3.2. Influence of Local Meteorological Conditions
The meteorological conditions changed from winter to spring during the measurement period, and the monthly average temperatures were 0.5, 6.0, 9.1, and 12.0 °C during February, March, April, and May, respectively. The temperatures were close to the normal monthly averages for February and May, while March was 4 °C and April was 2 °C warmer than normal. Daily temperatures were below zero during several days in February, and peak day-time temperatures were in the range of 15 to 20 °C during some of the warmer periods. Ground-level temperature inversions are well known to have a strong influence on air quality in Gothenburg [56
] and several strong inversions and associated periods of poor air quality occurred in March, while the effects of inversions on air quality were less important during other periods.
shows that the local wind speed has a relatively strong effect on the mean Na and K concentrations. Na concentrations increase considerably with wind speed above 3 m s−1
, while the highest K concentrations are observed with wind speeds below 3 m s−1
. Figure 4
illustrates that the wind direction also has an important effect on the concentrations of alkali-containing particles. High concentrations of Na-containing particles are observed with westerly winds from the nearby sea, while the highest K concentrations are observed for easterly winds from the inland region. Figure 5
shows that local temperature also has a substantial impact on the K concentrations. The concentrations of K (and Na) increase with decreasing temperature below 2 °C. The observed correlations between high Na concentrations and high wind speeds and westerly winds strongly indicate that the observed Na-rich particles mainly consist of sea salt particles. High K concentrations are, on the other hand, favored by low temperatures, low wind speeds, and winds from the inland, which suggests that they may originate from biomass burning in the nearby region during cold periods. It appears that the local meteorological conditions have an important impact on the concentrations of Na and K in PM1
. However, the observed concentrations show large variations under similar meteorological conditions, which indicates that regional production of particles may not be sufficient to explain the observations.
3.3. Influence of Air Mass History
To further evaluate the origin of the detected particles, back trajectories of air masses reaching Gothenburg were calculated and analyzed by group analysis. The results from a six-group solution are presented in Figure 6
, where the traces for individual back trajectories are indicated. Figure 7
shows the resulting mean trajectories for the six group solutions and the relative contribution to each cluster in percent. Some of the clusters consist of trajectories with a complex pattern, while some were more directed from a certain region. The individual clusters are attributed to different regions of origin for the air masses: Central (C), Eastern (E), Northern (N), and Western Europe (W), as well as clusters with Atlantic (A) and Polar (P) origin.
summarizes mean values for the alkali, PM10
, and trace gas concentrations depending on the type of air mass reaching Gothenburg together with mean meteorological data. These data sets are only included from time periods where at least two consecutive air mass trajectories are attributed to the same group type. The highest mean Na concentration and the highest Na/K ratio are observed in air masses with an Atlantic origin (trajectory cluster No. 5). The highest mean K concentrations and lowest Na/K ratios are observed when the air masses have an Eastern or a Central European origin. In these cases, the observed Na/K ratios are consistent with alkali-containing particles originating from solid fuel combustion processes [32
]. NO, NOx, and PM10
concentrations are also high for air mass types 1 and 2 compared with other types of air masses. The air masses originating from Northern and Western Europe, as well as from the Polar region, appear to consist of local polluted air with different degrees of marine character.
To further address the importance of regional emissions and long-range transport, the results from a short episode (March 24 to April 2, 2007) are illustrated in Figure 8
and Figure 9
]. The period is characterized by large daily variations in temperature and relatively strong night-time temperature inversions. Figure 8
a shows that the air masses reaching Gothenburg during this period originated in Eastern Europe, while the air mass origin shifted before and after this period. In addition to solid fuel combustion, fires are common in Eastern Europe during this period of the year owing to agricultural burns and may potentially influence air quality in Northern Europe during short episodes. Figure 8
b shows the distribution of fires in Europe (at 1 km resolution) during the short episode, where each dot on the map indicates a fire detected with the moderate resolution imaging spectroradiometer (MODIS) aboard the Terra satellite [59
a shows a close up of the measured Na and K concentrations together with wind direction (Figure 9
b) and wind speed (Figure 9
c) data during the same period as covered in Figure 8
. The K concentration was relatively high during this period, while Na was low during most of the period. An interesting effect was that both K and Na appear to follow a diurnal pattern during this particular period (24 Mar–1 Apr). The K concentrations often reached their highest values before noon, while Na peaked during the evening. The wind direction data (Figure 9
b) indicate that the increase in Na concentration was related to a weak sea breeze that was observed daily in the afternoon and evening. The wind speed increased slightly during the sea breeze, but it was fairly low, and the Na signal may be attributed with limited formation of sea salt particles in the region near Gothenburg. The modulated K signal was likely the result of a combination of long-range transport and regional emissions from biomass burning that were trapped near the ground during the relatively cold nights with strong night-time temperature inversions.
The results in Figure 10
indicate that the concentrations of Na and K are generally correlated, in spite of occasional variations. Cluster 1 has a closer linkage in the winter season, while Cluster 2 has no apparent seasonal trend coupling. This agrees with the temperature dependence of K mass concentrations illustrated in Figure 11
a, which shows that low temperatures favor Cluster 1, while Cluster 2 shows no clear dependence on temperature. They are both favored by low wind speeds (Figure 11
3.4. Comparison between Alkali-AMS and Chemical Transport Modeling Results
The experimental data are compared with results from model simulations with the EMEP MSC-W CTM, with a special emphasis on the influence of biomass burning and fossil fuel combustion. Figure 12
shows the measured total concentration of PM2.5
at the urban background station Femman and the modeled total concentration of carbonaceous PM from biomass burning (the sum of organic aerosol and elemental carbon from residential biomass combustion and open biomass fires). During the period 17 February–20 May 2007, the measured hourly PM2.5
concentration varies from 0 to about 50 µg m−3
, and the highest concentrations occur during a handful of major peaks. The highest PM2.5
concentrations were observed during the period 23 March–2 April (two concentration peaks)—at the same time, the modeled concentration of particles from open fire and residential biomass burning is also high. There is a relatively high degree of correlation between the measured PM2.5
and the modeled biomass burning PM during the campaign period (r = 0.81, for hourly, moving 24 h mean concentrations; r = 0.72 for hourly concentrations). The modeled biomass combustion PM may constitute a considerable fraction of the total PM2.5
during some episodes. The average modeled biomass carbonaceous PM during the period is about 1.3 µg m−3
, which is about 12% of the average measured PM2.5
concentration (11 µg m−3
The measured K and particle number concentrations in the two combustion-related components (Clusters 1 and 2) are also compared to the results from the EMEP MSC-W model simulations. As K is not explicitly modeled, the comparison is based on model results for other particulate components that can be tracked to different source types. The model results for primary emitted fine particles (PPM2.5
) from anthropogenic sources and open fires are used (Figure 13
and Figure 14
). The modeled PPM2.5
is split into PPM2.5
from (i) residential biomass combustion, (ii) open vegetation fires (including both wildfires and agricultural burning), and (iii) other anthropogenic sources (predominantly fossil fuel sources).
shows a comparison between modeled concentrations of PPM2.5
from biomass combustion (residential + open fires) and the measured K concentration in Cluster 1. Overall, the modeled concentrations show a high degree of correlation with experimental data (see also Table 2
; R = 0.64 for 24 h moving average concentrations) The main exception is a cold and windy six-day period in February, where the model produces significantly lower values (for the first five days). The meteorological conditions during this period result in a high demand for local residential heating, and we attribute this discrepancy to an underestimation of contributions from local sources—the total PM2.5
concentrations at the urban background station Femman were low during the first four days of the “Cluster 1 episode”, and this indicates that source(s) of the elevated K-concentrations measured with the AMS may have been located relatively close to the university site and possibly only impacting a relatively small area; the wind direction was rather stable during the whole episode. Local episodes of this type are difficult to model accurately with large scale chemical transport models owing to the low spatial resolution of the model simulations and emission data. The model simulations also confirm the importance of contributions from open fires, in particular, agricultural burns, in late March (compare with Figure 8
and Figure 9
illustrates the modeled concentration of PPM2.5
from fossil fuel combustion and the measured K concentration in Cluster 2. The correlation between the model and measurements is lower for Cluster 2 than for Cluster 1 (see also Table 2
; R = 0.61 for 24 h moving average concentrations)—this is not surprising because the model PPM2.5
(from other sources than biomass burning) represents sums of many different sources, including, for example, traffic exhaust emissions, coal combustion, industrial processes, and waste burning. However, several features in the experimental data are qualitatively represented by the model results.
A separate set of model simulations were performed to investigate the correlation between the AMS measurements and individual primary particle emission categories. The correlation coefficients between measured concentrations and modeled PPM2.5
from different emission sources are given in Table 2
. The measured concentrations in Cluster 1 are most strongly correlated with modeled biomass combustion particles, with a maximum r = 0.76 observed for total PPM2.5
from biomass combustion (residential combustion + open biomass burning). The correlation with modeled fossil fuel components is relatively low. In general, modeled concentrations are somewhat better correlated with measured number concentrations than with K concentrations. Although the measured Na concentrations are very low in Cluster 1, the correlation (not shown) between these and the modeled concentrations are as good as for K for most components. On the basis of the relatively high correlations observed, we conclude that the Cluster 1 particles observed with the Alkali-AMS are mainly emitted from biomass combustion.
The correlation coefficients between the measured concentrations in Cluster 2 and various model components are also given in Table 2
. Cluster 2 corresponds less well to any modeled compound than Cluster 1 does; the highest correlations are found for emissions from the agricultural sector (SNAP-10; not including open burning of biomass, r = 0.65 when compared with the Cluster 2 number concentration) and for the sum of all “non-biomass” emission sources (r = 0.63). The correlation is especially low for particles from residential biomass combustion (r < 0.23). This indicates that Cluster 2 is at least not dominated by particles from residential biomass combustion in the region around Gothenburg. For Cluster 2, the correlation coefficients are similar for particle number and K concentrations, while they are lower for Na concentrations (not shown; r < 0.36 for all model components in Table 2
except for residential/non-industrial fossil fuel combustion, for which r = 0.61 for Na in Cluster 2). The lower correlations for Na have not been studied in detail, but one possible explanation may be interference from sea salt particles at the defined border between Cluster 2 and 3 (see Figure 1
To investigate if emissions from any single geographical region could be responsible for a large fraction of the Cluster 2 particles, simplified model simulations, which track primary particle emissions from a number of areas in Europe (Sweden, Norway, Poland/Czechia/Slovakia (PCS), Eastern Europe (EEU = Russia, Belarus, the Baltic states, Finland, Ukraine) and Western Europe (WEU = Denmark, Germany, BeNeLux, France), the British Isles (United Kingdom, Ireland)), were performed with the EMEP MSC-W model. These simulations indicated that different Cluster 2 peaks correspond to different source regions; no single region seems to dominate Cluster 2 (see Figure S3
). The highest measured concentrations in Cluster 2 (late April) seem to be associated with air masses from Western Europe. The broad peak at the end of March is associated with air from Eastern Europe and, for the later part, also with more southerly air that has passed Poland and other parts of Central Europe; Swedish emissions also influenced Gothenburg during this period. Several other peaks can be related to emissions in PCS, WEU, EEU, and Sweden.
We conclude that Cluster 2 seems to represent emissions from several different types of combustion sources and different source regions that contribute to air pollution in Gothenburg by long-range transport.
3.5. Comparison with Earlier Studies
The present results agree with measurements performed in Gothenburg during 2003 and 2004 with the Alkali-AMS that only monitored one type of alkali ion at the time [31
]. Mass concentrations of alkali in ambient air varied in the range of 0.02–100 ng m−3
and the number of alkali-containing particles varied between 0.1 and 100 cm−3
. The detected aerosol was concluded to be dominated by emissions from combustion of biomass and fossil fuels, with a significant contribution from sea-salt particles only during intrusion of marine air.
A long term measurement of Na and K in an urban and marine environment by Ooki et al. [59
] reported an average K/Na ratio of 1.8 for the fine particles (D < 1.1 µm); the study concluded that the primary source of the fine particles was a domestic refuse incineration plant located within 15 km of the measurement site. This finding, K/Na ratio of 1.8, by Ooki et al. [59
] agrees with the K/Na ratio of 1.2–1.7 reported by Mamane [60
] from the direct household refuse incinerator aerosol measurements with the fine particles (D < 2.5 µm). In the present work, the air masses originating from Western Europe (W) had an average K/Na value of 2.2, which may suggest the emissions were associated with refuse combustions. A slightly higher value of K/Na than Mamane [60
] can be explained by different combusted source materials. In European countries, the usage of both small and large scale biomass burning systems is common; especially during the cold winter period, usage of domestic heating systems with biomass fuel may contribute to the higher K/Na ratio.
Stohl et al. [61
] reported measurements of Arctic air pollution owing to biomass burning associated with agricultural fire activities in Eastern Europe during the spring; this type of fire smoke can reach the Arctic region and potentially reduce the snow albedo and change the rate of snow/ice melting. The filter samples from the Zeppelin mountain station in Svalbard indicated elevated concentrations of levoglucosan, K, and CO for the biomass burning pollution events from 27 April to 9 May 2006. These results suggest that the monitoring of K and CO can be used as a good indicator for the potential contribution of air pollution from biomass burning in the Arctic region. As the SI-AMS has single-particle analysis capability, it has further potential to obtain more detailed information.