CO2 was measured with cavity ring-down spectroscopy (CRDS) (O'Keefe and Deacon, 1988) using a modified flight Picarro G2301-m greenhouse gas monitor with a response time, t90, of less than 1 s, the time that is needed at a step change to reach 90 % of the final value. The instrument produces a CO2, H2O and CH4 value once every second which is obtained by sequentially measuring the three species for around 0.3 s per species.

For determining the sulfur emission factor in the form of SO2, a modified Thermo 43iTLE trace gas monitor was used. An internal UV flash lamp stimulates fluorescence of the SO2 which is proportional to its volume mixing ratio (VMR) (Luke, 1997). The instrument is custom-modified through the elimination of a “hydrocarbon kicker”, and larger pump speed yielding a response time, t90, of about 2 s to allow flow rates for the detection of short and distinct plumes. The SO2 instrument is cross-sensitive to NO, with a SO2 VMR reading corresponding to 1.5 % of the VMR of NO. The removal of the kicker, which is simply a Teflon tube coil, also makes the instrument cross sensitive to aromatic volatile organic compounds (VOC) with about 1 % of the VOC VMR, but in most cases, ships emit very little VOC (Williams et al., 2009), so this is only a problem when measuring in proximity to large VOC sources, such as refineries, since this may cause fluctuating background readings.

The emission factor of NOx was obtained with a custom modified Thermo 42TL trace gas monitor. A larger pump is used to obtain lower instrument pressures, and a modification in the software allows for the user to externally control whether NO, NOx or the zero background should be measured, instead of continuously switching between measurement and zero background. The VMR of NO is determined by a chemiluminescent reaction of NO with ozone. The intensity of the light emitted is proportional to the VMR of NO (Kley and McFarland, 1980). The instrument uses a catalytic converter that converts the NO2 to NO, so the sum of NO and NO2 (NOx) is obtained. The instrument has a response time, t90, of 1 s.

An instrument based on electric mobility, the Engine Exhaust Particle Sizer (EEPS, TSI 3090), was applied to analyze the number size distribution in the range from 5.6 to 560 nm in 32 size channels. The particles in a stream of sampled air are charged by a corona and then forced to move in an electrical field which deflects them towards a column of electrodes (Johnson et al., 2004). The EEPS is originally intended for fast particle sizing of engine exhaust, and due to its fast simultaneous sampling at 10 Hz and response time, t90, of 0.5 s it also was found to be suitable for measurements of particulate ship emissions (Hallquist et al., 2013; Jonsson et al., 2011), even from aircraft (Beecken et al., 2014).

The size distribution of bigger particles was measured with an Optical Particle Sizer (OPS, TSI 3330). The OPS measures the backscattered intensity of light pulses onto a stream of sample air with particles. The number and size of the particles is determined from the detected backscattered light flashes. The diameter of the detected particles ranges from 0.3 to 10 µm and is binned into 16 size channels. The instruments response time, t90, is 2 s.

The total number of particles was determined with TSI 3787 General Purpose Water-Based Condensation Particle Counter (CPC) (Hering et al., 2005). Sampled particles are grown in a supersaturated chamber and afterwards optically counted. This CPC measures particles bigger than 5 nm up to approximately 1 µm with a response time, t90, of less than 0.3 s. The CPC was only used during the 2012 campaign.

The calculation of emission factors is similar for gas and particle data. The evaluation of sampled plumes is illustrated in Fig. 1. After the identification of a plume, a baseline is determined and subtracted from the in-plume values. The background-corrected data, given in mixing ratios units (here in ppb or ppm), is integrated over the plume for each substituent X, and then normalized against the integrated CO2 values according to Eq. (1). Furthermore, the calculated ratio is converted to a mass-based emission factor, i.e., mass of pollutant versus mass of fuel, by scaling with the molecular weights of the species X and fuel; the latter obtained as molecular weight of carbon corrected with the assumed carbon content of the fuel, i.e., 87 %. Note that for the calculation of fuel sulfur content the species X is replaced with pure sulfur, e.g., 20 gSO2 kgfuel-1 corresponds to a fuel sulfur content (FSC) of 1 %. In the case of NOx, its molecular weight is assumed to correspond to NO2, following the IMO technical code MEPC.177(58) (MEPC, 2008).

It is common to compare the performance of different engines, especially for NOx, by using load-based emission factors, i.e., mass of pollutant versus generated crank shaft power. In order to obtain this value (specific emission factor) the mass-based emission factors is multiplied with the brake-specific fuel consumption (BSFC), which relates the consumed fuel to the generated power. This number, which varies between 160–250 gfuel kWh-1 depending on ship type, was in this study obtained from the STEAM model data (Jalkanen et al., 2009, 2012), which in turn is based on ship specific data from the IHS Maritime ship register (IHS Global, 2014). In the cases of no registered BSFC, a default value of 200 gNOx kWh-1 was assumed.

EFXg/kgfuel=MXg/mol×∫Xppb-XbgdppbdtM(C)g/mol0.87×∫CO2ppm-CO2,bgdppmdt

For the calculation of the particle number emission factor EF(PN) in Eq. (2), the excess number concentration per volume unit in the plumes was related to the excess mass concentration per volume unit of CO2 (assuming Tavg= 290 K, pavg= 101 325 Pa) using the ideal gas law, with the ideal gas constant R= 8.314 J mol-1 K-1. The emission factor of CO2 is here calculated using Eq. (1). EFPN#/kgfuel=∫PN#/cm3-PNbgd#/cm3dt∫CO2kg/cm3-CO2,bgdkg/cm3dt×EFCO2kg/kgfuel CO2kg/cm3=CO2ppm×M(CO2)g/mol×pPaRJ(mol×K)×TK×10-15

The calculation of the emission factor of the particle mass is done analogously to Eq. (1) by substituting particle number (PN) with particle mass (PM). A unity density of 1 g cm-3 was arbitrarily assumed for the sake of qualitative comparison, although diesel particle density varies with composition and size between 0.5 and 1.23 g cm-3 for particles between 50 and 150 nm (Barone et al., 2011; Virtanen et al., 2002; Petzold et al., 2008). For the estimation of the emission factor of the particle mass, the particle sized data from the EEPS and OPS instruments were used. The results for the particle sizers were additionally compensated for by the actual size dependent diffusion losses under laminar flow conditions (Hinds, 1999).

The geometric mean diameter (GMD) was calculated using Eq. (4) (Hinds, 1999). It should be noted that the EEPS measures the particle diameter Dp depending on the electro-mobility of the particles while the OPS measures diameters depending on the optical properties of the particles. GMDnm=exp⁡∑ni×ln⁡Dpi,nmN

The symbols ni and N denote the number of particles in the respective size bin and the total number of particles of all size bins, respectively. Dpi is the center diameter of the respective size bin.

Calibrations of the gas-phase instruments were performed repeatedly during the measurement campaigns. The calibration gases were obtained from the Russian D.I. Mendeleyev Institute for Metrology (VNIIM) Standard Materials Service.

In 2011, cylinders with known gas mixtures were used for CO2 (401 ppm ± 3 %, 356 ppm ± 3 %) and NO (250 ppb ± 10 %). SO2 was calibrated with a dynamic gas calibrator, based on mass flow controller GGS-03-03 from OOO Monitoring, that mixes a high concentrated SO2 (53.4 ppm) with zero air by controlling the respective mass flow. SO2 was calibrated in several steps up to 529 ppb. Because the maximum flow of the gas calibrator was less than the flow of the measurement system, the calibration gas was filled in Tedlar gas sampling bags which were subsequently used in the calibration. This procedure was only carried out twice during the campaign.

In 2012, a high-flow dynamic gas calibrator (Thermo 146i), in conjunction with a zero-air supply (Thermo 1160), was used to dilute SO2 at 63.7 ppm ± 3 % and NO at 64 ppm ± 5 % in several calibration steps to volume mixing ratios between 0 and 300 ppb for both gases. For CO2 two calibration mixtures of 365 ppm ± 3 % and 418 ppm ± 3 % were used. This gas calibrator eliminated the need for Tedlar bags and therefore the calibration could be done several times each measurement day.

The measurement precision of the gas-phase instruments was estimated from the standard deviation during the calibrations, over a period between 30 and 120 s. The measurement precision of the SO2 instrument was recorded as 3.6 % in 2012, while for 2011 it was assumed to be 5 % due to fewer calibrations. For the NOx analyzer, the standard deviation of the response was recorded to be 0.5 % in 2011 and 1.3 % in 2012, respectively.

The plume samples were corrected using calibration factors. The uncertainty due to the interpolation of the calibration factors is estimated by evaluating the mean standard deviation between two adjacent calibration points. It was 0.2 and 0.7 % for CO2 respectively for 2011 and 2012. For SO2, it was 4.7 and 2.0 % and for NO, 4.0 and 2.1 % for these years.

The size response of the particle sizers was validated for both campaigns with vaporized Diethylhexyl Sebacate (DEHS) with particle diameters, Dp, between 200 and 300 nm during the campaigns with good agreement.

The results of repeatedly measured plumes from the same ship were used to estimate the precision, i.e., random uncertainty, of the emission factor measurements from the helicopter. The 1 ×σ precision values were 19.5 % for SO2 and 23.7 % for NOx, respectively, based on the mean precision of 12 ships that were measured at least three times.

For the ground-based measurements, the random uncertainty is expected to be smaller, since the plumes were present for a considerably longer time, and the random uncertainties above are therefore considered as an upper level.

The systematic uncertainties consist of the calibration uncertainties and the uncertainty by the model-based retrieval of the BSFC. Furthermore, other studies indicate that the sulfur content may be systematically underestimated by 1–19 % when applying ratio measurement of SO2 over CO2, hence assuming that all sulfur is emitted in the form of SO2 (Schlager et al., 2006; Agrawal et al., 2008; Moldanová et al., 2009, 2013; Balzani Lööv et al., 2014). Since the conclusions from these studies are quite inconsistent, this potential uncertainty is not included in the error estimation here.

Following the IMO guidelines, the carbon mass fraction in fuel varies between 85 and 87.5 % (MEPC, 2005). The carbon mass fraction of heavy fuel oil is closer to the lower end and diesel oil closer to the higher end of this interval. In this study a carbon mass fraction of 87 % was assumed in the calculation of the emission factors. The maximum error due to this assumption is approximately 2.4 %.

The overall measurement uncertainty is calculated as the root of sum of squares (RSS) of the systematic and random uncertainties. Where the uncertainties of the two campaigns were combined, the higher uncertainty was taken into account. This yields the total uncertainties of 21 and 26 % for the mass-based emission factors of SO2 and NOx, respectively. For the calculation of the specific emission factor for NOx an additional uncertainty of 11 % for the BSFC data is added, yielding a total uncertainty of 29 %.

The uncertainties above are comparable to estimates that were done in a previous study using the same system (Beecken et al., 2014), where the uncertainties in the mass-based emission factors were 20 % for SO2 and 24 % for NOx. Alföldy et al. (2013) report similar uncertainties of 23 % for SO2 and 26 % NOx. It should be noted that the uncertainty for emission factor of SO2 of 20 % was found for FSCs of around 1 %. In a study by Mellqvist et al. (2015), the emission of a ship which was known to run voluntarily on marine gas oil and hence with FSC of 0.1 % or below was repeatedly measured, yet at different occasions, using the same system at a fixed site. The measurements indicate an average FSC of 0.06 % with a standard deviation of about 0.03 %. Under the assumption that the FSC used by the ship would be the same during all measurements, this would indicate an uncertainty of about 50 % for the EF(SO2) for ships running on low FSC around 0.1 %.

According to the instrument certificate, the EEPS was analyzed by the manufacturer (TSI) against a scanning mobility particle sizer (SMPS) system for particle size distribution accuracy and a CPC (TSI CPC 3022, Dp,min= 7 nm) for total number accuracy using both 100 nm classified and polydisperse emery oil. According to the manufacturer's certificate, the deviation of particle size distribution was found to be less than 7 % and the deviation in total number less than 20 %.

Cross-comparison measurements of the EEPS were performed at our laboratory with a SMPS (TSI DMA 3081 and TSI CPC 3787) with ammonium sulfate at concentrations between 1.85 × 1011 and 8.36 × 1011 particles m-3. It was found that the GMDs in a size region around 30 nm measured with the EEPS are around 14 % below those measured with the SMPS. In this study it is assumed that the CPC counts all particles. The SMPS System which was used in this comparison was validated with standardized polystyrene latex spheres (PLS) of known sizes between 70 and 500 nm. From the deviations, it was seen that the particle diameters were underestimated by the SMPS by less than 1 % at an offset of less than 7 nm. A comparison with the CPC indicated an underestimation of the total particle number of about 30 % by the EEPS. A similar discrepancy has been observed in another study (Jonsson et al., 2011), when comparing the same type of instruments.

In this study it was not possible to perform any cross validations for the OPS. Instead the manufacturer's quality assurance certificates have to be relied on for error estimation, corresponding to an uncertainty in size resolution of 3.5 % and in number better than 10 % for particles around 0.5 µm.

The STEAM model generates ship specific emissions of CO2, SO2, NOx and particulates at the time and location of the actual ships (Jalkanen et al., 2009, 2012; Jalkanen and Johansson, 2013). The model uses as input values the position reports generated by the automatic identification system (AIS), this system is on-board every vessel that weighs more than 300 t throughout the globe. The AIS system provides automatic updates of the positions and instantaneous speeds of ships at intervals of a few seconds. The model requires as input also the detailed technical specifications of all fuel consuming systems on-board and other relevant technical details of the ships, taken from the IHS Maritime ship register (IHS Global, 2014), for all the ships.

The propelling power of each ship is predicted as a function of its speed. In STEAM, the fuel type and sulfur content for different engines are assigned on a per vessel basis and for main and auxiliary engines separately. If the sulfur content of the fuel is known explicitly, it is used by the model. In any other case the sulfur content is determined by engine properties (engine power, angular velocity and stroke type) according to the classification proposed by Kuiken (2008). The NOx emissions are modeled according to IMO three tier approach as a function of engine angular velocity (revolutions per minute; rpm). For vessels built before the year 2000, the so-called Tier 0 ships, the NOx emission factors 10 % above the Tier 1 level are assigned (Starcrest, 2012). Emission factors for PM are determined based on the FSC as described in Jalkanen et al. (2012). This approach assumes a linear relationship between sulfate aerosol formation and fuel sulfur content, but engine load level changes to sulfur–sulfate conversion efficiency were not modeled. Note that when comparing the results of modeled data with the measured data, in STEAM the gas-phase emissions of sulfur were calculated as SOx, which is the sum of SO2 and SO3, while during the campaigns just SO2 was measured. However, in the comparison it is assumed that the abundance of SO3 is negligible.

The frequency distribution of the measured SO2 emission factors is shown in Fig. 4. The distributions of the ground-based measurements show that there are two prominent modes separated by the gap at 12 gSO2 kgfuel-1 with about 50 % of the measurements on either side. The median emission in the lower mode is about 4.6 gSO2 kgfuel-1 while the first and third quartiles can be found at 2.7 and 7.5 gSO2 kgfuel-1, respectively. The corresponding median in the higher mode is 18.2, and 15.4 and 21.3 gSO2 kgfuel-1 for the first and third quartiles. The distribution of the helicopter-borne measured emission factors are almost entirely located around 20 gSO2 kgfuel-1.

The emission factors of SO2 for different ship types are shown in Fig. 5. Nearly all values are below 20 gSO2 kgfuel-1. However, a clear difference in the sulfur emission can be seen for the different types. Vessels which are operated mostly in domestic waters, i.e., fast ferries, Nevsky class cargo vessels, Volgoneft tankers and tugs, emitted less than 10 gSO2 kgfuel-1, indicating low fuel sulfur content, while the internationally operating ships had higher fuel sulfur contents.

The measured SO2 emission factors indicate that there was a reduction of 13 % in the sulfur emission factors between 2011 and 2012, with 80 % of the plumes corresponding to emission factors below 21.2 gSO2 kgfuel-1 in 2011 and below 18.4 gSO2 kgfuel-1 in 2012. The results obtained from ground-based measurement in Neva Bay in 2011 and 2012 indicate that 90 and 97 %, respectively, of the ships complied with IMO FSC limit of 1 %, when taking the measurement uncertainty into account. The 32 ships measured outside Neva Bay from the helicopter all complied with the IMO sulfur limits.

In Fig. 6 it can be seen that the NOx emission factors are distributed around a single peak. The median of the NOx emission related to the amount of consumed fuel can be found at 58 gNOx kgfuel-1 and the first and third quartiles at 44 and 70 gNOx kgfuel-1. The measured median NOx emission factor in this study is 12 % below the average value found by Williams et al. (2009), probably due the fact that most ships were running at low speed with relatively low loads (Borkowski et al., 2011). These lower values are consistent with other studies (Alföldy et al., 2013; Pirjola et al., 2014) also taking place in harbor areas or channels where ships were running at reduced speed (Cappa et al., 2014).

For the power related emission the corresponding median is at 12.1 gNOx kWh-1 and the first and third quartiles at 9.1 and 14.4 gNOx kWh-1. The NOx emission factors are shown for different ship types in Fig. 5.

The normalized size distributions in number, EF(PN), and mass units, EF(PM), for individual plume measurements are shown in Fig. 7. Ninety percent of the measured particles were smaller than 70 nm. It can be seen that the 10th to the 90th percentile range of the GMDs is between 24 and 53 nm. In a similar study (Jonsson et al., 2011), in the harbor of Gothenburg, Sweden, measurements were carried out from about the same distance as in this study, and in this case the GMD values were between 21 and 39 nm for six selected ships, consistent with the data given in this paper. The graph showing EF(PN) also indicates the presence of a second smaller particle mode with diameters of about 10 nm, probably corresponding to fresh particles produced in the flue gas, which is also observed in other studies (Hallquist et al., 2013; Moldanová et al., 2013). Around 70 % of the total measured EF(PM) particulate mass below 10 µm consists of particles smaller than 300 nm. In the distribution of the particle mass emission factor, two separate size regions were identified to contribute to the mass, one for particles from 30 to 300 nm and the other for particles above 2 µm. This is based on the assumption that all measured particles have spherical shape and unit density.

A comparison of the total particle numbers from measurements with the CPC and the combined measurements with the EEPS and OPS shows high correlation (R2=0.98) and a root mean square error (RMSE) of 0.17 × 1016 particles kgfuel-1. However, the CPC results show 34 % higher values than the combined particle sizers.

In Fig. 8, the frequency distributions of the measured emission factors of total particle numbers for each instrument are presented, whereas the result for the frequency distribution for particulate mass, EF(PM), is shown in Fig. 9. The statistical distributions of the particle emission factors for number and mass are shown for the different ship types in Fig. 5.

Altogether, the number and mass emission factors measured by the particle sizers lie within the ranges of 0.7 to 2.7 × 1016 particles kgfuel-1 and of 0.2 to 3.4 gPM kgfuel-1, respectively. These ranges compare well with the results found in other studies (Petzold et al., 2008; Murphy et al., 2009; Jonsson et al., 2011; Lack et al., 2011; Alföldy et al., 2013; Beecken et al., 2014; Pirjola et al., 2014) between 0.3 and 2.55 particles kgfuel-1 or accordingly for particulate mass, between 0.4 and 3.77 gPM kgfuel-1.

The differences between measured and modeled emission factors by STEAM are summarized for each ship type in Fig. 10. The data for each ship was modeled considering the actual ship speed at the time of the plume measurement to estimate the engine load.

When comparing modeled and measured SO2 emission factors, it can be seen that there is good agreement for passenger ships in international traffic and only a slight positive bias for the model for cargo and tanker ships. This hence indicates that the assigned model FSC for these ships is approximately correct. However, there are also many inland vessels for which there is a large positive bias in the model, indicating that the assigned model FSC of 1 % is much too high since the domestically running cargo and tanker ships actually had a measured FSC of around 0.4 % or less. This reflects the restrictions of fuel used in inland waterway traffic, which to our knowledge prohibit the use of heavy fuel oil within the city borders due to ship-operation safety reasons. Improvements to the FSC predictions of especially inland vessels needs local knowledge and geographical restrictions. In future modeling work, the fuel assignment of inland waterway traffic must be considered in a more realistic manner because fuel type and sulfur content assignment will have an impact on both SOx and PM emission factors.

This fact was pointed out to the air quality authorities of the City of Saint Petersburg (Krylov et al., 2012), who assumed a FSC of 1 and 1.5 % for the primary fuel of 70 and 30 % of the ships, respectively, when carrying out air quality modeling. They later adapted the emission factors in their modeling to 0.17 and 1 %, based on the FSC data given in this paper with the consequence that shipping area had considerably less impact on air quality in the Saint Petersburg than originally estimated, especially for sulfur but also particles.

The modeled emission factors of NOx match well with the measurements for passenger ships and domestic tankers, as shown in Fig. 10. The average difference is around 3 gNOx kgfuel-1 (+4 % relative to average) and the spread for individual ships is in the order of the measurement uncertainty for NOx. For domestic cargo ships, the average difference between the model and measurement results is 9 gNOx kgfuel-1 (-11 %) lower than the measured emissions.

Significant differences between the model and the measurement results can be seen for international running tankers (+40 %) and tug boats (+84 %). Even though only three tug boats were measured, they showed significantly lower NOx emissions than other ship types in the low-load conditions in the ship channel of Neva Bay.

The low values of measured NOx emission factors for certain, very new, vessels were observed already at IMO Tier 3 level. The measurements indicate that certain, recently built vessels already operate following the IMO tier 3 regulations. Other ships showing low NOx emissions are known to run with engines capable of using both gas and diesel. In the case of dual-fuel engines, low-load operation in STEAM leads to switch from gas mode to diesel mode. The speed limit of 10 kn is already low enough to trigger this behavior in the model and NOx emission factors defined by engine rpm and IMO NOx curve is then applied.

The modeling of particulate matter emissions is complex due to the uncertainties in assumptions about the FSC, engine load and the mass and composition of the emitted particles used. Large differences between modeled and measured emissions can be seen for particles in Fig. 10. The deviations found might be partly due to the assumed unity density (see for example Virtanen et al., 2002, Petzold et al., 2008, Murphy et al., 2009, Barone et al., 2011, Kuwata et al., 2012, and Pennington et al., 2013, for the typical range of densities), the limited size range for the measured data and assumptions made in STEAM. With respect to the latter, there are several uncertainty sources regarding the modeling of the particulate matter emissions.

First, the instantaneous main and auxiliary engine power level predictions will have an impact on modeled engine loads. Load levels of engines, in turn, will have an impact on calculated emission factors and instantaneous values of specific fuel consumption, thus changing the mass-based emission factors in the model. For the model, engine load and load balancing is more straightforward for a single two-stroke main engine than for a setup of several four-stroke main engines, in the case of which the number of active main engines may vary depending on variable power needs of the vessel. A comparison of the model and measured results for EF(PM) of ships using two-stroke engines did not show a significant improvement in this study. Unfortunately, the lack of observations from the ships' engine rooms regarding the operational state of the engines hinders a more detailed analysis. However, it is unlikely that these uncertainties are the single cause for the deviations between measured and modeled results of more than 100 %.

Second, the FSC will have a major impact on the modeled PM emission factor. In STEAM, the cheapest possible fuel (with higher sulfur content) is assigned to vessels, defined by geographical limitations (SECA/non-SECA, local legislation for port areas) and technical feasibility of using residual fuel. A recalculation with the model using FSC from the measurement of the individual ships as shown in the bottom plot in Fig. 10 did not lead to a major improvement.

Third, some differences between the modeled and measured results can be explained by unknown parameters for certain domestic ships, for which standard parameters for small vessels were used. Small vessels in domestic operations do not need to undergo the IMO registry procedure and the level of technical details of these vessels in STEAM database is low. For this reason, small vessels are assigned the generic tugboat type, which is bound to lead to inaccuracies in vessel performance and emissions calculations.

Fourth, the conditions of experimental measurements, on which STEAM emission factors for PM are based (see Jalkanen et al., 2012, for details), do not necessarily correspond to the measurement conditions used in the plume chasing approach used in the current work. The emission factors of fresh exhaust are quite different from those of the aged plume and sample analysis in laboratory conditions may have an impact on PM mass determination when compared to in situ measurements. The aerosol chemistry after the emission of the plume is not trivial, and considering that the plumes are already aged by several minutes there are significant changes in number and mass of the emitted particles. Direct comparison of PM results from plume chasing studies with emission modeling would necessitate the use of PM emission factors which represent fresh exhaust, and consecutive modeling should be done using a plume model with a detailed description of aerosol processes. This was not done during the current work, however, and it requires further study. It may be necessary to develop separate ship emission modeling schemes for short-range studies (both in space and time) of aerosols and for regional scale modeling, unless regional transport models can include relevant aerosol physics modules describing gas/particle partitioning in a short timescale (Robinson et al., 2007; Tian et al., 2014).

It was clearly observed in the modeling results that the misallocation of FSC for ships, especially for domestic traffic using low sulfur fuel, will easily lead to SOx and PM emission factors of which the latter are over twice higher than what was measured in this work. However, the model does not allow for higher fuel sulfur content for vessels than what is allowed by current legislation unless the user assigns the fuel sulfur content manually. Currently, there is no centralized registry for the properties of fuel used in each vessel, making emission modeling challenging for SOx and PM. In this regard, the work reported improves the knowledge of the fuel sulfur content of Baltic Sea shipping.