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Air pollution at the single-particle level: integrating atmospheric measurements with mathematical models

Citation

Bhave, Prakash Viththal (2003) Air pollution at the single-particle level: integrating atmospheric measurements with mathematical models. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechETD:etd-05252003-091827

Abstract

Particulate air pollution is of growing concern in the United States and around the world. Elevated concentrations of aerosols (solid particles and liquid droplets suspended in air) are correlated with increased cases of lung cancer, cardiopulmonary disorders, and human mortality. A detailed understanding of the size, chemical composition, and concentration of atmospheric particles is needed to assess their effects on human health, as well as on regional visibility and global climate. One can acquire such knowledge through direct measurements, or by utilizing mathematical air quality models. New and innovative instruments allow us to measure the size and composition of individual particles, rather than to infer aerosol chemical properties from bulk particulate matter samples. Concurrently, air quality models have been developed to numerically simulate the emissions of discrete particles, and their transport and chemical evolution in the atmosphere. This thesis focuses on how to integrate and compare measurements taken by state-of-the-science single-particle instruments with the air pollutant properties calculated using state-of-the-science mathematical models. A 1996 field experiment conducted in the Los Angeles air basin serves as the case study for this thesis research. Comparisons of model calculations against single-particle observations identify specific areas where model improvements are needed, and also identify important areas for future instrumental development. These comparisons contribute to our understanding of atmospheric pollution at the single-particle level, and ultimately, may provide tremendous value to policy makers who are seeking least-cost solutions to urban and regional air quality problems. After presenting initial omparisons of single-particle measurements and model results, efforts to quantify and categorize the single-particle chemical composition data are described. The quantitatively reconstructed single-particle measurements are compared with mathematical model calculations of the atmospheric aerosol mixing characteristics. Finally, an example is presented of how the model and measurement combination enhance our ability to reduce particulate pollution in the air we breathe.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:model evaluation; neural network; single particle mass spectrometry; source-oriented model
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Environmental Science and Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Seinfeld, John H. (advisor)
  • Cass, Glen Rowan (advisor)
Thesis Committee:
  • Seinfeld, John H. (chair)
  • Kleeman, Micheal J.
  • Wennberg, Paul O.
  • Flagan, Richard C.
Defense Date:25 March 2003
Record Number:CaltechETD:etd-05252003-091827
Persistent URL:http://resolver.caltech.edu/CaltechETD:etd-05252003-091827
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2037
Collection:CaltechTHESIS
Deposited By: Imported from ETD-db
Deposited On:27 May 2003
Last Modified:28 Jul 2014 23:07

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