Grantee Research Project Results

1999 Progress Report: Tightly Coupling Environmental Models with Spatial Analyses in High-Performance Computing and Communications Environments

EPA Grant Number: R825205
Title: Tightly Coupling Environmental Models with Spatial Analyses in High-Performance Computing and Communications Environments
Investigators: Karimi, Hassan A.
Institution: MCNC / North Carolina Supercomputing Center
EPA Project Officer: Aja, Hayley
Project Period: October 28, 1996 through October 27, 1999 (Extended to October 27, 2001)
Project Period Covered by this Report: October 28, 1998 through October 27, 1999
Project Amount: $581,082
RFA: High Performance Computing (1996) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Environmental Statistics , Human Health

Objective:

The objectives of this project are to: (1) investigate efficient spatial analysis algorithms for the processing of emissions in air quality modeling systems; (2) design and develop parallel algorithms for the adaptive grid (to reduce uncertainty) and the spatial analyses for the processing of emissions in air quality modeling; (3) investigate tightly coupling of spatial analyses, emissions models, and air quality models (AQMs) for adaptive grid air quality modeling in high-performance computing and communications (HPCC) environments; (4) refine the existing adaptive grid (e.g., adaptation criteria) for air quality modeling; (5) perform exploratory adaptive air quality modeling simulations on high-performance computing (HPC) environments; (6) investigate generalization of developed techniques for other environmental applications; and (7) make recommendations for future research and operational use of developed procedures.

Progress Summary:

Adaptive grids have the potential to reduce uncertainties in the predictions of AQMs. Through a process called grid clustering, adaptive grids allocate more computational resources over areas of interest in large domains. With increased resolution in such areas, adaptive grid AQMs can capture the details of atmospheric dynamics and chemistry more efficiently than their uniform grid counterparts. The vicinities of emission sources are of particular interest with their large pollutant concentration gradients and rapid chemical kinetics that transform primary (emitted) pollutants into complex intermediates, towards their ultimate fate as secondary pollutants. Identifying the location of emission sources over a grid that is continuously moving during the AQM simulations requires efficient geographic information system (GIS) algorithms. Several advanced GIS algorithms, including polygon/polygon intersection and line/polygon intersection, and an adaptive grid technique, based on the Dynamic Solution Adaptive Grid Algorithm (DSAGA3D) of Benson and McRae (1991), were developed. To identify the location of emission sources over the adaptive grid, the GIS algorithms and the adaptive grid technique were linked. A prototype of the developed GIS algorithms and the adaptive grid technique currently is being tested using different case studies.

Future Activities:

Our plan for the final year of the project is to complete the testing of the developed GIS algorithms and the adaptive grid technique. We will publish the results in scientific journals and conference proceedings.

Journal Articles on this Report : 1 Displayed | Download in RIS Format

Publications Views
Other project views:	All 5 publications	1 publications in selected types	All 1 journal articles

Publications
Type	Citation	Project	Document Sources
Journal Article	Hwang D, Karimi HA, Byun DW. Uncertainty analysis of environmental models within GIS environments. Computers & Geosciences 1998;24(2):119-130.	R825205 (1999)	Full-text: ScienceDirect-Full Text HTML Exit Abstract: ScienceDirect-Abstract Exit Other: ScienceDirect-Full Text PDF Exit

Supplemental Keywords:

air, chemical transport, innovation technology, engineering, mathematics, modeling, analytical, GIS, air quality., RFA, Scientific Discipline, Ecosystem Protection/Environmental Exposure & Risk, Ecological Risk Assessment, computing technology, adaptive grid model, cross media problem solving, data analysis, parallel numerical solvers, air quality modeling, emissions data, load balancing techniques, geographical information systems, HPCC, spatial modeling, tightly coupling environmental models, GIS, information technology, parallel computing, computer science

Progress and Final Reports:

Original Abstract

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.