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C H Spiegelman

Publications and source records attributed to C H Spiegelman.

3 recordsLinked to original sources

A transparent tool for seemingly difficult calibrations: the parallel calibration method.

A new easy-to-understand calibration method for the analysis of spectral data is developed. The "parallel calibration" method is logically simple and intuitive yet often provides an improvement over more complex standard calibration methods. A description of the algorithm with a technical justification for the parallel algorithm is presented, underscoring the simplicity of the approach. In addition, performance as compared to that of the standard methods of classical least-squares (CLS) and partial least-squares (PLS) regression is studied. Calibrations are carried out on a computer-generated simulation data set as well as two scientific data sets. The results show that the parallel method gives results comparable to or better than those of CLS and PLS methods in terms of mean squared error.

Algorithms↗

Reported emissions of organic gases are not consistent with observations.

Regulatory agencies and photochemical models of ozone rely on self-reported industrial emission rates of organic gases. Incorrect self-reported emissions can severely impact on air quality models and regulatory decisions. We compared self-reported emissions of organic gases in Houston, Texas, to measurements at a receptor site near the Houston ship channel, a major petrochemical complex. We analyzed hourly observations of total nonmethane organic carbon and 54 hydrocarbon compounds from C-2 to C-9 for the period June through November, 1993. We were able to demonstrate severe inconsistencies between reported emissions and major sources as derived from the data using a multivariate receptor model. The composition and the location of the sources as deduced from the data are not consistent with the reported industrial emissions. On the other hand, our observationally based methods did correctly identify the location and composition of a relatively small nearby chemical plant. This paper provides strong empirical evidence that regulatory agencies and photochemical models are making predictions based on inaccurate industrial emissions.

Journal Article↗

Sensitivity of trends in geometric mean blood levels to random measurement errors.

A statistical model is investigated that expresses observations, such as blood lead levels, as an additive function of true levels and random measurement errors. Both empirical results (obtained from a series of computer simulation experiments) and theoretical results indicate how certain summary statistics for the observations vary in response to random measurement errors. Such results are applied to a very large data base of pediatric blood lead levels collected in New York City during 1970-1976, and they indicate that the observed trends in geometric mean blood lead levels are not significantly altered by the possible presence of measurement errors.

Child, Preschool↗