Figure 1a shows the time series of PM1 components measured by ACSM (i.e., OA, sulfate, nitrate, ammonium, and chloride) and AE-33 (i.e., BC) in Dublin during December 2016. The campaign-averaged PM1 concentration was 8.6 µg m-3, ranging from < 0.5 to 146.8 µg m-3 (Table 1). The chemical composition of PM1 was dominated by OA, which on average accounted for 57 % (4.9 µg m-3) of the total PM1 mass, followed by BC, accounting for 23 % (2.0 µg m-3) of the total PM1 mass. Nitrate (8 % or 0.7 µg m-3), sulfate (5 % or 0.4 µg m-3), ammonium (4 % or 0.3 µg m-3), and chloride (3 % or 0.2 µg m-3) accounted for minor fractions of PM1.

Frequent pollution spikes with high OA and BC concentration (> 8.0 µg m-3) were observed in the evening during the pollution periods (P1–P3), while, during clean periods (C1–C3), all PM1 components were below 6.0 µg m-3 (Fig. 1). For one particulate pollution peak in the evening on 2 December 2016, the OA concentration increased up to 82.0 µg m-3, ∼ 17 times the OA average concentration, while BC concentration increased to 49.7 µg m-3, ∼ 25 times the BC average (Table 1). The simultaneous increase in both BC and OA during evening hours is a strong indication that these pollutants were emitted from a similar source, i.e., residential heating. In addition to emission sources, meteorological conditions such as wind speed and temperature were also important parameters in driving particulate air pollution. The temperature was ∼ 1.5 times lower during the pollution periods (5.9 ∘C, on average) than during the clean periods (8.7 ∘C; Fig. S2). In addition, the wind speed was ∼ 2.5 times lower (2.8 m s-1 vs. 7.3 m s-1) during pollution periods than during clean periods. These conditions commonly lead to increased air pollution in winter months due to a shallower boundary layer and less dispersion of primary emissions. The dominance of OA highlights the importance of its source apportionment to identify and quantify the major pollution sources.

To investigate the sources of the OA, unconstrained PMF (i.e., free PMF) was first applied to the ambient organic mass spectra. Hydrocarbon-like OA (HOA), solid-fuel-burning OA (SFOA), and oxygenated OA (OOA) were identified in the free PMF runs (see Fig. S3–S4 and more details in the Supplement). The free PMF solutions with higher numbers of factors provided no new meaningful factors. HOA is usually associated with traffic emission and its diurnal pattern is expected to show morning and/or evening rush hour peaks as found in other European cities, e.g., in London (Allan et al., 2010) and Paris (Crippa et al., 2013). However, HOA was mixed with SFOA in this three-factor solution because the HOA profile contained a higher-than-expected contribution from m/z 60 (0.006), which is regarded as a marker fragment for biomass burning (BB) (Alfarra et al., 2007). Moreover, both HOA and SFOA showed diurnal patterns with peak concentrations occurring in the evening and going into the night, indicating significant contributions from residential heating sources. Solid fuels like peat, wood, and coal have been reported to be the primary heating sources by a small proportion of households in Dublin (e.g., < 5 % of the household using solid fuels vs. ∼ 95 % of the households using electricity or natural gas) according to census data by the Central Statistics Office (CSO, 2016). However, free PMF was not capable of separating these three types of solid fuels at the same time if they were contributing to the nighttime peaks with temporal covariation (Lin et al., 2017). Therefore, SFOA in the free PMF solution contained mixed contributions from peat, wood, and coal burning.

To reduce the mix between HOA and SFOA, and to evaluate the contribution of different types of solid fuels, the reference profiles of HOA (Crippa et al., 2013), peat, wood, and coal (Lin et al., 2017) were constrained with the a value approach using ME-2 (Canonaco et al., 2013). A sensitivity test by varying the a values from 0 to 0.5 with an interval of 0.1 was performed and the correlation between the resolved factors with BC measurements was evaluated and compared to choose the best solution (Fig. S5). The HOA reference profile was taken from the Paris study (Crippa et al., 2013) and a small a value (e.g., 0–0.2) or tight constraint was expected because the HOA profiles do not show significant variability when compared to different cities in Europe (Canonaco et al., 2013; Crippa et al., 2014). The reference profiles of peat, wood, and coal were taken from our previous study in which an ACSM was used to characterize the primary OA emissions directly from burning these fuels (Lin et al., 2017). High a values (e.g., 0.3–0.5) or a loose constraint led to potential mixing between these heating-related factors, especially when their time series showed temporal covariation (i.e., all showed higher concentration at night). At high a values, the mixing between these factors was evidenced by the sudden drop of correlation coefficient between the time series of the peat factor and BCwb, with R dropping from 0.82 at an a value of 0.3 to the R of 0.47 at an a value of 0.4 (Fig. S5), while the correlation between the corresponding profile of peat also dropped from 0.96 to 0.90, confirming the mixing between peat and other factors (e.g., wood). In contrast, a lower a value (e.g., 0–0.2) reduced mixing and improved the separation by tightly constraining their individual profiles. As shown in Fig. S5, at an a value of 0.1, the time series of peat showed a good correlation with BCwb (R= 0.88), while the profile of peat was also tightly correlated with the reference peat profile (R= 0.99). Therefore, an a value of 0.1 was chosen as the most optimal ME-2 solution.

The mass spectra and time series of HOA, peat, coal, wood, and OOA are shown in Fig. 3. The HOA profile is dominated by signals at m/z 27, 29, 41, 43, 55, and 57, characteristic of aliphatic hydrocarbons. Many studies have shown that HOA is usually associated with traffic emissions in urban environments (Canagaratna et al., 2004; Schneider et al., 2005; Platt et al., 2017) and the HOA / BCtr ratio has been reported to be an important parameter to determine the type of fuels used (e.g., diesel or gasoline) (DeWitt et al., 2015). For example, HOA / BCtr ratios in the range of 0.03–0.61 have been reported to be associated with diesel vehicular emission, while the range of 0.9–1.7 for HOA / BCtr ratios is associated with gasoline vehicular emissions (DeWitt et al., 2015). In this study, the average HOA / BCtr ratio was 0.69±0.09 during the day (08:00–15:00, all times are in local time; Fig. S6). This ratio is close to the HOA / BCtr range (0.03–0.61) associated with the diesel vehicular emission and very similar to the ratio (0.61) reported in Paris (Crippa et al., 2013). Therefore, during the day, HOA was likely to be associated with diesel vehicular emissions. However, from 16:00 in the afternoon, the HOA / BCtr ratio started to increase and the ratio increased up to ∼ 9.0 in the evening, which was significantly higher than the values associated with gasoline vehicular emissions (0.9–1.7). Therefore, HOA during the night could not be attributed to the emissions from diesel- or gasoline-powered vehicles. Instead, both the diurnal cycle of HOA and HOA / BCtr ratio indicate the HOA in the evening was mainly associated with heating sources. According to the census data from CSO (2016), natural gas, electricity, oil, wood, coal, and peat were the major types of heating sources in Dublin. Among these fuels, oil is most likely to be the source of HOA during the night because oil, gasoline, and diesel are expected to have similar mass spectra as indicated by various ambient measurement and lab experiments (Canagaratna et al., 2004; Schneider et al., 2005; Platt et al., 2017). Assuming HOAs were merely from traffic during the day and the traffic HOA / BCtr ratio (0.69) were stable, the traffic HOA associated with traffic during the evening (18:00–23:00) was estimated at 16 % of total HOA, with the other 84 % being associated with oil heating. Over the whole period, 28 % of HOA was attributed to traffic. As shown in Fig. 2, HOA, on average, accounted for 25 % of OA over the entire period. It was, therefore, estimated that traffic-related HOA accounted for 7 % of the total OA, and oil-related HOA accounted for 18 % of the total OA.

Despite Dublin being a large city, the impact from traffic also depends on the distance from the roads, wind speed, wind direction, etc. Therefore, the traffic impact is not very pronounced at the residential measurement location. Actually, to further evaluate the impact of traffic emissions on urban air quality, a recent campaign was conducted in Dublin by simultaneously measuring the chemical composition of PM1 at both the curbside and at the same urban background site in this study. It was found that, while the diurnal cycle of HOA at the curbside shows typical rush hour peaks, the HOA at the same urban background shows no clear traffic-related patterns. The latter confirms our conclusion that the traffic emission contribution to HOA at the urban background site is minor (Lin et al., 2019).

Wood, peat, and coal are three types of solid fuels and their profiles were highly associated with their composition (Lin et al., 2017). In the mass spectral signatures for the wood and peat OA factors, the contribution of the signal at m/z 60 (that is, f60) and 73 (f73) to the total organics is associated with fragmentation of levoglucosan. f60 and f73 are, therefore, often regarded as tracers for biomass burning emissions (Alfarra et al., 2007; Cubison et al., 2011; Dall'Osto et al., 2013). In contrast, the mass spectral signature for the coal OA factor does not have any contribution from m/z 60 due to the lack of levoglucosan in this fossil fuel (Zhang et al., 2008). The time series of wood, peat, and coal all showed peak concentrations in the evening (Fig. 3b), corresponding to the time of residential heating activities. The solid fuel factors (the sum of wood, peat, and coal), on average, accounted for 50 % of OA, highlighting its important role in driving the pollution events in Dublin (Fig. 2). Among these solid fuels, peat (30 %) shows a contribution 2.5–3.8 times higher than that of wood (12 %) and coal (8 %).

In addition to the primary factors, an OOA factor was also resolved and its profile was characterized by a significant contribution from f44, which is higher than other primary factors. However, similar to other primary factors, OOA also showed peak concentrations during the evening, indicating OOA was dominated by local sources from residential heating. This is further confirmed by the wind rose, which shows higher concentrations of OOA were associated with the lower wind speed from the north-northwest direction, consistent with that for the BC polar plots (Fig. S7). The local contribution from heating sources to OOA was probably associated with the condensation of semi-volatile species and/or aging of primary aerosol emitted from biomass burning (Tiitta et al., 2016). The local and regional sources of OOA will be further discussed below. OOA, on average, accounted for 26 % of OA, comparable to HOA (25 %) and peat (30 %).

Carnsore Point is located in a rural area on the southeast coast of Ireland, ∼ 150 km south of Dublin. The campaign at Carnsore Point was conducted over the same period as that in Dublin during December 2016 (Fig. 1b). However, the average PM1 concentration was only 2.2 µg m-3, ∼ 4 times lower than that in Dublin. OA was the most dominant component, on average accounting for 34 % (0.7 µg m-3) of PM1, followed by nitrate (22 % or 0.5 µg m-3), BC (17 % or 0.4 µg m-3), sulfate (12 % or 0.3 µg m-3), ammonium (12 % or 0.3 µg m-3), and chloride (3 % or 0.1 µg m-3). The fraction of inorganic secondary aerosol (the sum of nitrate, sulfate, and ammonium) was 46 %, indicating that secondary formation over long-range transport was important at this rural site. This is consistent with the wind rose of sulfate and nitrate, which shows that higher sulfate and nitrate concentrations were associated with wind from the east-southeast direction, pointing to a major source from the UK and/or mainland Europe (Fig. S7). BC is associated with solid fuel combustion and/or biomass combustion. The wind rose of BC shows a major source from northwest areas, pointing to sources from the nearby villages and towns. Therefore, the aerosol measured at Carnsore Point was impacted by both international long-range transport and the emissions from nearby villages and towns, highly associated with the wind direction.

To investigate the sources of OA, free PMF was first conducted on the organic mass spectra. Three factors were identified, including two organic factors (i.e., OOA and SFOA) and one inorganic factor of sea salt (see Fig. S8 and more details in the Supplement). The profile of the sea salt factor was characterized by its fragments of m/z 37 (37Cl+), 58 (23Na35Cl+), 60 (23Na37Cl+), and 83 (23Na237Cl+), which were typical of sea salt fragmentation as found in our previous study where sea salt solution was atomized and directly characterized by AMS (Ovadnevaite et al., 2012). Note that other m/z values that belonged to sea salt, like m/z 23 (23Na+) or 81 (23Na235Cl+), did not appear in the OA factor profile as they were mainly associated with inorganic ions, which were not included in the OM matrix for PMF analysis. Also, the standard fragmentation table (Allan et al., 2004) in ACSM does not include sea salt, which is, however, ubiquitous in the marine environment. As a result, all the sea salt fragmentation ions (i.e., m/z 37, 58, 60, and 83) were included as “organic”. Therefore, the true OAs were corrected by subtracting the sea salt contribution at Carnsore Point. However, the profile of sea salt factor resolved by free PMF was not “clean” with some interference from other mass spectral fragments even at solutions with a higher number of factors (Fig. S8). To better quantify the contribution of sea salt, ME-2 was utilized to constrain its reference profile. A tight constraint (a value of 0.05) was applied because the sea salt factor was not expected to vary significantly.

To evaluate the contribution from different solid fuels to the total SFOA factor, as well as the oil heating factor, the reference profiles of wood, peat, coal, and HOA were also constrained with the a value approach using ME-2. The sensitivity test with varying a values (0–0.5) shows the average relative contribution of the factors did not vary significantly (only by a few percent) within the considered a values (Fig. S9). Figure 4 shows the reference profile and time series of all the factors obtained using the a value of 0.1. The profiles of the HOA, wood, peat, and coal were similar to those found in Dublin as expected because a tight constraint was applied with ME-2 at both locations. HOA, peat, coal, and wood were the primary OA factors. Among these primary factors, the peat factor was dominant, accounting for 16 % of the total OA mass. The wind rose for the peat factor shows higher concentrations were associated with wind direction from the northwest at a wind speed of 2–4 m s-1, indicating a source region from nearby villages and towns, consistent with that for BC (Fig. S7). HOA, coal, and wood were the minor OA factors, accounting for 4 %, 6 %, and 3 % of OA, respectively. In addition to these primary OA factors, an OOA factor was resolved. The profile of OOA featured an f44 of 0.29, which was higher than f44 of 0.19 for the OOA in Dublin, suggesting the OOA was more oxidized and had undergone more photochemical processing before reaching Carnsore Point. OOA, on average, accounted for 71 % of OA at Carnsore Point, more than twice that in Dublin (26 %), again suggesting the importance of secondary formation and/or aging of primary aerosol at this rural site. The wind rose of OOA shows higher concentrations (1–2 µg m-3) of OOA were associated with the wind from the east and southeast directions (Fig. S7), pointing to a source from the UK and/or other European countries.

The profile of the sea salt factor was characterized by the prominent signals at m/z 37, 58, 60, and 83, corresponding to sea salt fragmentation (Ovadnevaite et al., 2012). Sea salt particles are formed at the sea surface through wave breaking and higher wind speed is usually associated with higher sea salt concentrations (Ovadnevaite et al., 2012). The time series of sea salt showed higher concentration at higher wind speed when the concentration of other factors including peat and OOA factors was very low (Fig. 4). Note that a scaling factor of 11 should be applied to calculate the real sea salt concentration from PMF–ACSM results after comparing with HR-AMS sea salt (SS) concentration (Fig. S10) because the sea salt was not calibrated in the ACSM system. The wind rose for sea salt shows higher concentrations were associated with wind from the south to west at a wind speed of 6–12 m s-1 (Fig. S11), pointing to a source from the oceanic direction instead of the continental direction. In contrast, the sea salt showed a very low concentration (< 0.1 µg m-3) at low wind speed (< 6 m s-1), suggesting insufficient sea salt production at low wind speed.

Figure 1c shows the time series of PM1 composition measured by an ACSM and AE-33 in Birr during December 2015. The campaign-averaged PM1 concentration was 4.8 µg m-3, ranging from < 0.5 to 63.0 µg m-3 (Table 1). The PM1 chemical composition was dominated by OA, on average, accounting for 62 % (2.9 µg m-3) of OA, followed by BC (15 % or 0.7 µg m-3), sulfate (10 % or 0.5 µg m-3), ammonium (5 % or 0.3 µg m-3), nitrate (4 % or 0.2 µg m-3), and chloride (4 % or 0.2 µg m-3). The time series of OA and BC both showed spike concentrations in the evening, indicating a source from nearby heating activities. The peak OA concentration was 42.1 µg m-3, observed in the evening on 13 December 2015, accompanied by a peak concentration of BC (5.8 µg m-3), sulfate (8.8 µg m-3), ammonium (2.5 µg m-3), nitrate (2.1 µg m-3), and chloride (1.7 µg m-3). Source apportionment of OA using ME-2 showed the spikes were mainly due to solid fuel burning (Fig. S12). On average, solid fuels (the sum of peat, coal, and wood) accounted for 48 % (1.2 µg m-3) of OA (Table 2). The peat factor was the most dominant solid fuel factor, on average, accounting for 27 % (0.7 µg m-3) of OA. During the pollution peak, the contribution from peat increased to 66 % (or 22.2 µg m-3), highlighting its dominance. Coal and wood factors, on average, accounted for 12 % (or 0.3 µg m-3) and 9 % (or 0.2 µg m-3) of the OA, respectively. During the pollution peak, the coal and wood factor concentrations increased to 1.6 and 1.7 µg m-3, respectively. However, the fractions of coal and wood factors were only 4 % and 5 %, respectively. Similarly, the OOA contribution was higher during the pollution peaks than its average value (3.7 vs. 0.9 µg m-3), but its fraction was only 10 % compared to the average of 35 %. The wind roses for OOA, peat, and BC all showed that their higher concentrations were associated with low wind speed (< 5 m s-1) from no specific wind direction (Fig. S7), consistent with fact that the measurement site was surrounded by residential households.

Figure 1d shows the time series of PM1 components at Mace Head in January 2013. The average PM1 concentration was 0.7 µg m-3, which was the lowest among the four sites, primarily due to the dominant influence of marine air masses at this location. OA dominated the chemical composition of PM1, on average, accounting for 44 % (or 0.3 µg m-3) of OA, followed by BC (18 %, 0.1 µg m-3) and nitrate (15 % or 0.1 µg m-3). Sulfate (9 % or < 0.1 µg m-3), ammonium (8 % or < 0.1 µg m-3), and chloride (6 % or < 0.1 µg m-3) accounted for the other 24 % of PM1. OA spikes with a concentration of ∼ 5 µg m-3 were observed in the evening on 1 January and 5 January 2013. Source apportionment of OA with ME-2 shows these spikes were from the heating sources oil, peat, coal, and wood (Fig. S13). Among these primary sources, peat was the greatest OA factor, on average, accounting for 22 % (or 0.06 µg m-3) of OA. The contribution from the peat factor increased to 32 % (1.3 µg m-3) during the pollution peak (Table 2). The wind dependency of the peat factor showed that higher concentration of peat was associated with wind from the east at wind speeds < 5 m s-1 (Fig. S7), pointing to a source from nearby villages and towns. OOA, on average, accounted for 43 % of OA, making it the most dominant OA factor at Mace Head. The wind rose for OOA, sulfate, and nitrate showed that their highest contributions were associated with easterly wind at relatively high wind speed (> 5 m s-1), suggesting strong regional transport sources. Finally, an inorganic factor of sea salt was resolved at Mace Head. The wind rose for sea salt shows its highest contribution was associated with westerly wind at high wind speed (> 15 m s-1), pointing to sea salt production in the Atlantic Ocean during periods with high wind speeds (Fig. S11).