Identification of platform exhaust on the RV Investigator

• Published in: Atmospheric measurement techniques Vol. 12; no. 6; pp. 3019 - 3038
• Main Authors: Humphries, Ruhi S, McRobert, Ian M, Ponsonby, Will A, Ward, Jason P, Keywood, Melita D, Loh, Zoe M, Krummel, Paul B, Harnwell, James
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 04.06.2019; Copernicus Publications
• Subjects: Air-sea interaction; Algorithms; Analysis; Atmosphere; Atmospheric data; Black carbon; Carbon; Carbon dioxide; Carbon monoxide; Combustion; Data; Design and construction; Earth; Earth surface; Filters; Incineration; Maintenance and repair; Marine sciences; Mixing ratio; Oceans; Parameter identification; Research vessels; Ships; Statistical analysis; Statistical methods; Technology application; Temporal variations; Vessels; Waste disposal; Wind; Wind speed
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-12-3019-2019

Oceans cover over 70 % of the Earth's surface. Ship-based measurements are an important component in developing an understanding of atmosphere of this vast region. A common problem that impacts the quality of atmospheric data collected from marine research vessels is exhaust from both diesel combustion and waste incineration from the ship itself. Described here is an algorithm, developed for the recently commissioned Australian blue-water research vessel (RV) Investigator, that identifies exhaust periods in sampled air. The RV Investigator, with two dedicated atmospheric laboratories, represents an unprecedented opportunity for high-quality measurements of the marine atmosphere. The algorithm avoids using ancillary data such as wind speed and direction, and instead utilises components of the exhaust itself – aerosol number concentration, black carbon concentration, and carbon monoxide and carbon dioxide mixing ratios. The exhaust signal is identified within each of these parameters individually before they are combined and an additional window filter is applied. The algorithm relies heavily on statistical methods, rather than setting thresholds that are too rigid to accommodate potential temporal changes. The algorithm is more effective than traditional wind-based filters in removing exhaust data without removing exhaust-free data, which commonly occurs with traditional filters. In application to the current dataset, the algorithm identifies 26 % of the wind filter's “clean” data as exhaust, and recovers 5 % of data falsely removed by the wind filter. With suitable testing, the algorithm has the potential to be applied to other ship-based atmospheric measurements where suitable measurements exist.