Strategies for the Control of Particulate Air Quality: Least-Cost Solutions Based on Receptor-Oriented Models

Abstract

An approach to the identification of minimum cost solutions to regional particulate air quality problems is described that is potentially more accurate and less resource-intensive than prior methods based on atmospheric diffusion models. A receptor-oriented modeling procedure is used to determine the source contributions to ambient air quality at each monitoring site in an air basin. The air quality model is matched to a linear programming algorithm and is used to compute the least costly combination of emission controls needed to meet air quality objectives. The method is illustrated by application to the problem of particulate air quality control using historically available data for the Los Angeles basin.

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