Practical applications of open-path Fourier transform infrared spectrometry and classical least squares automated spectral interpretation routines for atmospheric measurements

Open-path Fourier transform infrared spectrometry (FT-IR) has been under development for the last five to six years and is quickly becoming a valuable tool in the environmental scientist's arsenal of analytical techniques. Based on FT-IR technology that has been in use in the laboratory for 25 years, open-path FT-IR spectrometry is a reliable and versatile means of monitoring multiple volatile organic compounds simultaneously.; There are two components of environmental monitoring with the open-path FT-IR method. The first component is the instrumentation and on-site collection of data. The second component of environmental monitoring with the open-path FT-IR method is data analysis and interpretation.; There are many factors that must be considered in the process of collecting data using the open-path FT-IR method. The advantages and limitations of open-path FT-IR are numerous and vary from one application of the method to the next. The practical advantages and limitations of data collection using the open-path FT-IR method will be discussed within this dissertation. These advantages and limitations are then illustrated by real-world applications of the open-path FT-IR method at industrial sites.; Once open-path FT-IR data is collected, it must then be analyzed. There are two approaches that can be followed when analyzing open-path FT-IR data. The first is to manually look at and interpret each field spectrum collected, and quantify these spectra using an interactive spectral subtraction program. The second approach to open-path FT-IR data analysis is to rely upon an automated spectral interpretation routine that is based on least squares methods. The relative merits of these two approaches will be discussed. In particular, the effects of spectral resolution on the results of the automated spectral interpretation routine are investigated in depth.