A Long-Path DOAS (LP-DOAS) developed by GIST was used to measure simultaneously atmospheric formaldehyde, monoaromatic hydrocarbons, and other trace gases over 740 and 2000 m beam paths at an urban site in Seoul, Korea during two field campaigns in February and August 2003. The mean concentrations of formaldehyde, benzene, toluene, m-xylene and p-xylene were measured to be 6.32 (±2.41), 0.78 (±0.32), 3.32 (±1.87), 0.52 (±0.24), and 0.38 (±0.18) ppbv during the two measurement periods. Based on the analysis of the ratio of the OH·-NO 2 to OH·-VOCs rate constants, it was possible to draw a conclusion that OH· reacted preferentially with NO 2 during the two measurement periods. Diurnal variation of formaldehyde was not typical of photochemically generated components during the two measurement periods in Seoul. Results from the linear regression fit analysis and diurnal variation of the measured species support that formaldehyde measured could be from primary sources (vehicle exhaust and heating burners) in winter and from primary and secondary sources (photochemical process) in summer. Monoaromatic hydrocarbons were likely emitted from the primary sources.

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We propose a hard expectation-maximization-based normalized matched filter (EM-NMF) for the detection of chemical warfare agent (CWA) clouds under background contamination. The NMF, which is one of the most powerful detectors, requires background statistics calculated from a background training dataset. However, in practice, because the training dataset is likely to contain CWA-on background pixels, the performance of the NMF is severely degraded. This phenomenon is referred to as background contamination. To address this issue, we propose an algorithm that estimates the posterior probability of each pixel belonging to either the background or the CWA class. The optimal posterior probabilities are obtained by maximizing the loglikelihood of a contaminated dataset using the EM algorithm. Based on the posterior probability, we extract CWA-free background pixels from the contaminated dataset and design a hard EM-NMF with extracted CWA-free background pixels. We demonstrate that the proposed algorithm is an effective solution for background contamination, via experimental results conducted with actual CWA data measured by a Bruker HI-90 instrument in an outdoor setting as well as synthetic CWA data.

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