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 most common local contamination source is often emissions from power generation. Typically, power generation burns hydrocarbon fuels (such as diesel) and emits a range of combustion products that are often the target species being measured in the background atmosphere.

Identification of periods of contamination is performed via a variety of methods depending on the contamination source and the target research question. A commonly used and reasonably reliable method for identification of local point source contaminants is by simple wind direction and speed criteria e.g.and references therein. This method aims to capture the exhaust plume diffusion processes using the two wind measurements as proxies. It is a robust method in environments where background composition is similar to the contamination source, such as in urban areas. However, because of the oversimplified parameterisation, very conservative bounds are often required, which results in the removal of often significant numbers of contaminant-free data. In addition, this method assumes relatively uniform flow characteristics and will fail when atmospheric recirculation results in measurements of contaminated air from directions outside the specified range. Figure  exemplifies this issue, where cloud condensation nuclei (CCN) number concentrations are found to be unreasonably high for the marine dataset used here, even after a wind speed and direction filter is utilised.

Depending on the environment, a combination of wind criteria and in situ composition measurements can be used to help overcome the recirculation issue. For example, high concentrations of nitrogen oxides (NOx) produced from combustion processes will react rapidly with background ozone (O3), resulting in O3-depleted air, which will only regenerate hours downwind through NOx chemistry and photolysis processes . The use of O3 can improve wind-based filters to help identify recirculation, depending on the timescale of interest e.g.. However, the problem of false-positive identification remains as long as measurements of ancillary data are used for identification. Ideally, identification of contaminated air would use only measurements of species emitted directly by the source itself in order to minimise false-positive contaminant identification and maximise the usable data from a dataset.

In the current study, 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 and references therein. Emitted species include carbon dioxide (CO2), carbon monoxide (CO), NOx, hydrocarbons and high concentrations of aerosols (condensation nuclei, CN), which include those whose composition is primarily black carbon (BC) as well as those that can act as CCN. Measurements of CO, CO2, BC and CN are utilised for the development of this exhaust identification algorithm as they have clear signals above the background atmosphere and are measured routinely on the vessel.

Figure  shows an example period of data from the vessel that illustrates the different signals resulting from exhaust influence that must be characterised in the algorithm. Exhaust influence in CN, CO and CO2 data is obvious with striking enhancements above variable background signals throughout the sample period. BC data are generally close to zero, with exhaust influence obvious when a signal appears out of the noise. Strong perturbations over extended periods, such as those observed on 18 May, are indicative of direct exhaust influence. Smaller signals, such as those observed in CN data on 19 May, or in BC data on 20 May, indicate a more dilute influence, with sampling likely occurring on the wavering edge of the exhaust plume.

Not all measured parameters respond to the exhaust to the same extent, or necessarily at all. A few examples of this are shown when looking at the time series of the parameters (Fig. ). Generally when this occurs, a signal is observed in CN data, but is absent in the other species. This is likely a result of the magnitude of differences in exhaust signal in each parameter, as well as sensitivity of the measurement techniques of the different species. Figure  clearly shows the magnitude differences of the various instruments with exhaust strikes. Exhaust strikes in CN are observed as perturbations of almost 4 orders of magnitude, while those in BC, CO and CO2 are factors of 10, 0.2 and 0.01, respectively. Being a simple counting instrument, the condensation particle counter (CPC) is sensitive to particle concentrations down to 1 cm-3. For the CO and CO2 measurements, although precision is high, the flow-through-cell technique utilised results in physical integration of the sample over a minute, thereby smoothing out any perturbations. For BC measurements, the detection limit of the instrument is 0.05 µgm-3 over a 10 min average. At 1 Hz time resolution, we are still able to get a useful signal (for the current purpose) at 0.01 µgm-3 mass resolution; however the instrument is clearly missing significant exhaust influence.

The RV Investigator is a blue-water research vessel capable of traversing from the ice edge to the Equator. The types of atmospheres it encounters range from pristine background, to continental (e.g. while sampling near the coast), to urban environments (e.g. while in port). An important objective of this algorithm is the ability to distinguish the local ship exhaust from the atmosphere of interest – a task which becomes particularly difficult in the more polluted environments such as those downwind of large urban centres. In this study, the dataset utilised for development contains influences from urban and background marine regions (as shown in Figs.  and ) by which differentiation from ship exhaust can be achieved. The ship track of the utilised voyage is shown in Fig. .

In this study, an algorithm is developed that produces an exhaust identification product that is published alongside other publicly available datasets from this platform. The algorithm aims to accurately identify exhaust from the ship itself, distinct from other polluted atmospheres such as urban centres, and minimise false-positive identification in order to retain as much valuable data from this mobile platform as possible. 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.

The RV Investigator (schematic shown in Fig. ) is a state-of-the-art research platform commissioned in 2015 by the Australian government. The vessel is designed for blue-water research and is capable of spending up to 300 d per year at sea, with a single voyage up to 60 d and over 10 000 nautical miles. Propulsion and power are provided by two diesel–electric engines together with three 3000 kW, nine-cylinder diesel engines. Exhaust from diesel combustion, together with waste incineration, which is emitted from a separate but co-located flue, provides the largest source of contamination to atmospheric measurements aboard the platform.

The vessel has been purpose built with two dedicated atmospheric laboratories along with a custom-designed air sampling inlet located above the ship's bow, approximately 18.4 m above sea level. The aerosol laboratory is situated directly underneath the air sampling inlet fore of the anchor well, such that the distance between sampling and instrumentation, and thus sample losses, is minimised (total distance to the aerosol laboratory's sampling manifold is ∼8 m). The aerosol laboratory houses instrumentation for the measurement of aerosols and the reactive gas ozone. The air chemistry laboratory is situated further aft in the vessel at the fore of the superstructure (total distance from main sample inlet to the air chemistry laboratory's sampling manifold is ∼38 m), and houses instrumentation for the measurement of less reactive atmospheric species such as greenhouse gases and volatile organic compounds.

The RV Investigator houses a range of permanent instrumentation. These instruments are run continuously throughout every voyage of the RV Investigator (except for when instruments are removed for maintenance or faults) and after data have been calibrated, and quality assurance and control procedures have been performed, data are made publicly available. Of particular relevance to this study is the measurement of CO, CO2, BC, CN and meteorological measurements. Each of these parameters will be described in detail in future publications documenting the ongoing measurements of the vessel; however a brief overview of these measurements is given here. For this analysis, CCN data are utilised as an independent parameter by which the exhaust identifier is tested. The dataset considered in this paper utilised CN and CCN data captured by instrumentation deployed specifically for this voyage, and thus will be described separately. It is worth noting that both CN and CCN instrumentation have more recently become part of the permanent ongoing instrument suite and will be described in a future publication and made publicly available alongside other aerosol data from the platform.

An important outcome of the current work is to make publicly available an exhaust identification data product that will be published alongside other atmospheric datasets from the vessel in order to assist data users in their analyses. For the present paper, the exhaust identification product has been developed using data from the RV Investigator voyage IN2016_V03 (see , for voyage track), and data utilised and produced in this paper are available from .

The primary task of the algorithm is that of distinguishing between two distinctly different signals in our data. Because of the magnitude of the difference, a first pass of the exhaust identification is simply an application of outlier detection algorithms. However, on closer inspection, the variability of the exhaust signal due to variations in source strength, dilution and plume location sampling, as well as the shear length of time that the exhaust can influence measurements (from seconds to days), makes many of the more well-known detection algorithms unsuitable to this problem. This is discussed more in Appendix Sect.  where a number of algorithms, including fast Fourier transform, z score and modified z score, double exponential smoothing, and histogram methods, were tested and found to be unsuitable. Hence this complicates the goal of the algorithm to differentiate between these two distinct but varying signals (i.e. exhaust and ambient in a range of environments).

Exhaust identification is performed primarily utilising the intersection between four parameters commonly emitted in fossil fuel combustion processes, namely CO, CO2, BC and CN. Figure  shows the variability of these species during periods of exhaust influence and within background air (defined here as not influenced by exhaust from the measurement platform, the RV Investigator). Distinct signals are observed in all four variables; however it is important to note that not all signals respond simultaneously. This concept is discussed in detail later in the paper.

Because of the differences in their exhaust responses, identification is performed on each of the parameters separately at 1 Hz, after which they are combined (aligned by time) and an additional window filter is applied to remove neighbouring values that are not captured completely by the parameters themselves. Each instrument connected to the Investigator's sampling system will also exhibit temporal variations in their responses to exhaust strikes due to differences in residence and detector response times. Because of this, it is impossible to create a single exhaust identification product that can be applied to every instrument that collects data on this platform. To effectively achieve a perfectly exhaust-free dataset for each instrument without removing substantial data that are free from exhaust, identification should ideally be performed on each dataset individually. Nevertheless, the creation of this exhaust identifier product is useful in that it creates a first-pass filter that identifies the vast majority of the exhaust influence. With this in mind, a relatively conservative approach is adopted in order to strike a balance between not identifying periods of exhaust influence, and the false-positive identification of background data as exhaust. Since the product is not used to filter published datasets, but instead is published alongside other data, it is left to the end user to determine whether more stringent criteria should be applied to specific datasets than the relatively conservative approach adopted here.

Figure  shows a subset of data to illustrate the exhaust filter when applied to the CCN dataset. CCN are used here as an independent dataset to test the exhaust filter algorithm and ensure its applicability beyond the parameters used in the algorithm itself. In addition, exhaust strikes are easily visible in the CCN dataset, making it useful for this purpose.

Although not 100 % effective, the algorithm removes the vast majority of exhaust influence and its effectiveness, particularly compared to other methods, is clearly apparent (Fig. ). It is clear from Fig. , where each parameter of the filter is applied separately, that none of the parameters are capable of entirely capturing the exhaust influence individually. The CN filter is the most effective, presumably because the exhaust signal is orders of magnitude higher than background values and the response time is rapid. Nevertheless, a significant fraction of exhaust periods make it through the CN filter. When all parameters are used together, the exhaust filter improves dramatically, although a small fraction of exhaust values remain. 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.

Figure  shows different combinations of the individual filters to demonstrate the effectiveness of each filter. Combining both Figs.  and indicates that CN is the most effective parameter, followed by BC, CO and CO2. By itself, CN removes the vast majority of the exhaust influence, but alone is incomplete. While this suggests that a simple filter utilising CN and only one of the other three parameters could be used to produce a similarly effective filter, in practice, having all three measurements (i.e. BC, CO and CO2) provides important redundancy. Currently, if problems occur with the CN measurements, the effectiveness of the exhaust filter is significantly reduced, as shown by Fig. h. Given the importance of the CN data to being able to effectively identify exhaust, instrumental redundancy for CN measurements is an important feature of the platform that is currently being implemented.

Interestingly, there are some periods which still show short periods of exhaust in the filtered CCN data, as shown in Fig. . Here, the exhaust is easily identified by the CN filter algorithm; however after applying the exhaust to the time-synchronised CCN data, the exhaust signal is delayed in the CCN data by about 10 s, presumably due to the longer residence time of the CCN instrument. It is possible to alter the algorithm in such a way that the 20 min window applies to any period identified as exhaust (rather than having the threshold described in Sect. ); however this has the immediate ramification of large losses of data that would otherwise be classified as background, which would be unacceptable for this purpose. Instead, this exhaust identifier has been designed to be used as an initial step, and if more stringent bounds are required by the end user, a more strict window filter can be applied at that time. In addition, individual datasets should be analysed for any remaining exhaust to account for differences in residence time and sampling regimes.

Application of the algorithm to other atmospheric datasets is an important verification step beyond that of CCN, which is a very similar measurement to that of CN measurements. In Fig. , 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; however 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.

Comparison of the algorithm to the traditional wind-based filter shows significant advantages. When applied to this dataset, the algorithm is able to recover 5 % (1 h) of data that the wind filter identified as exhaust, and removes 26 % (37 h) of data that the wind filter identified as clean. This is shown most clearly in Fig.  (also apparent in Figs.  and ). Data recovery is obvious in this figure from data present between relative wind directions of 90 and 270∘, while the high concentrations observed outside these ranges, which are exhaust signal, is removed by the algorithm. From the time series case study of Fig. , it can be seen that many of the exhaust signals missed by the wind filter are those on the edges of a large exhaust period, or simply just small exhaust strikes that might occur when the ship is turning.

A ship exhaust identification algorithm is described that utilises only components of the combustion exhaust, rather than commonly utilised ancillary data such as wind speed and direction. CO, CO2, BC and CN data are used as exhaust indicators and together with surrounding time-window removal, a robust exhaust identification method results. Statistical methods feature heavily in the algorithm in order to avoid, as much as possible, cut-off thresholds that can be subjective. The algorithm exhibits significantly improved performance compared to more traditional filters, identifying all of the exhaust periods (26 % of data identified as clean by wind filters were identified as exhaust by this algorithm), as well as recovering data falsely identified by other overzealous or indiscriminate methods (the algorithm recovered 5 % of data that wind-based filters removed), thereby optimising usable data. The algorithm is applied directly to data from the RV Investigator for which it was specifically developed and the resulting data product will be made available alongside other publicly available data from the research platform.