G-AQFS: Grid Computing Exploitation for the Management of Air Quality in Presence of Complex Meteorological-Circulations

By Aloisio, G.; Cafaro, M. et al. | Journal of Digital Information Management, June 2004 | Go to article overview

G-AQFS: Grid Computing Exploitation for the Management of Air Quality in Presence of Complex Meteorological-Circulations


Aloisio, G., Cafaro, M., Cesari, R., Mangia, C., Marra, G. P., Miglietta, M., Mirto, M., Rizza, U., Schipa, I., Tanzarella, A., Journal of Digital Information Management


Abstract. Leveraging Grid Computing technology, i.e. the virtualization of distributed computing and data resources such as processing, network bandwidth and storage capacity to create a single system image, we present a Grid Air Quality Forecast System (G-AQFS). The Modeling system consists of meteorological and dispersion models coupled in cascade. The computational workflow of the Modeling system is defined by means of DAGs (Direct Acyclic Graph). A simple system is presented to manage and schedule the computational Grid resources. In particular, the algorithm developed for the Work Flow Scheduler named Depth-First Search Job with Priority (DFSP) is illustrated. As case study the system has been applied over Salento area, in the Apulia region (South-eastern Italy), to simulate ground level ozone concentration. Model predictions have been compared with field measurements, with reasonable results.

Keywords: Air Quality and Atmospheric Modeling, Computational Grid, Grid Computing, Globus Toolkit

1. Introduction

The management of air quality is a quite complex task: it involves identification of the sources of materials emitted into the atmosphere, estimation of the emission rates of pollutants, understanding of transport and diffusion of the substances and knowledge of the physical and chemical transformation processes that can occur during that transport. Mathematical models, putting together all these aspects, can represent a fundamental tool not only to assist environmental authorities in planning control measures, but also to improve the understanding of the emissions, chemistry, and meteorology used to drive them.

Coastal areas are often preferred sites for industrial development. The meteorology of such regions can adversely affect transport and dispersion of air pollutants and cannot be generally obtained with simplified models, which assume that the flow is stationary and homogeneous. [1]. In the presence of coastlines and orography, we have complex circulations, characterized by large horizontal and vertical variations of meteorological parameters, that are caused by the different diurnal heating cycle, at the sea/land boundary [2]. In particular, in a flat straight peninsula, small scale temporal and spatial variations of the wind field and of the boundary layer structures are present, for the development and overlapping of different thermal circulations. The ground level impact of pollutants is determined by non-stationary 3D trajectories, which should be computed for a correct pollutant transport and dispersion calculation. Therefore, a combined Modeling system, that couples atmospheric flows with dispersion and chemistry is needed. This is particularly true for photochemical pollution, where non-linear chemistry is combined with meteorological effects, that strongly influence the maximum ozone concentration: primary and secondary pollutants may also be transported far away from the area where they are emitted and produced. It happens very often that high ozone levels are reached not close to the areas in which precursors are emitted, but in areas downwind the source.

The emerging Grid technology offers the resources needed to perform complex atmospheric and climate simulations. These simulations may ultimately be used to assess the impacts of global climate change at the regional scale. According to Foster et al., Computational Grid [3] is a collection of distributed, possibly heterogeneous resources which can be used as an ensemble to execute large-scale applications. By using these resources, it is possible to access information about the grid components, locate and schedule resources, communicate between nodes, access programs and data sets within data archives, measure and analyze performance and finally authenticate users and resources. We exploit the Globus Toolkit [4], the de facto middleware standard for computational grid, offering the power and security needed to develop atmospheric Modeling applications. …

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