Abstract
Abstract. 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.
Highlights
When undertaking atmospheric composition and chemistry measurements, a common issue that impacts data quality is the ability to effectively identify and potentially filter out sources of contamination
The application of the window removes most of the remaining exhaust-affected data, resulting in a dataset that can be confidently used in subsequent analyses of the background atmosphere
Aerosol size distributions, measured using a scanning mobility particle sizer (GRIMM SMPS model 5.420 with M-DMA installed, GRIMM Aerosol Technik, Ainring, Germany), are shown as raw data, as well as with both the wind-based filter and the exhaust algorithm applied. Both filter methods are effective at removing much of the exhaust influence; the exhaust algorithm shows distinct advantages for more accurate exhaust identification, recovering more exhaust-free data and removing exhaust-laden data compared with the wind filter
Summary
When undertaking atmospheric composition and chemistry measurements, a common issue that impacts data quality is the ability to effectively identify and potentially filter out sources of contamination. An exhaust identification algorithm is developed for application to data collected on board Australia’s new marine research vessel (RV) Investigator utilising measurements of species emitted directly by combustion processes occurring on the ship – namely diesel combustion and waste incineration. Both combustion processes (hereafter referred to as “exhaust”) have similar emissions relative to the background atmosphere (Resitoglu et al, 2015; Johnke, 1999; Jones and Harrison, 2016, and references therein). The exhaust product is developed utilising a dataset exemplifying the range of atmospheres that are sampled and is validated by applying it to measurements of CCN that were measured simultaneously
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