MOVES-Matrix and Distributed Computing For Region-Level Line Source Dispersion Analysis

This dissertation builds a framework for regional-level micro-scale pollutant dispersion analysis using MOVES-Matrix and distributed computing across multiple dispersion models (CALINE3, CALINE4, CAL3QHC, R-LINE, and AERMOD). The advanced framework for line source dispersion analysis results in huge savings in computing cost and time compared to traditional methods. However, due to the limited number of links allowed for individual model runs, line source dispersion analysis in regional-level is challenging. Preparing the extensive inputs for use in regional-level analysis across all models is also challenging (e.g., road geometry information, meteorology inputs, receptor locations, etc.). Therefore, advanced techniques to efficiently prepare the extensive input datasets are needed. This research addresses the variety of complexities across five dispersion models. Advanced techniques are implemented to efficiently prepare the extensive input datasets that are needed to undertake complex regional analyses. A case study for a large-scale transportation project (e.g., HOV to HOT conversion) is implemented to: 1) assess model performance, 2) and assess the relative environmental impacts defined by the tools for complex projects.

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