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Abstract: Two methods were described to estimate interference in the measurements of infrared (IR) photoacoustic multi-gas analyzer (PAMGA). One is IR spectroscopic analysis (IRSA) and the other is mathematical simulation. An Innova 1412 analyzer (AirTech Instruments, Ballerup, Denmark) with two different filter configurations was used to provide examples that demonstrate the two methods. The filter configuration in Example #1 consists of methane (CH₄), methanol (MeOH), ethanol (EtOH), nitrous oxide (N₂O), carbon dioxide (CO₂), and water vapor (H₂O), and in Example #2 of ammonia (NH₃), MeOH, EtOH, N₂O, CO₂, and H₂O. The interferences of NH₃ as a non-target gas in Example #1 were measured to validate the two methods. The interferences of H₂O and NH₃ as target gases in Example #2 were also measured to evaluate the analyzer’s internal cross compensation algorithm. Both simulation and experimental results showed that the interference between the target gases could be eliminated by the internal cross compensation algorithm. But the interferences of non-target gases on target gases could not be addressed by the internal cross compensation, while they could be assessed by the IRSA and mathematical simulation methods. If the IR spectrum of a non-target gas overlaps with that of target gas A at filter A, it could affect not only gas A (primary interference), but also other target gases by secondary interference (because the IR spectrum of gas A overlaps with gas B at filter B and thus affects gas B measurements). The IRSA and mathematical simulation methods can be used to estimate the interference in IR PAMGA measurements prior to purchase or calibration of the unit.

Abstract: The present study examined the sensitivity of sulfur dioxide (SO₂), nitric oxide (NO), and nitrogen dioxide (NO₂) concentrations to the important factors affecting air quality inside a public transportation bus. Additionally, this study quantified the in-bus contaminant concentrations in relation to the ranked statistically significant variables. The independent variables to which the monitored contaminant concentrations are the most sensitive to were determined using regression trees and the analysis of variance. A comprehensive one-year database, of the monitored contaminant concentrations and the independent factors that affect an indoor microenvironment (meteorology, monitoring periods, outdoor sources, and ventilation settings) was developed to study the sensitivity of monitored in-bus contaminants. SO₂ concentrations were extremely sensitive to the month, weather conditions, and heavy vehicles. NO concentrations were sensitive to the month/season, ventilation, and ambient temperature; while NO₂ concentrations were additionally sensitive to the monitoring period and the ambient mixing ratio. Quantified in-bus relationships revealed NO and NO₂ concentrations to be less than 0.6 ppm and 0.1 ppm, respectively. SO₂ concentrations of 0.4 ppm were observed in the fall-winter months, when the lead heavy vehicles were at a minimum density of 56 per hour; < 0.4 ppm SO₂ concentrations remained for the rest of the year.