TY - JOUR
T1 - An algorithm for real-time tomography of gas concentrations, using prior information about spatial derivatives
JF - Atmospheric Environment
Y1 - 2001/06//
SP - 2827
EP - 2835
A1 - Phillip N. Price
A1 - Marc L. Fischer
A1 - Ashok J. Gadgil
A1 - Richard G. Sextro
KW - Air Flow
KW - computed tomography
KW - Concentration mapping
KW - Optical remote sensing
KW - pollutant dispersion
AB - We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as smooth basis function minimization (the best method previously available), yet is about 100 times faster.
VL - 35
IS - 16
JO - Atmospheric Environment
ER -
TY - JOUR
T1 - Rapid Measurement and Mapping of Tracer Gas Concentrations in a Large Indoor Space
JF - Atmospheric Environment
Y1 - 2001/
SP - 2837
EP - 2844
A1 - Marc L. Fischer
A1 - Phillip N. Price
A1 - Tracy L. Thatcher
A1 - Carrie A. Schwalbe
A1 - Mathias J. Craig
A1 - Emily E. Wood
A1 - Richard G. Sextro
A1 - Ashok J. Gadgil
KW - Air Flow
KW - computed tomography
KW - Optical remote sensing
KW - Tracer gas measurement
AB - Rapid mapping of gas concentrations in air benefits studies of atmospheric phenomena ranging from pollutant dispersion to surface layer meteorology. Here we demonstrate a technique that combines multiple-open-path tunable-diode-laser spectroscopy and computed tomography to map tracer gas concentrations with approximately 0.5 m spatial and 7 s temporal resolution. Releasing CH4 as a tracer gas in a large (7 m×9 m×11 m high) ventilated chamber, we measured path-integrated CH4 concentrations over a planar array of 28 “long” (2–10 m) optical paths, recording a complete sequence of measurements every 7 s during the course of hour-long experiments. Maps of CH4 concentration were reconstructed from the long path data using a computed tomography algorithm that employed simulated annealing to search for a best fit solution. The reconstructed maps were compared with simultaneous measurements from 28 “short” (0.5 m) optical paths located in the same measurement plane. On average, the reconstructed maps capture ∼74% of the variance in the short path measurements. The accuracy of the reconstructed maps is limited, in large part, by the number of optical paths and the time required for the measurement. Straightforward enhancements to the instrumentation will allow rapid mapping of three-dimensional gas concentrations in indoor and outdoor air, with sub-second temporal resolution.
VL - 35
ER -
TY - JOUR
T1 - Stationary and Time-Dependent Indoor Tracer-Gas Concentration Profiles Measured by OP-FTIR Remote Sensing and SBFM Computed Tomography
JF - Atmospheric Environment
Y1 - 1997/03//
SP - 727
EP - 740
A1 - Anushka C. Drescher
A1 - Dooyong Park
A1 - Michael G. Yost
A1 - Ashok J. Gadgil
A1 - Steven P. Levine
A1 - William W. Nazaroff
KW - computed tomography
KW - gas monitoring
KW - indoor air quality
KW - Remote sensing
AB - Measurement of gas concentrations in indoor air using optical remote sensing (ORS) and computed tomography (CT) has been suggested but not thoroughly investigated. We present experiments in which one time-varying and 11 different steady-state tracer-gas concentration profiles were generated in a ventilated chamber and sampled in a horizontal plane by an open-path Fourier transform infrared (OP-FTIR) spectrometer for subsequent CT inversion. CT reconstructions were performed using the recently developed smooth basis function minimization (SBFM) technique. The CT reconstructions were compared with simultaneously gathered point-sample concentration measurements. Agreement between the two sampling methods was qualitatively very good, with concentration profiles generated by both methods showing the same features of peak location and shape. Quantitative agreement was generally good to within 50%. We discuss the sources of discrepancy and suggest directions for future research, especially with regard to monitoring time-dependent processes. With further refinements in the SBFM algorithm and improvements in optical remote sensing hardware, this technique promises to yield rapid and accurate measurements of the spatial distribution of gases in indoor environments.
VL - 31
IS - 5
U1 - 3

ER -
TY - JOUR
T1 - Novel Approach for Tomographic Reconstruction of Gas Concentration Distributions in Air: Use of Smooth Basis Functions and Simulated Annealing
JF - Atmospheric Environment
Y1 - 1996/03//
SP - 929
EP - 940
A1 - Anushka C. Drescher
A1 - Ashok J. Gadgil
A1 - Phillip N. Price
A1 - William W. Nazaroff
KW - computed tomography
KW - gas monitoring
KW - Remote sensing
KW - simulated annealing
AB - Optical remote sensing and iterative computed tomography (CT) can be applied to measure the spatial distribution of gaseous pollutant concentrations. We conducted chamber experiments to test this combination of techniques using an open path Fourier transform infrared spectrometer (OP-FTIR) and a standard algebraic reconstruction technique (ART). Although ART converged to solutions that showed excellent agreement with the measured ray-integral concentrations, the solutions were inconsistent with simultaneously gathered point-sample concentration measurements. A new CT method was developed that combines (1) the superposition of bivariate Gaussians to represent the concentration distribution and (2) a simulated annealing minimization routine to find the parameters of the Gaussian basis functions that result in the best fit to the ray-integral concentration data. This method, named smooth basis function minimization (SBFM), generated reconstructions that agreed well, both qualitatively and quantitatively, with the concentration profiles generated from point sampling. We present an analysis of two sets of experimental data that compares the performance of ART and SBFM. We conclude that SBFM is a superior CT reconstruction method for practical indoor and outdoor air monitoring applications.
VL - 30
IS - 6
U1 - 3

ER -