯exibility regarding the choice of an appropriate spatiotemporal map, offers a complete assessment of the mapping uncertainty and contributes to the scienti®c understanding of the underlying natural phenomenon. Valuable insight is gained by studying a spatiotemporal data set representing water-level elevations at the Equus Beds aquifer (Kansas). Numerical results show that, as was expected in theory, classical geostatistics analysis is obtained as a special case of the considerably more general BME approach. Moreover, modern geostatistical analysis in terms of BME offers more accurate and informative results in practice, by incorporating various sources of physical knowledge that cannot be processed by the classical methods.

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Section: Introductionmentioning

confidence: 99%

Modern geostatistics: computational BME analysis in the light of uncertain physical knowledge - the Equus Beds study

Serre

Christakos

1999

Stochastic Environmental Research and Risk Assessment (SERRA)

106

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“…The visualization of diverse atmospheric data allows making easy to understand the air quality physical and chemical process. In order for this data to provide useful insight into the workings of the atmosphere it must be visualized in a form that users can readily interpret [1].Visualization has been proved to be an effective tool for presenting results of air pollution modelling [2]. Human visual perception offers excellent capabilities that facilitate knowledge construction and the analysis of spatial-temporal relations [3].…”

Section: Introductionmentioning

confidence: 99%

Advances in 3D visualization of air quality data

José

Pérez

Gonzalez

2012

Usage, Usability, and Utility of 3D City Models – European COST Action TU0801

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Abstract. The air quality models produce a considerable amount of data, raw data can be hard to conceptualize, particularly when the size of the data sets can be terabytes, so to understand the atmospheric processes and consequences of air pollution it is necessary to analyse the results of the air pollution simulations. The basis of the development of the visualization is shaped by the requirements of the different group of users. We show different possibilities to represent 3D atmospheric data and geographic data. We present several examples developed with IDV software, which is a generic tool that can be used directly with the simulation results. The rest of solutions are specific applications developed by the authors which are the integration of different tools and technologies. In the case of the buildings has been necessary to make a 3D model from the buildings data using COLLADA standard format. In case of the Google Earth approach, for the atmospheric part we use Ferret software. In the case of gvSIG.-3D for the atmospheric visualization we have used different geometric figures available: "QuadPoints", "Polylines", "Spheres" and isosurfaces. The last one is also displayed following the VRML standard.

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“…The primary challenge is to convert massive amounts of data and scientific knowledge into easily understandable format for decision-making. Earth science applications in general, and water resources management applications in particular, generate large amounts of data relating to physical phenomenon in space and time (Koussoulakou, 1994). The multidimensional nature of this data makes it necessary to develop computer visualization techniques to explore and interpret it.…”

Section: Introductionmentioning

confidence: 99%

Simulation modeling and visualization of hydrologic response of an agricultural watershed

Namwamba

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This study developed a set of techniques to assist the decision-making process in agricultural watershed management As local governments develop water reservoirs for water storage and use, there is need to accurately forecast not only the amounts of runoff going into reservoirs, but also the patterns and potential quantities of peak flow. Major components of the study involved the acctirate modeling of the hydrology of an agricultural watershed, the enhancement of the presentation of simulated hydrology results to decision makers, and the exploration of alternative management options in an agricultural watershed. The objectives of this study were to: (1) evaluate the performance of a distributed-parameter hydrologic model to simulate daily runoff of an agricultural watershed, (2) visualize the simulation results generated by the model from observed data, and (3) use the visualization procedvires developed to examine scenarios of impacts of alternative land management practices on the streamflow from an agricultural watershed. Geographic information systems (GIS) techniques were used to prepare input data for the Precipitation and Runoff Modeling System (PRMS) to simulate surface hydrology of an agricultural watershed. Results from the simulation were then used as input for a dynamic visualization process. The visualization procedures developed in this study assisted in the examination of different scenarios of streamflow resulting from alternative land management practices. New procedures were developed for the evaluation, application and visualization of results of a widely used hydrologic modeling system. 1 CHAPTER 1 GENERAL INTRODUCTION Overview Accurate prediction of streamflow and surface runoff in complex watersheds and drainage basins is becoming urgently necessary. This is because of the potential for flood damage and the increasing concern about the adverse effects of non-point source pollution on surface water quality. This study evaluated the perfomiiance of a precipitation and runoff model in an agricultural watershed, developed procedures to visualize simulation results from the model, and visualized results of simulations of different land management scenarios. Thus, a distributed-parameter hydrologic modeling system was used to simulate surface runoff from a watershed in east central Iowa. A geographic information system (GIS) was used in input data preparation. The results from the simulation model were then used as input to a visualization process. The visualization process developed in this research was used to display simulated streamflow resulting from different land management practices. Through visualization of modeling results, decision-makers can obtain improved understanding of phenomena and, hopefully, make more informed management decisions. Watershed Hydrologic Modeling The computer modeling component of the research focused on the Precipitation Runoff Modeling System (PRMS) developed by Leavesley et a/.(1983). Previous studies that used PRMS have been confined mainly to watersheds und...

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Spatial–Temporal Analysis of Urban Air Pollution

Cited by 10 publications

References 13 publications

Modern geostatistics: computational BME analysis in the light of uncertain physical knowledge - the Equus Beds study

Modern geostatistics: computational BME analysis in the light of uncertain physical knowledge - the Equus Beds study

Advances in 3D visualization of air quality data

Simulation modeling and visualization of hydrologic response of an agricultural watershed

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