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Optics & Infrared Sensing

Optics & Infrared Sensing

Atmospheric Background Modeling

Performance of chemical sensors measured in a laboratory almost always overestimates the performance of these same sensors in the real world. Frequently the major cause for this poorer performance is the presence of variable concentrations of background chemicals in the air. Examples include humidity, smoke from a menthol cigarettes and exhaust from diesel engines, which depends in part on the source of the diesel, the load on the engine and how well the engine is tuned-up. For sensors based on infrared spectroscopy, we have developed a Monte-Carlo model that uses PNNL's infrared spectral database to estimate the variability of infrared absorption due to background chemicals for several generic scenarios, including polluted urban environments with various levels of diesel exhaust. The included chemicals and their ranges of variations were selected based on scouring the scientific literature for this information. This model is used in selecting optimum sets of sensing wavelengths and predicting real-world performance in the early stages of instrument development rather than waiting until a fieldable prototype sensor is built to examine

Figure 1  Modeled spectral response of a sensor due to background chemical variations.
Figure 1 Modeled spectral response of a sensor due to background chemical variations. This is 1400 predicted responses of a photo-acoustic sensor as a function of infrared frequency to randomly selected amounts of 76 background chemicals including water and carbon dioxide. The spectral resolution has been degraded by the relatively broad laser linewidth used in this model.
Contact: Bret Cannon

Optics & Infrared Sensing

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